From 45ebc1617ab82d3c801bbd697f6ed4317db70d65 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Fri, 26 Jun 2020 20:22:35 +0200
Subject: [PATCH 01/33] =?UTF-8?q?Bump=20version:=200.3.3=20=E2=86=92=200.3?=
 =?UTF-8?q?.4.dev0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 sdv/__init__.py | 2 +-
 setup.cfg       | 2 +-
 setup.py        | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/sdv/__init__.py b/sdv/__init__.py
index 5213dd4ca..8caed53d9 100644
--- a/sdv/__init__.py
+++ b/sdv/__init__.py
@@ -6,7 +6,7 @@
 
 __author__ = """MIT Data To AI Lab"""
 __email__ = 'dailabmit@gmail.com'
-__version__ = '0.3.3'
+__version__ = '0.3.4.dev0'
 
 from sdv.demo import get_available_demos, load_demo
 from sdv.metadata import Metadata
diff --git a/setup.cfg b/setup.cfg
index 24389c418..6a2ef34f0 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.3
+current_version = 0.3.4.dev0
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\.(?P<release>[a-z]+)(?P<candidate>\d+))?
diff --git a/setup.py b/setup.py
index 0c8189cdf..81283021b 100644
--- a/setup.py
+++ b/setup.py
@@ -93,6 +93,6 @@
     test_suite='tests',
     tests_require=tests_require,
     url='https://github.com/sdv-dev/SDV',
-    version='0.3.3',
+    version='0.3.4.dev0',
     zip_safe=False,
 )

From 2b52f86201ceee8785ed9353f212a27dd67ca556 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Wed, 1 Jul 2020 16:42:12 +0200
Subject: [PATCH 02/33] WIP

---
 sdv/metadata.py |  2 +-
 sdv/modeler.py  |  6 +++-
 sdv/sampler.py  | 80 ++++++++++++++++++++++++++++++++++++++++---------
 sdv/sdv.py      | 13 ++++----
 4 files changed, 80 insertions(+), 21 deletions(-)

diff --git a/sdv/metadata.py b/sdv/metadata.py
index dbf01ca60..94f3e8e55 100644
--- a/sdv/metadata.py
+++ b/sdv/metadata.py
@@ -624,7 +624,7 @@ def validate(self, tables=None):
 
             self._validate_table(table_name, table_meta, table)
             self._validate_circular_relationships(table_name)
-            self._validate_parents(table_name)
+            # self._validate_parents(table_name)
 
     def _check_field(self, table, field, exists=False):
         """Validate the existance of the table and existance (or not) of field."""
diff --git a/sdv/modeler.py b/sdv/modeler.py
index aecfbd91a..7e766deb6 100644
--- a/sdv/modeler.py
+++ b/sdv/modeler.py
@@ -27,6 +27,7 @@ def __init__(self, metadata, model=GaussianCopula, model_kwargs=None):
         self.metadata = metadata
         self.model = model
         self.model_kwargs = dict() if model_kwargs is None else model_kwargs
+        self.table_sizes = dict()
 
     def _get_extension(self, child_name, child_table, foreign_key):
         """Generate list of extension for child tables.
@@ -78,7 +79,7 @@ def cpa(self, table_name, tables, foreign_key=None):
             table_name (str):
                 Name of the table to model.
             tables (dict):
-                Dict of tables tha have been already modeled.
+                Dict of original tables.
             foreign_key (str):
                 Name of the foreign key that references this table. Used only when applying
                 CPA on a child table.
@@ -87,6 +88,7 @@ def cpa(self, table_name, tables, foreign_key=None):
             pandas.DataFrame:
                 table data with the extensions created while modeling its children.
         """
+
         LOGGER.info('Modeling %s', table_name)
 
         if tables:
@@ -94,6 +96,8 @@ def cpa(self, table_name, tables, foreign_key=None):
         else:
             table = self.metadata.load_table(table_name)
 
+        self.table_sizes[table_name] = len(table)
+
         extended = self.metadata.transform(table_name, table)
 
         primary_key = self.metadata.get_primary_key(table_name)
diff --git a/sdv/sampler.py b/sdv/sampler.py
index b67f3e87a..8e0639b2b 100644
--- a/sdv/sampler.py
+++ b/sdv/sampler.py
@@ -23,13 +23,14 @@ class Sampler:
     primary_key = None
     remaining_primary_key = None
 
-    def __init__(self, metadata, models, model, model_kwargs):
+    def __init__(self, metadata, models, model, model_kwargs, table_sizes):
         self.metadata = metadata
         self.models = models
         self.primary_key = dict()
         self.remaining_primary_key = dict()
         self.model = model
         self.model_kwargs = model_kwargs
+        self.table_sizes = table_sizes
 
     def _reset_primary_keys_generators(self):
         """Reset the primary key generators."""
@@ -161,9 +162,9 @@ def _sample_children(self, table_name, sampled):
         table_rows = sampled[table_name]
         for child_name in self.metadata.get_children(table_name):
             for _, row in table_rows.iterrows():
-                self._sample_table(child_name, table_name, row, sampled)
+                self._sample_child_rows(child_name, table_name, row, sampled)
 
-    def _sample_table(self, table_name, parent_name, parent_row, sampled):
+    def _sample_child_rows(self, table_name, parent_name, parent_row, sampled):
         extension = self._get_extension(parent_row, table_name)
 
         model = self.model(**self.model_kwargs)
@@ -184,7 +185,49 @@ def _sample_table(self, table_name, parent_name, parent_row, sampled):
 
         self._sample_children(table_name, sampled)
 
-    def sample(self, table_name, num_rows, reset_primary_keys=False, sample_children=True):
+    def _get_pdfs(self, parent_rows, child_name):
+        pdfs = dict()
+        for parent_id, row in parent_rows.iterrows():
+            extension = self._get_extension(row, child_name)
+            model = self.model(**self.model_kwargs)
+            model.set_parameters(extension)
+            pdfs[parent_id] = model.model.probability_density
+
+        return pdfs
+
+    def _find_parent_id(self, row, pdfs):
+        likelihoods = dict()
+        for parent_id, pdf in pdfs.items():
+            try:
+                likelihoods[parent_id] = pdf(row)
+            except np.linalg.LinAlgError:
+                likelihoods[parent_id] = None
+
+        likelihoods = pd.Series(likelihoods)
+        likelihoods = likelihoods.fillna(likelihoods.mean()).fillna(0)
+
+        # weights = likelihoods.values / likelihoods.sum()
+
+        return likelihoods.idxmax()
+
+    def _find_parent_ids(self, table_name, parent_name, sampled_rows, sampled=None):
+        ratio = self.table_sizes[parent_name] / self.table_sizes[table_name]
+        num_parent_rows = max(int(round(len(sampled_rows) * ratio)), 1)
+        parent_model = self.models[parent_name]
+        parent_rows = self._sample_rows(parent_model, num_parent_rows, parent_name)
+
+        foreign_key = self.metadata.get_foreign_key(parent_name, table_name)
+        primary_key = self.metadata.get_primary_key(parent_name)
+        pdfs = self._get_pdfs(parent_rows.set_index(primary_key), table_name)
+
+        parent_ids = list()
+        for _, row in sampled_rows.iterrows():
+            parent_ids.append(self._find_parent_id(row, pdfs))
+
+        return parent_ids
+
+    def sample(self, table_name, num_rows=None, reset_primary_keys=False,
+               sample_children=True, sample_parents=True):
         """Sample one table.
 
         Child tables will be sampled when ``sample_children`` is ``True``.
@@ -195,11 +238,14 @@ def sample(self, table_name, num_rows, reset_primary_keys=False, sample_children
             table_name (str):
                 Table name to sample.
             num_rows (int):
-                Amount of rows to sample.
+                Amount of rows to sample. If ``None``, sample the same number of rows
+                as there were in the original table.
             reset_primary_keys (bool):
                 Whether or not reset the primary keys generators. Defaults to ``False``.
             sample_children (bool):
                 Whether or not sample child tables. Defaults to ``True``.
+            sample_parents (bool):
+                Whether or not sample parent tables. Defaults to ``True``.
 
         Returns:
             dict or pandas.DataFrame:
@@ -211,16 +257,21 @@ def sample(self, table_name, num_rows, reset_primary_keys=False, sample_children
         if reset_primary_keys:
             self._reset_primary_keys_generators()
 
-        model = self.models[table_name]
+        if num_rows is None:
+            num_rows = self.table_sizes[table_name]
 
+        model = self.models[table_name]
         sampled_rows = self._sample_rows(model, num_rows, table_name)
 
         parents = self.metadata.get_parents(table_name)
-        if parents:
-            parent_name = list(parents)[0]
-            foreign_key = self.metadata.get_foreign_key(parent_name, table_name)
-            parent_id = self._get_primary_keys(parent_name, 1)[1][0]
-            sampled_rows[foreign_key] = parent_id
+        if parents and sample_parents:
+            data_fields = list(self.metadata.get_dtypes(table_name, ids=False).keys())
+            table_data = sampled_rows[data_fields]
+            for parent_name in parents:
+                parent_ids = self._find_parent_ids(table_name, parent_name, table_data)
+                foreign_key = self.metadata.get_foreign_key(parent_name, table_name)
+                # parent_ids = self._get_primary_keys(parent_name, num_rows)[1]
+                sampled_rows[foreign_key] = parent_ids
 
         if sample_children:
             sampled_data = {
@@ -237,7 +288,7 @@ def sample(self, table_name, num_rows, reset_primary_keys=False, sample_children
         else:
             return self._transform_synthesized_rows(sampled_rows, table_name)
 
-    def sample_all(self, num_rows=5, reset_primary_keys=False):
+    def sample_all(self, num_rows=None, reset_primary_keys=False):
         """Samples the entire dataset.
 
         ``sample_all`` returns a dictionary with all the tables of the dataset sampled.
@@ -249,9 +300,10 @@ def sample_all(self, num_rows=5, reset_primary_keys=False):
 
         Args:
             num_rows (int):
-                Number of rows to be sampled on the parent tables.
+                Number of rows to be sampled on the first parent tables. If ``None``,
+                sample the same number of rows as in the original tables.
             reset_primary_keys (bool):
-                Wheter or not reset the primary key generators.
+                Whether or not reset the primary key generators.
 
         Returns:
             dict:
diff --git a/sdv/sdv.py b/sdv/sdv.py
index f309801a0..4f05282b7 100644
--- a/sdv/sdv.py
+++ b/sdv/sdv.py
@@ -67,16 +67,18 @@ def fit(self, metadata, tables=None, root_path=None):
 
         self.modeler = Modeler(self.metadata, self.model, self.model_kwargs)
         self.modeler.model_database(tables)
-        self.sampler = Sampler(self.metadata, self.modeler.models, self.model, self.model_kwargs)
+        self.sampler = Sampler(self.metadata, self.modeler.models, self.model,
+                               self.model_kwargs, self.modeler.table_sizes)
 
-    def sample(self, table_name, num_rows, sample_children=True, reset_primary_keys=False):
+    def sample(self, table_name, num_rows=None, sample_children=True, reset_primary_keys=False):
         """Sample ``num_rows`` rows from the indicated table.
 
         Args:
             table_name (str):
                 Name of the table to sample from.
             num_rows (int):
-                Amount of rows to sample.
+                Amount of rows to sample. If ``None``, sample the same number of rows
+                as there were in the original table.
             sample_children (bool):
                 Whether or not to sample children tables. Defaults to ``True``.
             reset_primary_keys (bool):
@@ -100,12 +102,13 @@ def sample(self, table_name, num_rows, sample_children=True, reset_primary_keys=
             reset_primary_keys=reset_primary_keys
         )
 
-    def sample_all(self, num_rows=5, reset_primary_keys=False):
+    def sample_all(self, num_rows=None, reset_primary_keys=False):
         """Sample the entire dataset.
 
         Args:
             num_rows (int):
-                Amount of rows to sample. Defaults to ``5``.
+                Number of rows to be sampled on the first parent tables. If ``None``,
+                sample the same number of rows as in the original tables.
             reset_primary_keys (bool):
                 Wheter or not reset the primary key generators. Defaults to ``False``.
 

From 8eae9c7b4069b3bfc01396c8d72c5c03a41801b7 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Wed, 1 Jul 2020 23:44:41 +0200
Subject: [PATCH 03/33] Multi-parent support

---
 sdv/models/copulas.py |  47 ++++++++++-------
 sdv/models/utils.py   |  10 ++--
 sdv/sampler.py        | 115 +++++++++++++++++++++++-------------------
 setup.py              |   2 +-
 4 files changed, 102 insertions(+), 72 deletions(-)

diff --git a/sdv/models/copulas.py b/sdv/models/copulas.py
index 61c72bd9f..ff1665d50 100644
--- a/sdv/models/copulas.py
+++ b/sdv/models/copulas.py
@@ -1,5 +1,7 @@
 import numpy as np
-from copulas import multivariate, univariate
+from copulas import EPSILON
+from copulas.multivariate import GaussianMultivariate
+from copulas.univariate import GaussianUnivariate
 
 from sdv.models.base import SDVModel
 from sdv.models.utils import (
@@ -29,7 +31,7 @@ class GaussianCopula(SDVModel):
         4  1.925887
     """
 
-    DISTRIBUTION = univariate.GaussianUnivariate
+    DISTRIBUTION = GaussianUnivariate
     distribution = None
     model = None
 
@@ -46,7 +48,7 @@ def fit(self, table_data):
                 Data to be fitted.
         """
         table_data = impute(table_data)
-        self.model = multivariate.GaussianMultivariate(distribution=self.distribution)
+        self.model = GaussianMultivariate(distribution=self.distribution)
         self.model.fit(table_data)
 
     def sample(self, num_samples):
@@ -79,11 +81,20 @@ def get_parameters(self):
             values.append(row[:index + 1])
 
         self.model.covariance = np.array(values)
-        for distribution in self.model.distribs.values():
-            if distribution.std is not None:
-                distribution.std = np.log(distribution.std)
+        params = self.model.to_dict()
+        univariates = dict()
+        for name, univariate in zip(params.pop('columns'), params['univariates']):
+            univariates[name] = univariate
+            if 'scale' in univariate:
+                scale = univariate['scale']
+                if scale == 0:
+                    scale = EPSILON
 
-        return flatten_dict(self.model.to_dict())
+                univariate['scale'] = np.log(scale)
+
+        params['univariates'] = univariates
+
+        return flatten_dict(params)
 
     def _prepare_sampled_covariance(self, covariance):
         """Prepare a covariance matrix.
@@ -126,15 +137,20 @@ def _unflatten_gaussian_copula(self, model_parameters):
                 Model parameters ready to recreate the model.
         """
 
-        distribution_kwargs = {
-            'fitted': True,
+        univariate_kwargs = {
             'type': model_parameters['distribution']
         }
 
-        distribs = model_parameters['distribs']
-        for distribution in distribs.values():
-            distribution.update(distribution_kwargs)
-            distribution['std'] = np.exp(distribution['std'])
+        columns = list()
+        univariates = list()
+        for column, univariate in model_parameters['univariates'].items():
+            columns.append(column)
+            univariate.update(univariate_kwargs)
+            univariate['scale'] = np.exp(univariate['scale'])
+            univariates.append(univariate)
+
+        model_parameters['univariates'] = univariates
+        model_parameters['columns'] = columns
 
         covariance = model_parameters['covariance']
         model_parameters['covariance'] = self._prepare_sampled_covariance(covariance)
@@ -156,8 +172,5 @@ def set_parameters(self, parameters):
         parameters.setdefault('distribution', self.distribution)
 
         parameters = self._unflatten_gaussian_copula(parameters)
-        for param in parameters['distribs'].values():
-            param.setdefault('type', self.distribution)
-            param.setdefault('fitted', True)
 
-        self.model = multivariate.GaussianMultivariate.from_dict(parameters)
+        self.model = GaussianMultivariate.from_dict(parameters)
diff --git a/sdv/models/utils.py b/sdv/models/utils.py
index a839ecfb5..241a20048 100644
--- a/sdv/models/utils.py
+++ b/sdv/models/utils.py
@@ -20,11 +20,15 @@ def flatten_array(nested, prefix=''):
     for index in range(len(nested)):
         prefix_key = '__'.join([prefix, str(index)]) if len(prefix) else str(index)
 
-        if isinstance(nested[index], (list, np.ndarray)):
-            result.update(flatten_array(nested[index], prefix=prefix_key))
+        value = nested[index]
+        if isinstance(value, (list, np.ndarray)):
+            result.update(flatten_array(value, prefix=prefix_key))
+
+        elif isinstance(value, dict):
+            result.update(flatten_dict(value, prefix=prefix_key))
 
         else:
-            result[prefix_key] = nested[index]
+            result[prefix_key] = value
 
     return result
 
diff --git a/sdv/sampler.py b/sdv/sampler.py
index 8e0639b2b..706d8c064 100644
--- a/sdv/sampler.py
+++ b/sdv/sampler.py
@@ -37,24 +37,33 @@ def _reset_primary_keys_generators(self):
         self.primary_key = dict()
         self.remaining_primary_key = dict()
 
-    def _transform_synthesized_rows(self, synthesized, table_name):
+    def _finalize(self, sampled_data):
         """Reverse transform synthetized data.
 
         Args:
-            synthesized (pandas.DataFrame):
-                Generated data from model
-            table_name (str):
-                Name of the table.
+            sampled_data (dict):
+                Generated data
 
         Return:
             pandas.DataFrame:
                 Formatted synthesized data.
         """
-        reversed_data = self.metadata.reverse_transform(table_name, synthesized)
+        final_data = dict()
+        for table_name, table_rows in sampled_data.items():
+            parents = self.metadata.get_parents(table_name)
+            if parents:
+                for parent_name in parents:
+                    parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)
+                    foreign_key = self.metadata.get_foreign_key(parent_name, table_name)
+                    table_rows[foreign_key] = parent_ids
+
+            reversed_data = self.metadata.reverse_transform(table_name, table_rows)
+
+            fields = self.metadata.get_fields(table_name)
 
-        fields = self.metadata.get_fields(table_name)
+            final_data[table_name] = reversed_data[list(fields.keys())]
 
-        return reversed_data[list(fields.keys())]
+        return final_data
 
     def _get_primary_keys(self, table_name, num_rows):
         """Return the primary key and amount of values for the requested table.
@@ -119,7 +128,7 @@ def _get_primary_keys(self, table_name, num_rows):
 
         return primary_key, primary_key_values
 
-    def _get_extension(self, parent_row, table_name):
+    def _extract_parameters(self, parent_row, table_name):
         """Get the params from a generated parent row.
 
         Args:
@@ -128,7 +137,6 @@ def _get_extension(self, parent_row, table_name):
             table_name (str):
                 Name of the table to make the model for.
         """
-
         prefix = '__{}__'.format(table_name)
         keys = [key for key in parent_row.keys() if key.startswith(prefix)]
         new_keys = {key: key[len(prefix):] for key in keys}
@@ -165,64 +173,84 @@ def _sample_children(self, table_name, sampled):
                 self._sample_child_rows(child_name, table_name, row, sampled)
 
     def _sample_child_rows(self, table_name, parent_name, parent_row, sampled):
-        extension = self._get_extension(parent_row, table_name)
+        parameters = self._extract_parameters(parent_row, table_name)
 
         model = self.model(**self.model_kwargs)
-        model.set_parameters(extension)
-        num_rows = max(round(extension['child_rows']), 0)
+        model.set_parameters(parameters)
+        num_rows = max(round(parameters['child_rows']), 0)
 
-        sampled_rows = self._sample_rows(model, num_rows, table_name)
+        table_rows = self._sample_rows(model, num_rows, table_name)
 
         parent_key = self.metadata.get_primary_key(parent_name)
         foreign_key = self.metadata.get_foreign_key(parent_name, table_name)
-        sampled_rows[foreign_key] = parent_row[parent_key]
+        table_rows[foreign_key] = parent_row[parent_key]
 
         previous = sampled.get(table_name)
         if previous is None:
-            sampled[table_name] = sampled_rows
+            sampled[table_name] = table_rows
         else:
-            sampled[table_name] = pd.concat([previous, sampled_rows]).reset_index(drop=True)
+            sampled[table_name] = pd.concat([previous, table_rows]).reset_index(drop=True)
 
         self._sample_children(table_name, sampled)
 
     def _get_pdfs(self, parent_rows, child_name):
         pdfs = dict()
         for parent_id, row in parent_rows.iterrows():
-            extension = self._get_extension(row, child_name)
+            parameters = self._extract_parameters(row, child_name)
             model = self.model(**self.model_kwargs)
-            model.set_parameters(extension)
+            model.set_parameters(parameters)
             pdfs[parent_id] = model.model.probability_density
 
         return pdfs
 
-    def _find_parent_id(self, row, pdfs):
+    def _find_parent_id(self, row, pdfs, num_rows):
         likelihoods = dict()
         for parent_id, pdf in pdfs.items():
             try:
-                likelihoods[parent_id] = pdf(row)
+                likelihoods[parent_id] = max(pdf(row), 0.0)
             except np.linalg.LinAlgError:
+                # Singular matrix
                 likelihoods[parent_id] = None
 
         likelihoods = pd.Series(likelihoods)
-        likelihoods = likelihoods.fillna(likelihoods.mean()).fillna(0)
+        mean = likelihoods.mean()
+        if (likelihoods == 0).all():
+            # All rows got 0 likelihood, fallback to num_rows
+            likelihoods = num_rows
+        elif pd.isnull(mean) or mean == 0:
+            # No row got likelihood > 0, but some got singlar matrix
+            # Fallback to num_rows on the singular matrix rows
+            likelihoods = likelihoods.fillna(num_rows)
+        else:
+            # at least one row got likelihood > 0, so fill the
+            # singular matrix rows with the mean
+            likelihoods = likelihoods.fillna(mean)
 
-        # weights = likelihoods.values / likelihoods.sum()
+        total = likelihoods.sum()
+        if total == 0:
+            weights = np.ones(len(likelihoods))
+        else:
+            weights = likelihoods.values / likelihoods.sum()
 
-        return likelihoods.idxmax()
+        return np.random.choice(likelihoods.index, p=weights)
 
-    def _find_parent_ids(self, table_name, parent_name, sampled_rows, sampled=None):
-        ratio = self.table_sizes[parent_name] / self.table_sizes[table_name]
-        num_parent_rows = max(int(round(len(sampled_rows) * ratio)), 1)
-        parent_model = self.models[parent_name]
-        parent_rows = self._sample_rows(parent_model, num_parent_rows, parent_name)
+    def _find_parent_ids(self, table_name, parent_name, sampled_data):
+        table_rows = sampled_data[table_name]
+        if parent_name in sampled_data:
+            parent_rows = sampled_data[parent_name]
+        else:
+            ratio = self.table_sizes[parent_name] / self.table_sizes[table_name]
+            num_parent_rows = max(int(round(len(table_rows) * ratio)), 1)
+            parent_model = self.models[parent_name]
+            parent_rows = self._sample_rows(parent_model, num_parent_rows, parent_name)
 
-        foreign_key = self.metadata.get_foreign_key(parent_name, table_name)
         primary_key = self.metadata.get_primary_key(parent_name)
         pdfs = self._get_pdfs(parent_rows.set_index(primary_key), table_name)
+        num_rows = parent_rows['__' + table_name + '__child_rows'].clip(0)
 
         parent_ids = list()
-        for _, row in sampled_rows.iterrows():
-            parent_ids.append(self._find_parent_id(row, pdfs))
+        for _, row in table_rows.iterrows():
+            parent_ids.append(self._find_parent_id(row, pdfs, num_rows))
 
         return parent_ids
 
@@ -253,7 +281,6 @@ def sample(self, table_name, num_rows=None, reset_primary_keys=False,
                   and child tables.
                 - Returns a ``pandas.DataFrame`` when ``sample_children`` is ``False``.
         """
-
         if reset_primary_keys:
             self._reset_primary_keys_generators()
 
@@ -261,32 +288,18 @@ def sample(self, table_name, num_rows=None, reset_primary_keys=False,
             num_rows = self.table_sizes[table_name]
 
         model = self.models[table_name]
-        sampled_rows = self._sample_rows(model, num_rows, table_name)
-
-        parents = self.metadata.get_parents(table_name)
-        if parents and sample_parents:
-            data_fields = list(self.metadata.get_dtypes(table_name, ids=False).keys())
-            table_data = sampled_rows[data_fields]
-            for parent_name in parents:
-                parent_ids = self._find_parent_ids(table_name, parent_name, table_data)
-                foreign_key = self.metadata.get_foreign_key(parent_name, table_name)
-                # parent_ids = self._get_primary_keys(parent_name, num_rows)[1]
-                sampled_rows[foreign_key] = parent_ids
+        table_rows = self._sample_rows(model, num_rows, table_name)
 
         if sample_children:
             sampled_data = {
-                table_name: sampled_rows
+                table_name: table_rows
             }
 
             self._sample_children(table_name, sampled_data)
-
-            for table, sampled_rows in sampled_data.items():
-                sampled_data[table] = self._transform_synthesized_rows(sampled_rows, table)
-
-            return sampled_data
+            return self._finalize(sampled_data)
 
         else:
-            return self._transform_synthesized_rows(sampled_rows, table_name)
+            return self._finalize({table_name: table_rows})[table_name]
 
     def sample_all(self, num_rows=None, reset_primary_keys=False):
         """Samples the entire dataset.
diff --git a/setup.py b/setup.py
index 81283021b..bca8362a0 100644
--- a/setup.py
+++ b/setup.py
@@ -15,7 +15,7 @@
     'exrex>=0.9.4,<0.11',
     'numpy>=1.15.4,<1.17',
     'pandas>=0.23.4,<0.25',
-    'copulas>=0.2.5,<0.3',
+    'copulas>=0.3,<0.4',
     'rdt>=0.2.1,<0.3',
     'graphviz>=0.13.2',
     'sdmetrics>=0.0.1,<0.0.2'

From bf50f63d33cdb5f548d637287e9b0fa53538c555 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 2 Jul 2020 15:49:58 +0200
Subject: [PATCH 04/33] Run numbered examples only

---
 Makefile                                      |   2 +-
 examples/0. Quickstart - README.ipynb         | 506 ++++++------------
 ...uickstart - Single Table - In Memory.ipynb | 117 ++--
 .... Quickstart - Single Table - Census.ipynb | 159 +++---
 .../3. Quickstart - Multitable - Files.ipynb  | 165 +++---
 examples/4. Anonymization.ipynb               |  72 ++-
 ...5. Generate Metadata from Dataframes.ipynb |  51 +-
 examples/Demo - Walmart.ipynb                 | 185 ++++++-
 examples/Evaluation.ipynb                     | 193 +++++++
 examples/demo_metadata.json                   |  26 +-
 10 files changed, 831 insertions(+), 645 deletions(-)
 create mode 100644 examples/Evaluation.ipynb

diff --git a/Makefile b/Makefile
index bdcbeac21..31f1d6d6f 100644
--- a/Makefile
+++ b/Makefile
@@ -110,7 +110,7 @@ test-readme: ## run the readme snippets
 
 .PHONY: test-tutorials
 test-tutorials: ## run the tutorial notebooks
-	jupyter nbconvert --execute --ExecutePreprocessor.timeout=600 examples/*.ipynb --stdout > /dev/null
+	jupyter nbconvert --execute --ExecutePreprocessor.timeout=600 examples/?.\ *.ipynb --stdout > /dev/null
 
 .PHONY: test
 test: test-unit test-readme test-tutorials ## test everything that needs test dependencies
diff --git a/examples/0. Quickstart - README.ipynb b/examples/0. Quickstart - README.ipynb
index fa7ba905b..f6f2fecdc 100644
--- a/examples/0. Quickstart - README.ipynb	
+++ b/examples/0. Quickstart - README.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -48,7 +48,7 @@
        "    'approved': {'type': 'boolean'}}}}}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +100,7 @@
        " 9               9           9 2019-01-29 12:10:48    99.9      True}"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -188,10 +188,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7fc333a8f710>"
+       "<graphviz.dot.Digraph at 0x7f3aba585080>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {
     "scrolled": false
    },
@@ -211,27 +211,43 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-06-25 16:03:03,773 - INFO - modeler - Modeling users\n",
-      "2020-06-25 16:03:03,778 - INFO - modeler - Modeling sessions\n",
-      "2020-06-25 16:03:03,783 - INFO - modeler - Modeling transactions\n",
-      "2020-06-25 16:03:03,791 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,803 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,812 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,819 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,825 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,834 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,840 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,845 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,861 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,891 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,909 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,935 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,954 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,972 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:03,990 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:04,018 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:04,141 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-06-25 16:03:04,326 - INFO - modeler - Modeling Complete\n"
+      "2020-07-02 14:24:31,338 - INFO - modeler - Modeling users\n",
+      "2020-07-02 14:24:31,339 - INFO - metadata - Loading transformer CategoricalTransformer for field country\n",
+      "2020-07-02 14:24:31,340 - INFO - metadata - Loading transformer CategoricalTransformer for field gender\n",
+      "2020-07-02 14:24:31,341 - INFO - metadata - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-02 14:24:31,356 - INFO - modeler - Modeling sessions\n",
+      "2020-07-02 14:24:31,357 - INFO - metadata - Loading transformer CategoricalTransformer for field device\n",
+      "2020-07-02 14:24:31,357 - INFO - metadata - Loading transformer CategoricalTransformer for field os\n",
+      "2020-07-02 14:24:31,371 - INFO - modeler - Modeling transactions\n",
+      "2020-07-02 14:24:31,372 - INFO - metadata - Loading transformer DatetimeTransformer for field timestamp\n",
+      "2020-07-02 14:24:31,373 - INFO - metadata - Loading transformer NumericalTransformer for field amount\n",
+      "2020-07-02 14:24:31,373 - INFO - metadata - Loading transformer BooleanTransformer for field approved\n",
+      "2020-07-02 14:24:31,386 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,396 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
+      "  return getattr(obj, method)(*args, **kwds)\n",
+      "2020-07-02 14:24:31,404 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/pandas/core/frame.py:7143: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  baseCov = np.cov(mat.T)\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2451: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  c *= np.true_divide(1, fact)\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2451: RuntimeWarning: invalid value encountered in multiply\n",
+      "  c *= np.true_divide(1, fact)\n",
+      "2020-07-02 14:24:31,413 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,418 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,425 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,431 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,435 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,447 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,471 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,485 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,502 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,516 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,530 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,544 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,563 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,629 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:24:31,734 - INFO - modeler - Modeling Complete\n"
      ]
     }
    ],
@@ -244,321 +260,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(102)\u001b[0;36m_prepare_sampled_covariance\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    101 \u001b[0;31m        \u001b[0;32mimport\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 102 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquare_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    103 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> covariance\n",
-      "[[1.726615061615442], [1.0454201815611962e-17, 0.8202611657476363], [0.906087951592798, 0.0, 0.9063498818810053], [0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [1.7211659999181501, 0.09407387578039639, 0.9062265705680096, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.7265698690747444], [0.1962595829950089, -0.4101010550927784, 0.9064318910717786, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15363369110349148, 1.7265890069169954], [1.6501160558524515, 0.34588621287899607, 0.9060859813924124, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.6848330864411356, 0.089532714183855, 1.7265426703918063], [0.19506154837332199, -0.41017105995034014, 0.9064258143689862, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15319193719168775, 1.7265715342431274, 0.08933484748381948, 1.726601450683982], [1.558118079154194, -0.49788243931513426, 0.9061755927291784, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.4967074003515342, 0.5906071587381734, 1.287493411569124, 0.5910547002051788, 1.7265085917492808], [0.19622238674880996, -0.4101312643965461, 0.9063303749716511, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15392250979132835, 1.7265715878572472, 0.08897912790316595, 1.726633522580159, 0.5911723429044428, 1.7265328687279475], [-1.426683586477038, 0.6340766049086322, -0.9064943110193431, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.350356266672644, -0.7728329555360014, -1.1105383964628412, -0.7729561856331211, -1.7046109781197039, -0.7727141956567691, 1.7265200912725471], [0.19613513187055676, -0.4101447540378119, 0.9060999824108501, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1536514208926178, 1.726564276607309, 0.08894497870076296, 1.7266086430031098, 0.5907859264695859, 1.7266240785900109, -0.7726518888264287, 1.7265194550742498], [-0.19600331514505734, 0.4100687094013292, -0.9060002638533913, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.15335524134834988, -1.7265840276560094, -0.08909093050930667, -1.7265435834346627, -0.5903898337829075, -1.7265249406712062, 0.7728873327661734, -1.7266499172273617, 1.726589663707634]]\n",
-      "ipdb> n\n",
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(103)\u001b[0;36m_prepare_sampled_covariance\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    102 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquare_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 103 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    104 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> covariance\n",
-      "array([[ 1.72661506e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 1.04542018e-17,  8.20261166e-01,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 9.06087952e-01,  0.00000000e+00,  9.06349882e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         1.19209290e-07,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.19209290e-07,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  1.19209290e-07,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         1.19209290e-07,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.19209290e-07,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  1.19209290e-07,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         1.19209290e-07,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.19209290e-07,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  1.19209290e-07,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         1.19209290e-07,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 1.72116600e+00,  9.40738758e-02,  9.06226571e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.72656987e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 1.96259583e-01, -4.10101055e-01,  9.06431891e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.53633691e-01,  1.72658901e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 1.65011606e+00,  3.45886213e-01,  9.06085981e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.68483309e+00,  8.95327142e-02,\n",
-      "         1.72654267e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 1.95061548e-01, -4.10171060e-01,  9.06425814e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.53191937e-01,  1.72657153e+00,\n",
-      "         8.93348475e-02,  1.72660145e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 1.55811808e+00, -4.97882439e-01,  9.06175593e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.49670740e+00,  5.90607159e-01,\n",
-      "         1.28749341e+00,  5.91054700e-01,  1.72650859e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 1.96222387e-01, -4.10131264e-01,  9.06330375e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.53922510e-01,  1.72657159e+00,\n",
-      "         8.89791279e-02,  1.72663352e+00,  5.91172343e-01,\n",
-      "         1.72653287e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [-1.42668359e+00,  6.34076605e-01, -9.06494311e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00, -1.35035627e+00, -7.72832956e-01,\n",
-      "        -1.11053840e+00, -7.72956186e-01, -1.70461098e+00,\n",
-      "        -7.72714196e-01,  1.72652009e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [ 1.96135132e-01, -4.10144754e-01,  9.06099982e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  1.53651421e-01,  1.72656428e+00,\n",
-      "         8.89449787e-02,  1.72660864e+00,  5.90785926e-01,\n",
-      "         1.72662408e+00, -7.72651889e-01,  1.72651946e+00,\n",
-      "         0.00000000e+00],\n",
-      "       [-1.96003315e-01,  4.10068709e-01, -9.06000264e-01,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-      "         0.00000000e+00, -1.53355241e-01, -1.72658403e+00,\n",
-      "        -8.90909305e-02, -1.72654358e+00, -5.90389834e-01,\n",
-      "        -1.72652494e+00,  7.72887333e-01, -1.72664992e+00,\n",
-      "         1.72658966e+00]])\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ipdb> ll\n",
-      "\u001b[1;32m     90 \u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m_prepare_sampled_covariance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m     91 \u001b[0m        \"\"\"Prepare a covariance matrix.\n",
-      "\u001b[1;32m     92 \u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m     93 \u001b[0m        \u001b[0mArgs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m     94 \u001b[0m            \u001b[0mcovariance\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m     95 \u001b[0m                \u001b[0mcovariance\u001b[0m \u001b[0mafter\u001b[0m \u001b[0munflattening\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m     96 \u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m     97 \u001b[0m        \u001b[0mResult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m     98 \u001b[0m            \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m     99 \u001b[0m                \u001b[0msymmetric\u001b[0m \u001b[0mPositive\u001b[0m \u001b[0msemi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdefinite\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m    100 \u001b[0m        \"\"\"\n",
-      "\u001b[1;32m    101 \u001b[0m        \u001b[0;32mimport\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m    102 \u001b[0m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquare_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m--> 103 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[1;32m    104 \u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m    105 \u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcheck_matrix_symmetric_positive_definite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m    106 \u001b[0m            \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_positive_definite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m    107 \u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m    108 \u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m    109 \u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\n",
-      "ipdb> n\n",
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(105)\u001b[0;36m_prepare_sampled_covariance\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    104 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 105 \u001b[0;31m        \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcheck_matrix_symmetric_positive_definite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    106 \u001b[0;31m            \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_positive_definite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> n\n",
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(106)\u001b[0;36m_prepare_sampled_covariance\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    105 \u001b[0;31m        \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcheck_matrix_symmetric_positive_definite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 106 \u001b[0;31m            \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_positive_definite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    107 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> n\n",
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(108)\u001b[0;36m_prepare_sampled_covariance\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    107 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 108 \u001b[0;31m        \u001b[0;32mreturn\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    109 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> n\n",
-      "--Return--\n",
-      "[[1.726928289354223, -6.284976501719013e-05, 0.9060437009442075, 1.1799638567951807e-18, -1.7530700091068226e-20, 5.17606189171064e-21, ...], [-6.284976501719013e-05, 0.8203273652660589, -3.601422526398147e-05, 5.414945862916836e-17, 3.245206248848917e-20, -1.2006964972373577e-20, ...], [0.9060437009442075, -3.601422526398147e-05, 0.9064526869220875, 1.933376368463285e-17, -2.6655267441977074e-20, 2.5829833603018656e-21, ...], [1.1799638567951807e-18, 5.414945862916836e-17, 1.933376368463285e-17, 1.192092915444104e-07, -1.1365716705948445e-20, 4.7277921079772496e-21, ...], [-1.7530700091068226e-20, 3.245206248848917e-20, -2.6655267441977074e-20, -1.1365716705948445e-20, 1.1920929154436114e-07, 5.437058680680646e-21, ...], [5.17606189171064e-21, -1.2006964972373577e-20, 2.5829833603018656e-21, 4.7277921079772496e-21, 5.437058680680646e-21, 1.1920929154437684e-07, ...], ...]\n",
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(108)\u001b[0;36m_prepare_sampled_covariance\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    107 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 108 \u001b[0;31m        \u001b[0;32mreturn\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    109 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> n\n",
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(145)\u001b[0;36m_unflatten_gaussian_copula\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    144 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 145 \u001b[0;31m        \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    146 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> c\n",
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(102)\u001b[0;36m_prepare_sampled_covariance\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    101 \u001b[0;31m        \u001b[0;32mimport\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 102 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquare_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    103 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> covariance\n",
-      "[[1.9735777830655135], [1.0081332489157226e-18, 0.07922245304646469], [1.8944415499749927, 0.0, 1.8945678304393692], [0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [1.9731403682292472, 0.009080369609414676, 1.8945052811636178, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.9736633430154003], [1.8257571570695899, -0.03951915463265521, 1.8943818752505202, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.8223719529889824, 1.9735960424607375], [1.9662149071445283, 0.03332947346115095, 1.8943702463439986, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.9696074413161604, 1.8155029069801496, 1.9735786685725154], [1.8259699330878305, -0.039591165816158624, 1.8946306798075692, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.8222344112809918, 1.973660492989475, 1.8155381885055653, 1.9736509880140303], [1.9574634628424201, -0.0480675268053235, 1.8944800309305712, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.9514242514903646, 1.8641779050506904, 1.931397843569703, 1.8644699021432583, 1.97360470087791], [1.8261599075098556, -0.039602178471882965, 1.8944795379664088, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.8221689799836889, 1.9736216215949314, 1.8159480875771647, 1.9736090474517491, 1.8643658311250595, 1.9735713191144897], [-1.9447451350012899, 0.06124906062526714, -1.8941152019092415, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.9372479886935285, -1.8815955695505688, -1.9143447992000526, -1.8819069289711103, -1.9715806948911605, -1.8818122561418829, 1.9735728616891475], [1.826265421380993, -0.03969242272676249, 1.8947570439794252, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.821544416662928, 1.9736431265524037, 1.815795088268001, 1.9735745553644184, 1.8640973954156812, 1.9736683290141885, -1.8818260533698774, 1.9736371138967417], [-1.8260997940060133, 0.039593106594922045, -1.8945455094034567, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.8221756212795035, -1.9736399789373342, -1.8157747174291041, -1.973587031691748, -1.863968052730401, -1.9737028348459618, 1.8816417352261723, -1.9736384778314904, 1.9735905131918667]]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ipdb> c\n",
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(102)\u001b[0;36m_prepare_sampled_covariance\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    101 \u001b[0;31m        \u001b[0;32mimport\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 102 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquare_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    103 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> covariance\n",
-      "[[1.7798122161501428], [8.418645096312187e-18, 0.6606961152453757], [1.1193765416016843, 0.0, 1.119151425141485], [0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.1920928955078125e-07], [1.7754388866407922, 0.07580182865822005, 1.1193005781250343, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.7797523138925522], [0.5470801791882579, -0.3302484465683945, 1.1192475956082857, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5129529949615216, 1.779801879956687], [1.718206887546536, 0.2784760244595479, 1.119072525637323, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.746240556959611, 0.4609048552481457, 1.7798064162607745], [0.5470062693558146, -0.3303697163214912, 1.1192392868938212, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5128535772731618, 1.7797855289798796, 0.4614315710843969, 1.7798640319936492], [1.6441999827502363, -0.4009517141082254, 1.1191904874368754, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.5946523925660905, 0.8650486386888843, 1.4262448365733487, 0.8650813439515379, 1.7798317994703388], [0.546920059994199, -0.3302738771226835, 1.1191467637807095, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5131699084433272, 1.7797358524773224, 0.4613957209455638, 1.7798293469122717, 0.865178931907089, 1.779727156681622], [-1.5381861979492537, 0.5108352793295525, -1.119103691856403, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.4767659633854602, -1.011712169525542, -1.2837980435462297, -1.0115506970319363, -1.7621372594453895, -1.0115631089643444, 1.7798513072348816], [0.547088849554847, -0.3303011887943328, 1.119126575120807, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5129183354605175, 1.779756666332634, 0.46107358199142606, 1.7797463173063606, 0.8648122390861165, 1.7797904461596725, -1.0117639532832405, 1.7798273537990834], [-0.5470493658634797, 0.33018152099185216, -1.119089754226156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5129960724566422, -1.7798135506798376, -0.4609291114365542, -1.779840914364154, -0.8650801853648311, -1.7798114433503558, 1.0116215056600828, -1.7798428944094702, 1.7798232994357222]]\n",
-      "ipdb> c\n",
-      "> \u001b[0;32m/home/xals/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m(102)\u001b[0;36m_prepare_sampled_covariance\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    101 \u001b[0;31m        \u001b[0;32mimport\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 102 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquare_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    103 \u001b[0;31m        \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> covariance\n",
-      "[[None], [None, None], [None, None, None], [None, None, None, None]]\n",
-      "ipdb> q\n"
-     ]
-    },
-    {
-     "ename": "BdbQuit",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mBdbQuit\u001b[0m                                   Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-16-2368c2d72bb7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msdv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sdv.py\u001b[0m in \u001b[0;36msample_all\u001b[0;34m(self, num_rows, reset_primary_keys)\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'SDV instance has not been fitted'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_primary_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset_primary_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36msample_all\u001b[0;34m(self, num_rows, reset_primary_keys)\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_parents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m                 \u001b[0msampled_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msampled_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, table_name, num_rows, reset_primary_keys, sample_children)\u001b[0m\n\u001b[1;32m    228\u001b[0m             }\n\u001b[1;32m    229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sample_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled_rows\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msampled_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_sample_children\u001b[0;34m(self, table_name, sampled)\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mchild_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtable_rows\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sample_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_sample_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent_row\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_sample_table\u001b[0;34m(self, table_name, parent_name, parent_row, sampled)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0msampled\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mprevious\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled_rows\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sample_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_primary_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_children\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_sample_children\u001b[0;34m(self, table_name, sampled)\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mchild_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtable_rows\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sample_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_sample_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent_row\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_sample_table\u001b[0;34m(self, table_name, parent_name, parent_row, sampled)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m         \u001b[0mnum_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'child_rows'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m in \u001b[0;36mset_parameters\u001b[0;34m(self, parameters)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'distribution'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistribution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m         \u001b[0mparameters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unflatten_gaussian_copula\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'univariates'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m             \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistribution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m in \u001b[0;36m_unflatten_gaussian_copula\u001b[0;34m(self, model_parameters)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_parameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'covariance'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m         \u001b[0mmodel_parameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'covariance'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_sampled_covariance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m in \u001b[0;36m_prepare_sampled_covariance\u001b[0;34m(self, covariance)\u001b[0m\n\u001b[1;32m    100\u001b[0m         \"\"\"\n\u001b[1;32m    101\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquare_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m         \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/models/copulas.py\u001b[0m in \u001b[0;36m_prepare_sampled_covariance\u001b[0;34m(self, covariance)\u001b[0m\n\u001b[1;32m    100\u001b[0m         \"\"\"\n\u001b[1;32m    101\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mipdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msquare_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m         \u001b[0mcovariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.6/bdb.py\u001b[0m in \u001b[0;36mtrace_dispatch\u001b[0;34m(self, frame, event, arg)\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;31m# None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'line'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'call'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/lib/python3.6/bdb.py\u001b[0m in \u001b[0;36mdispatch_line\u001b[0;34m(self, frame)\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_here\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbreak_here\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquitting\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mBdbQuit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrace_dispatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mBdbQuit\u001b[0m: "
-     ]
+     "data": {
+      "text/plain": [
+       "{'users':    user_id country gender  age\n",
+       " 0        0      ES      F   50\n",
+       " 1        1      FR    NaN   61\n",
+       " 2        2      UK      F   19\n",
+       " 3        3     USA      F   37\n",
+       " 4        4      UK      M   14\n",
+       " 5        5     USA      M   20\n",
+       " 6        6      FR      F   41\n",
+       " 7        7      UK    NaN   15\n",
+       " 8        8     USA      F   17\n",
+       " 9        9      UK      M   27,\n",
+       " 'sessions':    session_id  user_id  device       os\n",
+       " 0           0        5  mobile  android\n",
+       " 1           1        3  tablet      ios\n",
+       " 2           2        5  mobile      ios\n",
+       " 3           3        2  mobile      ios\n",
+       " 4           4        1  mobile      ios\n",
+       " 5           5        8  mobile      ios\n",
+       " 6           6        3  mobile      ios\n",
+       " 7           7        2  tablet      ios\n",
+       " 8           8        8  tablet      ios,\n",
+       " 'transactions':     transaction_id  session_id                     timestamp       amount  \\\n",
+       " 0                0           1 2019-01-05 19:44:17.109082880   108.520480   \n",
+       " 1                1           0 2019-01-05 19:44:17.109082880   108.520446   \n",
+       " 2                2           4 2019-01-05 19:44:17.109082880   108.520456   \n",
+       " 3                3           4 2019-01-05 19:44:17.109082880   108.520463   \n",
+       " 4                4           0 2019-01-05 19:44:17.109082880   108.520451   \n",
+       " 5                5           4 2019-01-14 10:59:23.654829312  -293.744075   \n",
+       " 6                6           0 2019-01-08 21:06:22.124677120  -157.555578   \n",
+       " 7                7           4 2019-01-05 19:44:17.109082880   108.520405   \n",
+       " 8                8           4 2019-01-20 05:49:39.108628736  1436.388992   \n",
+       " 9                9           0 2019-01-13 01:21:03.496772608  -688.014399   \n",
+       " 10              10           5 2019-01-15 19:14:13.132234496    92.573682   \n",
+       " 11              11           0 2019-01-15 19:13:47.834800128    88.733610   \n",
+       " 12              12           6 2019-01-15 19:13:25.752608512    87.612329   \n",
+       " 13              13           6 2019-01-15 19:14:06.131340544    91.149239   \n",
+       " 14              14           4 2019-01-05 19:44:17.109082880   108.520315   \n",
+       " 15              15           4 2019-01-10 14:54:25.195068416  -405.271997   \n",
+       " 16              16           0 2019-01-06 11:03:17.424094208   321.267007   \n",
+       " 17              17           1 2019-01-15 19:13:30.394386944    89.473644   \n",
+       " 18              18           5 2019-01-15 19:13:23.178177536    87.084668   \n",
+       " 19              19           5 2019-01-15 19:13:53.814526976    87.328847   \n",
+       " 20              20           6 2019-01-15 19:13:47.778595840    87.698023   \n",
+       " 21              21           0 2019-01-05 19:44:17.109082880   108.520444   \n",
+       " 22              22           4 2019-01-03 13:01:55.231368192  -171.151299   \n",
+       " 23              23           4 2019-01-10 03:03:38.631104256  -711.512501   \n",
+       " 24              24           5 2019-01-15 19:13:39.923056128    92.779222   \n",
+       " 25              25           5 2019-01-15 19:13:54.239550976    87.046705   \n",
+       " 26              26           1 2019-01-15 19:13:51.475357952    89.768894   \n",
+       " 27              27           6 2019-01-15 19:13:38.378663168    85.864682   \n",
+       " 28              28           7 2019-01-20 22:53:01.679367168    84.550410   \n",
+       " 29              29           7 2019-01-20 22:53:01.672930560    84.739006   \n",
+       " 30              30           1 2019-01-20 22:53:01.444472832    84.790641   \n",
+       " 31              31           8 2019-01-20 22:53:01.404470784    84.595276   \n",
+       " 32              32           4 2019-01-05 19:44:17.109082880   108.520411   \n",
+       " 33              33           4 2019-01-15 17:22:02.305563904  -602.769803   \n",
+       " 34              34           1 2019-01-15 04:01:37.883063808  -493.475733   \n",
+       " 35              35           0 2019-01-15 19:13:20.536071936    82.887047   \n",
+       " 36              36           5 2019-01-15 19:13:50.685021696    90.577240   \n",
+       " 37              37           6 2019-01-15 19:14:05.128847616    91.543630   \n",
+       " 38              38           6 2019-01-15 19:13:10.650768896    90.478404   \n",
+       " 39              39           7 2019-01-20 22:53:01.680873472    84.876897   \n",
+       " 40              40           7 2019-01-20 22:53:01.735595520    85.080808   \n",
+       " 41              41           0 2019-01-20 22:53:01.332932352    84.584933   \n",
+       " 42              42           8 2019-01-20 22:53:01.451664640    84.779657   \n",
+       " \n",
+       "     approved  \n",
+       " 0      False  \n",
+       " 1      False  \n",
+       " 2      False  \n",
+       " 3      False  \n",
+       " 4      False  \n",
+       " 5       True  \n",
+       " 6       True  \n",
+       " 7      False  \n",
+       " 8       True  \n",
+       " 9       True  \n",
+       " 10      True  \n",
+       " 11      True  \n",
+       " 12      True  \n",
+       " 13      True  \n",
+       " 14     False  \n",
+       " 15      True  \n",
+       " 16      True  \n",
+       " 17      True  \n",
+       " 18      True  \n",
+       " 19      True  \n",
+       " 20      True  \n",
+       " 21     False  \n",
+       " 22      True  \n",
+       " 23      True  \n",
+       " 24      True  \n",
+       " 25      True  \n",
+       " 26      True  \n",
+       " 27      True  \n",
+       " 28      True  \n",
+       " 29      True  \n",
+       " 30      True  \n",
+       " 31      True  \n",
+       " 32     False  \n",
+       " 33      True  \n",
+       " 34      True  \n",
+       " 35      True  \n",
+       " 36      True  \n",
+       " 37      True  \n",
+       " 38      True  \n",
+       " 39      True  \n",
+       " 40      True  \n",
+       " 41      True  \n",
+       " 42      True  }"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
diff --git a/examples/1. Quickstart - Single Table - In Memory.ipynb b/examples/1. Quickstart - Single Table - In Memory.ipynb
index 6a07eeb32..b9f48bbd2 100644
--- a/examples/1. Quickstart - Single Table - In Memory.ipynb	
+++ b/examples/1. Quickstart - Single Table - In Memory.ipynb	
@@ -232,14 +232,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-02-01 19:45:45,775 - INFO - modeler - Modeling data\n",
-      "2020-02-01 19:45:45,776 - INFO - metadata - Loading transformer NumericalTransformer for field integer\n",
-      "2020-02-01 19:45:45,777 - INFO - metadata - Loading transformer NumericalTransformer for field float\n",
-      "2020-02-01 19:45:45,777 - INFO - metadata - Loading transformer CategoricalTransformer for field categorical\n",
-      "2020-02-01 19:45:45,778 - INFO - metadata - Loading transformer BooleanTransformer for field bool\n",
-      "2020-02-01 19:45:45,779 - INFO - metadata - Loading transformer NumericalTransformer for field nullable\n",
-      "2020-02-01 19:45:45,779 - INFO - metadata - Loading transformer DatetimeTransformer for field datetime\n",
-      "2020-02-01 19:45:45,824 - INFO - modeler - Modeling Complete\n"
+      "2020-07-02 14:25:11,511 - INFO - modeler - Modeling data\n",
+      "2020-07-02 14:25:11,512 - INFO - metadata - Loading transformer NumericalTransformer for field integer\n",
+      "2020-07-02 14:25:11,512 - INFO - metadata - Loading transformer NumericalTransformer for field float\n",
+      "2020-07-02 14:25:11,513 - INFO - metadata - Loading transformer CategoricalTransformer for field categorical\n",
+      "2020-07-02 14:25:11,513 - INFO - metadata - Loading transformer BooleanTransformer for field bool\n",
+      "2020-07-02 14:25:11,513 - INFO - metadata - Loading transformer NumericalTransformer for field nullable\n",
+      "2020-07-02 14:25:11,514 - INFO - metadata - Loading transformer DatetimeTransformer for field datetime\n",
+      "2020-07-02 14:25:11,551 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:11,564 - INFO - modeler - Modeling Complete\n"
      ]
     }
    ],
@@ -291,52 +292,82 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.080051</td>\n",
-       "      <td>a</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaT</td>\n",
+       "      <td>b</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2010-01-21 11:39:29.986688000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.015712</td>\n",
-       "      <td>a</td>\n",
-       "      <td>False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>2009-12-08 11:20:58.439345408</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2010-02-17 13:51:34.408565760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.142979</td>\n",
-       "      <td>a</td>\n",
-       "      <td>True</td>\n",
-       "      <td>4.971408</td>\n",
-       "      <td>2010-01-08 00:32:21.629585920</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.304326</td>\n",
+       "      <td>c</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2010-02-25 12:07:19.982103552</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.133913</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.174580</td>\n",
        "      <td>a</td>\n",
        "      <td>True</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>2010-01-14 21:37:22.051623936</td>\n",
+       "      <td>2010-01-21 04:04:59.336169472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.159480</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.208637</td>\n",
        "      <td>a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.026796</td>\n",
+       "      <td>b</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.166949</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2010-01-28 23:23:34.873413888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.086972</td>\n",
+       "      <td>b</td>\n",
        "      <td>False</td>\n",
-       "      <td>4.294080</td>\n",
-       "      <td>2010-01-09 21:09:11.245925888</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2010-01-08 09:44:47.101891840</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -344,18 +375,24 @@
       ],
       "text/plain": [
        "   index  integer     float categorical   bool  nullable  \\\n",
-       "0      0        1  0.080051           a    NaN       NaN   \n",
-       "1      1        0 -0.015712           a  False       NaN   \n",
-       "2      2        1  0.142979           a   True  4.971408   \n",
-       "3      3        1  0.133913           a   True       NaN   \n",
-       "4      4        1  0.159480           a  False  4.294080   \n",
+       "0      0      NaN       NaN           b  False       NaN   \n",
+       "1      1      NaN       NaN         NaN   True       NaN   \n",
+       "2      2      3.0  0.304326           c  False       NaN   \n",
+       "3      3      2.0  0.174580           a   True       NaN   \n",
+       "4      4      3.0  0.208637           a    NaN       NaN   \n",
+       "5      5      1.0  0.026796           b    NaN       NaN   \n",
+       "6      6      2.0  0.166949         NaN  False       NaN   \n",
+       "7      7      1.0  0.086972           b  False       NaN   \n",
        "\n",
        "                       datetime  \n",
-       "0                           NaT  \n",
-       "1 2009-12-08 11:20:58.439345408  \n",
-       "2 2010-01-08 00:32:21.629585920  \n",
-       "3 2010-01-14 21:37:22.051623936  \n",
-       "4 2010-01-09 21:09:11.245925888  "
+       "0 2010-01-21 11:39:29.986688000  \n",
+       "1 2010-02-17 13:51:34.408565760  \n",
+       "2 2010-02-25 12:07:19.982103552  \n",
+       "3 2010-01-21 04:04:59.336169472  \n",
+       "4                           NaT  \n",
+       "5                           NaT  \n",
+       "6 2010-01-28 23:23:34.873413888  \n",
+       "7 2010-01-08 09:44:47.101891840  "
       ]
      },
      "execution_count": 5,
diff --git a/examples/2. Quickstart - Single Table - Census.ipynb b/examples/2. Quickstart - Single Table - Census.ipynb
index 595fe502c..6857b7827 100644
--- a/examples/2. Quickstart - Single Table - Census.ipynb	
+++ b/examples/2. Quickstart - Single Table - Census.ipynb	
@@ -312,23 +312,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-06-25 23:39:05,857 - INFO - modeler - Modeling census\n",
-      "2020-06-25 23:39:05,857 - INFO - metadata - Loading transformer NumericalTransformer for field age\n",
-      "2020-06-25 23:39:05,858 - INFO - metadata - Loading transformer CategoricalTransformer for field workclass\n",
-      "2020-06-25 23:39:05,858 - INFO - metadata - Loading transformer NumericalTransformer for field fnlwgt\n",
-      "2020-06-25 23:39:05,859 - INFO - metadata - Loading transformer CategoricalTransformer for field education\n",
-      "2020-06-25 23:39:05,859 - INFO - metadata - Loading transformer NumericalTransformer for field education-num\n",
-      "2020-06-25 23:39:05,860 - INFO - metadata - Loading transformer CategoricalTransformer for field marital-status\n",
-      "2020-06-25 23:39:05,860 - INFO - metadata - Loading transformer CategoricalTransformer for field occupation\n",
-      "2020-06-25 23:39:05,860 - INFO - metadata - Loading transformer CategoricalTransformer for field relationship\n",
-      "2020-06-25 23:39:05,861 - INFO - metadata - Loading transformer CategoricalTransformer for field race\n",
-      "2020-06-25 23:39:05,861 - INFO - metadata - Loading transformer CategoricalTransformer for field sex\n",
-      "2020-06-25 23:39:05,862 - INFO - metadata - Loading transformer NumericalTransformer for field capital-gain\n",
-      "2020-06-25 23:39:05,863 - INFO - metadata - Loading transformer NumericalTransformer for field capital-loss\n",
-      "2020-06-25 23:39:05,863 - INFO - metadata - Loading transformer NumericalTransformer for field hours-per-week\n",
-      "2020-06-25 23:39:05,863 - INFO - metadata - Loading transformer CategoricalTransformer for field native-country\n",
-      "2020-06-25 23:39:05,864 - INFO - metadata - Loading transformer CategoricalTransformer for field income\n",
-      "2020-06-25 23:39:06,119 - INFO - modeler - Modeling Complete\n"
+      "2020-07-02 14:25:31,656 - INFO - modeler - Modeling census\n",
+      "2020-07-02 14:25:31,657 - INFO - metadata - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-02 14:25:31,657 - INFO - metadata - Loading transformer CategoricalTransformer for field workclass\n",
+      "2020-07-02 14:25:31,658 - INFO - metadata - Loading transformer NumericalTransformer for field fnlwgt\n",
+      "2020-07-02 14:25:31,658 - INFO - metadata - Loading transformer CategoricalTransformer for field education\n",
+      "2020-07-02 14:25:31,658 - INFO - metadata - Loading transformer NumericalTransformer for field education-num\n",
+      "2020-07-02 14:25:31,659 - INFO - metadata - Loading transformer CategoricalTransformer for field marital-status\n",
+      "2020-07-02 14:25:31,659 - INFO - metadata - Loading transformer CategoricalTransformer for field occupation\n",
+      "2020-07-02 14:25:31,659 - INFO - metadata - Loading transformer CategoricalTransformer for field relationship\n",
+      "2020-07-02 14:25:31,660 - INFO - metadata - Loading transformer CategoricalTransformer for field race\n",
+      "2020-07-02 14:25:31,660 - INFO - metadata - Loading transformer CategoricalTransformer for field sex\n",
+      "2020-07-02 14:25:31,661 - INFO - metadata - Loading transformer NumericalTransformer for field capital-gain\n",
+      "2020-07-02 14:25:31,661 - INFO - metadata - Loading transformer NumericalTransformer for field capital-loss\n",
+      "2020-07-02 14:25:31,662 - INFO - metadata - Loading transformer NumericalTransformer for field hours-per-week\n",
+      "2020-07-02 14:25:31,662 - INFO - metadata - Loading transformer CategoricalTransformer for field native-country\n",
+      "2020-07-02 14:25:31,663 - INFO - metadata - Loading transformer CategoricalTransformer for field income\n",
+      "2020-07-02 14:25:31,831 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:31,928 - INFO - modeler - Modeling Complete\n"
      ]
     }
    ],
@@ -385,91 +386,91 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>42</td>\n",
+       "      <td>35</td>\n",
        "      <td>Private</td>\n",
-       "      <td>47585</td>\n",
-       "      <td>Assoc-voc</td>\n",
+       "      <td>468185</td>\n",
+       "      <td>HS-grad</td>\n",
        "      <td>9</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Prof-specialty</td>\n",
-       "      <td>Not-in-family</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Sales</td>\n",
+       "      <td>Husband</td>\n",
        "      <td>White</td>\n",
        "      <td>Male</td>\n",
-       "      <td>2398</td>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
+       "      <td>2039</td>\n",
+       "      <td>436</td>\n",
+       "      <td>48</td>\n",
        "      <td>United-States</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>56</td>\n",
+       "      <td>47</td>\n",
        "      <td>Private</td>\n",
-       "      <td>92870</td>\n",
+       "      <td>104447</td>\n",
        "      <td>HS-grad</td>\n",
-       "      <td>13</td>\n",
+       "      <td>7</td>\n",
        "      <td>Married-civ-spouse</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>Not-in-family</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Husband</td>\n",
        "      <td>White</td>\n",
        "      <td>Male</td>\n",
-       "      <td>3624</td>\n",
-       "      <td>269</td>\n",
-       "      <td>30</td>\n",
+       "      <td>-703</td>\n",
+       "      <td>81</td>\n",
+       "      <td>53</td>\n",
        "      <td>United-States</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>54</td>\n",
+       "      <td>20</td>\n",
        "      <td>Private</td>\n",
-       "      <td>218711</td>\n",
+       "      <td>231391</td>\n",
        "      <td>HS-grad</td>\n",
-       "      <td>13</td>\n",
+       "      <td>11</td>\n",
        "      <td>Never-married</td>\n",
-       "      <td>Other-service</td>\n",
+       "      <td>Exec-managerial</td>\n",
        "      <td>Not-in-family</td>\n",
        "      <td>White</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>4420</td>\n",
-       "      <td>-269</td>\n",
-       "      <td>43</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>654</td>\n",
+       "      <td>-8</td>\n",
+       "      <td>57</td>\n",
        "      <td>United-States</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>31</td>\n",
-       "      <td>?</td>\n",
-       "      <td>71625</td>\n",
-       "      <td>Some-college</td>\n",
-       "      <td>14</td>\n",
+       "      <td>35</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>223275</td>\n",
+       "      <td>Masters</td>\n",
+       "      <td>12</td>\n",
        "      <td>Married-civ-spouse</td>\n",
-       "      <td>Prof-specialty</td>\n",
-       "      <td>Husband</td>\n",
+       "      <td>Sales</td>\n",
+       "      <td>Not-in-family</td>\n",
        "      <td>White</td>\n",
        "      <td>Male</td>\n",
-       "      <td>3196</td>\n",
-       "      <td>898</td>\n",
-       "      <td>51</td>\n",
+       "      <td>-392</td>\n",
+       "      <td>-178</td>\n",
+       "      <td>71</td>\n",
        "      <td>United-States</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>37</td>\n",
+       "      <td>26</td>\n",
        "      <td>Private</td>\n",
-       "      <td>184276</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>14</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Craft-repair</td>\n",
+       "      <td>-11408</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Machine-op-inspct</td>\n",
        "      <td>Husband</td>\n",
        "      <td>White</td>\n",
        "      <td>Male</td>\n",
-       "      <td>4785</td>\n",
-       "      <td>907</td>\n",
-       "      <td>32</td>\n",
+       "      <td>5799</td>\n",
+       "      <td>13</td>\n",
+       "      <td>47</td>\n",
        "      <td>United-States</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
@@ -478,26 +479,26 @@
        "</div>"
       ],
       "text/plain": [
-       "   age workclass  fnlwgt      education  education-num       marital-status  \\\n",
-       "0   42   Private   47585      Assoc-voc              9        Never-married   \n",
-       "1   56   Private   92870        HS-grad             13   Married-civ-spouse   \n",
-       "2   54   Private  218711        HS-grad             13        Never-married   \n",
-       "3   31         ?   71625   Some-college             14   Married-civ-spouse   \n",
-       "4   37   Private  184276      Bachelors             14        Never-married   \n",
+       "   age workclass  fnlwgt education  education-num       marital-status  \\\n",
+       "0   35   Private  468185   HS-grad              9   Married-civ-spouse   \n",
+       "1   47   Private  104447   HS-grad              7   Married-civ-spouse   \n",
+       "2   20   Private  231391   HS-grad             11        Never-married   \n",
+       "3   35   Private  223275   Masters             12   Married-civ-spouse   \n",
+       "4   26   Private  -11408   HS-grad              8   Married-civ-spouse   \n",
        "\n",
-       "        occupation    relationship    race      sex  capital-gain  \\\n",
-       "0   Prof-specialty   Not-in-family   White     Male          2398   \n",
-       "1            Sales   Not-in-family   White     Male          3624   \n",
-       "2    Other-service   Not-in-family   White   Female          4420   \n",
-       "3   Prof-specialty         Husband   White     Male          3196   \n",
-       "4     Craft-repair         Husband   White     Male          4785   \n",
+       "           occupation    relationship    race    sex  capital-gain  \\\n",
+       "0               Sales         Husband   White   Male          2039   \n",
+       "1        Adm-clerical         Husband   White   Male          -703   \n",
+       "2     Exec-managerial   Not-in-family   White   Male           654   \n",
+       "3               Sales   Not-in-family   White   Male          -392   \n",
+       "4   Machine-op-inspct         Husband   White   Male          5799   \n",
        "\n",
        "   capital-loss  hours-per-week  native-country  income  \n",
-       "0             3              44   United-States   <=50K  \n",
-       "1           269              30   United-States   <=50K  \n",
-       "2          -269              43   United-States   <=50K  \n",
-       "3           898              51   United-States   <=50K  \n",
-       "4           907              32   United-States   <=50K  "
+       "0           436              48   United-States   <=50K  \n",
+       "1            81              53   United-States   <=50K  \n",
+       "2            -8              57   United-States   <=50K  \n",
+       "3          -178              71   United-States   <=50K  \n",
+       "4            13              47   United-States   <=50K  "
       ]
      },
      "execution_count": 7,
@@ -520,7 +521,7 @@
     {
      "data": {
       "text/plain": [
-       "-43.51455835774797"
+       "-43.151506729008716"
       ]
      },
      "execution_count": 8,
diff --git a/examples/3. Quickstart - Multitable - Files.ipynb b/examples/3. Quickstart - Multitable - Files.ipynb
index b4183f965..d00a5989e 100644
--- a/examples/3. Quickstart - Multitable - Files.ipynb	
+++ b/examples/3. Quickstart - Multitable - Files.ipynb	
@@ -11,21 +11,38 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-02-01 19:45:58,147 - INFO - modeler - Modeling customers\n",
-      "2020-02-01 19:45:58,148 - INFO - metadata - Loading table customers\n",
-      "2020-02-01 19:45:58,156 - INFO - metadata - Loading transformer CategoricalTransformer for field cust_postal_code\n",
-      "2020-02-01 19:45:58,157 - INFO - metadata - Loading transformer NumericalTransformer for field phone_number1\n",
-      "2020-02-01 19:45:58,158 - INFO - metadata - Loading transformer NumericalTransformer for field credit_limit\n",
-      "2020-02-01 19:45:58,158 - INFO - metadata - Loading transformer CategoricalTransformer for field country\n",
-      "2020-02-01 19:45:58,174 - INFO - modeler - Modeling orders\n",
-      "2020-02-01 19:45:58,175 - INFO - metadata - Loading table orders\n",
-      "2020-02-01 19:45:58,179 - INFO - metadata - Loading transformer NumericalTransformer for field order_total\n",
-      "2020-02-01 19:45:58,183 - INFO - modeler - Modeling order_items\n",
-      "2020-02-01 19:45:58,183 - INFO - metadata - Loading table order_items\n",
-      "2020-02-01 19:45:58,187 - INFO - metadata - Loading transformer CategoricalTransformer for field product_id\n",
-      "2020-02-01 19:45:58,187 - INFO - metadata - Loading transformer NumericalTransformer for field unit_price\n",
-      "2020-02-01 19:45:58,187 - INFO - metadata - Loading transformer NumericalTransformer for field quantity\n",
-      "2020-02-01 19:45:58,824 - INFO - modeler - Modeling Complete\n"
+      "2020-07-02 14:25:44,066 - INFO - modeler - Modeling customers\n",
+      "2020-07-02 14:25:44,067 - INFO - metadata - Loading table customers\n",
+      "2020-07-02 14:25:44,074 - INFO - metadata - Loading transformer CategoricalTransformer for field cust_postal_code\n",
+      "2020-07-02 14:25:44,074 - INFO - metadata - Loading transformer NumericalTransformer for field phone_number1\n",
+      "2020-07-02 14:25:44,075 - INFO - metadata - Loading transformer NumericalTransformer for field credit_limit\n",
+      "2020-07-02 14:25:44,076 - INFO - metadata - Loading transformer CategoricalTransformer for field country\n",
+      "2020-07-02 14:25:44,094 - INFO - modeler - Modeling orders\n",
+      "2020-07-02 14:25:44,095 - INFO - metadata - Loading table orders\n",
+      "2020-07-02 14:25:44,098 - INFO - metadata - Loading transformer NumericalTransformer for field order_total\n",
+      "2020-07-02 14:25:44,101 - INFO - modeler - Modeling order_items\n",
+      "2020-07-02 14:25:44,102 - INFO - metadata - Loading table order_items\n",
+      "2020-07-02 14:25:44,106 - INFO - metadata - Loading transformer CategoricalTransformer for field product_id\n",
+      "2020-07-02 14:25:44,107 - INFO - metadata - Loading transformer NumericalTransformer for field unit_price\n",
+      "2020-07-02 14:25:44,108 - INFO - metadata - Loading transformer NumericalTransformer for field quantity\n",
+      "2020-07-02 14:25:44,120 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,131 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,138 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,147 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,155 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,164 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,171 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,177 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,183 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,189 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,196 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,210 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,231 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,258 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,281 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,303 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,370 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:25:44,496 - INFO - modeler - Modeling Complete\n"
      ]
     }
    ],
@@ -45,9 +62,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-02-01 19:45:58,874 - INFO - metadata - Loading table customers\n",
-      "2020-02-01 19:45:58,878 - INFO - metadata - Loading table orders\n",
-      "2020-02-01 19:45:58,881 - INFO - metadata - Loading table order_items\n"
+      "2020-07-02 14:25:46,641 - INFO - metadata - Loading table customers\n",
+      "2020-07-02 14:25:46,646 - INFO - metadata - Loading table orders\n",
+      "2020-07-02 14:25:46,649 - INFO - metadata - Loading table order_items\n"
      ]
     }
    ],
@@ -97,41 +114,41 @@
        "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>63145</td>\n",
-       "      <td>5941487535</td>\n",
-       "      <td>1694</td>\n",
-       "      <td>UK</td>\n",
+       "      <td>7811758514</td>\n",
+       "      <td>510</td>\n",
+       "      <td>US</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>11371</td>\n",
-       "      <td>8369261990</td>\n",
-       "      <td>501</td>\n",
-       "      <td>UK</td>\n",
+       "      <td>20166</td>\n",
+       "      <td>6720163478</td>\n",
+       "      <td>317</td>\n",
+       "      <td>SPAIN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
        "      <td>11371</td>\n",
-       "      <td>6106157274</td>\n",
-       "      <td>1287</td>\n",
-       "      <td>CANADA</td>\n",
+       "      <td>7447861719</td>\n",
+       "      <td>1371</td>\n",
+       "      <td>SPAIN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>6096</td>\n",
-       "      <td>4243270462</td>\n",
-       "      <td>1584</td>\n",
-       "      <td>UK</td>\n",
+       "      <td>63145</td>\n",
+       "      <td>6835026704</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>US</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
-       "      <td>6096</td>\n",
-       "      <td>5705933008</td>\n",
-       "      <td>584</td>\n",
-       "      <td>UK</td>\n",
+       "      <td>11371</td>\n",
+       "      <td>5371158268</td>\n",
+       "      <td>739</td>\n",
+       "      <td>FRANCE</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -139,11 +156,11 @@
       ],
       "text/plain": [
        "   customer_id cust_postal_code  phone_number1  credit_limit country\n",
-       "0            0            63145     5941487535          1694      UK\n",
-       "1            1            11371     8369261990           501      UK\n",
-       "2            2            11371     6106157274          1287  CANADA\n",
-       "3            3             6096     4243270462          1584      UK\n",
-       "4            4             6096     5705933008           584      UK"
+       "0            0            63145     7811758514           510      US\n",
+       "1            1            20166     6720163478           317   SPAIN\n",
+       "2            2            11371     7447861719          1371   SPAIN\n",
+       "3            3            63145     6835026704          1024      US\n",
+       "4            4            11371     5371158268           739  FRANCE"
       ]
      },
      "execution_count": 3,
@@ -286,32 +303,32 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>581</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1784</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>707</td>\n",
+       "      <td>1864</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>878</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>285</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1485</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>840</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2435</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -319,11 +336,11 @@
       ],
       "text/plain": [
        "   order_id  customer_id  order_total\n",
-       "0         0            1          581\n",
-       "1         1            1          707\n",
-       "2         2            1          878\n",
-       "3         3            2          285\n",
-       "4         4            3          840"
+       "0         0            0         1784\n",
+       "1         1            1         1864\n",
+       "2         2            4         2677\n",
+       "3         3            1         1485\n",
+       "4         4            1         2435"
       ]
      },
      "execution_count": 5,
@@ -456,41 +473,41 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2</td>\n",
        "      <td>6</td>\n",
-       "      <td>62</td>\n",
-       "      <td>3</td>\n",
+       "      <td>208</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>7</td>\n",
        "      <td>10</td>\n",
-       "      <td>108</td>\n",
-       "      <td>5</td>\n",
+       "      <td>116</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
        "      <td>0</td>\n",
-       "      <td>6</td>\n",
+       "      <td>10</td>\n",
        "      <td>57</td>\n",
-       "      <td>3</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "      <td>114</td>\n",
+       "      <td>3</td>\n",
        "      <td>6</td>\n",
+       "      <td>-24</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>38</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -499,11 +516,11 @@
       ],
       "text/plain": [
        "   order_item_id  order_id product_id  unit_price  quantity\n",
-       "0              0         0          6          62         3\n",
-       "1              1         0         10         108         5\n",
-       "2              2         0          6          57         3\n",
-       "3              3         1         10         114         6\n",
-       "4              4         1         10          81         4"
+       "0              0         2          6         208         7\n",
+       "1              1         7         10         116         2\n",
+       "2              2         0         10          57         6\n",
+       "3              3         3          6         -24         1\n",
+       "4              4         3          6          38         4"
       ]
      },
      "execution_count": 7,
diff --git a/examples/4. Anonymization.ipynb b/examples/4. Anonymization.ipynb
index 65382e218..5ff4caa51 100644
--- a/examples/4. Anonymization.ipynb	
+++ b/examples/4. Anonymization.ipynb	
@@ -185,13 +185,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-02-01 19:46:01,587 - INFO - modeler - Modeling anonymized\n",
-      "2020-02-01 19:46:01,589 - INFO - metadata - Loading transformer CategoricalTransformer for field name\n",
-      "2020-02-01 19:46:01,589 - INFO - metadata - Loading transformer CategoricalTransformer for field credit_card_number\n",
-      "2020-02-01 19:46:01,640 - INFO - modeler - Modeling normal\n",
-      "2020-02-01 19:46:01,641 - INFO - metadata - Loading transformer CategoricalTransformer for field name\n",
-      "2020-02-01 19:46:01,641 - INFO - metadata - Loading transformer CategoricalTransformer for field credit_card_number\n",
-      "2020-02-01 19:46:01,658 - INFO - modeler - Modeling Complete\n"
+      "2020-07-02 14:26:26,044 - INFO - modeler - Modeling anonymized\n",
+      "2020-07-02 14:26:26,044 - INFO - metadata - Loading transformer CategoricalTransformer for field name\n",
+      "2020-07-02 14:26:26,045 - INFO - metadata - Loading transformer CategoricalTransformer for field credit_card_number\n",
+      "2020-07-02 14:26:26,087 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:26:26,092 - INFO - modeler - Modeling normal\n",
+      "2020-07-02 14:26:26,092 - INFO - metadata - Loading transformer CategoricalTransformer for field name\n",
+      "2020-07-02 14:26:26,093 - INFO - metadata - Loading transformer CategoricalTransformer for field credit_card_number\n",
+      "2020-07-02 14:26:26,109 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:26:26,113 - INFO - modeler - Modeling Complete\n"
      ]
     }
    ],
@@ -250,44 +252,37 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
-       "      <td>Brandon</td>\n",
-       "      <td>4134540887507040</td>\n",
+       "      <td>Pamela</td>\n",
+       "      <td>4801288395665668</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>Larry</td>\n",
-       "      <td>4288700396707168</td>\n",
+       "      <td>Kimberly</td>\n",
+       "      <td>4801288395665668</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
-       "      <td>Brandon</td>\n",
-       "      <td>4869967025488786</td>\n",
+       "      <td>Pamela</td>\n",
+       "      <td>4801288395665668</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>Brandon</td>\n",
-       "      <td>4288700396707168</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Larry</td>\n",
-       "      <td>4869967025488786</td>\n",
+       "      <td>Kimberly</td>\n",
+       "      <td>4592405566223480</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   index     name credit_card_number\n",
-       "0      0  Brandon   4134540887507040\n",
-       "1      1    Larry   4288700396707168\n",
-       "2      2  Brandon   4869967025488786\n",
-       "3      3  Brandon   4288700396707168\n",
-       "4      4    Larry   4869967025488786"
+       "   index      name credit_card_number\n",
+       "0      0    Pamela   4801288395665668\n",
+       "1      1  Kimberly   4801288395665668\n",
+       "2      2    Pamela   4801288395665668\n",
+       "3      3  Kimberly   4592405566223480"
       ]
      },
      "execution_count": 7,
@@ -337,19 +332,19 @@
        "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>Joe</td>\n",
-       "      <td>1111222233334444</td>\n",
+       "      <td>8888888888888888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>Bill</td>\n",
+       "      <td>Joe</td>\n",
        "      <td>0000000000000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
-       "      <td>Jeff</td>\n",
-       "      <td>1111222233334444</td>\n",
+       "      <td>Joe</td>\n",
+       "      <td>8888888888888888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -357,23 +352,16 @@
        "      <td>Bill</td>\n",
        "      <td>8888888888888888</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Jeff</td>\n",
-       "      <td>0000000000000000</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "   index  name credit_card_number\n",
-       "0      0   Joe   1111222233334444\n",
-       "1      1  Bill   0000000000000000\n",
-       "2      2  Jeff   1111222233334444\n",
-       "3      3  Bill   8888888888888888\n",
-       "4      4  Jeff   0000000000000000"
+       "0      0   Joe   8888888888888888\n",
+       "1      1   Joe   0000000000000000\n",
+       "2      2   Joe   8888888888888888\n",
+       "3      3  Bill   8888888888888888"
       ]
      },
      "execution_count": 8,
diff --git a/examples/5. Generate Metadata from Dataframes.ipynb b/examples/5. Generate Metadata from Dataframes.ipynb
index 5768ce084..91b001036 100644
--- a/examples/5. Generate Metadata from Dataframes.ipynb	
+++ b/examples/5. Generate Metadata from Dataframes.ipynb	
@@ -163,27 +163,26 @@
     {
      "data": {
       "text/plain": [
-       "{'tables': {'users': {'fields': {'user_id': {'type': 'id',\n",
-       "     'subtype': 'integer'},\n",
-       "    'gender': {'type': 'categorical'},\n",
-       "    'age': {'type': 'numerical', 'subtype': 'integer'},\n",
-       "    'country': {'type': 'categorical'}},\n",
+       "{'tables': {'users': {'fields': {'gender': {'type': 'categorical'},\n",
+       "    'user_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'country': {'type': 'categorical'},\n",
+       "    'age': {'type': 'numerical', 'subtype': 'integer'}},\n",
        "   'primary_key': 'user_id'},\n",
-       "  'sessions': {'fields': {'user_id': {'type': 'id',\n",
+       "  'sessions': {'fields': {'session_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'user_id': {'type': 'id',\n",
        "     'subtype': 'integer',\n",
        "     'ref': {'table': 'users', 'field': 'user_id'}},\n",
        "    'os': {'type': 'categorical'},\n",
-       "    'device': {'type': 'categorical'},\n",
-       "    'session_id': {'type': 'id', 'subtype': 'integer'}},\n",
+       "    'device': {'type': 'categorical'}},\n",
        "   'primary_key': 'session_id'},\n",
        "  'transactions': {'fields': {'timestamp': {'type': 'datetime',\n",
        "     'format': '%Y-%m-%d'},\n",
-       "    'approved': {'type': 'boolean'},\n",
-       "    'amount': {'type': 'numerical', 'subtype': 'float'},\n",
-       "    'transaction_id': {'type': 'id', 'subtype': 'integer'},\n",
        "    'session_id': {'type': 'id',\n",
        "     'subtype': 'integer',\n",
-       "     'ref': {'table': 'sessions', 'field': 'session_id'}}},\n",
+       "     'ref': {'table': 'sessions', 'field': 'session_id'}},\n",
+       "    'transaction_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'approved': {'type': 'boolean'},\n",
+       "    'amount': {'type': 'numerical', 'subtype': 'float'}},\n",
        "   'primary_key': 'transaction_id'}}}"
       ]
      },
@@ -333,7 +332,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7f033c7a7358>"
+       "<graphviz.dot.Digraph at 0x7fc36bc596d8>"
       ]
      },
      "execution_count": 14,
@@ -370,10 +369,10 @@
        "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
        "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
        "</g>\n",
@@ -383,10 +382,10 @@
        "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
        "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
        "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
@@ -405,10 +404,10 @@
        "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
        "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
@@ -424,7 +423,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7f033c7a7470>"
+       "<graphviz.dot.Digraph at 0x7fc36bc59198>"
       ]
      },
      "execution_count": 15,
diff --git a/examples/Demo - Walmart.ipynb b/examples/Demo - Walmart.ipynb
index a4015bb0f..d3bbe80e9 100644
--- a/examples/Demo - Walmart.ipynb	
+++ b/examples/Demo - Walmart.ipynb	
@@ -100,9 +100,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-02-01 19:46:10,378 - INFO - metadata - Loading table stores\n",
-      "2020-02-01 19:46:10,386 - INFO - metadata - Loading table features\n",
-      "2020-02-01 19:46:10,401 - INFO - metadata - Loading table depts\n"
+      "2020-07-02 14:27:25,479 - INFO - metadata - Loading table stores\n",
+      "2020-07-02 14:27:25,486 - INFO - metadata - Loading table features\n",
+      "2020-07-02 14:27:25,502 - INFO - metadata - Loading table depts\n"
      ]
     }
    ],
@@ -213,7 +213,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7f60b94205f8>"
+       "<graphviz.dot.Digraph at 0x7f6909571860>"
       ]
      },
      "execution_count": 2,
@@ -253,35 +253,136 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-02-01 19:46:10,769 - INFO - modeler - Modeling stores\n",
-      "2020-02-01 19:46:10,769 - INFO - metadata - Loading transformer CategoricalTransformer for field Type\n",
-      "2020-02-01 19:46:10,770 - INFO - metadata - Loading transformer NumericalTransformer for field Size\n",
-      "2020-02-01 19:46:10,781 - INFO - modeler - Modeling features\n",
-      "2020-02-01 19:46:10,781 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
-      "2020-02-01 19:46:10,782 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown1\n",
-      "2020-02-01 19:46:10,782 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
-      "2020-02-01 19:46:10,783 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown4\n",
-      "2020-02-01 19:46:10,783 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown3\n",
-      "2020-02-01 19:46:10,784 - INFO - metadata - Loading transformer NumericalTransformer for field Fuel_Price\n",
-      "2020-02-01 19:46:10,785 - INFO - metadata - Loading transformer NumericalTransformer for field Unemployment\n",
-      "2020-02-01 19:46:10,785 - INFO - metadata - Loading transformer NumericalTransformer for field Temperature\n",
-      "2020-02-01 19:46:10,786 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown5\n",
-      "2020-02-01 19:46:10,786 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown2\n",
-      "2020-02-01 19:46:10,787 - INFO - metadata - Loading transformer NumericalTransformer for field CPI\n",
-      "2020-02-01 19:46:12,119 - INFO - modeler - Modeling depts\n",
-      "2020-02-01 19:46:12,119 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
-      "2020-02-01 19:46:12,119 - INFO - metadata - Loading transformer NumericalTransformer for field Weekly_Sales\n",
-      "2020-02-01 19:46:12,120 - INFO - metadata - Loading transformer NumericalTransformer for field Dept\n",
-      "2020-02-01 19:46:12,120 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
-      "/home/xals/.virtualenvs/SDV.merge/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
+      "2020-07-02 14:27:31,883 - INFO - modeler - Modeling stores\n",
+      "2020-07-02 14:27:31,883 - INFO - metadata - Loading transformer CategoricalTransformer for field Type\n",
+      "2020-07-02 14:27:31,884 - INFO - metadata - Loading transformer NumericalTransformer for field Size\n",
+      "2020-07-02 14:27:31,895 - INFO - modeler - Modeling depts\n",
+      "2020-07-02 14:27:31,895 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
+      "2020-07-02 14:27:31,896 - INFO - metadata - Loading transformer NumericalTransformer for field Weekly_Sales\n",
+      "2020-07-02 14:27:31,896 - INFO - metadata - Loading transformer NumericalTransformer for field Dept\n",
+      "2020-07-02 14:27:31,896 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
+      "2020-07-02 14:27:32,007 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,272 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,288 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,302 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,319 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,335 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,350 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,367 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,383 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,399 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,414 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,430 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,445 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,460 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,475 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,490 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,508 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,522 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,536 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,550 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,563 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,576 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,594 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,607 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,621 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,636 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,651 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,664 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,679 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,695 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,710 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,723 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,738 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,752 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,765 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,781 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,796 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,809 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,823 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,837 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,852 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,868 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,881 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,896 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,907 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,919 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:32,938 - INFO - modeler - Modeling features\n",
+      "2020-07-02 14:27:32,939 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
+      "2020-07-02 14:27:32,939 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown1\n",
+      "2020-07-02 14:27:32,940 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
+      "2020-07-02 14:27:32,941 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown4\n",
+      "2020-07-02 14:27:32,941 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown3\n",
+      "2020-07-02 14:27:32,941 - INFO - metadata - Loading transformer NumericalTransformer for field Fuel_Price\n",
+      "2020-07-02 14:27:32,942 - INFO - metadata - Loading transformer NumericalTransformer for field Unemployment\n",
+      "2020-07-02 14:27:32,942 - INFO - metadata - Loading transformer NumericalTransformer for field Temperature\n",
+      "2020-07-02 14:27:32,942 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown5\n",
+      "2020-07-02 14:27:32,942 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown2\n",
+      "2020-07-02 14:27:32,943 - INFO - metadata - Loading transformer NumericalTransformer for field CPI\n",
+      "2020-07-02 14:27:32,993 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,043 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,073 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,104 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,133 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,189 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,220 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,253 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,285 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,314 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,342 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,371 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-02 14:27:33,399 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,425 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,452 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,479 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,504 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,531 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,558 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,592 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,624 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,653 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,683 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,713 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,743 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,770 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,797 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,826 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,852 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,880 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,910 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,939 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,967 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:33,996 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,025 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,051 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,078 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,107 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,134 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,160 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,187 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,216 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,246 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,275 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,303 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,333 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 14:27:34,452 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
       "  return getattr(obj, method)(*args, **kwds)\n",
-      "2020-02-01 19:46:13,498 - INFO - modeler - Modeling Complete\n"
+      "2020-07-02 14:27:34,680 - INFO - modeler - Modeling Complete\n"
      ]
     }
    ],
@@ -314,7 +415,31 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-eecf8d026e72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msdv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sdv.py\u001b[0m in \u001b[0;36msample_all\u001b[0;34m(self, num_rows, reset_primary_keys)\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'SDV instance has not been fitted'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_primary_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset_primary_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36msample_all\u001b[0;34m(self, num_rows, reset_primary_keys)\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_parents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 333\u001b[0;31m                 \u001b[0msampled_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msampled_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, table_name, num_rows, reset_primary_keys, sample_children, sample_parents)\u001b[0m\n\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sample_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampled_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_finalize\u001b[0;34m(self, sampled_data)\u001b[0m\n\u001b[1;32m     58\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mparents\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mparent_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparents\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m                     \u001b[0mparent_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_find_parent_ids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m                     \u001b[0mforeign_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_foreign_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m                     \u001b[0mtable_rows\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mforeign_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparent_ids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_find_parent_ids\u001b[0;34m(self, table_name, parent_name, sampled_data)\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0mparent_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtable_rows\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m             \u001b[0mparent_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_find_parent_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpdfs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mparent_ids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_find_parent_id\u001b[0;34m(self, row, pdfs, num_rows)\u001b[0m\n\u001b[1;32m    212\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparent_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpdf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpdfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m                 \u001b[0mlikelihoods\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparent_id\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinAlgError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m                 \u001b[0;31m# Singular matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/copulas/multivariate/gaussian.py\u001b[0m in \u001b[0;36mprobability_density\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    133\u001b[0m         \"\"\"\n\u001b[1;32m    134\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m         \u001b[0mtransformed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transform_to_normal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultivariate_normal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/copulas/multivariate/gaussian.py\u001b[0m in \u001b[0;36m_transform_to_normal\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcolumn_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munivariate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munivariates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0mcolumn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m             \u001b[0mU\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munivariate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEPSILON\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mEPSILON\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mppf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mU\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/copulas/univariate/base.py\u001b[0m in \u001b[0;36mcdf\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    306\u001b[0m                 \u001b[0mCumulative\u001b[0m \u001b[0mdistribution\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpoints\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m         \"\"\"\n\u001b[0;32m--> 308\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumulative_distribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpercent_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mU\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/copulas/univariate/base.py\u001b[0m in \u001b[0;36mcumulative_distribution\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    523\u001b[0m         \"\"\"\n\u001b[1;32m    524\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpercent_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mU\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py\u001b[0m in \u001b[0;36mcdf\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlogcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py\u001b[0m in \u001b[0;36mcdf\u001b[0;34m(self, x, *args, **kwds)\u001b[0m\n\u001b[1;32m   1747\u001b[0m         \u001b[0mcond1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_support_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscale\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1748\u001b[0m         \u001b[0mcond2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mcond0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1749\u001b[0;31m         \u001b[0mcond\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcond0\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mcond1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1750\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcond\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtyp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1751\u001b[0m         \u001b[0mplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mcond0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbadvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "samples = sdv.sample_all()"
    ]
@@ -670,7 +795,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "data": {
diff --git a/examples/Evaluation.ipynb b/examples/Evaluation.ipynb
new file mode 100644
index 000000000..3d2d5bd7e
--- /dev/null
+++ b/examples/Evaluation.ipynb
@@ -0,0 +1,193 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>tables</th>\n",
+       "      <th>rows</th>\n",
+       "      <th>columns</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>airbnb-simplified</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5751408</td>\n",
+       "      <td>22</td>\n",
+       "      <td>airbnb user account information, including fir...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rossmann</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1018324</td>\n",
+       "      <td>19</td>\n",
+       "      <td>rossmann stores promotional information and it...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>walmart</td>\n",
+       "      <td>3</td>\n",
+       "      <td>429804</td>\n",
+       "      <td>20</td>\n",
+       "      <td>walmart stores information (size and type) wit...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                name  tables     rows  columns  \\\n",
+       "0  airbnb-simplified       2  5751408       22   \n",
+       "1           rossmann       2  1018324       19   \n",
+       "2            walmart       3   429804       20   \n",
+       "\n",
+       "                                         description  \n",
+       "0  airbnb user account information, including fir...  \n",
+       "1  rossmann stores promotional information and it...  \n",
+       "2  walmart stores information (size and type) wit...  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from sdv.demo import get_available_demos\n",
+    "\n",
+    "get_available_demos()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-06-26 00:11:41,404 - INFO - metadata - Loading table store\n",
+      "2020-06-26 00:11:41,434 - INFO - metadata - Loading table historical\n",
+      "2020-06-26 00:11:43,265 - INFO - modeler - Modeling store\n",
+      "2020-06-26 00:11:43,265 - INFO - metadata - Loading transformer NumericalTransformer for field CompetitionOpenSinceYear\n",
+      "2020-06-26 00:11:43,266 - INFO - metadata - Loading transformer NumericalTransformer for field CompetitionOpenSinceMonth\n",
+      "2020-06-26 00:11:43,266 - INFO - metadata - Loading transformer NumericalTransformer for field CompetitionDistance\n",
+      "2020-06-26 00:11:43,267 - INFO - metadata - Loading transformer BooleanTransformer for field Promo2\n",
+      "2020-06-26 00:11:43,267 - INFO - metadata - Loading transformer NumericalTransformer for field Promo2SinceYear\n",
+      "2020-06-26 00:11:43,268 - INFO - metadata - Loading transformer NumericalTransformer for field Promo2SinceWeek\n",
+      "2020-06-26 00:11:43,268 - INFO - metadata - Loading transformer CategoricalTransformer for field StoreType\n",
+      "2020-06-26 00:11:43,269 - INFO - metadata - Loading transformer CategoricalTransformer for field Assortment\n",
+      "2020-06-26 00:11:43,270 - INFO - metadata - Loading transformer CategoricalTransformer for field PromoInterval\n",
+      "2020-06-26 00:11:43,310 - INFO - modeler - Modeling historical\n",
+      "2020-06-26 00:11:43,311 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
+      "2020-06-26 00:11:43,311 - INFO - metadata - Loading transformer NumericalTransformer for field DayOfWeek\n",
+      "2020-06-26 00:11:43,311 - INFO - metadata - Loading transformer NumericalTransformer for field Promo\n",
+      "2020-06-26 00:11:43,312 - INFO - metadata - Loading transformer CategoricalTransformer for field StateHoliday\n",
+      "2020-06-26 00:11:43,312 - INFO - metadata - Loading transformer NumericalTransformer for field Open\n",
+      "2020-06-26 00:11:43,312 - INFO - metadata - Loading transformer NumericalTransformer for field SchoolHoliday\n",
+      "2020-06-26 00:11:43,312 - INFO - metadata - Loading transformer NumericalTransformer for field Customers\n",
+      "2020-06-26 00:13:14,951 - INFO - modeler - Modeling Complete\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-49542b34efbe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0msampled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msdv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfirst_table\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdemo\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDV/sdv/evaluation.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(synth, real, metadata, root_path, table_name, get_report)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0msynth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_validate_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msynth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroot_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m     \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msdmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mget_report\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/evaluation.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(metadata, real, synthetic)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMetricsReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0mreport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/report.py\u001b[0m in \u001b[0;36madd_metrics\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0mof\u001b[0m \u001b[0mthese\u001b[0m \u001b[0mmetrics\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \"\"\"\n\u001b[0;32m---> 96\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mmetric\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/evaluation.py\u001b[0m in \u001b[0;36m_metrics\u001b[0;34m(metadata, real, synthetic)\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mconstraint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdetection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mstatistical\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/detection/__init__.py\u001b[0m in \u001b[0;36mmetrics\u001b[0;34m(metadata, real_tables, synthetic_tables)\u001b[0m\n\u001b[1;32m     21\u001b[0m     \"\"\"\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdetector\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mLogisticDetector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdetector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_tables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic_tables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/detection/tabular/base.py\u001b[0m in \u001b[0;36mmetrics\u001b[0;34m(self, metadata, real_tables, synthetic_tables)\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0mMetric\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mnext\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \"\"\"\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_single_table_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_tables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic_tables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_child_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_tables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic_tables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/detection/tabular/base.py\u001b[0m in \u001b[0;36m_single_table_detection\u001b[0;34m(self, metadata, real_tables, synthetic_tables)\u001b[0m\n\u001b[1;32m     57\u001b[0m             auroc = self._compute_auroc(\n\u001b[1;32m     58\u001b[0m                 \u001b[0mreal_tables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtable_fields\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m                 synthetic_tables[table_name][table_fields])\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m             yield Metric(\n",
+      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/detection/tabular/base.py\u001b[0m in \u001b[0;36m_compute_auroc\u001b[0;34m(self, real_table, synthetic_table)\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mreal_table\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic_table\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msynthetic_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv import SDV\n",
+    "from sdv.demo import load_demo\n",
+    "from sdv.evaluation import evaluate\n",
+    "\n",
+    "scores = dict()\n",
+    "for demo in get_available_demos().name[1:]:\n",
+    "    metadata, tables = load_demo(demo, metadata=True)\n",
+    "    \n",
+    "    sdv = SDV()\n",
+    "    sdv.fit(metadata, tables)\n",
+    "    \n",
+    "    first_table = metadata.get_tables()[0]\n",
+    "    sampled = sdv.sample_all(len(tables[first_table]))\n",
+    "    \n",
+    "    score = evaluate(sampled, tables, metadata)\n",
+    "    scores[demo] = score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/demo_metadata.json b/examples/demo_metadata.json
index a8b345822..bb24a5963 100644
--- a/examples/demo_metadata.json
+++ b/examples/demo_metadata.json
@@ -2,10 +2,6 @@
     "tables": {
         "users": {
             "fields": {
-                "user_id": {
-                    "type": "id",
-                    "subtype": "integer"
-                },
                 "age": {
                     "type": "numerical",
                     "subtype": "integer"
@@ -13,6 +9,10 @@
                 "gender": {
                     "type": "categorical"
                 },
+                "user_id": {
+                    "type": "id",
+                    "subtype": "integer"
+                },
                 "country": {
                     "type": "categorical"
                 }
@@ -21,7 +21,11 @@
         },
         "sessions": {
             "fields": {
-                "os": {
+                "session_id": {
+                    "type": "id",
+                    "subtype": "integer"
+                },
+                "device": {
                     "type": "categorical"
                 },
                 "user_id": {
@@ -32,12 +36,8 @@
                         "field": "user_id"
                     }
                 },
-                "device": {
+                "os": {
                     "type": "categorical"
-                },
-                "session_id": {
-                    "type": "id",
-                    "subtype": "integer"
                 }
             },
             "primary_key": "session_id"
@@ -52,9 +52,6 @@
                     "type": "id",
                     "subtype": "integer"
                 },
-                "approved": {
-                    "type": "boolean"
-                },
                 "session_id": {
                     "type": "id",
                     "subtype": "integer",
@@ -66,6 +63,9 @@
                 "amount": {
                     "type": "numerical",
                     "subtype": "float"
+                },
+                "approved": {
+                    "type": "boolean"
                 }
             },
             "primary_key": "transaction_id"

From 33a8bff51d7e9f800fde579b54dc312bae7e671f Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 2 Jul 2020 15:51:13 +0200
Subject: [PATCH 05/33] Update tests

---
 examples/Evaluation.ipynb     | 193 ----------------------------------
 sdv/metadata.py               |   6 --
 sdv/sampler.py                |  15 +--
 tests/integration/test_sdv.py |  36 +++++++
 tests/models/test_copulas.py  |  18 ++--
 tests/test_metadata.py        |  22 ----
 tests/test_modeler.py         |   1 +
 tests/test_sampler.py         | 109 ++++++++++---------
 tests/test_sdv.py             |   2 +-
 9 files changed, 114 insertions(+), 288 deletions(-)
 delete mode 100644 examples/Evaluation.ipynb
 create mode 100644 tests/integration/test_sdv.py

diff --git a/examples/Evaluation.ipynb b/examples/Evaluation.ipynb
deleted file mode 100644
index 3d2d5bd7e..000000000
--- a/examples/Evaluation.ipynb
+++ /dev/null
@@ -1,193 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>tables</th>\n",
-       "      <th>rows</th>\n",
-       "      <th>columns</th>\n",
-       "      <th>description</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>airbnb-simplified</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5751408</td>\n",
-       "      <td>22</td>\n",
-       "      <td>airbnb user account information, including fir...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>rossmann</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1018324</td>\n",
-       "      <td>19</td>\n",
-       "      <td>rossmann stores promotional information and it...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>walmart</td>\n",
-       "      <td>3</td>\n",
-       "      <td>429804</td>\n",
-       "      <td>20</td>\n",
-       "      <td>walmart stores information (size and type) wit...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                name  tables     rows  columns  \\\n",
-       "0  airbnb-simplified       2  5751408       22   \n",
-       "1           rossmann       2  1018324       19   \n",
-       "2            walmart       3   429804       20   \n",
-       "\n",
-       "                                         description  \n",
-       "0  airbnb user account information, including fir...  \n",
-       "1  rossmann stores promotional information and it...  \n",
-       "2  walmart stores information (size and type) wit...  "
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "from sdv.demo import get_available_demos\n",
-    "\n",
-    "get_available_demos()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-06-26 00:11:41,404 - INFO - metadata - Loading table store\n",
-      "2020-06-26 00:11:41,434 - INFO - metadata - Loading table historical\n",
-      "2020-06-26 00:11:43,265 - INFO - modeler - Modeling store\n",
-      "2020-06-26 00:11:43,265 - INFO - metadata - Loading transformer NumericalTransformer for field CompetitionOpenSinceYear\n",
-      "2020-06-26 00:11:43,266 - INFO - metadata - Loading transformer NumericalTransformer for field CompetitionOpenSinceMonth\n",
-      "2020-06-26 00:11:43,266 - INFO - metadata - Loading transformer NumericalTransformer for field CompetitionDistance\n",
-      "2020-06-26 00:11:43,267 - INFO - metadata - Loading transformer BooleanTransformer for field Promo2\n",
-      "2020-06-26 00:11:43,267 - INFO - metadata - Loading transformer NumericalTransformer for field Promo2SinceYear\n",
-      "2020-06-26 00:11:43,268 - INFO - metadata - Loading transformer NumericalTransformer for field Promo2SinceWeek\n",
-      "2020-06-26 00:11:43,268 - INFO - metadata - Loading transformer CategoricalTransformer for field StoreType\n",
-      "2020-06-26 00:11:43,269 - INFO - metadata - Loading transformer CategoricalTransformer for field Assortment\n",
-      "2020-06-26 00:11:43,270 - INFO - metadata - Loading transformer CategoricalTransformer for field PromoInterval\n",
-      "2020-06-26 00:11:43,310 - INFO - modeler - Modeling historical\n",
-      "2020-06-26 00:11:43,311 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
-      "2020-06-26 00:11:43,311 - INFO - metadata - Loading transformer NumericalTransformer for field DayOfWeek\n",
-      "2020-06-26 00:11:43,311 - INFO - metadata - Loading transformer NumericalTransformer for field Promo\n",
-      "2020-06-26 00:11:43,312 - INFO - metadata - Loading transformer CategoricalTransformer for field StateHoliday\n",
-      "2020-06-26 00:11:43,312 - INFO - metadata - Loading transformer NumericalTransformer for field Open\n",
-      "2020-06-26 00:11:43,312 - INFO - metadata - Loading transformer NumericalTransformer for field SchoolHoliday\n",
-      "2020-06-26 00:11:43,312 - INFO - metadata - Loading transformer NumericalTransformer for field Customers\n",
-      "2020-06-26 00:13:14,951 - INFO - modeler - Modeling Complete\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-49542b34efbe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0msampled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msdv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfirst_table\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdemo\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/evaluation.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(synth, real, metadata, root_path, table_name, get_report)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0msynth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_validate_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msynth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroot_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m     \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msdmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mget_report\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/evaluation.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(metadata, real, synthetic)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMetricsReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0mreport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/report.py\u001b[0m in \u001b[0;36madd_metrics\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0mof\u001b[0m \u001b[0mthese\u001b[0m \u001b[0mmetrics\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \"\"\"\n\u001b[0;32m---> 96\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mmetric\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/evaluation.py\u001b[0m in \u001b[0;36m_metrics\u001b[0;34m(metadata, real, synthetic)\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mconstraint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdetection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mstatistical\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/detection/__init__.py\u001b[0m in \u001b[0;36mmetrics\u001b[0;34m(metadata, real_tables, synthetic_tables)\u001b[0m\n\u001b[1;32m     21\u001b[0m     \"\"\"\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdetector\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mLogisticDetector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdetector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_tables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic_tables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/detection/tabular/base.py\u001b[0m in \u001b[0;36mmetrics\u001b[0;34m(self, metadata, real_tables, synthetic_tables)\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0mMetric\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mnext\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \"\"\"\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_single_table_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_tables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic_tables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_child_detection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_tables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic_tables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/detection/tabular/base.py\u001b[0m in \u001b[0;36m_single_table_detection\u001b[0;34m(self, metadata, real_tables, synthetic_tables)\u001b[0m\n\u001b[1;32m     57\u001b[0m             auroc = self._compute_auroc(\n\u001b[1;32m     58\u001b[0m                 \u001b[0mreal_tables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtable_fields\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m                 synthetic_tables[table_name][table_fields])\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m             yield Metric(\n",
-      "\u001b[0;32m~/Projects/MIT/SDMetrics/sdmetrics/detection/tabular/base.py\u001b[0m in \u001b[0;36m_compute_auroc\u001b[0;34m(self, real_table, synthetic_table)\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mreal_table\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msynthetic_table\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msynthetic_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
-     ]
-    }
-   ],
-   "source": [
-    "from sdv import SDV\n",
-    "from sdv.demo import load_demo\n",
-    "from sdv.evaluation import evaluate\n",
-    "\n",
-    "scores = dict()\n",
-    "for demo in get_available_demos().name[1:]:\n",
-    "    metadata, tables = load_demo(demo, metadata=True)\n",
-    "    \n",
-    "    sdv = SDV()\n",
-    "    sdv.fit(metadata, tables)\n",
-    "    \n",
-    "    first_table = metadata.get_tables()[0]\n",
-    "    sampled = sdv.sample_all(len(tables[first_table]))\n",
-    "    \n",
-    "    score = evaluate(sampled, tables, metadata)\n",
-    "    scores[demo] = score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/sdv/metadata.py b/sdv/metadata.py
index 94f3e8e55..efa610e58 100644
--- a/sdv/metadata.py
+++ b/sdv/metadata.py
@@ -575,11 +575,6 @@ def _validate_circular_relationships(self, parent, children=None):
         for child in children:
             self._validate_circular_relationships(parent, self.get_children(child))
 
-    def _validate_parents(self, table_name):
-        """Make sure that the table has only one parent."""
-        if len(self.get_parents(table_name)) > 1:
-            raise MetadataError('Table {} has more than one parent.'.format(table_name))
-
     def validate(self, tables=None):
         """Validate this metadata.
 
@@ -624,7 +619,6 @@ def validate(self, tables=None):
 
             self._validate_table(table_name, table_meta, table)
             self._validate_circular_relationships(table_name)
-            # self._validate_parents(table_name)
 
     def _check_field(self, table, field, exists=False):
         """Validate the existance of the table and existance (or not) of field."""
diff --git a/sdv/sampler.py b/sdv/sampler.py
index 706d8c064..3aecd076d 100644
--- a/sdv/sampler.py
+++ b/sdv/sampler.py
@@ -38,7 +38,11 @@ def _reset_primary_keys_generators(self):
         self.remaining_primary_key = dict()
 
     def _finalize(self, sampled_data):
-        """Reverse transform synthetized data.
+        """Do the final touches to the generated data.
+
+        This method reverts the previous transformations to go back
+        to values in the original space and also adds the parent
+        keys in case foreign key relationships exist between the tables.
 
         Args:
             sampled_data (dict):
@@ -226,11 +230,7 @@ def _find_parent_id(self, row, pdfs, num_rows):
             # singular matrix rows with the mean
             likelihoods = likelihoods.fillna(mean)
 
-        total = likelihoods.sum()
-        if total == 0:
-            weights = np.ones(len(likelihoods))
-        else:
-            weights = likelihoods.values / likelihoods.sum()
+        weights = likelihoods.values / likelihoods.sum()
 
         return np.random.choice(likelihoods.index, p=weights)
 
@@ -245,7 +245,8 @@ def _find_parent_ids(self, table_name, parent_name, sampled_data):
             parent_rows = self._sample_rows(parent_model, num_parent_rows, parent_name)
 
         primary_key = self.metadata.get_primary_key(parent_name)
-        pdfs = self._get_pdfs(parent_rows.set_index(primary_key), table_name)
+        parent_rows = parent_rows.set_index(primary_key)
+        pdfs = self._get_pdfs(parent_rows, table_name)
         num_rows = parent_rows['__' + table_name + '__child_rows'].clip(0)
 
         parent_ids = list()
diff --git a/tests/integration/test_sdv.py b/tests/integration/test_sdv.py
new file mode 100644
index 000000000..2314aee8b
--- /dev/null
+++ b/tests/integration/test_sdv.py
@@ -0,0 +1,36 @@
+from sdv import SDV, load_demo
+
+
+def test_sdv():
+    metadata, tables = load_demo(metadata=True)
+
+    sdv = SDV()
+    sdv.fit(metadata, tables)
+
+    # Sample all
+    sampled = sdv.sample_all()
+
+    assert set(sampled.keys()) == {'users', 'sessions', 'transactions'}
+    assert len(sampled['users']) == 10
+
+    # Sample with children
+    sampled = sdv.sample('users', reset_primary_keys=True)
+
+    assert set(sampled.keys()) == {'users', 'sessions', 'transactions'}
+    assert len(sampled['users']) == 10
+
+    # Sample without children
+    users = sdv.sample('users', sample_children=False)
+
+    assert users.shape == tables['users'].shape
+    assert list(users.columns) == list(tables['users'].columns)
+
+    sessions = sdv.sample('sessions', sample_children=False)
+
+    assert sessions.shape == tables['sessions'].shape
+    assert list(sessions.columns) == list(tables['sessions'].columns)
+
+    transactions = sdv.sample('transactions', sample_children=False)
+
+    assert transactions.shape == tables['transactions'].shape
+    assert list(transactions.columns) == list(tables['transactions'].columns)
diff --git a/tests/models/test_copulas.py b/tests/models/test_copulas.py
index 0dd0579d1..56a14215b 100644
--- a/tests/models/test_copulas.py
+++ b/tests/models/test_copulas.py
@@ -26,8 +26,11 @@ def test__unflatten_gaussian_copula(self):
 
         # Run
         model_parameters = {
-            'distribs': {
-                'foo': {'std': 0.5}
+            'univariates': {
+                'foo': {
+                    'scale': 0.0,
+                    'loc': 5
+                },
             },
             'covariance': [[0.4, 0.1], [0.1]],
             'distribution': 'GaussianUnivariate'
@@ -36,13 +39,14 @@ def test__unflatten_gaussian_copula(self):
 
         # Asserts
         expected = {
-            'distribs': {
-                'foo': {
-                    'fitted': True,
-                    'std': 1.6487212707001282,
+            'univariates': [
+                {
+                    'scale': 1.0,
+                    'loc': 5,
                     'type': 'GaussianUnivariate'
                 }
-            },
+            ],
+            'columns': ['foo'],
             'distribution': 'GaussianUnivariate',
             'covariance': [[0.4, 0.2], [0.2, 0.0]]
         }
diff --git a/tests/test_metadata.py b/tests/test_metadata.py
index a322bc7c7..137a258b8 100644
--- a/tests/test_metadata.py
+++ b/tests/test_metadata.py
@@ -218,28 +218,6 @@ def test__dict_metadata_dict(self):
         }
         assert result == expected
 
-    def test__validate_parents_no_error(self):
-        """Test that any error is raised with a supported structure"""
-        # Setup
-        mock = MagicMock(spec_set=Metadata)
-        mock.get_parents.return_value = []
-
-        # Run
-        Metadata._validate_parents(mock, 'demo')
-
-        # Asserts
-        mock.get_parents.assert_called_once_with('demo')
-
-    def test__validate_parents_raise_error(self):
-        """Test that a ValueError is raised because the bad structure"""
-        # Setup
-        mock = MagicMock(spec_set=Metadata)
-        mock.get_parents.return_value = ['foo', 'bar']
-
-        # Run
-        with pytest.raises(MetadataError):
-            Metadata._validate_parents(mock, 'demo')
-
     @patch('sdv.metadata.Metadata._analyze_relationships')
     @patch('sdv.metadata.Metadata._dict_metadata')
     def test___init__default_metadata_dict(self, mock_meta, mock_relationships):
diff --git a/tests/test_modeler.py b/tests/test_modeler.py
index bf162d6c8..f701e2469 100644
--- a/tests/test_modeler.py
+++ b/tests/test_modeler.py
@@ -69,6 +69,7 @@ def test_cpa_with_tables_no_primary_key(self):
         modeler.model = Mock(spec=SDVModel)
         modeler.model_kwargs = dict()
         modeler.models = dict()
+        modeler.table_sizes = {'data': 5}
         modeler.metadata.transform.return_value = pd.DataFrame({'data': [1, 2, 3]})
         modeler.metadata.get_primary_key.return_value = None
 
diff --git a/tests/test_sampler.py b/tests/test_sampler.py
index a5eddf75f..fd3e0daa0 100644
--- a/tests/test_sampler.py
+++ b/tests/test_sampler.py
@@ -15,7 +15,13 @@ def test___init__(self):
         """Test create a default instance of Sampler class"""
         # Run
         models = {'test': Mock()}
-        sampler = Sampler('test_metadata', models, SDVModel, dict())
+        sampler = Sampler(
+            'test_metadata',
+            models,
+            SDVModel,
+            {'model': 'kwargs'},
+            {'table': 'sizes'}
+        )
 
         # Asserts
         assert sampler.metadata == 'test_metadata'
@@ -23,7 +29,8 @@ def test___init__(self):
         assert sampler.primary_key == dict()
         assert sampler.remaining_primary_key == dict()
         assert sampler.model == SDVModel
-        assert sampler.model_kwargs == dict()
+        assert sampler.model_kwargs == {'model': 'kwargs'}
+        assert sampler.table_sizes == {'table': 'sizes'}
 
     def test__reset_primary_keys_generators(self):
         """Test reset values"""
@@ -39,23 +46,50 @@ def test__reset_primary_keys_generators(self):
         assert sampler.primary_key == dict()
         assert sampler.remaining_primary_key == dict()
 
-    def test__transform_synthesized_rows(self):
-        """Test transform synthesized rows"""
+    def test__finalize(self):
+        """Test finalize"""
         # Setup
-        metadata_reverse_transform = pd.DataFrame({'foo': [0, 1], 'bar': [2, 3], 'tar': [4, 5]})
-
         sampler = Mock(spec=Sampler)
         sampler.metadata = Mock(spec=Metadata)
-        sampler.metadata.reverse_transform.return_value = metadata_reverse_transform
-        sampler.metadata.get_fields.return_value = {'foo': 'some data', 'tar': 'some data'}
+        sampler.metadata.get_parents.return_value = ['b', 'c']
+
+        sampler.metadata.reverse_transform.side_effect = lambda x, y: y
+
+        sampler.metadata.get_fields.return_value = {
+            'a': 'some data',
+            'b': 'some data',  # fk
+            'c': 'some data'   # fk
+        }
+
+        sampler._find_parent_ids.side_effect = [
+            [2, 3],
+            [4, 5]
+        ]
+        sampler.metadata.get_foreign_key.side_effect = [
+            'b',
+            'c',
+        ]
 
         # Run
-        synthesized = pd.DataFrame({'data': [1, 2, 3]})
-        result = Sampler._transform_synthesized_rows(sampler, synthesized, 'test')
+        sampled_data = {
+            'test': pd.DataFrame({
+                'a': [0, 1],  # actual data
+                'z': [6, 7]   # not used
+            })
+        }
+        result = Sampler._finalize(sampler, sampled_data)
 
         # Asserts
-        expected = pd.DataFrame({'foo': [0, 1], 'tar': [4, 5]})
-        pd.testing.assert_frame_equal(result.sort_index(axis=1), expected.sort_index(axis=1))
+        assert isinstance(result, dict)
+        expected = pd.DataFrame({
+            'a': [0, 1],
+            'b': [2, 3],
+            'c': [4, 5],
+        })
+        pd.testing.assert_frame_equal(
+            result['test'].sort_index(axis=1),
+            expected.sort_index(axis=1)
+        )
 
     def test__get_primary_keys_none(self):
         """Test returns a tuple of none when a table doesn't have a primary key"""
@@ -133,7 +167,7 @@ def test__get_primary_keys_raises_value_error_remaining(self):
         with pytest.raises(ValueError):
             Sampler._get_primary_keys(sampler, 'test', 5)
 
-    def test__get_extension(self):
+    def test__extract_parameters(self):
         """Test get extension"""
         # Setup
         sampler = Mock(spec=Sampler)
@@ -141,7 +175,7 @@ def test__get_extension(self):
         # Run
         parent_row = pd.Series([[0, 1], [1, 0]], index=['__foo__field', '__foo__field2'])
         table_name = 'foo'
-        result = Sampler._get_extension(sampler, parent_row, table_name)
+        result = Sampler._extract_parameters(sampler, parent_row, table_name)
 
         # Asserts
         expected = {'field': [0, 1], 'field2': [1, 0]}
@@ -191,14 +225,14 @@ def test__sample_children(self):
             ['child C', 'test', pd.Series([22], index=['field'], name=1), sampled],
             ['child C', 'test', pd.Series([33], index=['field'], name=2), sampled],
         ]
-        actual_calls = sampler._sample_table.call_args_list
+        actual_calls = sampler._sample_child_rows.call_args_list
         for result_call, expected_call in zip(actual_calls, expected_calls):
             assert result_call[0][0] == expected_call[0]
             assert result_call[0][1] == expected_call[1]
             assert result_call[0][3] == expected_call[3]
             pd.testing.assert_series_equal(result_call[0][2], expected_call[2])
 
-    def test__sample_table_sampled_empty(self):
+    def test__sample_child_rows_sampled_empty(self):
         """Test sample table when sampled is still an empty dict."""
         # Setup
         model = Mock(spec=SDVModel)
@@ -208,7 +242,7 @@ def test__sample_table_sampled_empty(self):
         sampler.model = model
         sampler.model_kwargs = dict()
 
-        sampler._get_extension.return_value = {'child_rows': 5}
+        sampler._extract_parameters.return_value = {'child_rows': 5}
 
         table_model_mock = Mock()
         sampler.models = {'test': table_model_mock}
@@ -222,10 +256,10 @@ def test__sample_table_sampled_empty(self):
         # Run
         parent_row = pd.Series({'id': 0})
         sampled = dict()
-        Sampler._sample_table(sampler, 'test', 'parent', parent_row, sampled)
+        Sampler._sample_child_rows(sampler, 'test', 'parent', parent_row, sampled)
 
         # Asserts
-        sampler._get_extension.assert_called_once_with(parent_row, 'test')
+        sampler._extract_parameters.assert_called_once_with(parent_row, 'test')
         sampler._sample_rows.assert_called_once_with(model, 5, 'test')
 
         assert sampler._sample_children.call_count == 1
@@ -240,7 +274,7 @@ def test__sample_table_sampled_empty(self):
             expected_sampled
         )
 
-    def test__sample_table_sampled_not_empty(self):
+    def test__sample_child_rows_sampled_not_empty(self):
         """Test sample table when sampled previous sampled rows exist."""
         # Setup
         model = Mock(spec=SDVModel)
@@ -249,7 +283,7 @@ def test__sample_table_sampled_not_empty(self):
         sampler = Mock(spec=Sampler)
         sampler.model = model
         sampler.model_kwargs = dict()
-        sampler._get_extension.return_value = {'child_rows': 5}
+        sampler._extract_parameters.return_value = {'child_rows': 5}
 
         table_model_mock = Mock()
         sampler.models = {'test': table_model_mock}
@@ -270,11 +304,10 @@ def test__sample_table_sampled_not_empty(self):
                 'parent_id': [0, 0, 0, 0, 0]
             })
         }
-        Sampler._sample_table(sampler, 'test', 'parent', parent_row, sampled)
+        Sampler._sample_child_rows(sampler, 'test', 'parent', parent_row, sampled)
 
         # Asserts
-        sampler._get_extension.assert_called_once_with(parent_row, 'test')
-        # sampler._get_model.assert_called_once_with({'child_rows': 5}, table_model_mock)
+        sampler._extract_parameters.assert_called_once_with(parent_row, 'test')
         sampler._sample_rows.assert_called_once_with(model, 5, 'test')
 
         assert sampler._sample_children.call_count == 1
@@ -308,31 +341,3 @@ def sample_side_effect(table, num_rows):
         assert sampler._reset_primary_keys_generators.call_count == 1
         pd.testing.assert_frame_equal(result['table a'], pd.DataFrame({'foo': range(3)}))
         pd.testing.assert_frame_equal(result['table c'], pd.DataFrame({'foo': range(3)}))
-
-    def test_sample_table_with_parents(self):
-        """Test sample table with parents."""
-        sampler = Mock(spec=Sampler)
-        sampler.metadata = Mock(spec=Metadata)
-        sampler.metadata.get_parents.return_value = ['test_parent']
-        sampler.metadata.get_foreign_key.return_value = 'id'
-        sampler.models = {'test': 'some model'}
-        sampler._get_primary_keys.return_value = None, pd.Series({'id': 0})
-        sampler._sample_rows.return_value = pd.DataFrame({'id': [0, 1]})
-
-        Sampler.sample(sampler, 'test', 5)
-        sampler.metadata.get_parents.assert_called_once_with('test')
-        sampler.metadata.get_foreign_key.assert_called_once_with('test_parent', 'test')
-
-    def test_sample_no_sample_children(self):
-        """Test sample no sample children"""
-        # Setup
-        sampler = Mock(spec=Sampler)
-        sampler.models = {'test': 'model'}
-        sampler.metadata.get_parents.return_value = None
-
-        # Run
-        Sampler.sample(sampler, 'test', 5, sample_children=False)
-        sampler._transform_synthesized_rows.assert_called_once_with(
-            sampler._sample_rows.return_value,
-            'test'
-        )
diff --git a/tests/test_sdv.py b/tests/test_sdv.py
index f1373129e..21c61f7ef 100644
--- a/tests/test_sdv.py
+++ b/tests/test_sdv.py
@@ -86,7 +86,7 @@ def test_sample_all_fitted(self):
 
         # Asserts
         assert result == 'test'
-        sdv.sampler.sample_all.assert_called_once_with(5, reset_primary_keys=False)
+        sdv.sampler.sample_all.assert_called_once_with(None, reset_primary_keys=False)
 
     def test_sample_all_not_fitted(self):
         """Check that the sample_all raise an exception when is not fitted."""

From 92da737989324ffd544c9897891f4656dde02fd8 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 2 Jul 2020 16:52:38 +0200
Subject: [PATCH 06/33] Fix test on py35

---
 tests/integration/test_sdv.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/integration/test_sdv.py b/tests/integration/test_sdv.py
index 2314aee8b..be9b40325 100644
--- a/tests/integration/test_sdv.py
+++ b/tests/integration/test_sdv.py
@@ -23,14 +23,14 @@ def test_sdv():
     users = sdv.sample('users', sample_children=False)
 
     assert users.shape == tables['users'].shape
-    assert list(users.columns) == list(tables['users'].columns)
+    assert set(users.columns) == set(tables['users'].columns)
 
     sessions = sdv.sample('sessions', sample_children=False)
 
     assert sessions.shape == tables['sessions'].shape
-    assert list(sessions.columns) == list(tables['sessions'].columns)
+    assert set(sessions.columns) == set(tables['sessions'].columns)
 
     transactions = sdv.sample('transactions', sample_children=False)
 
     assert transactions.shape == tables['transactions'].shape
-    assert list(transactions.columns) == list(tables['transactions'].columns)
+    assert set(transactions.columns) == set(tables['transactions'].columns)

From 5b041f465d30ce9bf69603cd50ea917ae581f309 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 2 Jul 2020 18:56:37 +0200
Subject: [PATCH 07/33] Vectorize and search for parents only if necessary

---
 sdv/sampler.py | 38 ++++++++++++++++++++------------------
 1 file changed, 20 insertions(+), 18 deletions(-)

diff --git a/sdv/sampler.py b/sdv/sampler.py
index 3aecd076d..2dd318e7d 100644
--- a/sdv/sampler.py
+++ b/sdv/sampler.py
@@ -57,9 +57,10 @@ def _finalize(self, sampled_data):
             parents = self.metadata.get_parents(table_name)
             if parents:
                 for parent_name in parents:
-                    parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)
                     foreign_key = self.metadata.get_foreign_key(parent_name, table_name)
-                    table_rows[foreign_key] = parent_ids
+                    if foreign_key not in table_rows:
+                        parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)
+                        table_rows[foreign_key] = parent_ids
 
             reversed_data = self.metadata.reverse_transform(table_name, table_rows)
 
@@ -198,6 +199,7 @@ def _sample_child_rows(self, table_name, parent_name, parent_row, sampled):
         self._sample_children(table_name, sampled)
 
     def _get_pdfs(self, parent_rows, child_name):
+        """Build a model for each parent row and get its pdf function."""
         pdfs = dict()
         for parent_id, row in parent_rows.iterrows():
             parameters = self._extract_parameters(row, child_name)
@@ -207,16 +209,7 @@ def _get_pdfs(self, parent_rows, child_name):
 
         return pdfs
 
-    def _find_parent_id(self, row, pdfs, num_rows):
-        likelihoods = dict()
-        for parent_id, pdf in pdfs.items():
-            try:
-                likelihoods[parent_id] = max(pdf(row), 0.0)
-            except np.linalg.LinAlgError:
-                # Singular matrix
-                likelihoods[parent_id] = None
-
-        likelihoods = pd.Series(likelihoods)
+    def _find_parent_id(self, likelihoods, num_rows):
         mean = likelihoods.mean()
         if (likelihoods == 0).all():
             # All rows got 0 likelihood, fallback to num_rows
@@ -234,6 +227,19 @@ def _find_parent_id(self, row, pdfs, num_rows):
 
         return np.random.choice(likelihoods.index, p=weights)
 
+    def _get_likelihoods(self, table_rows, parent_rows, table_name):
+        likelihoods = dict()
+        for parent_id, row in parent_rows.iterrows():
+            parameters = self._extract_parameters(row, table_name)
+            model = self.model(**self.model_kwargs)
+            model.set_parameters(parameters)
+            try:
+                likelihoods[parent_id] = model.model.probability_density(table_rows)
+            except np.linalg.LinAlgError:
+                likelihoods[parent_id] = None
+
+        return pd.DataFrame(likelihoods, index=table_rows.index)
+
     def _find_parent_ids(self, table_name, parent_name, sampled_data):
         table_rows = sampled_data[table_name]
         if parent_name in sampled_data:
@@ -246,14 +252,10 @@ def _find_parent_ids(self, table_name, parent_name, sampled_data):
 
         primary_key = self.metadata.get_primary_key(parent_name)
         parent_rows = parent_rows.set_index(primary_key)
-        pdfs = self._get_pdfs(parent_rows, table_name)
         num_rows = parent_rows['__' + table_name + '__child_rows'].clip(0)
 
-        parent_ids = list()
-        for _, row in table_rows.iterrows():
-            parent_ids.append(self._find_parent_id(row, pdfs, num_rows))
-
-        return parent_ids
+        likelihoods = self._get_likelihoods(table_rows, parent_rows, table_name)
+        return likelihoods.apply(self._find_parent_id, axis=1, num_rows=num_rows)
 
     def sample(self, table_name, num_rows=None, reset_primary_keys=False,
                sample_children=True, sample_parents=True):

From 6c7be1a50280f244dd073b1bd981f9e0304734f8 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 2 Jul 2020 18:57:10 +0200
Subject: [PATCH 08/33] Update notebook examples

---
 examples/Demo - Walmart.ipynb | 872 +++++++++++++++++-----------------
 examples/demo_metadata.json   |  30 +-
 2 files changed, 450 insertions(+), 452 deletions(-)

diff --git a/examples/Demo - Walmart.ipynb b/examples/Demo - Walmart.ipynb
index d3bbe80e9..2e3303259 100644
--- a/examples/Demo - Walmart.ipynb	
+++ b/examples/Demo - Walmart.ipynb	
@@ -100,9 +100,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-02 14:27:25,479 - INFO - metadata - Loading table stores\n",
-      "2020-07-02 14:27:25,486 - INFO - metadata - Loading table features\n",
-      "2020-07-02 14:27:25,502 - INFO - metadata - Loading table depts\n"
+      "2020-07-02 18:50:09,230 - INFO - metadata - Loading table stores\n",
+      "2020-07-02 18:50:09,237 - INFO - metadata - Loading table features\n",
+      "2020-07-02 18:50:09,255 - INFO - metadata - Loading table depts\n"
      ]
     }
    ],
@@ -213,7 +213,7 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7f6909571860>"
+       "<graphviz.dot.Digraph at 0x7fc286fd97f0>"
       ]
      },
      "execution_count": 2,
@@ -261,128 +261,128 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-02 14:27:31,883 - INFO - modeler - Modeling stores\n",
-      "2020-07-02 14:27:31,883 - INFO - metadata - Loading transformer CategoricalTransformer for field Type\n",
-      "2020-07-02 14:27:31,884 - INFO - metadata - Loading transformer NumericalTransformer for field Size\n",
-      "2020-07-02 14:27:31,895 - INFO - modeler - Modeling depts\n",
-      "2020-07-02 14:27:31,895 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
-      "2020-07-02 14:27:31,896 - INFO - metadata - Loading transformer NumericalTransformer for field Weekly_Sales\n",
-      "2020-07-02 14:27:31,896 - INFO - metadata - Loading transformer NumericalTransformer for field Dept\n",
-      "2020-07-02 14:27:31,896 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
-      "2020-07-02 14:27:32,007 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,272 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,288 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,302 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,319 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,335 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,350 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,367 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,383 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,399 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,414 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,430 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,445 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,460 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,475 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,490 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,508 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,522 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,536 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,550 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,563 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,576 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,594 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,607 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,621 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,636 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,651 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,664 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,679 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,695 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,710 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,723 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,738 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,752 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,765 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,781 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,796 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,809 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,823 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,837 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,852 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,868 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,881 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,896 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,907 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,919 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:32,938 - INFO - modeler - Modeling features\n",
-      "2020-07-02 14:27:32,939 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
-      "2020-07-02 14:27:32,939 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown1\n",
-      "2020-07-02 14:27:32,940 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
-      "2020-07-02 14:27:32,941 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown4\n",
-      "2020-07-02 14:27:32,941 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown3\n",
-      "2020-07-02 14:27:32,941 - INFO - metadata - Loading transformer NumericalTransformer for field Fuel_Price\n",
-      "2020-07-02 14:27:32,942 - INFO - metadata - Loading transformer NumericalTransformer for field Unemployment\n",
-      "2020-07-02 14:27:32,942 - INFO - metadata - Loading transformer NumericalTransformer for field Temperature\n",
-      "2020-07-02 14:27:32,942 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown5\n",
-      "2020-07-02 14:27:32,942 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown2\n",
-      "2020-07-02 14:27:32,943 - INFO - metadata - Loading transformer NumericalTransformer for field CPI\n",
-      "2020-07-02 14:27:32,993 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,043 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,073 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,104 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,133 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,189 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,220 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,253 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,285 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,314 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,342 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,371 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
+      "2020-07-02 18:50:09,639 - INFO - modeler - Modeling stores\n",
+      "2020-07-02 18:50:09,640 - INFO - metadata - Loading transformer CategoricalTransformer for field Type\n",
+      "2020-07-02 18:50:09,640 - INFO - metadata - Loading transformer NumericalTransformer for field Size\n",
+      "2020-07-02 18:50:09,653 - INFO - modeler - Modeling features\n",
+      "2020-07-02 18:50:09,653 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
+      "2020-07-02 18:50:09,654 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown1\n",
+      "2020-07-02 18:50:09,654 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
+      "2020-07-02 18:50:09,654 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown4\n",
+      "2020-07-02 18:50:09,655 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown3\n",
+      "2020-07-02 18:50:09,656 - INFO - metadata - Loading transformer NumericalTransformer for field Fuel_Price\n",
+      "2020-07-02 18:50:09,656 - INFO - metadata - Loading transformer NumericalTransformer for field Unemployment\n",
+      "2020-07-02 18:50:09,657 - INFO - metadata - Loading transformer NumericalTransformer for field Temperature\n",
+      "2020-07-02 18:50:09,657 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown5\n",
+      "2020-07-02 18:50:09,657 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown2\n",
+      "2020-07-02 18:50:09,659 - INFO - metadata - Loading transformer NumericalTransformer for field CPI\n",
+      "2020-07-02 18:50:09,709 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:09,760 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:09,791 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:09,817 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:09,845 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:09,871 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:09,901 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:09,929 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:09,958 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:09,985 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,016 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,045 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,076 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,131 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,158 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,184 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,211 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,239 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,268 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,297 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,325 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,352 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,378 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,410 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,440 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,467 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,494 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,519 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,547 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,572 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,602 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,628 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,656 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,686 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,713 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,740 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,768 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,799 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,827 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,858 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,886 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,912 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,939 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,967 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:10,996 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,028 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,058 - INFO - modeler - Modeling depts\n",
+      "2020-07-02 18:50:11,058 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
+      "2020-07-02 18:50:11,059 - INFO - metadata - Loading transformer NumericalTransformer for field Weekly_Sales\n",
+      "2020-07-02 18:50:11,059 - INFO - metadata - Loading transformer NumericalTransformer for field Dept\n",
+      "2020-07-02 18:50:11,059 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
+      "2020-07-02 18:50:11,169 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,445 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,461 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,474 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,489 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,505 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,521 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,536 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,552 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,568 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,583 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,603 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-02 14:27:33,399 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,425 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,452 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,479 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,504 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,531 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,558 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,592 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,624 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,653 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,683 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,713 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,743 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,770 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,797 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,826 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,852 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,880 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,910 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,939 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,967 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:33,996 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,025 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,051 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,078 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,107 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,134 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,160 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,187 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,216 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,246 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,275 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,303 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,333 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:27:34,452 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,618 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,634 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,652 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,667 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,683 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,698 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,713 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,728 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,741 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,754 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,769 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,782 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,800 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,814 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,829 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,844 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,859 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,874 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,887 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,900 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,914 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,928 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,940 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,955 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,968 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,979 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:11,991 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:12,003 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:12,017 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:12,032 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:12,045 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:12,058 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:12,069 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:12,084 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-02 18:50:12,177 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
       "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
       "  return getattr(obj, method)(*args, **kwds)\n",
-      "2020-07-02 14:27:34,680 - INFO - modeler - Modeling Complete\n"
+      "2020-07-02 18:50:12,380 - INFO - modeler - Modeling Complete\n"
      ]
     }
    ],
@@ -417,29 +417,27 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-5-eecf8d026e72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msdv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sdv.py\u001b[0m in \u001b[0;36msample_all\u001b[0;34m(self, num_rows, reset_primary_keys)\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'SDV instance has not been fitted'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset_primary_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset_primary_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36msample_all\u001b[0;34m(self, num_rows, reset_primary_keys)\u001b[0m\n\u001b[1;32m    331\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_parents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 333\u001b[0;31m                 \u001b[0msampled_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msampled_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, table_name, num_rows, reset_primary_keys, sample_children, sample_parents)\u001b[0m\n\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sample_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampled_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_finalize\u001b[0;34m(self, sampled_data)\u001b[0m\n\u001b[1;32m     58\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mparents\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mparent_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mparents\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m                     \u001b[0mparent_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_find_parent_ids\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampled_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m                     \u001b[0mforeign_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_foreign_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m                     \u001b[0mtable_rows\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mforeign_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparent_ids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_find_parent_ids\u001b[0;34m(self, table_name, parent_name, sampled_data)\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0mparent_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtable_rows\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m             \u001b[0mparent_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_find_parent_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpdfs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mparent_ids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Projects/MIT/SDV/sdv/sampler.py\u001b[0m in \u001b[0;36m_find_parent_id\u001b[0;34m(self, row, pdfs, num_rows)\u001b[0m\n\u001b[1;32m    212\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparent_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpdf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpdfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m                 \u001b[0mlikelihoods\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mparent_id\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinAlgError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m                 \u001b[0;31m# Singular matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/copulas/multivariate/gaussian.py\u001b[0m in \u001b[0;36mprobability_density\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    133\u001b[0m         \"\"\"\n\u001b[1;32m    134\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m         \u001b[0mtransformed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transform_to_normal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    136\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultivariate_normal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcovariance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/copulas/multivariate/gaussian.py\u001b[0m in \u001b[0;36m_transform_to_normal\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcolumn_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munivariate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munivariates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0mcolumn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m             \u001b[0mU\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munivariate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEPSILON\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mEPSILON\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mppf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mU\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/copulas/univariate/base.py\u001b[0m in \u001b[0;36mcdf\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    306\u001b[0m                 \u001b[0mCumulative\u001b[0m \u001b[0mdistribution\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpoints\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m         \"\"\"\n\u001b[0;32m--> 308\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumulative_distribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpercent_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mU\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/copulas/univariate/base.py\u001b[0m in \u001b[0;36mcumulative_distribution\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    523\u001b[0m         \"\"\"\n\u001b[1;32m    524\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpercent_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mU\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py\u001b[0m in \u001b[0;36mcdf\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mlogcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.virtualenvs/SDV/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py\u001b[0m in \u001b[0;36mcdf\u001b[0;34m(self, x, *args, **kwds)\u001b[0m\n\u001b[1;32m   1747\u001b[0m         \u001b[0mcond1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_support_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscale\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1748\u001b[0m         \u001b[0mcond2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mcond0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1749\u001b[0;31m         \u001b[0mcond\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcond0\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mcond1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1750\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcond\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtyp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1751\u001b[0m         \u001b[0mplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mcond0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbadvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
+     "data": {
+      "text/plain": [
+       "{'stores': 45, 'features': 8190, 'depts': 421570}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
+   "source": [
+    "sdv.modeler.table_sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
     "samples = sdv.sample_all()"
    ]
@@ -453,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -486,32 +484,32 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>B</td>\n",
-       "      <td>108725</td>\n",
-       "      <td>0</td>\n",
+       "      <td>85496</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>71407</td>\n",
-       "      <td>1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>178862</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>A</td>\n",
-       "      <td>192291</td>\n",
-       "      <td>2</td>\n",
+       "      <td>B</td>\n",
+       "      <td>69654</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>B</td>\n",
-       "      <td>91790</td>\n",
-       "      <td>3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>211981</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>B</td>\n",
-       "      <td>147700</td>\n",
-       "      <td>4</td>\n",
+       "      <td>A</td>\n",
+       "      <td>131188</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -519,14 +517,14 @@
       ],
       "text/plain": [
        "  Type    Size  Store\n",
-       "0    B  108725      0\n",
-       "1    B   71407      1\n",
-       "2    A  192291      2\n",
-       "3    B   91790      3\n",
-       "4    B  147700      4"
+       "0    B   85496      3\n",
+       "1    A  178862      4\n",
+       "2    B   69654      5\n",
+       "3    A  211981      6\n",
+       "4    A  131188      7"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -537,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -578,107 +576,107 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2012-01-06 12:07:18.407483136</td>\n",
+       "      <td>2012-04-23 03:09:52.638271488</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
+       "      <td>3</td>\n",
        "      <td>False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3371.662534</td>\n",
-       "      <td>3.518252</td>\n",
-       "      <td>8.595226</td>\n",
-       "      <td>82.638090</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>-9803.940199</td>\n",
+       "      <td>3.561375</td>\n",
+       "      <td>8.838728</td>\n",
+       "      <td>67.162475</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>190.969301</td>\n",
+       "      <td>2703.17729</td>\n",
+       "      <td>186.471991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2012-02-04 17:43:32.634723840</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2011-04-19 08:45:12.429521664</td>\n",
+       "      <td>483.892955</td>\n",
+       "      <td>3</td>\n",
        "      <td>False</td>\n",
+       "      <td>7504.524416</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>3.495118</td>\n",
+       "      <td>7.360667</td>\n",
+       "      <td>42.785730</td>\n",
+       "      <td>2772.597105</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3.690170</td>\n",
-       "      <td>8.564543</td>\n",
-       "      <td>75.341551</td>\n",
-       "      <td>5178.361034</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>197.207944</td>\n",
+       "      <td>192.048268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2011-02-28 11:10:43.405782528</td>\n",
-       "      <td>3991.026002</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2011-01-30 22:13:59.841415680</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
        "      <td>False</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3.736181</td>\n",
-       "      <td>7.736966</td>\n",
-       "      <td>52.131744</td>\n",
-       "      <td>2939.548298</td>\n",
-       "      <td>2017.730801</td>\n",
-       "      <td>198.774994</td>\n",
+       "      <td>3.361946</td>\n",
+       "      <td>7.524812</td>\n",
+       "      <td>34.945770</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>192.100673</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2013-10-07 10:01:00.746535424</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2011-10-08 08:16:00.977235968</td>\n",
+       "      <td>4661.175670</td>\n",
+       "      <td>3</td>\n",
        "      <td>False</td>\n",
+       "      <td>3392.028528</td>\n",
+       "      <td>-5837.649003</td>\n",
+       "      <td>2.994273</td>\n",
+       "      <td>7.993152</td>\n",
+       "      <td>66.818180</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>2969.019454</td>\n",
-       "      <td>3.004613</td>\n",
-       "      <td>9.759439</td>\n",
-       "      <td>73.570879</td>\n",
-       "      <td>10744.780013</td>\n",
-       "      <td>4958.619653</td>\n",
-       "      <td>173.358556</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>197.921916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2011-12-29 13:02:32.885990144</td>\n",
-       "      <td>6395.942402</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2011-09-29 23:18:43.912751616</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
        "      <td>False</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-1840.929955</td>\n",
-       "      <td>3.882088</td>\n",
-       "      <td>10.158594</td>\n",
-       "      <td>77.868550</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>3.116659</td>\n",
+       "      <td>4.945393</td>\n",
+       "      <td>48.979036</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>197.546955</td>\n",
+       "      <td>2957.77612</td>\n",
+       "      <td>192.677797</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                           Date    MarkDown1  Store  IsHoliday  MarkDown4  \\\n",
-       "0 2012-01-06 12:07:18.407483136          NaN      0      False        NaN   \n",
-       "1 2012-02-04 17:43:32.634723840          NaN      0      False        NaN   \n",
-       "2 2011-02-28 11:10:43.405782528  3991.026002      0      False        NaN   \n",
-       "3 2013-10-07 10:01:00.746535424          NaN      0      False        NaN   \n",
-       "4 2011-12-29 13:02:32.885990144  6395.942402      0      False        NaN   \n",
+       "                           Date    MarkDown1  Store  IsHoliday    MarkDown4  \\\n",
+       "0 2012-04-23 03:09:52.638271488          NaN      3      False          NaN   \n",
+       "1 2011-04-19 08:45:12.429521664   483.892955      3      False  7504.524416   \n",
+       "2 2011-01-30 22:13:59.841415680          NaN      3      False          NaN   \n",
+       "3 2011-10-08 08:16:00.977235968  4661.175670      3      False  3392.028528   \n",
+       "4 2011-09-29 23:18:43.912751616          NaN      3      False          NaN   \n",
        "\n",
-       "     MarkDown3  Fuel_Price  Unemployment  Temperature     MarkDown5  \\\n",
-       "0  3371.662534    3.518252      8.595226    82.638090           NaN   \n",
-       "1          NaN    3.690170      8.564543    75.341551   5178.361034   \n",
-       "2          NaN    3.736181      7.736966    52.131744   2939.548298   \n",
-       "3  2969.019454    3.004613      9.759439    73.570879  10744.780013   \n",
-       "4 -1840.929955    3.882088     10.158594    77.868550           NaN   \n",
+       "     MarkDown3  Fuel_Price  Unemployment  Temperature    MarkDown5  \\\n",
+       "0 -9803.940199    3.561375      8.838728    67.162475          NaN   \n",
+       "1          NaN    3.495118      7.360667    42.785730  2772.597105   \n",
+       "2          NaN    3.361946      7.524812    34.945770          NaN   \n",
+       "3 -5837.649003    2.994273      7.993152    66.818180          NaN   \n",
+       "4          NaN    3.116659      4.945393    48.979036          NaN   \n",
        "\n",
-       "     MarkDown2         CPI  \n",
-       "0          NaN  190.969301  \n",
-       "1          NaN  197.207944  \n",
-       "2  2017.730801  198.774994  \n",
-       "3  4958.619653  173.358556  \n",
-       "4          NaN  197.546955  "
+       "    MarkDown2         CPI  \n",
+       "0  2703.17729  186.471991  \n",
+       "1         NaN  192.048268  \n",
+       "2         NaN  192.100673  \n",
+       "3         NaN  197.921916  \n",
+       "4  2957.77612  192.677797  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -689,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -723,42 +721,42 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2012-05-01 02:58:51.702994688</td>\n",
-       "      <td>16832.327095</td>\n",
-       "      <td>0</td>\n",
-       "      <td>77</td>\n",
+       "      <td>2011-04-22 15:24:18.057608704</td>\n",
+       "      <td>11196.989134</td>\n",
+       "      <td>3</td>\n",
+       "      <td>38</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2011-01-06 18:03:36.333044224</td>\n",
-       "      <td>52593.751325</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-35</td>\n",
+       "      <td>2010-02-06 19:21:02.431538688</td>\n",
+       "      <td>-14038.529503</td>\n",
+       "      <td>3</td>\n",
+       "      <td>29</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2012-09-19 18:41:44.623307520</td>\n",
-       "      <td>41113.554799</td>\n",
-       "      <td>0</td>\n",
-       "      <td>48</td>\n",
+       "      <td>2012-06-04 16:23:09.227934976</td>\n",
+       "      <td>-6519.738485</td>\n",
+       "      <td>3</td>\n",
+       "      <td>46</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2012-02-18 19:35:16.603736320</td>\n",
-       "      <td>46877.378050</td>\n",
-       "      <td>0</td>\n",
-       "      <td>21</td>\n",
+       "      <td>2011-08-09 17:18:54.910250752</td>\n",
+       "      <td>23194.918038</td>\n",
+       "      <td>3</td>\n",
+       "      <td>45</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2011-10-04 10:57:22.125225216</td>\n",
-       "      <td>47731.413911</td>\n",
-       "      <td>0</td>\n",
-       "      <td>60</td>\n",
+       "      <td>2010-09-01 23:10:54.986872576</td>\n",
+       "      <td>16761.426407</td>\n",
+       "      <td>3</td>\n",
+       "      <td>29</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -767,14 +765,14 @@
       ],
       "text/plain": [
        "                           Date  Weekly_Sales  Store  Dept  IsHoliday\n",
-       "0 2012-05-01 02:58:51.702994688  16832.327095      0    77      False\n",
-       "1 2011-01-06 18:03:36.333044224  52593.751325      0   -35      False\n",
-       "2 2012-09-19 18:41:44.623307520  41113.554799      0    48      False\n",
-       "3 2012-02-18 19:35:16.603736320  46877.378050      0    21      False\n",
-       "4 2011-10-04 10:57:22.125225216  47731.413911      0    60      False"
+       "0 2011-04-22 15:24:18.057608704  11196.989134      3    38      False\n",
+       "1 2010-02-06 19:21:02.431538688 -14038.529503      3    29      False\n",
+       "2 2012-06-04 16:23:09.227934976  -6519.738485      3    46      False\n",
+       "3 2011-08-09 17:18:54.910250752  23194.918038      3    45      False\n",
+       "4 2010-09-01 23:10:54.986872576  16761.426407      3    29      False"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -794,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "scrolled": false
    },
@@ -803,198 +801,198 @@
      "data": {
       "text/plain": [
        "{'features':                              Date     MarkDown1  Store  IsHoliday  \\\n",
-       " 0   2012-05-01 16:47:44.635517440   9856.327932      5      False   \n",
-       " 1   2011-03-02 09:17:23.001815040           NaN      5      False   \n",
-       " 2   2009-12-09 12:09:15.280852480           NaN      5      False   \n",
-       " 3   2012-09-27 22:00:49.812289792   7575.545504      5      False   \n",
-       " 4   2011-05-09 00:56:06.155403520           NaN      5      False   \n",
-       " 5   2011-09-07 10:54:38.274752256   -319.511710      5      False   \n",
-       " 6   2011-07-15 04:51:41.685670656           NaN      5      False   \n",
-       " 7   2010-10-26 23:57:49.155473408           NaN      5      False   \n",
-       " 8   2013-01-07 03:08:40.389892352    -25.338635      5      False   \n",
-       " 9   2014-02-11 19:59:19.362542592   8154.995622      5      False   \n",
-       " 10  2009-08-16 21:08:36.649604352           NaN      5       True   \n",
-       " 11  2011-04-07 23:12:30.381310976           NaN      5       True   \n",
-       " 12  2011-06-01 23:40:25.271012608           NaN      5      False   \n",
-       " 13  2010-11-25 16:27:01.937167872           NaN      5      False   \n",
-       " 14  2012-04-01 23:49:59.091133440           NaN      5      False   \n",
-       " 15  2011-11-29 06:44:29.486765568   5394.076595      5      False   \n",
-       " 16  2012-12-23 01:54:43.755820800    616.736543      5      False   \n",
-       " 17  2011-02-22 12:01:12.161310464           NaN      5      False   \n",
-       " 18  2012-09-14 09:12:48.080929792   4264.005722      5      False   \n",
-       " 19  2011-09-29 04:49:59.894278656           NaN      5       True   \n",
-       " 20  2011-07-06 18:19:10.024774400           NaN      5      False   \n",
-       " 21  2010-12-20 03:51:25.390490880   8089.734544      5      False   \n",
-       " 22  2011-12-13 08:37:11.030729472   9237.405853      5      False   \n",
-       " 23  2011-05-10 05:02:03.746472704           NaN      5      False   \n",
-       " 24  2011-09-16 10:58:14.326885632   2452.122989      5      False   \n",
-       " 25  2013-08-04 01:41:06.983268352   3136.407925      5      False   \n",
-       " 26  2012-02-27 13:58:51.523597568   4152.240138      5      False   \n",
-       " 27  2011-02-14 11:07:31.025720320           NaN      5      False   \n",
-       " 28  2010-12-31 09:46:24.324750592           NaN      5      False   \n",
-       " 29  2011-11-01 17:13:08.654945280   3392.664690      5      False   \n",
+       " 0   2012-03-09 19:29:04.121987584   4547.265066     48      False   \n",
+       " 1   2012-01-12 12:05:27.772331264           NaN     51      False   \n",
+       " 2   2012-10-09 23:52:58.454752256  13291.983302     51      False   \n",
+       " 3   2011-07-26 23:58:33.986580992           NaN     49      False   \n",
+       " 4   2012-01-17 02:27:51.821400832   6865.756770     50      False   \n",
+       " 5   2009-12-27 02:32:16.625306880           NaN     51      False   \n",
+       " 6   2011-07-12 23:01:03.955540224           NaN     48      False   \n",
+       " 7   2011-09-25 10:16:19.542280704   7114.339579     48      False   \n",
+       " 8   2011-05-06 02:00:24.919497728           NaN     48      False   \n",
+       " 9   2013-10-23 10:17:59.945650432   2267.991754     50      False   \n",
+       " 10  2011-05-18 08:31:22.419635968           NaN     48      False   \n",
+       " 11  2010-07-27 18:16:54.600127744           NaN     49      False   \n",
+       " 12  2010-11-04 00:37:47.996870656           NaN     51      False   \n",
+       " 13  2012-03-23 15:04:51.172607488  10192.668685     48      False   \n",
+       " 14  2007-11-14 22:28:51.711726848           NaN     48      False   \n",
+       " 15  2013-02-07 20:55:46.684175872   9146.998153     49      False   \n",
+       " 16  2011-07-18 00:03:42.998976512           NaN     50      False   \n",
+       " 17  2010-10-02 21:34:09.917561088           NaN     49      False   \n",
+       " 18  2010-03-12 18:25:31.509475328           NaN     51      False   \n",
+       " 19  2013-06-24 02:50:15.027540736   2943.979267     50      False   \n",
+       " 20  2010-09-24 15:42:39.457930752           NaN     48      False   \n",
+       " 21  2012-03-27 19:00:12.877346816   7750.605869     51      False   \n",
+       " 22  2010-05-26 23:28:27.091251968           NaN     48      False   \n",
+       " 23  2011-05-01 23:07:37.680983296           NaN     52      False   \n",
+       " 24  2012-10-07 16:56:39.076689152  11838.898938     51      False   \n",
+       " 25  2011-02-19 21:38:01.903840000           NaN     52      False   \n",
+       " 26  2012-01-18 06:02:24.764590592   7428.686729     52      False   \n",
+       " 27  2012-09-28 01:36:19.603453184   4962.148181     52      False   \n",
+       " 28  2011-07-28 19:21:49.952510208  12773.901269     48      False   \n",
+       " 29  2011-09-04 16:53:45.561477888           NaN     50      False   \n",
        " ..                            ...           ...    ...        ...   \n",
-       " 970 2011-10-05 18:36:28.353068288           NaN      5      False   \n",
-       " 971 2012-01-12 04:51:28.610967552           NaN      5      False   \n",
-       " 972 2012-03-24 12:11:51.862262272           NaN      5      False   \n",
-       " 973 2013-07-12 16:33:09.626688000   6561.766212      5      False   \n",
-       " 974 2011-07-07 11:50:18.363575808           NaN      5      False   \n",
-       " 975 2012-04-22 12:03:07.605280512    451.002188      5      False   \n",
-       " 976 2011-04-24 16:40:02.879920896           NaN      5      False   \n",
-       " 977 2013-05-10 19:00:49.186777088  14456.109439      5      False   \n",
-       " 978 2011-09-16 12:39:50.775564288           NaN      5      False   \n",
-       " 979 2011-01-27 23:51:25.038483968           NaN      5      False   \n",
-       " 980 2011-04-08 07:11:55.072327424           NaN      5      False   \n",
-       " 981 2012-04-14 11:31:04.547722496           NaN      5      False   \n",
-       " 982 2010-12-31 16:22:41.385283072           NaN      5      False   \n",
-       " 983 2014-05-03 06:44:59.234656512   7187.928500      5      False   \n",
-       " 984 2010-09-10 17:16:27.645561344           NaN      5       True   \n",
-       " 985 2012-02-26 19:44:58.053891840  12936.660878      5       True   \n",
-       " 986 2011-08-12 15:34:48.039582976   1592.820792      5      False   \n",
-       " 987 2011-01-04 14:20:20.553192192           NaN      5      False   \n",
-       " 988 2011-10-07 13:59:02.167729408           NaN      5      False   \n",
-       " 989 2012-11-21 05:42:10.223450880   5971.323886      5      False   \n",
-       " 990 2011-10-01 05:02:17.844523520           NaN      5      False   \n",
-       " 991 2012-02-23 17:58:47.762346752  -2849.677150      5      False   \n",
-       " 992 2011-04-20 10:01:13.460115456           NaN      5      False   \n",
-       " 993 2010-12-21 01:41:52.356143872           NaN      5      False   \n",
-       " 994 2011-01-17 09:10:37.772929536           NaN      5      False   \n",
-       " 995 2011-06-03 03:47:46.385799936           NaN      5      False   \n",
-       " 996 2009-01-13 10:16:37.091725312           NaN      5      False   \n",
-       " 997 2012-03-11 15:48:35.927031808           NaN      5       True   \n",
-       " 998 2010-11-17 15:02:44.981103616           NaN      5      False   \n",
-       " 999 2010-09-07 12:56:31.595172096           NaN      5      False   \n",
+       " 970 2011-06-14 18:48:47.646437632   7563.489728     48      False   \n",
+       " 971 2011-05-11 09:27:03.321238016   3763.579757     50      False   \n",
+       " 972 2012-10-27 08:44:16.987041024  -2107.559543     49      False   \n",
+       " 973 2011-03-26 17:36:28.323915264           NaN     48      False   \n",
+       " 974 2012-03-30 09:18:45.289797888           NaN     52      False   \n",
+       " 975 2012-12-25 00:57:24.386340608   3483.807111     51      False   \n",
+       " 976 2010-09-17 18:59:15.931293440           NaN     52      False   \n",
+       " 977 2012-04-18 06:40:20.102041344   2869.173605     50      False   \n",
+       " 978 2010-01-18 10:21:26.293353216           NaN     49      False   \n",
+       " 979 2010-08-13 00:08:32.324416256           NaN     50      False   \n",
+       " 980 2012-01-17 04:04:26.614297088           NaN     50      False   \n",
+       " 981 2011-06-23 01:40:48.209999104           NaN     48      False   \n",
+       " 982 2012-05-15 04:01:02.762140160   3935.159830     50      False   \n",
+       " 983 2011-03-03 10:20:57.530126848   5704.369183     52      False   \n",
+       " 984 2011-09-11 11:54:13.537061888  -3939.448245     49      False   \n",
+       " 985 2012-07-01 08:38:12.122587648           NaN     49      False   \n",
+       " 986 2013-01-15 14:24:51.253277952   7802.046675     52      False   \n",
+       " 987 2011-03-16 12:56:22.305051648           NaN     49      False   \n",
+       " 988 2012-02-09 21:21:51.383019776   3563.474259     50      False   \n",
+       " 989 2011-04-28 07:35:57.312682752           NaN     49      False   \n",
+       " 990 2012-07-06 11:32:42.706951424   4134.684319     48      False   \n",
+       " 991 2012-09-10 11:27:22.375229184   2670.997722     48      False   \n",
+       " 992 2010-04-19 00:59:34.282525696           NaN     51      False   \n",
+       " 993 2012-05-21 02:19:56.773740288    725.161402     51      False   \n",
+       " 994 2012-08-25 02:46:15.815232768   2736.817373     52      False   \n",
+       " 995 2011-11-27 15:29:59.302161664           NaN     48      False   \n",
+       " 996 2012-11-19 23:22:47.651802880           NaN     49      False   \n",
+       " 997 2012-03-06 18:23:43.093342208           NaN     51       True   \n",
+       " 998 2011-11-17 03:00:20.669487872           NaN     50      False   \n",
+       " 999 2010-10-18 08:04:04.443984128           NaN     51      False   \n",
        " \n",
-       "        MarkDown4    MarkDown3  Fuel_Price  Unemployment  Temperature  \\\n",
-       " 0    2843.666588  2084.265105    3.539294      8.026048    67.279415   \n",
-       " 1            NaN          NaN    3.136235      7.464250    35.839727   \n",
-       " 2            NaN          NaN    3.117027      8.804691    55.531072   \n",
-       " 3    6291.467352  7644.651968    3.950191      8.449126    63.312543   \n",
-       " 4            NaN          NaN    3.151100     10.724954    78.355700   \n",
-       " 5     319.073405  8048.361448    3.214550      6.730897    46.004140   \n",
-       " 6            NaN          NaN    3.073315      8.794787    46.230595   \n",
-       " 7            NaN          NaN    2.851889      8.455094    73.007249   \n",
-       " 8    -842.343741  1973.521805    3.219424           NaN    83.146804   \n",
-       " 9    4753.392021  2744.820760    4.389613      6.049940    55.535728   \n",
-       " 10           NaN          NaN    2.921131     10.411630    81.934195   \n",
-       " 11   -380.767318          NaN    3.588675     10.915397    41.862765   \n",
-       " 12   3841.062967  -692.534491    3.183958      8.449273    91.520699   \n",
-       " 13           NaN          NaN    2.676381     10.171808    59.799911   \n",
-       " 14           NaN          NaN    4.068388     11.061642    49.266088   \n",
-       " 15   4759.123311  3958.005275    3.860776      8.559916    54.371928   \n",
-       " 16  -2880.580355  6861.020784    3.241783      7.662614    44.933106   \n",
-       " 17           NaN          NaN    3.287047      8.232218    73.642978   \n",
-       " 18           NaN          NaN    3.553487      4.932510    70.156403   \n",
-       " 19           NaN  3498.711115    3.650312     10.192201    64.554441   \n",
-       " 20           NaN          NaN    3.711555      5.975218    45.371293   \n",
-       " 21           NaN  8008.053972    2.965420      6.715859    65.736442   \n",
-       " 22           NaN  5226.209147    3.770431      4.689591    48.907799   \n",
-       " 23           NaN          NaN    3.148174      7.048833    63.931931   \n",
-       " 24           NaN          NaN    3.228123      7.125082    54.541421   \n",
-       " 25  -2311.380219  9527.093555    4.376510      6.227941    39.120836   \n",
-       " 26    625.303990          NaN    3.789428      5.716800    99.128378   \n",
-       " 27           NaN          NaN    3.259632      8.651978    75.544242   \n",
-       " 28           NaN          NaN    3.703069     10.753015    40.004955   \n",
-       " 29           NaN          NaN    3.741760      6.060680   100.625665   \n",
-       " ..           ...          ...         ...           ...          ...   \n",
-       " 970          NaN          NaN    3.572695      7.966283    57.537589   \n",
-       " 971          NaN          NaN    3.828349      9.731679    82.126278   \n",
-       " 972          NaN          NaN    3.313500           NaN    46.785653   \n",
-       " 973  3054.127592  -724.702300    4.287166      9.651842    51.824356   \n",
-       " 974          NaN          NaN    3.761812      8.287437    88.088342   \n",
-       " 975 -3266.187881  8215.493689    3.642633      9.500544    29.831979   \n",
-       " 976          NaN          NaN    2.819650      7.832418    40.807527   \n",
-       " 977  4122.302492  6313.274115    4.174946      8.727237    70.737165   \n",
-       " 978  5838.158736          NaN    3.066125           NaN    31.147061   \n",
-       " 979          NaN          NaN    3.151944      7.658218    61.645832   \n",
-       " 980          NaN   630.957174    3.183844      9.862215    17.948335   \n",
-       " 981          NaN          NaN    3.271366      9.679093    61.315823   \n",
-       " 982          NaN          NaN    3.867379      8.191903    51.461519   \n",
-       " 983  2608.840965  -562.271212    3.781071           NaN    42.556422   \n",
-       " 984          NaN          NaN    2.956717      7.515493    75.537151   \n",
-       " 985  3915.581744  1272.550074    3.473630      5.244393    78.032536   \n",
-       " 986  2982.566125          NaN    2.981562      9.439555    62.315341   \n",
-       " 987          NaN          NaN    2.839158      9.016700    58.018581   \n",
-       " 988  3139.374304          NaN    3.952791      6.206879    68.542122   \n",
-       " 989  4102.145584 -2255.694045    4.091854      9.904070    48.245039   \n",
-       " 990          NaN          NaN    3.318221      9.150930   100.355027   \n",
-       " 991          NaN  -852.368857    3.281618      6.774089    63.196870   \n",
-       " 992          NaN          NaN    3.199449      6.715256    26.426601   \n",
-       " 993          NaN          NaN    3.339197     11.524258    56.354388   \n",
-       " 994          NaN          NaN    3.422232      7.571462    65.796130   \n",
-       " 995  1692.546761          NaN    3.178186      5.160924    43.172044   \n",
-       " 996          NaN          NaN    2.748483     11.166695    34.682053   \n",
-       " 997          NaN          NaN    3.713086      8.024620    35.345627   \n",
-       " 998          NaN          NaN    3.683783      8.076637    43.072280   \n",
-       " 999          NaN          NaN    2.804057      9.006230    79.267188   \n",
+       "        MarkDown4     MarkDown3  Fuel_Price  Unemployment  Temperature  \\\n",
+       " 0    2262.924323   4314.258484    2.944899     12.207513    44.866684   \n",
+       " 1     648.327530           NaN    3.415876      8.296209    79.755000   \n",
+       " 2    7464.648400   4818.694540    3.438865     10.375869    53.011465   \n",
+       " 3    7722.936675           NaN    3.238451      8.340640    81.331769   \n",
+       " 4     385.720439   2933.746873    3.352123      4.517977    33.216711   \n",
+       " 5            NaN           NaN    2.595952      8.423675    58.153432   \n",
+       " 6            NaN           NaN    3.337125      7.289977    68.198691   \n",
+       " 7    1376.144152  -1375.405701    3.586827      4.433586    57.457667   \n",
+       " 8            NaN    885.964999    3.132711      7.941049    82.624443   \n",
+       " 9    4380.021000   4861.726545    4.617105      7.636990    64.616449   \n",
+       " 10           NaN           NaN    3.490697      9.699187   113.890285   \n",
+       " 11           NaN           NaN    3.337917     10.428851    76.354063   \n",
+       " 12           NaN           NaN    3.016818     10.044523    52.925513   \n",
+       " 13           NaN   1505.002284    3.244111      7.329469    34.127333   \n",
+       " 14           NaN           NaN    2.497412     12.247448   100.350997   \n",
+       " 15   7982.863954   7539.825400    3.909380      5.689230    65.945165   \n",
+       " 16           NaN           NaN    2.895094      7.718521    54.750702   \n",
+       " 17           NaN           NaN    3.722465      7.339952    56.152632   \n",
+       " 18           NaN           NaN    2.665366      8.045471    78.257558   \n",
+       " 19  -3310.004199   -808.378062    4.379650      5.320308    39.604265   \n",
+       " 20           NaN   4604.622951    3.058964      8.584222    44.177016   \n",
+       " 21   3790.217545   3053.642374    3.091990      5.069122    25.777591   \n",
+       " 22           NaN           NaN    2.931042      9.062068    41.649615   \n",
+       " 23           NaN           NaN    3.721348      6.586825    67.785117   \n",
+       " 24   4610.034450   -617.120861    4.010528      8.686732    35.445785   \n",
+       " 25           NaN           NaN    3.249523      8.450699    38.639637   \n",
+       " 26   4005.965014    -28.792693    3.808513      9.188265    57.584018   \n",
+       " 27   6433.867544  -3996.702966    2.976900      8.991618     6.179927   \n",
+       " 28           NaN           NaN    3.208463     11.875517    50.400717   \n",
+       " 29           NaN           NaN    3.455848      7.377382    56.480212   \n",
+       " ..           ...           ...         ...           ...          ...   \n",
+       " 970  7130.653517    613.434417    3.798558      8.849840    45.081481   \n",
+       " 971  -950.449794   8369.741392    3.691277      8.687173    21.625873   \n",
+       " 972  1423.082811   3915.095969    3.716980      7.812599    38.891102   \n",
+       " 973          NaN           NaN    3.277952      7.881991    74.018427   \n",
+       " 974          NaN           NaN    3.444190      4.147388    79.128181   \n",
+       " 975  5645.978692   3893.099917    3.821702      8.410268    87.162204   \n",
+       " 976          NaN           NaN    2.640021      7.458197    35.588448   \n",
+       " 977 -2627.096894   -142.014936    3.320107      6.970687    49.700872   \n",
+       " 978          NaN           NaN    2.710125      9.927722    83.348488   \n",
+       " 979          NaN           NaN    3.139506      8.348364    46.708000   \n",
+       " 980          NaN           NaN    3.578857      7.555184    25.595379   \n",
+       " 981          NaN  -1147.726465    3.685507      9.407928    91.745352   \n",
+       " 982   375.025287  11130.134824    3.467141      6.285321    38.179293   \n",
+       " 983          NaN           NaN    2.675670     10.258619    50.599203   \n",
+       " 984          NaN           NaN    3.375990      9.175979    44.036130   \n",
+       " 985          NaN    649.171815    3.370836      6.948002    22.863602   \n",
+       " 986  1944.149217    896.663662    3.971550      6.242637    97.107161   \n",
+       " 987          NaN           NaN    3.524965      7.059279    65.469921   \n",
+       " 988  2550.079436    121.672409    2.898533      5.929999    58.612442   \n",
+       " 989          NaN           NaN    3.237647      6.992924    72.225708   \n",
+       " 990          NaN           NaN    4.075160      8.392256    80.837964   \n",
+       " 991 -1543.390402   5264.766267    3.157407      7.017727    81.515109   \n",
+       " 992          NaN           NaN    3.293817      8.719239    65.735676   \n",
+       " 993   932.568195   1845.259380    3.157982      7.494639    65.103553   \n",
+       " 994 -2905.284812  -1268.636611    3.776062      6.736775    46.083801   \n",
+       " 995          NaN           NaN    3.424730           NaN    79.231321   \n",
+       " 996  9027.387381           NaN    3.960882      8.911136    26.948635   \n",
+       " 997          NaN           NaN    3.764028      8.049413   111.723841   \n",
+       " 998          NaN   6261.182193    3.609560      6.024725    38.411214   \n",
+       " 999          NaN           NaN    3.051021      7.855145    75.446009   \n",
        " \n",
-       "         MarkDown5     MarkDown2         CPI  \n",
-       " 0    10204.869614   2819.319182  194.172590  \n",
-       " 1             NaN   1682.652584  137.004267  \n",
-       " 2             NaN           NaN  179.678323  \n",
-       " 3     7501.331616   4715.605494  161.759497  \n",
-       " 4             NaN           NaN  128.911283  \n",
-       " 5     6750.992820    797.112925  205.683144  \n",
-       " 6             NaN           NaN  203.634987  \n",
-       " 7             NaN           NaN  230.663986  \n",
-       " 8     3871.914754   3988.518657         NaN  \n",
-       " 9     1624.808895    735.714943  133.713539  \n",
-       " 10            NaN           NaN  197.575244  \n",
-       " 11            NaN           NaN  139.060197  \n",
-       " 12            NaN           NaN  194.510387  \n",
-       " 13            NaN           NaN   78.886026  \n",
-       " 14            NaN           NaN   81.669058  \n",
-       " 15    7758.050721           NaN  198.052999  \n",
-       " 16    2853.381375   9269.494345  104.932028  \n",
-       " 17            NaN           NaN  204.210702  \n",
-       " 18    3343.712458           NaN  178.168110  \n",
-       " 19            NaN           NaN  161.572206  \n",
-       " 20            NaN           NaN  128.629900  \n",
-       " 21            NaN           NaN  181.929594  \n",
-       " 22    3656.616824   1231.941589  193.633886  \n",
-       " 23            NaN     33.178516  198.074896  \n",
-       " 24   -1626.757019           NaN  105.550579  \n",
-       " 25     644.446061  10905.729749  137.971932  \n",
-       " 26    6373.039633           NaN  192.460300  \n",
-       " 27            NaN           NaN  213.233609  \n",
-       " 28            NaN           NaN  123.195502  \n",
-       " 29    2826.315133           NaN  195.701117  \n",
-       " ..            ...           ...         ...  \n",
-       " 970           NaN           NaN  148.906214  \n",
-       " 971           NaN           NaN  204.262748  \n",
-       " 972           NaN           NaN         NaN  \n",
-       " 973  -1499.994553  16305.139629  140.188752  \n",
-       " 974           NaN           NaN  161.099549  \n",
-       " 975   2685.234955   4271.729391  108.874111  \n",
-       " 976           NaN           NaN  188.576448  \n",
-       " 977   3914.364495   3982.010819  196.334916  \n",
-       " 978           NaN           NaN         NaN  \n",
-       " 979           NaN           NaN  158.554149  \n",
-       " 980           NaN           NaN  163.242219  \n",
-       " 981           NaN           NaN  184.637472  \n",
-       " 982           NaN           NaN   95.502993  \n",
-       " 983   5061.605364   4816.687238         NaN  \n",
-       " 984           NaN           NaN  150.901411  \n",
-       " 985   5375.719303  10141.431209  250.053444  \n",
-       " 986   6132.068608   7917.959484  120.500763  \n",
-       " 987           NaN           NaN  127.836518  \n",
-       " 988           NaN           NaN  206.249045  \n",
-       " 989   9976.937232   3037.291454  126.800358  \n",
-       " 990           NaN           NaN  239.016886  \n",
-       " 991   4704.812744           NaN  213.808756  \n",
-       " 992           NaN           NaN  136.904711  \n",
-       " 993           NaN           NaN  177.862208  \n",
-       " 994           NaN           NaN  166.085349  \n",
-       " 995           NaN           NaN  163.449486  \n",
-       " 996           NaN           NaN  136.797738  \n",
-       " 997    464.410675           NaN  186.260545  \n",
-       " 998           NaN           NaN  140.825171  \n",
-       " 999           NaN           NaN  168.683814  \n",
+       "        MarkDown5    MarkDown2         CPI  \n",
+       " 0    7218.910186          NaN  154.437715  \n",
+       " 1    4928.572337  1860.474257  126.006100  \n",
+       " 2    4381.712904  6491.080062  182.505905  \n",
+       " 3            NaN  2075.661361  170.252793  \n",
+       " 4   -1363.927516  6383.011111  181.850615  \n",
+       " 5            NaN          NaN  250.558631  \n",
+       " 6            NaN          NaN  213.846371  \n",
+       " 7    1644.489130  4966.726612  189.105468  \n",
+       " 8            NaN  4689.948004  152.931090  \n",
+       " 9    3291.191563  2344.032770  152.780297  \n",
+       " 10           NaN          NaN  195.602913  \n",
+       " 11           NaN          NaN  176.381561  \n",
+       " 12           NaN          NaN  147.881741  \n",
+       " 13   1668.882903          NaN  155.004680  \n",
+       " 14           NaN          NaN  193.442985  \n",
+       " 15   1437.517079  4874.558939  152.281926  \n",
+       " 16   8439.806431          NaN  130.530240  \n",
+       " 17           NaN          NaN  201.486725  \n",
+       " 18           NaN          NaN  188.760741  \n",
+       " 19  -6907.068884  3246.451314  108.120768  \n",
+       " 20           NaN          NaN  148.788886  \n",
+       " 21   5724.976324  4496.854628  190.036218  \n",
+       " 22           NaN          NaN  181.746008  \n",
+       " 23           NaN          NaN  200.412670  \n",
+       " 24   6637.078664 -1388.022496   87.164451  \n",
+       " 25           NaN          NaN  195.622231  \n",
+       " 26   2922.749710          NaN  224.233432  \n",
+       " 27  -3042.801607          NaN  131.909287  \n",
+       " 28   7063.102432          NaN  146.156666  \n",
+       " 29           NaN          NaN  176.694595  \n",
+       " ..           ...          ...         ...  \n",
+       " 970  2882.447318          NaN  148.323409  \n",
+       " 971  1918.923930          NaN  141.723698  \n",
+       " 972   151.136535  1221.530133  155.532651  \n",
+       " 973          NaN          NaN  213.405340  \n",
+       " 974          NaN          NaN  186.636286  \n",
+       " 975  2598.731935  -846.566855  190.957987  \n",
+       " 976          NaN          NaN  153.917260  \n",
+       " 977  8372.618269  1768.408869  249.068471  \n",
+       " 978          NaN          NaN  177.318454  \n",
+       " 979          NaN          NaN  208.719040  \n",
+       " 980          NaN   754.021358  108.580497  \n",
+       " 981          NaN          NaN  159.526588  \n",
+       " 982  7349.003146  -732.495611  173.559001  \n",
+       " 983          NaN          NaN  140.910196  \n",
+       " 984   401.319810          NaN  175.340840  \n",
+       " 985          NaN          NaN  171.683362  \n",
+       " 986  -465.292159  -744.114065  177.553651  \n",
+       " 987          NaN          NaN  144.344477  \n",
+       " 988   567.400188          NaN  213.833680  \n",
+       " 989          NaN          NaN  157.113353  \n",
+       " 990          NaN  -232.801449  132.592027  \n",
+       " 991   -72.622621          NaN  207.754643  \n",
+       " 992          NaN          NaN  132.575018  \n",
+       " 993  4955.778653  4683.646548  160.260401  \n",
+       " 994  2898.089751          NaN  129.801694  \n",
+       " 995          NaN          NaN         NaN  \n",
+       " 996          NaN   608.026190  115.452177  \n",
+       " 997          NaN          NaN  206.323679  \n",
+       " 998          NaN  7020.489423  178.966240  \n",
+       " 999          NaN          NaN  152.636567  \n",
        " \n",
        " [1000 rows x 12 columns]}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/examples/demo_metadata.json b/examples/demo_metadata.json
index bb24a5963..1becd1764 100644
--- a/examples/demo_metadata.json
+++ b/examples/demo_metadata.json
@@ -2,32 +2,32 @@
     "tables": {
         "users": {
             "fields": {
-                "age": {
-                    "type": "numerical",
-                    "subtype": "integer"
-                },
-                "gender": {
+                "country": {
                     "type": "categorical"
                 },
                 "user_id": {
                     "type": "id",
                     "subtype": "integer"
                 },
-                "country": {
+                "gender": {
                     "type": "categorical"
+                },
+                "age": {
+                    "type": "numerical",
+                    "subtype": "integer"
                 }
             },
             "primary_key": "user_id"
         },
         "sessions": {
             "fields": {
+                "device": {
+                    "type": "categorical"
+                },
                 "session_id": {
                     "type": "id",
                     "subtype": "integer"
                 },
-                "device": {
-                    "type": "categorical"
-                },
                 "user_id": {
                     "type": "id",
                     "subtype": "integer",
@@ -48,9 +48,9 @@
                     "type": "datetime",
                     "format": "%Y-%m-%d"
                 },
-                "transaction_id": {
-                    "type": "id",
-                    "subtype": "integer"
+                "amount": {
+                    "type": "numerical",
+                    "subtype": "float"
                 },
                 "session_id": {
                     "type": "id",
@@ -60,9 +60,9 @@
                         "field": "session_id"
                     }
                 },
-                "amount": {
-                    "type": "numerical",
-                    "subtype": "float"
+                "transaction_id": {
+                    "type": "id",
+                    "subtype": "integer"
                 },
                 "approved": {
                     "type": "boolean"

From 03f46a663cbfd704ffbf5179030f675104432c84 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 2 Jul 2020 19:24:19 +0200
Subject: [PATCH 09/33] Add tests for _find_parent_ids

---
 sdv/sampler.py        |  39 ++++++---------
 tests/test_sampler.py | 107 ++++++++++++++++++++++++++++++++++--------
 2 files changed, 103 insertions(+), 43 deletions(-)

diff --git a/sdv/sampler.py b/sdv/sampler.py
index 2dd318e7d..dc6be988f 100644
--- a/sdv/sampler.py
+++ b/sdv/sampler.py
@@ -171,13 +171,13 @@ def _sample_rows(self, model, num_rows, table_name):
 
         return sampled
 
-    def _sample_children(self, table_name, sampled):
-        table_rows = sampled[table_name]
+    def _sample_children(self, table_name, sampled_data):
+        table_rows = sampled_data[table_name]
         for child_name in self.metadata.get_children(table_name):
             for _, row in table_rows.iterrows():
-                self._sample_child_rows(child_name, table_name, row, sampled)
+                self._sample_child_rows(child_name, table_name, row, sampled_data)
 
-    def _sample_child_rows(self, table_name, parent_name, parent_row, sampled):
+    def _sample_child_rows(self, table_name, parent_name, parent_row, sampled_data):
         parameters = self._extract_parameters(parent_row, table_name)
 
         model = self.model(**self.model_kwargs)
@@ -190,37 +190,28 @@ def _sample_child_rows(self, table_name, parent_name, parent_row, sampled):
         foreign_key = self.metadata.get_foreign_key(parent_name, table_name)
         table_rows[foreign_key] = parent_row[parent_key]
 
-        previous = sampled.get(table_name)
+        previous = sampled_data.get(table_name)
         if previous is None:
-            sampled[table_name] = table_rows
+            sampled_data[table_name] = table_rows
         else:
-            sampled[table_name] = pd.concat([previous, table_rows]).reset_index(drop=True)
-
-        self._sample_children(table_name, sampled)
-
-    def _get_pdfs(self, parent_rows, child_name):
-        """Build a model for each parent row and get its pdf function."""
-        pdfs = dict()
-        for parent_id, row in parent_rows.iterrows():
-            parameters = self._extract_parameters(row, child_name)
-            model = self.model(**self.model_kwargs)
-            model.set_parameters(parameters)
-            pdfs[parent_id] = model.model.probability_density
+            sampled_data[table_name] = pd.concat([previous, table_rows]).reset_index(drop=True)
 
-        return pdfs
+        self._sample_children(table_name, sampled_data)
 
-    def _find_parent_id(self, likelihoods, num_rows):
+    @staticmethod
+    def _find_parent_id(likelihoods, num_rows):
         mean = likelihoods.mean()
         if (likelihoods == 0).all():
             # All rows got 0 likelihood, fallback to num_rows
             likelihoods = num_rows
         elif pd.isnull(mean) or mean == 0:
-            # No row got likelihood > 0, but some got singlar matrix
-            # Fallback to num_rows on the singular matrix rows
+            # Some rows got singlar matrix error and the rest were 0
+            # Fallback to num_rows on the singular matrix rows and
+            # keep 0s on the rest.
             likelihoods = likelihoods.fillna(num_rows)
         else:
-            # at least one row got likelihood > 0, so fill the
-            # singular matrix rows with the mean
+            # at least one row got a valid likelihood, so fill the
+            # rows that got a singular matrix error with the mean
             likelihoods = likelihoods.fillna(mean)
 
         weights = likelihoods.values / likelihoods.sum()
diff --git a/tests/test_sampler.py b/tests/test_sampler.py
index fd3e0daa0..a881e3dd8 100644
--- a/tests/test_sampler.py
+++ b/tests/test_sampler.py
@@ -1,6 +1,6 @@
-from unittest import TestCase
-from unittest.mock import Mock
+from unittest.mock import Mock, patch
 
+import numpy as np
 import pandas as pd
 import pytest
 
@@ -9,7 +9,7 @@
 from sdv.sampler import Sampler
 
 
-class TestSampler(TestCase):
+class TestSampler:
 
     def test___init__(self):
         """Test create a default instance of Sampler class"""
@@ -61,10 +61,7 @@ def test__finalize(self):
             'c': 'some data'   # fk
         }
 
-        sampler._find_parent_ids.side_effect = [
-            [2, 3],
-            [4, 5]
-        ]
+        sampler._find_parent_ids.return_value = [4, 5]
         sampler.metadata.get_foreign_key.side_effect = [
             'b',
             'c',
@@ -74,6 +71,7 @@ def test__finalize(self):
         sampled_data = {
             'test': pd.DataFrame({
                 'a': [0, 1],  # actual data
+                'b': [2, 3],  # existing fk key
                 'z': [6, 7]   # not used
             })
         }
@@ -90,6 +88,7 @@ def test__finalize(self):
             result['test'].sort_index(axis=1),
             expected.sort_index(axis=1)
         )
+        sampler._find_parent_ids.assert_called_once()
 
     def test__get_primary_keys_none(self):
         """Test returns a tuple of none when a table doesn't have a primary key"""
@@ -168,7 +167,7 @@ def test__get_primary_keys_raises_value_error_remaining(self):
             Sampler._get_primary_keys(sampler, 'test', 5)
 
     def test__extract_parameters(self):
-        """Test get extension"""
+        """Test extract parameters"""
         # Setup
         sampler = Mock(spec=Sampler)
 
@@ -206,24 +205,24 @@ def test__sample_children(self):
         sampler.metadata.get_children.return_value = ['child A', 'child B', 'child C']
 
         # Run
-        sampled = {
+        sampled_data = {
             'test': pd.DataFrame({'field': [11, 22, 33]})
         }
-        Sampler._sample_children(sampler, 'test', sampled)
+        Sampler._sample_children(sampler, 'test', sampled_data)
 
         # Asserts
         sampler.metadata.get_children.assert_called_once_with('test')
 
         expected_calls = [
-            ['child A', 'test', pd.Series([11], index=['field'], name=0), sampled],
-            ['child A', 'test', pd.Series([22], index=['field'], name=1), sampled],
-            ['child A', 'test', pd.Series([33], index=['field'], name=2), sampled],
-            ['child B', 'test', pd.Series([11], index=['field'], name=0), sampled],
-            ['child B', 'test', pd.Series([22], index=['field'], name=1), sampled],
-            ['child B', 'test', pd.Series([33], index=['field'], name=2), sampled],
-            ['child C', 'test', pd.Series([11], index=['field'], name=0), sampled],
-            ['child C', 'test', pd.Series([22], index=['field'], name=1), sampled],
-            ['child C', 'test', pd.Series([33], index=['field'], name=2), sampled],
+            ['child A', 'test', pd.Series([11], index=['field'], name=0), sampled_data],
+            ['child A', 'test', pd.Series([22], index=['field'], name=1), sampled_data],
+            ['child A', 'test', pd.Series([33], index=['field'], name=2), sampled_data],
+            ['child B', 'test', pd.Series([11], index=['field'], name=0), sampled_data],
+            ['child B', 'test', pd.Series([22], index=['field'], name=1), sampled_data],
+            ['child B', 'test', pd.Series([33], index=['field'], name=2), sampled_data],
+            ['child C', 'test', pd.Series([11], index=['field'], name=0), sampled_data],
+            ['child C', 'test', pd.Series([22], index=['field'], name=1), sampled_data],
+            ['child C', 'test', pd.Series([33], index=['field'], name=2), sampled_data],
         ]
         actual_calls = sampler._sample_child_rows.call_args_list
         for result_call, expected_call in zip(actual_calls, expected_calls):
@@ -341,3 +340,73 @@ def sample_side_effect(table, num_rows):
         assert sampler._reset_primary_keys_generators.call_count == 1
         pd.testing.assert_frame_equal(result['table a'], pd.DataFrame({'foo': range(3)}))
         pd.testing.assert_frame_equal(result['table c'], pd.DataFrame({'foo': range(3)}))
+
+    @patch('sdv.sampler.np.random.choice')
+    def test__find_parent_id_all_0(self, choice_mock):
+        """If all likelihoods are 0, use num_rows."""
+        likelihoods = pd.Series([0, 0, 0, 0])
+        num_rows = pd.Series([1, 2, 3, 4])
+
+        Sampler._find_parent_id(likelihoods, num_rows)
+
+        expected_weights = np.array([1 / 10, 2 / 10, 3 / 10, 4 / 10])
+
+        choice_mock.assert_called_once()
+        assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
+        np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)
+
+    @patch('sdv.sampler.np.random.choice')
+    def test__find_parent_id_all_singlar_matrix(self, choice_mock):
+        """If all likelihoods got singular matrix, use num_rows."""
+        likelihoods = pd.Series([None, None, None, None])
+        num_rows = pd.Series([1, 2, 3, 4])
+
+        Sampler._find_parent_id(likelihoods, num_rows)
+
+        expected_weights = np.array([1 / 10, 2 / 10, 3 / 10, 4 / 10])
+
+        choice_mock.assert_called_once()
+        assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
+        np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)
+
+    @patch('sdv.sampler.np.random.choice')
+    def test__find_parent_id_all_0_or_singlar_matrix(self, choice_mock):
+        """If likehoods are either 0 or NaN, fill the gaps with num_rows."""
+        likelihoods = pd.Series([0, None, 0, None])
+        num_rows = pd.Series([1, 2, 3, 4])
+
+        Sampler._find_parent_id(likelihoods, num_rows)
+
+        expected_weights = np.array([0, 2 / 6, 0, 4 / 6])
+
+        choice_mock.assert_called_once()
+        assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
+        np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)
+
+    @patch('sdv.sampler.np.random.choice')
+    def test__find_parent_id_some_good(self, choice_mock):
+        """If some likehoods are good, fill the gaps with num_rows."""
+        likelihoods = pd.Series([0.5, None, 1.5, None])
+        num_rows = pd.Series([1, 2, 3, 4])
+
+        Sampler._find_parent_id(likelihoods, num_rows)
+
+        expected_weights = np.array([0.5 / 4, 1 / 4, 1.5 / 4, 1 / 4])
+
+        choice_mock.assert_called_once()
+        assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
+        np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)
+
+    @patch('sdv.sampler.np.random.choice')
+    def test__find_parent_id_all_good(self, choice_mock):
+        """If all are good, use the likelihoods unmodified."""
+        likelihoods = pd.Series([0.5, 1, 1.5, 2])
+        num_rows = pd.Series([1, 2, 3, 4])
+
+        Sampler._find_parent_id(likelihoods, num_rows)
+
+        expected_weights = np.array([0.5 / 5, 1 / 5, 1.5 / 5, 2 / 5])
+
+        choice_mock.assert_called_once()
+        assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
+        np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)

From ca6dd51230be7d64ca46f298a740e9df9034ca07 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 2 Jul 2020 19:39:47 +0200
Subject: [PATCH 10/33] Fix test on py35

---
 tests/test_sampler.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/test_sampler.py b/tests/test_sampler.py
index a881e3dd8..6474780ad 100644
--- a/tests/test_sampler.py
+++ b/tests/test_sampler.py
@@ -88,7 +88,7 @@ def test__finalize(self):
             result['test'].sort_index(axis=1),
             expected.sort_index(axis=1)
         )
-        sampler._find_parent_ids.assert_called_once()
+        assert sampler._find_parent_ids.call_count == 1
 
     def test__get_primary_keys_none(self):
         """Test returns a tuple of none when a table doesn't have a primary key"""
@@ -351,7 +351,7 @@ def test__find_parent_id_all_0(self, choice_mock):
 
         expected_weights = np.array([1 / 10, 2 / 10, 3 / 10, 4 / 10])
 
-        choice_mock.assert_called_once()
+        assert choice_mock.call_count == 1
         assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
         np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)
 
@@ -365,7 +365,7 @@ def test__find_parent_id_all_singlar_matrix(self, choice_mock):
 
         expected_weights = np.array([1 / 10, 2 / 10, 3 / 10, 4 / 10])
 
-        choice_mock.assert_called_once()
+        assert choice_mock.call_count == 1
         assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
         np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)
 
@@ -379,7 +379,7 @@ def test__find_parent_id_all_0_or_singlar_matrix(self, choice_mock):
 
         expected_weights = np.array([0, 2 / 6, 0, 4 / 6])
 
-        choice_mock.assert_called_once()
+        assert choice_mock.call_count == 1
         assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
         np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)
 
@@ -393,7 +393,7 @@ def test__find_parent_id_some_good(self, choice_mock):
 
         expected_weights = np.array([0.5 / 4, 1 / 4, 1.5 / 4, 1 / 4])
 
-        choice_mock.assert_called_once()
+        assert choice_mock.call_count == 1
         assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
         np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)
 
@@ -407,6 +407,6 @@ def test__find_parent_id_all_good(self, choice_mock):
 
         expected_weights = np.array([0.5 / 5, 1 / 5, 1.5 / 5, 2 / 5])
 
-        choice_mock.assert_called_once()
+        assert choice_mock.call_count == 1
         assert list(choice_mock.call_args[0][0]) == list(likelihoods.index)
         np.testing.assert_array_equal(choice_mock.call_args[1]['p'], expected_weights)

From 9753a217955bfc222d925b06bef2ab5cdbf68d4a Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 2 Jul 2020 20:04:28 +0200
Subject: [PATCH 11/33] Add integration test for multi-parent dataset

---
 sdv/data/got_families/character_families.csv | 13 ++++
 sdv/data/got_families/characters.csv         |  8 ++
 sdv/data/got_families/families.csv           |  5 ++
 sdv/data/got_families/metadata.json          | 79 ++++++++++++++++++++
 tests/integration/test_sdv.py                | 36 +++++++++
 5 files changed, 141 insertions(+)
 create mode 100644 sdv/data/got_families/character_families.csv
 create mode 100644 sdv/data/got_families/characters.csv
 create mode 100644 sdv/data/got_families/families.csv
 create mode 100644 sdv/data/got_families/metadata.json

diff --git a/sdv/data/got_families/character_families.csv b/sdv/data/got_families/character_families.csv
new file mode 100644
index 000000000..6b5560db5
--- /dev/null
+++ b/sdv/data/got_families/character_families.csv
@@ -0,0 +1,13 @@
+character_id,family_id,generation,type
+1,1,8,father
+1,4,5,mother
+2,1,10,father
+2,2,4,mother
+3,3,7,both
+4,4,12,both
+5,1,10,father
+5,2,4,mother
+6,1,10,father
+6,2,4,mother
+7,1,10,father
+7,2,4,mother
diff --git a/sdv/data/got_families/characters.csv b/sdv/data/got_families/characters.csv
new file mode 100644
index 000000000..f37e21af5
--- /dev/null
+++ b/sdv/data/got_families/characters.csv
@@ -0,0 +1,8 @@
+age,character_id,name
+20,1,Jon
+16,2,Arya
+35,3,Tyrion
+18,4,Daenerys
+19,5,Sansa
+24,6,Robb
+15,7,Bran
diff --git a/sdv/data/got_families/families.csv b/sdv/data/got_families/families.csv
new file mode 100644
index 000000000..51745d59e
--- /dev/null
+++ b/sdv/data/got_families/families.csv
@@ -0,0 +1,5 @@
+family_id,name
+1,Stark
+2,Tully
+3,Lannister
+4,Targaryen
diff --git a/sdv/data/got_families/metadata.json b/sdv/data/got_families/metadata.json
new file mode 100644
index 000000000..9ecff24a5
--- /dev/null
+++ b/sdv/data/got_families/metadata.json
@@ -0,0 +1,79 @@
+{
+    "path": "",
+    "tables": [
+        {
+            "use": true,
+            "primary_key": "character_id",
+            "fields": [
+                {
+                    "regex": "^[1-9]{1,2}$",
+                    "type": "id",
+                    "name": "character_id"
+                },
+                {
+                    "type": "categorical",
+                    "name": "name"
+                },
+                {
+                    "subtype": "integer",
+                    "type": "numerical",
+                    "name": "age"
+                }
+            ],
+            "headers": true,
+            "path": "characters.csv",
+            "name": "characters"
+        },
+        {
+            "use": true,
+            "primary_key": "family_id",
+            "fields": [
+                {
+                    "regex": "^[1-9]$",
+                    "type": "id",
+                    "name": "family_id"
+                },
+                {
+                    "type": "categorical",
+                    "name": "name"
+                }
+            ],
+            "headers": true,
+            "path": "families.csv",
+            "name": "families"
+        },
+        {
+            "headers": true,
+            "path": "character_families.csv",
+            "use": true,
+            "name": "character_families",
+            "fields": [
+                {
+                    "type": "id",
+                    "ref": {
+                        "field": "character_id",
+                        "table": "characters"
+                    },
+                    "name": "character_id"
+                },
+                {
+                    "type": "id",
+                    "ref": {
+                        "field": "family_id",
+                        "table": "families"
+                    },
+                    "name": "family_id"
+                },
+                {
+                    "type": "categorical",
+                    "name": "type"
+                },
+                {
+                    "subtype": "integer",
+                    "type": "numerical",
+                    "name": "generation"
+                }
+            ]
+        }
+    ]
+}
diff --git a/tests/integration/test_sdv.py b/tests/integration/test_sdv.py
index be9b40325..f72cb22b4 100644
--- a/tests/integration/test_sdv.py
+++ b/tests/integration/test_sdv.py
@@ -34,3 +34,39 @@ def test_sdv():
 
     assert transactions.shape == tables['transactions'].shape
     assert set(transactions.columns) == set(tables['transactions'].columns)
+
+
+def test_sdv_multiparent():
+    metadata, tables = load_demo('got_families', metadata=True)
+
+    sdv = SDV()
+    sdv.fit(metadata, tables)
+
+    # Sample all
+    sampled = sdv.sample_all()
+
+    assert set(sampled.keys()) == {'characters', 'families', 'character_families'}
+    assert len(sampled['characters']) == 7
+
+    # Sample with children
+    sampled = sdv.sample('characters', reset_primary_keys=True)
+
+    assert set(sampled.keys()) == {'characters', 'character_families'}
+    assert len(sampled['characters']) == 7
+    assert 'family_id' in sampled['character_families']
+
+    # Sample without children
+    characters = sdv.sample('characters', sample_children=False)
+
+    assert characters.shape == tables['characters'].shape
+    assert set(characters.columns) == set(tables['characters'].columns)
+
+    families = sdv.sample('families', sample_children=False)
+
+    assert families.shape == tables['families'].shape
+    assert set(families.columns) == set(tables['families'].columns)
+
+    character_families = sdv.sample('character_families', sample_children=False)
+
+    assert character_families.shape == tables['character_families'].shape
+    assert set(character_families.columns) == set(tables['character_families'].columns)

From 60dbcf74280c370911ef17f72c5d50f72981efc3 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 2 Jul 2020 22:59:49 +0200
Subject: [PATCH 12/33] Allow creating a metadata with multiple parents

---
 sdv/metadata.py        | 86 +++++++++++++++++++++++++++++++++---------
 sdv/models/copulas.py  |  2 +-
 tests/test_metadata.py | 11 +-----
 3 files changed, 72 insertions(+), 27 deletions(-)

diff --git a/sdv/metadata.py b/sdv/metadata.py
index efa610e58..f19ed985c 100644
--- a/sdv/metadata.py
+++ b/sdv/metadata.py
@@ -240,6 +240,30 @@ def get_tables(self):
         """
         return list(self._metadata['tables'].keys())
 
+    def get_field_meta(self, table_name, field_name):
+        """Get the metadata dict for a table.
+
+        Args:
+            table_name (str):
+                Name of the table to which the field belongs.
+            field_name (str):
+                Name of the field to get data for.
+
+        Returns:
+            dict:
+                field metadata
+
+        Raises:
+            ValueError:
+                If the table or the field do not exist in this metadata.
+        """
+        field_meta = self.get_fields(table_name).get(field_name)
+        if field_meta is None:
+            raise ValueError(
+                'Table "{}" does not contain a field name "{}"'.format(table_name, field_name))
+
+        return copy.deepcopy(field_meta)
+
     def get_fields(self, table_name):
         """Get table fields metadata.
 
@@ -291,11 +315,9 @@ def get_foreign_key(self, parent, child):
             ValueError:
                 If the relationship does not exist.
         """
-        primary = self.get_primary_key(parent)
-
         for name, field in self.get_fields(child).items():
             ref = field.get('ref')
-            if ref and ref['field'] == primary:
+            if ref and ref['table'] == parent:
                 return name
 
         raise ValueError('{} is not parent of {}'.format(parent, child))
@@ -370,8 +392,13 @@ def get_dtypes(self, table_name, ids=False):
 
             if ids and field_type == 'id':
                 if (name != table_meta.get('primary_key')) and not field.get('ref'):
-                    raise MetadataError(
-                        'id field `{}` is neither a primary or a foreign key'.format(name))
+                    for child_table in self.get_children(table_name):
+                        if name == self.get_foreign_key(table_name, child_table):
+                            break
+
+                    else:
+                        raise MetadataError(
+                            'id field `{}` is neither a primary or a foreign key'.format(name))
 
             if ids or (field_type != 'id'):
                 dtypes[name] = dtype
@@ -672,14 +699,17 @@ def add_field(self, table, field, field_type, field_subtype=None, properties=Non
     def _get_key_subtype(field_meta):
         """Get the appropriate key subtype."""
         field_type = field_meta['type']
+
         if field_type == 'categorical':
             field_subtype = 'string'
+
         elif field_type in ('numerical', 'id'):
             field_subtype = field_meta['subtype']
             if field_subtype not in ('integer', 'string'):
                 raise ValueError(
                     'Invalid field "subtype" for key field: "{}"'.format(field_subtype)
                 )
+
         else:
             raise ValueError(
                 'Invalid field "type" for key field: "{}"'.format(field_type)
@@ -740,7 +770,11 @@ def add_relationship(self, parent, child, foreign_key=None):
                     * The child table already has a parent.
                     * The new relationship closes a relationship circle.
         """
-        # Validate table and field names
+        # Validate tables exists
+        self.get_table_meta(parent)
+        self.get_table_meta(child)
+
+        # Validate field names
         primary_key = self.get_primary_key(parent)
         if not primary_key:
             raise ValueError('Parent table "{}" does not have a primary key'.format(parent))
@@ -748,33 +782,51 @@ def add_relationship(self, parent, child, foreign_key=None):
         if foreign_key is None:
             foreign_key = primary_key
 
+        parent_key_meta = copy.deepcopy(self.get_field_meta(parent, primary_key))
+        child_key_meta = copy.deepcopy(self.get_field_meta(child, foreign_key))
+
         # Validate relationships
-        if self.get_parents(child):
-            raise ValueError('Table "{}" already has a parent'.format(child))
+        child_ref = child_key_meta.get('ref')
+        if child_ref:
+            raise ValueError(
+                'Field "{}.{}" already defines a relationship'.format(child, foreign_key))
 
         grandchildren = self.get_children(child)
         if grandchildren:
             self._validate_circular_relationships(parent, grandchildren)
 
-        # Copy primary key details over to the foreign key
-        foreign_key_details = copy.deepcopy(self.get_fields(parent)[primary_key])
-        foreign_key_details['ref'] = {
+        # Make sure that the parent key is an id
+        if parent_key_meta['type'] != 'id':
+            parent_key_meta['subtype'] = self._get_key_subtype(parent_key_meta)
+            parent_key_meta['type'] = 'id'
+
+        # Update the child key meta
+        child_key_meta['subtype'] = self._get_key_subtype(child_key_meta)
+        child_key_meta['type'] = 'id'
+        child_key_meta['ref'] = {
             'table': parent,
             'field': primary_key
         }
 
         # Make sure that key subtypes are the same
-        foreign_meta = self.get_fields(child).get(foreign_key)
-        if foreign_meta:
-            foreign_subtype = self._get_key_subtype(foreign_meta)
-            if foreign_subtype != foreign_key_details['subtype']:
-                raise ValueError('Primary and Foreign key subtypes mismatch')
+        if child_key_meta['subtype'] != parent_key_meta['subtype']:
+            raise ValueError('Parent and Child key subtypes mismatch')
+
+        # Make a backup
+        metadata_backup = copy.deepcopy(self._metadata)
 
-        self._metadata['tables'][child]['fields'][foreign_key] = foreign_key_details
+        self._metadata['tables'][parent]['fields'][primary_key] = parent_key_meta
+        self._metadata['tables'][child]['fields'][foreign_key] = child_key_meta
 
         # Re-analyze the relationships
         self._analyze_relationships()
 
+        try:
+            self.validate()
+        except MetadataError:
+            self._metadata = metadata_backup
+            raise
+
     def _get_field_details(self, data, fields):
         """Get or build all the fields metadata.
 
diff --git a/sdv/models/copulas.py b/sdv/models/copulas.py
index ff1665d50..fcd8a3c8b 100644
--- a/sdv/models/copulas.py
+++ b/sdv/models/copulas.py
@@ -152,7 +152,7 @@ def _unflatten_gaussian_copula(self, model_parameters):
         model_parameters['univariates'] = univariates
         model_parameters['columns'] = columns
 
-        covariance = model_parameters['covariance']
+        covariance = model_parameters.get('covariance')
         model_parameters['covariance'] = self._prepare_sampled_covariance(covariance)
 
         return model_parameters
diff --git a/tests/test_metadata.py b/tests/test_metadata.py
index 137a258b8..812ac7bcd 100644
--- a/tests/test_metadata.py
+++ b/tests/test_metadata.py
@@ -381,6 +381,7 @@ def test_get_dtypes_error_id(self):
         }
         metadata = Mock(spec_set=Metadata)
         metadata.get_table_meta.return_value = table_meta
+        metadata.get_children.return_value = []
         metadata._DTYPES = Metadata._DTYPES
 
         # Run
@@ -608,23 +609,16 @@ def test_get_primary_key(self):
     def test_get_foreign_key(self):
         """Test get foreign key"""
         # Setup
-        primary_key = 'a_primary_key'
         fields = {
             'a_field': {
                 'ref': {
+                    'table': 'parent',
                     'field': 'a_primary_key'
                 },
                 'name': 'a_field'
-            },
-            'p_field': {
-                'ref': {
-                    'field': 'another_key_field'
-                },
-                'name': 'p_field'
             }
         }
         metadata = Mock(spec_set=Metadata)
-        metadata.get_primary_key.return_value = primary_key
         metadata.get_fields.return_value = fields
 
         # Run
@@ -632,7 +626,6 @@ def test_get_foreign_key(self):
 
         # Asserts
         assert result == 'a_field'
-        metadata.get_primary_key.assert_called_once_with('parent')
         metadata.get_fields.assert_called_once_with('child')
 
     def test_reverse_transform(self):

From 8ab9734d2e6e8343865309975097d269c19c33d5 Mon Sep 17 00:00:00 2001
From: Carles Sala <c.sala.stq@gmail.com>
Date: Sat, 4 Jul 2020 14:36:24 +0200
Subject: [PATCH 13/33] Add benchmark module

---
 EVALUATION.md    | 93 +++++++++++++++++++++++++++++++++++++++++++++-
 sdv/benchmark.py | 96 ++++++++++++++++++++++++++++++++++++++++++++++++
 sdv/demo.py      |  8 ++--
 3 files changed, 192 insertions(+), 5 deletions(-)
 create mode 100644 sdv/benchmark.py

diff --git a/EVALUATION.md b/EVALUATION.md
index 5d64ac6db..a8343184f 100644
--- a/EVALUATION.md
+++ b/EVALUATION.md
@@ -12,7 +12,7 @@ generate a simple standardized score.
 After you have modeled your databased and generated samples out of the SDV models
 you will be left with a dictionary that contains table names and dataframes.
 
-For exmple, if we model and sample the demo dataset:
+For example, if we model and sample the demo dataset:
 
 ```python3
 from sdv import SDV
@@ -44,3 +44,94 @@ the value will be negative.
 
 For further options, including visualizations and more detailed reports, please refer to
 the [SDMetrics](https://github.com/sdv-dev/SDMetrics) library.
+
+
+## SDV Benchmark
+
+SDV also provides a simple functionality to evaluate the performance of SDV across a
+collection of demo datasets or custom datasets hosted in a local folder.
+
+In order to execute this evaluation you can execute the function `sdv.benchmark.run_benchmark`:
+
+```python3
+from sdv.benchmark import run_benchmark
+
+scores = run_benchmark()
+```
+
+This function has the following arguments:
+
+* `datasets`: List of dataset names, which can either be names of demo datasets or
+  names of custom datasets stored in a local folder.
+* `datasets_path`: Path where the custom datasets are stored. If not provided, the
+  dataset names are interpreted as demo datasets.
+* `distributed`: Whether to execute the benchmark using Dask. Defaults to True.
+* `timeout`: Maximum time allowed for each dataset to be modeled, sampled and evaluated.
+  Any dataset that takes longer to run will return a score of `None`.
+
+For example, the following command will run the SDV benchmark on all the given demo datasets
+using `dask` and a timeout of 60 seconds:
+
+```python
+scores = run_benchmark(
+    datasets=['DCG_v1', 'trains_v1', 'UTube_v1'],
+    distributed=True,
+    timeout=60
+)
+```
+
+And the result will be a DataFrame containing a table with the columns `dataset`, `score`:
+
+| dataset | score |
+|:-------:|:-----:|
+| DCG_v1  | -14.49341665631863 |
+| trains_v1  | -30.26840342069557 |
+| UTube_v1  | -8.57618576332235 |
+
+Additionally, if some dataset has raised an error or has reached the timeout, an `error`
+column will be added indicating the details.
+
+### Demo Datasets
+
+The collection of datasets can be seen using the `sdv.demo.get_demo_demos`,
+which returns a table with a description of the dataset properties:
+
+```python3
+from sdv.demo import get_available_demos
+
+demos = get_available_demos()
+```
+
+The result is a table indicating the name of the dataset and a few properties, such as the
+number of tables that compose the dataset and the total number of rows and columns:
+
+| name                  |   tables |    rows |   columns |
+|-----------------------|----------|---------|-----------|
+| UTube_v1              |        2 |    2735 |        10 |
+| SAP_v1                |        4 | 3841029 |        71 |
+| NCAA_v1               |        9 |  202305 |       333 |
+| airbnb-simplified     |        2 | 5751408 |        22 |
+| Atherosclerosis_v1    |        4 |   12781 |       307 |
+| rossmann              |        3 | 2035533 |        21 |
+| walmart               |        4 |  544869 |        24 |
+| AustralianFootball_v1 |        4 |  139179 |       193 |
+| Pyrimidine_v1         |        2 |     296 |        38 |
+| world_v1              |        3 |    5302 |        39 |
+| Accidents_v1          |        3 | 1463093 |        87 |
+| trains_v1             |        2 |      83 |        15 |
+| legalActs_v1          |        5 | 1754397 |        50 |
+| DCG_v1                |        2 |    8258 |         9 |
+| imdb_ijs_v1           |        7 | 5647694 |        50 |
+| SalesDB_v1            |        4 | 6735507 |        35 |
+| MuskSmall_v1          |        2 |     568 |       173 |
+| KRK_v1                |        1 |    1000 |         9 |
+| Chess_v1              |        2 |    2052 |        57 |
+| Telstra_v1            |        5 |  148021 |        23 |
+| mutagenesis_v1        |        3 |   10324 |        26 |
+| PremierLeague_v1      |        4 |   11308 |       250 |
+| census                |        1 |   32561 |        15 |
+| FNHK_v1               |        3 | 2113275 |        43 |
+| imdb_MovieLens_v1     |        7 | 1249411 |        58 |
+| financial_v1          |        8 | 1079680 |        84 |
+| ftp_v1                |        2 |   96491 |        13 |
+| Triazine_v1           |        2 |    1302 |        35 |
diff --git a/sdv/benchmark.py b/sdv/benchmark.py
new file mode 100644
index 000000000..7641bd1f5
--- /dev/null
+++ b/sdv/benchmark.py
@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+
+import logging
+import multiprocessing
+import os
+import signal
+import sys
+from datetime import datetime
+
+import pandas as pd
+
+from sdv import Metadata, SDV
+from sdv.demo import get_available_demos, load_demo
+from sdv.evaluation import evaluate
+
+LOGGER = logging.getLogger(__name__)
+
+
+def _score_dataset(dataset, datasets_path, output):
+    start = datetime.now()
+
+    try:
+        if datasets_path is None:
+            metadata, tables = load_demo(dataset, metadata=True)
+        else:
+            metadata = Metadata(os.path.join(datasets_path, dataset, 'metadata.json'))
+            tables = metadata.load_tables()
+
+        sdv = SDV()
+        LOGGER.info('Modeling dataset %s', dataset)
+        sdv.fit(metadata, tables)
+
+        LOGGER.info('Sampling dataset %s', dataset)
+        sampled = sdv.sample_all(10)
+
+        LOGGER.info('Evaluating dataset %s', dataset)
+        score = evaluate(sampled, metadata=metadata)
+
+        LOGGER.info('%s: %s - ELAPSED: %s', dataset, score, datetime.now() - start)
+        output.update({
+            'dataset': dataset,
+            'score': score,
+        })
+
+    except Exception as ex:
+        error = '{}: {}'.format(type(ex).__name__, str(ex))
+        LOGGER.error('%s: %s - ELAPSED: %s', dataset, error, datetime.now() - start)
+        output.update({
+            'dataset': dataset,
+            'error': error
+        })
+
+
+def score_dataset(dataset, datasets_path, timeout=None):
+    with multiprocessing.Manager() as manager:
+        output = manager.dict()
+        process = multiprocessing.Process(
+            target=_score_dataset,
+            args=(dataset, datasets_path, output)
+        )
+
+        process.start()
+        process.join(timeout)
+        process.terminate()
+
+        if not output:
+            LOGGER.warn('%s: TIMEOUT', dataset)
+            return {
+                'dataset': dataset,
+                'error': 'timeout'
+            }
+
+        return dict(output)
+
+
+def benchmark(datasets=None, datasets_path=None, distributed=True, timeout=None):
+    if datasets is None:
+        if datasets_path is None:
+            datasets = get_available_demos().name
+        else:
+            datasets = os.listdir(datasets_path)
+
+    if distributed:
+        import dask
+
+        global score_dataset
+        score_dataset = dask.delayed(score_dataset)
+
+    scores = list()
+    for dataset in datasets:
+        scores.append(score_dataset(dataset, datasets_path, timeout))
+
+    if distributed:
+        scores = dask.compute(*scores)
+
+    return pd.DataFrame(scores)
diff --git a/sdv/demo.py b/sdv/demo.py
index 672130ada..5e723b4b0 100644
--- a/sdv/demo.py
+++ b/sdv/demo.py
@@ -104,9 +104,9 @@ def _download(dataset_name, data_path):
         zf.extractall(data_path)
 
 
-def _load(dataset_name, data_path):
-    if not os.path.exists(DATA_PATH):
-        os.makedirs(DATA_PATH)
+def _get_dataset_path(dataset_name, data_path):
+    if not os.path.exists(data_path):
+        os.makedirs(data_path)
 
     if not os.path.exists(os.path.join(data_path, dataset_name)):
         _download(dataset_name, data_path)
@@ -151,7 +151,7 @@ def _load_dummy():
 
 
 def _load_demo_dataset(dataset_name, data_path):
-    data_path = _load(dataset_name, data_path)
+    dataset_path = _get_dataset_path(dataset_name, data_path)
     meta = Metadata(metadata=os.path.join(data_path, 'metadata.json'))
     tables = meta.load_tables()
     return meta, tables

From 0cd81bdbd08fdc2b099c233b3466efd7cd6dc52e Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Sat, 4 Jul 2020 14:42:15 +0200
Subject: [PATCH 14/33] Fix lint

---
 sdv/benchmark.py | 4 +---
 sdv/demo.py      | 2 +-
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/sdv/benchmark.py b/sdv/benchmark.py
index 7641bd1f5..df4365d0e 100644
--- a/sdv/benchmark.py
+++ b/sdv/benchmark.py
@@ -3,13 +3,11 @@
 import logging
 import multiprocessing
 import os
-import signal
-import sys
 from datetime import datetime
 
 import pandas as pd
 
-from sdv import Metadata, SDV
+from sdv import SDV, Metadata
 from sdv.demo import get_available_demos, load_demo
 from sdv.evaluation import evaluate
 
diff --git a/sdv/demo.py b/sdv/demo.py
index 5e723b4b0..9ef350904 100644
--- a/sdv/demo.py
+++ b/sdv/demo.py
@@ -152,7 +152,7 @@ def _load_dummy():
 
 def _load_demo_dataset(dataset_name, data_path):
     dataset_path = _get_dataset_path(dataset_name, data_path)
-    meta = Metadata(metadata=os.path.join(data_path, 'metadata.json'))
+    meta = Metadata(metadata=os.path.join(dataset_path, 'metadata.json'))
     tables = meta.load_tables()
     return meta, tables
 

From daefd3b05a36b1aeea3243c5372a9b72d15b4595 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Sat, 4 Jul 2020 14:51:00 +0200
Subject: [PATCH 15/33] =?UTF-8?q?Bump=20version:=200.3.4.dev0=20=E2=86=92?=
 =?UTF-8?q?=200.3.4.dev1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 sdv/__init__.py | 2 +-
 setup.cfg       | 2 +-
 setup.py        | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/sdv/__init__.py b/sdv/__init__.py
index 8caed53d9..3a0f29f7d 100644
--- a/sdv/__init__.py
+++ b/sdv/__init__.py
@@ -6,7 +6,7 @@
 
 __author__ = """MIT Data To AI Lab"""
 __email__ = 'dailabmit@gmail.com'
-__version__ = '0.3.4.dev0'
+__version__ = '0.3.4.dev1'
 
 from sdv.demo import get_available_demos, load_demo
 from sdv.metadata import Metadata
diff --git a/setup.cfg b/setup.cfg
index 6a2ef34f0..d39776a09 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.4.dev0
+current_version = 0.3.4.dev1
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\.(?P<release>[a-z]+)(?P<candidate>\d+))?
diff --git a/setup.py b/setup.py
index bca8362a0..3784752e2 100644
--- a/setup.py
+++ b/setup.py
@@ -93,6 +93,6 @@
     test_suite='tests',
     tests_require=tests_require,
     url='https://github.com/sdv-dev/SDV',
-    version='0.3.4.dev0',
+    version='0.3.4.dev1',
     zip_safe=False,
 )

From 83bd3471c777ca40aced2ca43a221b020e0a01d6 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Sat, 4 Jul 2020 14:53:03 +0200
Subject: [PATCH 16/33] Add release notes for v0.3.4

---
 HISTORY.md | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/HISTORY.md b/HISTORY.md
index b2a2e996d..00b164949 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -1,5 +1,16 @@
 # History
 
+## 0.3.4 - 2020-07-04
+
+## New Features
+
+* Support for Multiple Parents - [Issue #162](https://github.com/sdv-dev/SDV/issues/162) by @csala
+* Sample by default the same number of rows as in the original table - [Issue #163](https://github.com/sdv-dev/SDV/issues/163) by @csala
+
+### General Improvements
+
+* Add benchmark - [Issue #165](https://github.com/sdv-dev/SDV/issues/165) by @csala
+
 ## 0.3.3 - 2020-06-26
 
 ### General Improvements

From 729a59b0d25d672660f96aca32bb2425e75d60fa Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Sat, 4 Jul 2020 14:55:51 +0200
Subject: [PATCH 17/33] =?UTF-8?q?Bump=20version:=200.3.4.dev1=20=E2=86=92?=
 =?UTF-8?q?=200.3.4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 sdv/__init__.py | 2 +-
 setup.cfg       | 2 +-
 setup.py        | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/sdv/__init__.py b/sdv/__init__.py
index 3a0f29f7d..e56c24244 100644
--- a/sdv/__init__.py
+++ b/sdv/__init__.py
@@ -6,7 +6,7 @@
 
 __author__ = """MIT Data To AI Lab"""
 __email__ = 'dailabmit@gmail.com'
-__version__ = '0.3.4.dev1'
+__version__ = '0.3.4'
 
 from sdv.demo import get_available_demos, load_demo
 from sdv.metadata import Metadata
diff --git a/setup.cfg b/setup.cfg
index d39776a09..9890ee3b2 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.4.dev1
+current_version = 0.3.4
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\.(?P<release>[a-z]+)(?P<candidate>\d+))?
diff --git a/setup.py b/setup.py
index 3784752e2..53c783ad9 100644
--- a/setup.py
+++ b/setup.py
@@ -93,6 +93,6 @@
     test_suite='tests',
     tests_require=tests_require,
     url='https://github.com/sdv-dev/SDV',
-    version='0.3.4.dev1',
+    version='0.3.4',
     zip_safe=False,
 )

From 21a5fdc27fc88139db80e858f77178aafcdce1cc Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Sat, 4 Jul 2020 14:56:10 +0200
Subject: [PATCH 18/33] =?UTF-8?q?Bump=20version:=200.3.4=20=E2=86=92=200.3?=
 =?UTF-8?q?.5.dev0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 sdv/__init__.py | 2 +-
 setup.cfg       | 2 +-
 setup.py        | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/sdv/__init__.py b/sdv/__init__.py
index e56c24244..d8e94dd37 100644
--- a/sdv/__init__.py
+++ b/sdv/__init__.py
@@ -6,7 +6,7 @@
 
 __author__ = """MIT Data To AI Lab"""
 __email__ = 'dailabmit@gmail.com'
-__version__ = '0.3.4'
+__version__ = '0.3.5.dev0'
 
 from sdv.demo import get_available_demos, load_demo
 from sdv.metadata import Metadata
diff --git a/setup.cfg b/setup.cfg
index 9890ee3b2..e4bf6d2c7 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.4
+current_version = 0.3.5.dev0
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\.(?P<release>[a-z]+)(?P<candidate>\d+))?
diff --git a/setup.py b/setup.py
index 53c783ad9..c8a4a56b5 100644
--- a/setup.py
+++ b/setup.py
@@ -93,6 +93,6 @@
     test_suite='tests',
     tests_require=tests_require,
     url='https://github.com/sdv-dev/SDV',
-    version='0.3.4',
+    version='0.3.5.dev0',
     zip_safe=False,
 )

From a54702957f43234d9e717b24320fcecf2f4801ad Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 13:20:27 +0200
Subject: [PATCH 19/33] Tabular models, work in progress

---
 sdv/{metadata.py => metadata/__init__.py} | 141 ++-----
 sdv/metadata/errors.py                    |   2 +
 sdv/metadata/table.py                     | 489 ++++++++++++++++++++++
 sdv/metadata/visualization.py             | 120 ++++++
 sdv/tabular/__init__.py                   |   0
 sdv/tabular/base.py                       | 180 ++++++++
 sdv/tabular/copulas.py                    | 186 ++++++++
 sdv/tabular/utils.py                      | 219 ++++++++++
 8 files changed, 1224 insertions(+), 113 deletions(-)
 rename sdv/{metadata.py => metadata/__init__.py} (90%)
 create mode 100644 sdv/metadata/errors.py
 create mode 100644 sdv/metadata/table.py
 create mode 100644 sdv/metadata/visualization.py
 create mode 100644 sdv/tabular/__init__.py
 create mode 100644 sdv/tabular/base.py
 create mode 100644 sdv/tabular/copulas.py
 create mode 100644 sdv/tabular/utils.py

diff --git a/sdv/metadata.py b/sdv/metadata/__init__.py
similarity index 90%
rename from sdv/metadata.py
rename to sdv/metadata/__init__.py
index f19ed985c..fc91ad549 100644
--- a/sdv/metadata.py
+++ b/sdv/metadata/__init__.py
@@ -9,6 +9,17 @@
 import pandas as pd
 from rdt import HyperTransformer, transformers
 
+from sdv.metadata import visualization
+from sdv.metadata.errors import MetadataError
+from sdv.metadata.table import Table
+
+__all__ = [
+    'Metadata',
+    'MetadataError',
+    'Table',
+    'visualization'
+]
+
 LOGGER = logging.getLogger(__name__)
 
 
@@ -965,119 +976,7 @@ def to_json(self, path):
         with open(path, 'w') as out_file:
             json.dump(self._metadata, out_file, indent=4)
 
-    @staticmethod
-    def _get_graphviz_extension(path):
-        if path:
-            path_splitted = path.split('.')
-            if len(path_splitted) == 1:
-                raise ValueError('Path without graphviz extansion.')
-
-            graphviz_extension = path_splitted[-1]
-
-            if graphviz_extension not in graphviz.backend.FORMATS:
-                raise ValueError(
-                    '"{}" not a valid graphviz extension format.'.format(graphviz_extension)
-                )
-
-            return '.'.join(path_splitted[:-1]), graphviz_extension
-
-        return None, None
-
-    def _visualize_add_nodes(self, plot):
-        """Add nodes into a `graphviz.Digraph`.
-
-        Each node represent a metadata table.
-
-        Args:
-            plot (graphviz.Digraph)
-        """
-        for table in self.get_tables():
-            # Append table fields
-            fields = []
-
-            for name, value in self.get_fields(table).items():
-                if value.get('subtype') is not None:
-                    fields.append('{} : {} - {}'.format(name, value['type'], value['subtype']))
-
-                else:
-                    fields.append('{} : {}'.format(name, value['type']))
-
-            fields = r'\l'.join(fields)
-
-            # Append table extra information
-            extras = []
-
-            primary_key = self.get_primary_key(table)
-            if primary_key is not None:
-                extras.append('Primary key: {}'.format(primary_key))
-
-            parents = self.get_parents(table)
-            for parent in parents:
-                foreign_key = self.get_foreign_key(parent, table)
-                extras.append('Foreign key ({}): {}'.format(parent, foreign_key))
-
-            path = self.get_table_meta(table).get('path')
-            if path is not None:
-                extras.append('Data path: {}'.format(path))
-
-            extras = r'\l'.join(extras)
-
-            # Add table node
-            title = r'{%s|%s\l|%s\l}' % (table, fields, extras)
-            plot.node(table, label=title)
-
-    def _visualize_add_edges(self, plot):
-        """Add edges into a `graphviz.Digraph`.
-
-        Each edge represents a relationship between two metadata tables.
-
-        Args:
-            plot (graphviz.Digraph)
-        """
-        for table in self.get_tables():
-            for parent in list(self.get_parents(table)):
-                plot.edge(
-                    parent,
-                    table,
-                    label='   {}.{} -> {}.{}'.format(
-                        table, self.get_foreign_key(parent, table),
-                        parent, self.get_primary_key(parent)
-                    ),
-                    arrowhead='crow'
-                )
-
-    def visualize(self, path=None):
-        """Plot metadata usign graphviz.
-
-        Try to generate a plot using graphviz.
-        If a ``path`` is provided save the output into a file.
-
-        Args:
-            path (str):
-                Output file path to save the plot, it requires a graphviz
-                supported extension. If ``None`` do not save the plot.
-                Defaults to ``None``.
-        """
-        filename, graphviz_extension = self._get_graphviz_extension(path)
-        plot = graphviz.Digraph(
-            'Metadata',
-            format=graphviz_extension,
-            node_attr={
-                "shape": "Mrecord",
-                "fillcolor": "lightgoldenrod1",
-                "style": "filled"
-            },
-        )
-
-        self._visualize_add_nodes(plot)
-        self._visualize_add_edges(plot)
-
-        if filename:
-            plot.render(filename=filename, cleanup=True, format=graphviz_extension)
-        else:
-            return plot
-
-    def __str__(self):
+    def __repr__(self):
         tables = self.get_tables()
         relationships = [
             '    {}.{} -> {}.{}'.format(
@@ -1099,3 +998,19 @@ def __str__(self):
             tables,
             '\n'.join(relationships)
         )
+
+    def visualize(self, path=None):
+        """Plot metadata usign graphviz.
+
+        Generate a plot using graphviz.
+        If a ``path`` is provided save the output into a file.
+
+        Args:
+            path (str):
+                Output file path to save the plot. It requires a graphviz
+                supported extension. If ``None`` do not save the plot and
+                just return the ``graphviz.Digraph`` object.
+                Defaults to ``None``.
+        """
+        return visualization.visualize(self, path)
+
diff --git a/sdv/metadata/errors.py b/sdv/metadata/errors.py
new file mode 100644
index 000000000..8e1bada44
--- /dev/null
+++ b/sdv/metadata/errors.py
@@ -0,0 +1,2 @@
+class MetadataError(Exception):
+    pass
diff --git a/sdv/metadata/table.py b/sdv/metadata/table.py
new file mode 100644
index 000000000..85b129d78
--- /dev/null
+++ b/sdv/metadata/table.py
@@ -0,0 +1,489 @@
+import copy
+import json
+import logging
+import os
+
+import numpy as np
+import pandas as pd
+from faker import Faker
+from rdt import HyperTransformer, transformers
+
+from sdv.metadata.error import MetadataError
+
+LOGGER = logging.getLogger(__name__)
+
+
+class Table:
+    """Table Metadata.
+
+    The Metadata class provides a unified layer of abstraction over the metadata
+    of a single Table, which includes both the necessary details to load the data
+    from the filesystem and to know how to parse and transform it to numerical data.
+
+    Args:
+        metadata (str or dict):
+            Path to a ``json`` file that contains the metadata or a ``dict`` representation
+            of ``metadata`` following the same structure.
+        root_path (str):
+            The path to which the dataset is located. Defaults to ``None``.
+    """
+
+    _metadata = None
+    _hyper_transformer = None
+    _root_path = None
+
+    _FIELD_TEMPLATES = {
+        'i': {
+            'type': 'numerical',
+            'subtype': 'integer',
+        },
+        'f': {
+            'type': 'numerical',
+            'subtype': 'float',
+        },
+        'O': {
+            'type': 'categorical',
+        },
+        'b': {
+            'type': 'boolean',
+        },
+        'M': {
+            'type': 'datetime',
+        }
+    }
+    _DTYPES = {
+        ('categorical', None): 'object',
+        ('boolean', None): 'bool',
+        ('numerical', None): 'float',
+        ('numerical', 'float'): 'float',
+        ('numerical', 'integer'): 'int',
+        ('datetime', None): 'datetime64',
+        ('id', None): 'int',
+        ('id', 'integer'): 'int',
+        ('id', 'string'): 'str'
+    }
+
+    def __init__(self, metadata=None, root_path=None, field_names=None, primary_key=None,
+                 field_types=None, anonymize_fields=None, constraints=None,):
+        self._metadata = metadata or dict()
+        self._hyper_transformer = None
+        self._root_path = None
+
+        self._field_names = field_names
+        self._primary_key = primary_key
+        self._field_types = field_types or {}
+        self._anonymize_fields = anonymize_fields
+        self._constraints = constraints
+
+    def _get_field_dtype(self, field_name, field_metadata):
+        field_type = field_metadata['type']
+        field_subtype = field_metadata.get('subtype')
+        dtype = self._DTYPES.get((field_type, field_subtype))
+        if not dtype:
+            raise MetadataError(
+                'Invalid type and subtype combination for field {}: ({}, {})'.format(
+                    field_name, field_type, field_subtype)
+            )
+
+        return dtype
+
+    def get_dtypes(self, ids=False):
+        """Get a ``dict`` with the ``dtypes`` for each field of the table.
+
+        Args:
+            ids (bool):
+                Whether or not include the id fields. Defaults to ``False``.
+
+        Returns:
+            dict:
+                Dictionary that contains the field names and data types.
+
+        Raises:
+            ValueError:
+                If a field has an invalid type or subtype.
+        """
+        dtypes = dict()
+        for name, field_meta in self._metadata['fields'].items():
+            field_type = field_meta['type']
+
+            if ids or (field_type != 'id'):
+                dtypes[name] = self._get_field_dtype(name, field_meta)
+
+        return dtypes
+
+    def _get_faker(self):
+        """Return the faker object to anonymize data.
+
+        Returns:
+            function:
+                Faker function to generate new data instances with ``self.anonymize`` arguments.
+
+        Raises:
+            ValueError:
+                A ``ValueError`` is raised if the faker category we want don't exist.
+        """
+        if isinstance(self._anonymize, (tuple, list)):
+            category, *args = self._anonymize
+        else:
+            category = self._anonymize
+            args = tuple()
+
+        try:
+            faker_method = getattr(Faker(), category)
+
+            def faker():
+                return faker_method(*args)
+
+            return faker
+        except AttributeError:
+            raise ValueError('Category "{}" couldn\'t be found on faker'.format(self.anonymize))
+
+    def _anonymize(self, data):
+        """Anonymize data and save the anonymization mapping in-memory."""
+        # TODO: Do this by column
+        faker = self._get_faker()
+        uniques = data.unique()
+        fake_data = [faker() for x in range(len(uniques))]
+
+        mapping = dict(zip(uniques, fake_data))
+        MAPS[id(self)] = mapping
+
+        return data.map(mapping)
+
+    def _build_fields_metadata(self, data):
+        """Build all the fields metadata.
+
+        Args:
+            data (pandas.DataFrame):
+                Data to be analyzed.
+
+        Returns:
+            dict:
+                Dict of valid fields.
+
+        Raises:
+            ValueError:
+                If a column from the data analyzed is an unsupported data type
+        """
+        field_names = self._field_names or data.columns
+
+        fields_metadata = dict()
+        for field_name in field_names:
+            if not field_name in data:
+                raise ValueError('Field {} not found in given data'.format(field_name))
+
+            field_meta = self._field_types.get(field_name)
+            if field_meta:
+                # Validate the given meta
+                self._get_field_dtype(field_name, field_meta)
+            else:
+                dtype = data[field_name].dtype
+                field_template = self._FIELD_TEMPLATES.get(dtype.kind)
+                if field_template is None:
+                    raise ValueError('Unsupported dtype {} in column {}'.format(dtype, field_name))
+
+                field_meta = copy.deepcopy(field_template)
+
+            fields_metadata[field_name] = field_meta
+
+        return fields_metadata
+
+    def _get_pii_fields(self):
+        """Get the ``pii_category`` for each field that contains PII.
+
+        Returns:
+            dict:
+                pii field names and categories.
+        """
+        pii_fields = dict()
+        for name, field in self._metadata['fields'].items():
+            if field['type'] == 'categorical' and field.get('pii', False):
+                pii_fields[name] = field['pii_category']
+
+        return pii_fields
+
+    def _get_transformers(self, dtypes):
+        """Create the transformer instances needed to process the given dtypes.
+
+        Temporary drop-in replacement of ``HyperTransformer._analyze`` method,
+        before RDT catches up.
+
+        Args:
+            dtypes (dict):
+                mapping of field names and dtypes.
+            pii_fields (dict):
+                mapping of pii field names and categories.
+
+        Returns:
+            dict:
+                mapping of field names and transformer instances.
+        """
+        transformers_dict = dict()
+        pii_fields = self._get_pii_fields()
+        for name, dtype in dtypes.items():
+            dtype = np.dtype(dtype)
+            if dtype.kind == 'i':
+                transformer = transformers.NumericalTransformer(dtype=int)
+            elif dtype.kind == 'f':
+                transformer = transformers.NumericalTransformer(dtype=float)
+            elif dtype.kind == 'O':
+                anonymize = pii_fields.get(name)
+                transformer = transformers.CategoricalTransformer(anonymize=anonymize)
+            elif dtype.kind == 'b':
+                transformer = transformers.BooleanTransformer()
+            elif dtype.kind == 'M':
+                transformer = transformers.DatetimeTransformer()
+            else:
+                raise ValueError('Unsupported dtype: {}'.format(dtype))
+
+            LOGGER.info('Loading transformer %s for field %s',
+                        transformer.__class__.__name__, name)
+            transformers_dict[name] = transformer
+
+        return transformers_dict
+
+    def _fit_hyper_transformer(self, data):
+        """Create and return a new ``rdt.HyperTransformer`` instance.
+
+        First get the ``dtypes`` and then use them to build a transformer dictionary
+        to be used by the ``HyperTransformer``.
+
+        Returns:
+            rdt.HyperTransformer
+        """
+        dtypes = self.get_dtypes(ids=False)
+        transformers_dict = self._get_transformers(dtypes)
+        self._hyper_transformer = HyperTransformer(transformers=transformers_dict)
+        self._hyper_transformer.fit(data[list(dtypes.keys())])
+
+    def set_primary_key(self, field_name):
+        """Set the primary key of this table.
+
+        The field must exist and either be an integer or categorical field.
+
+        Args:
+            field_name (str):
+                Name of the field to be used as the new primary key.
+
+        Raises:
+            ValueError:
+                If the table or the field do not exist or if the field has an
+                invalid type or subtype.
+        """
+        if field_name is not None:
+            if field_name not in self.get_fields():
+                raise ValueError('Field "{}" does not exist in this table'.format(field_name))
+
+            field_metadata = self._metadata['fields'][field_name]
+            field_subtype = self._get_key_subtype(field_metadata)
+
+            field_metadata.update({
+                'type': 'id',
+                'subtype': field_subtype
+            })
+
+        self._primary_key = field_name
+
+    def fit(self, data):
+        """Fit this metadata to the given data.
+
+        Args:
+            data (pandas.DataFrame):
+                Table to be analyzed.
+        """
+        self._field_names = self._field_names or list(data.columns)
+        self._metadata['fields'] = self._build_fields_metadata(data)
+        self.set_primary_key(self._primary_key)
+
+        # TODO: Treat/Learn constraints
+
+        self._fit_hyper_transformer(data)
+
+    def get_fields(self):
+        """Get fields metadata.
+
+        Returns:
+            dict:
+                Mapping of field names and their metadata dicts.
+        """
+        return self._metadata['fields']
+
+    def transform(self, data):
+        """Transform the given data.
+
+        Args:
+            data (pandas.DataFrame):
+                Table data.
+
+        Returns:
+            pandas.DataFrame:
+                Transformed data.
+        """
+
+        # TODO: Do this by column
+        # if self.anonymize:
+        #     data = data.map(MAPS[id(self)])
+
+        fields = list(self._hyper_transformer.transformers.keys())
+        return self._hyper_transformer.transform(data[fields])
+
+    def reverse_transform(self, data):
+        """Reverse the transformed data to the original format.
+
+        Args:
+            data (pandas.DataFrame):
+                Data to be reverse transformed.
+
+        Returns:
+            pandas.DataFrame
+        """
+        reversed_data = self._hyper_transformer.reverse_transform(data)
+
+        fields = self._metadata['fields']
+        for name, dtype in self.get_dtypes(ids=True).items():
+            field_type = fields[name]['type']
+            if field_type == 'id':
+                field_data = pd.Series(np.arange(len(reversed_data)))
+            else:
+                field_data = reversed_data[name]
+
+            reversed_data[name] = field_data.dropna().astype(dtype)
+
+        return reversed_data[self._field_names]
+
+    def get_children(self):
+        """Get tables for which this table is parent.
+
+        Returns:
+            set:
+                Set of children for this table.
+        """
+        return self._children
+
+    def get_parents(self):
+        """Get tables for with this table is child.
+
+        Returns:
+            set:
+                Set of parents for this table.
+        """
+        return self._parents
+
+    def get_field(self, field_name):
+        """Get the metadata dict for a field.
+
+        Args:
+            field_name (str):
+                Name of the field to get data for.
+
+        Returns:
+            dict:
+                field metadata
+
+        Raises:
+            ValueError:
+                If the table or the field do not exist in this metadata.
+        """
+        field_meta = self._metadata['fields'].get(field_name)
+        if field_meta is None:
+            raise ValueError('Invalid field name "{}"'.format(field_name))
+
+        return copy.deepcopy(field_meta)
+
+    # def _read_csv_dtypes(self):
+    #     """Get the dtypes specification that needs to be passed to read_csv."""
+    #     dtypes = dict()
+    #     for name, field in self._metadata['fields'].items():
+    #         field_type = field['type']
+    #         if field_type == 'categorical':
+    #             dtypes[name] = str
+    #         elif field_type == 'id' and field.get('subtype', 'integer') == 'string':
+    #             dtypes[name] = str
+
+    #     return dtypes
+
+    # def _parse_dtypes(self, data):
+    #     """Convert the data columns to the right dtype after loading the CSV."""
+    #     for name, field in self._metadata['fields'].items():
+    #         field_type = field['type']
+    #         if field_type == 'datetime':
+    #             datetime_format = field.get('format')
+    #             data[name] = pd.to_datetime(data[name], format=datetime_format, exact=False)
+    #         elif field_type == 'numerical' and field.get('subtype') == 'integer':
+    #             data[name] = data[name].dropna().astype(int)
+    #         elif field_type == 'id' and field.get('subtype', 'integer') == 'integer':
+    #             data[name] = data[name].dropna().astype(int)
+
+    #     return data
+
+    # def load(self):
+    #     """Load table data.
+
+    #     First load the CSV with the right dtypes and then parse the columns
+    #     to the final dtypes.
+
+    #     Returns:
+    #         pandas.DataFrame:
+    #             DataFrame with the contents of the table.
+    #     """
+    #     relative_path = os.path.join(self.root_path, self.path)
+    #     dtypes = self._read_csv_dtypes()
+
+    #     data = pd.read_csv(relative_path, dtype=dtypes)
+    #     data = self._parse_dtypes(data)
+
+    #     return data
+
+    @staticmethod
+    def _get_key_subtype(field_meta):
+        """Get the appropriate key subtype."""
+        field_type = field_meta['type']
+
+        if field_type == 'categorical':
+            field_subtype = 'string'
+
+        elif field_type in ('numerical', 'id'):
+            field_subtype = field_meta['subtype']
+            if field_subtype not in ('integer', 'string'):
+                raise ValueError(
+                    'Invalid field "subtype" for key field: "{}"'.format(field_subtype)
+                )
+
+        else:
+            raise ValueError(
+                'Invalid field "type" for key field: "{}"'.format(field_type)
+            )
+
+        return field_subtype
+
+    def _check_field(self, field, exists=False):
+        """Validate the existance of the table and existance (or not) of field."""
+        table_fields = self.get_fields(table)
+        if exists and (field not in table_fields):
+            raise ValueError('Field "{}" does not exist in table "{}"'.format(field, table))
+
+        if not exists and (field in table_fields):
+            raise ValueError('Field "{}" already exists in table "{}"'.format(field, table))
+
+    # ###################### #
+    # Metadata Serialization #
+    # ###################### #
+
+    def to_dict(self):
+        """Get a dict representation of this metadata.
+
+        Returns:
+            dict:
+                dict representation of this metadata.
+        """
+        return copy.deepcopy(self._metadata)
+
+    def to_json(self, path):
+        """Dump this metadata into a JSON file.
+
+        Args:
+            path (str):
+                Path of the JSON file where this metadata will be stored.
+        """
+        with open(path, 'w') as out_file:
+            json.dump(self._metadata, out_file, indent=4)
diff --git a/sdv/metadata/visualization.py b/sdv/metadata/visualization.py
new file mode 100644
index 000000000..1eb1dca6d
--- /dev/null
+++ b/sdv/metadata/visualization.py
@@ -0,0 +1,120 @@
+import graphviz
+
+
+def _get_graphviz_extension(path):
+    if path:
+        path_splitted = path.split('.')
+        if len(path_splitted) == 1:
+            raise ValueError('Path without graphviz extansion.')
+
+        graphviz_extension = path_splitted[-1]
+
+        if graphviz_extension not in graphviz.backend.FORMATS:
+            raise ValueError(
+                '"{}" not a valid graphviz extension format.'.format(graphviz_extension)
+            )
+
+        return '.'.join(path_splitted[:-1]), graphviz_extension
+
+    return None, None
+
+def _add_nodes(metadata, digraph):
+    """Add nodes into a `graphviz.Digraph`.
+
+    Each node represent a metadata table.
+
+    Args:
+        metadata (Metadata):
+            Metadata object to plot.
+        digraph (graphviz.Digraph):
+            graphviz.Digraph being built
+    """
+    for table in metadata.get_tables():
+        # Append table fields
+        fields = []
+
+        for name, value in metadata.get_fields(table).items():
+            if value.get('subtype') is not None:
+                fields.append('{} : {} - {}'.format(name, value['type'], value['subtype']))
+
+            else:
+                fields.append('{} : {}'.format(name, value['type']))
+
+        fields = r'\l'.join(fields)
+
+        # Append table extra information
+        extras = []
+
+        primary_key = metadata.get_primary_key(table)
+        if primary_key is not None:
+            extras.append('Primary key: {}'.format(primary_key))
+
+        parents = metadata.get_parents(table)
+        for parent in parents:
+            foreign_key = metadata.get_foreign_key(parent, table)
+            extras.append('Foreign key ({}): {}'.format(parent, foreign_key))
+
+        path = metadata.get_table_meta(table).get('path')
+        if path is not None:
+            extras.append('Data path: {}'.format(path))
+
+        extras = r'\l'.join(extras)
+
+        # Add table node
+        title = r'{%s|%s\l|%s\l}' % (table, fields, extras)
+        digraph.node(table, label=title)
+
+
+def _add_edges(metadata, digraph):
+    """Add edges into a `graphviz.Digraph`.
+
+    Each edge represents a relationship between two metadata tables.
+
+    Args:
+        digraph (graphviz.Digraph)
+    """
+    for table in metadata.get_tables():
+        for parent in list(metadata.get_parents(table)):
+            digraph.edge(
+                parent,
+                table,
+                label='   {}.{} -> {}.{}'.format(
+                    table, metadata.get_foreign_key(parent, table),
+                    parent, metadata.get_primary_key(parent)
+                ),
+                arrowhead='oinv'
+            )
+
+
+def visualize(metadata, path=None):
+    """Plot metadata usign graphviz.
+
+    Try to generate a plot using graphviz.
+    If a ``path`` is provided save the output into a file.
+
+    Args:
+        metadata (Metadata):
+            Metadata object to plot.
+        path (str):
+            Output file path to save the plot, it requires a graphviz
+            supported extension. If ``None`` do not save the plot.
+            Defaults to ``None``.
+    """
+    filename, graphviz_extension = _get_graphviz_extension(path)
+    digraph = graphviz.Digraph(
+        'Metadata',
+        format=graphviz_extension,
+        node_attr={
+            "shape": "Mrecord",
+            "fillcolor": "lightgoldenrod1",
+            "style": "filled"
+        },
+    )
+
+    _add_nodes(metadata, digraph)
+    _add_edges(metadata, digraph)
+
+    if filename:
+        digraph.render(filename=filename, cleanup=True, format=graphviz_extension)
+    else:
+        return digraph
diff --git a/sdv/tabular/__init__.py b/sdv/tabular/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/sdv/tabular/base.py b/sdv/tabular/base.py
new file mode 100644
index 000000000..940282fd4
--- /dev/null
+++ b/sdv/tabular/base.py
@@ -0,0 +1,180 @@
+from sdv.metadata import Table
+
+
+ANONYMIZATION_MAPS = {}
+
+
+class BaseTabularModel():
+    """Base class for all the tabular models.
+
+    The ``BaseTabularModel`` class defines the common API that all the
+    TabularModels need to implement, as well as common functionality.
+    """
+
+    _metadata = None
+
+    def __init__(self, field_names=None, primary_key=None, field_types=None,
+                 anonymize_fields=None, constraints=None, table_metadata=None,
+                 model_kwargs=None, *args, **kwargs):
+        """Initialize a Tabular Model.
+
+        Args:
+            field_names (list[str]):
+                List of names of the fields that need to be modeled
+                and included in the generated output data. Any additional
+                fields found in the data will be ignored and will not be
+                included in the generated output, except if they have
+                been added as primary keys or fields to anonymize.
+                If ``None``, all the fields found in the data are used.
+            primary_key (str, list[str] or dict[str, dict]):
+                Specification about which field or fields are the
+                primary key of the table and information about how
+                to generate them.
+            field_types (dict[str, dict]):
+                Dictinary specifying the data types and subtypes
+                of the fields that will be modeled.
+            anonymize_fields (dict[str, str]):
+                Dict specifying which fields to anonymize and what faker
+                category they belong to.
+            constraints (list[dict]):
+                List of dicts specifying field and inter-field constraints.
+                TODO: Format TBD
+            table_metadata (dict or metadata.Table):
+                Table metadata instance or dict representation.
+                If given alongside any other metadata-related arguments, an
+                exception will be raised.
+                If not given at all, it will be built using the other
+                arguments or learned from the data.
+            *args, **kwargs:
+                Subclasses will add any arguments or keyword arguments needed.
+        """
+        if table_metadata is not None:
+            if isinstance(table_metadata, dict):
+                table_metadata = Table(table_metadata)
+
+            for arg in (field_names, primary_key, field_types, anonymize, constraints):
+                if arg:
+                    raise ValueError(
+                        'If table_metadata is given {} must be None'.format(arg.__name__))
+
+            self._metadata = table_metadata
+
+        else:
+            self._field_names = field_names
+            self._primary_key = primary_key
+            self._field_types = field_types
+            self._anonymize_fields = anonymize_fields
+            self._constraints = constraints
+
+        self._model_kwargs = model_kwargs
+
+    def _fit_metadata(self, data):
+        metadata = Table(
+            field_names=self._field_names,
+            primary_key=self._primary_key,
+            field_types=self._field_types,
+            anonymize_fields=self._anonymize_fields,
+            constraints=self._constraints,
+        )
+        metadata.fit(data)
+
+        self._metadata = metadata
+
+    def fit(self, data):
+        """Fit this model to the data.
+
+        If table metadata has not been given, learn it from the data.
+
+        Args:
+            data (pandas.DataFrame or str):
+                Data to fit the model to. It can be passed as a
+                ``pandas.DataFrame`` or as an ``str``.
+                If an ``str`` is passed, it is assumed to be
+                the path to a CSV file which can be loaded using
+                ``pandas.read_csv``.
+        """
+        if self._metadata is None:
+            self._fit_metadata(data)
+
+        transformed = self._metadata.transform(data)
+        self._fit(transformed)
+
+    def get_metadata(self):
+        """Get metadata about the table.
+
+        This will return an ``sdv.metadata.Table`` object containing
+        the information about the data that this model has learned.
+
+        This Table metadata will contain some common information,
+        such as field names and data types, as well as additional
+        information that each Sub-class might add, such as the
+        observed data field distributions and their parameters.
+
+        Returns:
+            sdv.metadata.Table:
+                Table metadata.
+        """
+        return self._metadata
+
+    def sample(self, size=None, values=None):
+        """Sample rows from this table.
+
+        Args:
+            size (int):
+                Number of rows to sample. If not given the model
+                will generate as many rows as there were in the
+                data passed to the ``fit`` method.
+            values (dict):    <- FUTURE
+                Fixed values to use for knowledge-based sampling.
+                In case the model does not support knowledge-based
+                sampling, a discard+resample strategy will be used.
+
+        Returns:
+            pandas.DataFrame:
+                Sampled data.
+        """
+        sampled = self._sample(size)
+        return self._metadata.reverse_transform(sampled)
+
+    def get_parameters(self):
+        """Get the parameters learned from the data.
+
+        The result is a flat dict (single level) which contains
+        all the necessary parameters to be able to reproduce
+        this model.
+
+        Subclasses which are not parametric, such as DeepLearning
+        based models, raise a NonParametricError indicating that
+        this method is not supported for their implementation.
+
+        Returns:
+            parameters (dict):
+                flat dict (single level) which contains all the
+                necessary parameters to be able to reproduce
+                this model.
+
+        Raises:
+            NonParametricError:
+                If the model is not parametric or cannot be described
+                using a simple dictionary.
+        """
+        raise NotImplementedError()
+
+    @classmethod
+    def from_parameters(cls):
+        """Regenerate a previously learned model from its parameters.
+
+        Subclasses which are not parametric, such as DeepLearning
+        based models, raise a NonParametricError indicating that
+        this method is not supported for their implementation.
+
+        Returns:
+            BaseTabularModel:
+                New instance with its parameters set.
+
+        Raises:
+            NonParametricError:
+                If the model is not parametric or cannot be described
+                using a simple dictionary.
+        """
+        raise NotImplementedError()
diff --git a/sdv/tabular/copulas.py b/sdv/tabular/copulas.py
new file mode 100644
index 000000000..389fdc6ff
--- /dev/null
+++ b/sdv/tabular/copulas.py
@@ -0,0 +1,186 @@
+import numpy as np
+import copulas
+
+from sdv.tabular.base import BaseTabularModel
+from sdv.tabular.utils import (
+    check_matrix_symmetric_positive_definite, flatten_dict, make_positive_definite,
+    square_matrix, unflatten_dict)
+
+
+class GaussianCopula(BaseTabularModel):
+    """Model wrapping ``copulas.multivariate.GaussianMultivariate`` copula.
+
+    Args:
+        distribution (copulas.univariate.Univariate or str):
+            Copulas univariate distribution to use.
+    """
+
+    DISTRIBUTION = copulas.univariate.GaussianUnivariate
+    _distribution = None
+    _model = None
+
+    HYPERPARAMETERS = {
+        'distribution': {
+            'type': 'str or copulas.univariate.Univariate',
+            'default': 'copulas.univariate.Univariate',
+            'choices': [
+                'copulas.univariate.Univariate',
+                'copulas.univariate.GaussianUnivariate',
+                'copulas.univariate.GammaUnivariate',
+                'copulas.univariate.BetaUnivariate',
+                'copulas.univariate.StudentTUnivariate',
+                'copulas.univariate.GaussianKDE',
+                'copulas.univariate.TruncatedGaussian',
+            ]
+        }
+    }
+
+    def __init__(self, distribution=None, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._distribution = distribution or self.DISTRIBUTION
+
+    def _update_metadata(self):
+        parameters = self._model.to_dict()
+        univariates = parameters['univariates']
+        columns = parameters['columns']
+
+        fields = self._metadata.get_fields()
+        for field_name, univariate in zip(columns, univariates):
+            field_meta = fields[field_name]
+            field_meta['distribution'] = univariate['type']
+
+    def _fit(self, data):
+        """Fit the model to the table.
+
+        Args:
+            table_data (pandas.DataFrame):
+                Data to be fitted.
+        """
+        self._model = copulas.multivariate.GaussianMultivariate(distribution=self._distribution)
+        self._model.fit(data)
+        # self._update_metadata()
+
+    def _sample(self, size):
+        """Sample ``size`` rows from the model.
+
+        Args:
+            size (int):
+                Amount of rows to sample.
+
+        Returns:
+            pandas.DataFrame:
+                Sampled data.
+        """
+        return self._model.sample(size)
+
+    def get_parameters(self):
+        """Get copula model parameters.
+
+        Compute model ``covariance`` and ``distribution.std``
+        before it returns the flatten dict.
+
+        Returns:
+            dict:
+                Copula flatten parameters.
+        """
+        values = list()
+        triangle = np.tril(self._model.covariance)
+
+        for index, row in enumerate(triangle.tolist()):
+            values.append(row[:index + 1])
+
+        self._model.covariance = np.array(values)
+        params = self._model.to_dict()
+        univariates = dict()
+        for name, univariate in zip(params.pop('columns'), params['univariates']):
+            univariates[name] = univariate
+            if 'scale' in univariate:
+                scale = univariate['scale']
+                if scale == 0:
+                    scale = copulas.EPSILON
+
+                univariate['scale'] = np.log(scale)
+
+        params['univariates'] = univariates
+
+        return flatten_dict(params)
+
+    def _prepare_sampled_covariance(self, covariance):
+        """Prepare a covariance matrix.
+
+        Args:
+            covariance (list):
+                covariance after unflattening model parameters.
+
+        Result:
+            list[list]:
+                symmetric Positive semi-definite matrix.
+        """
+        covariance = np.array(square_matrix(covariance))
+        covariance = (covariance + covariance.T - (np.identity(covariance.shape[0]) * covariance))
+
+        if not check_matrix_symmetric_positive_definite(covariance):
+            covariance = make_positive_definite(covariance)
+
+        return covariance.tolist()
+
+    def _unflatten_gaussian_copula(self, model_parameters):
+        """Prepare unflattened model params to recreate Gaussian Multivariate instance.
+
+        The preparations consist basically in:
+
+            - Transform sampled negative standard deviations from distributions into positive
+              numbers
+
+            - Ensure the covariance matrix is a valid symmetric positive-semidefinite matrix.
+
+            - Add string parameters kept inside the class (as they can't be modelled),
+              like ``distribution_type``.
+
+        Args:
+            model_parameters (dict):
+                Sampled and reestructured model parameters.
+
+        Returns:
+            dict:
+                Model parameters ready to recreate the model.
+        """
+
+        univariate_kwargs = {
+            'type': model_parameters['distribution']
+        }
+
+        columns = list()
+        univariates = list()
+        for column, univariate in model_parameters['univariates'].items():
+            columns.append(column)
+            univariate.update(univariate_kwargs)
+            if 'scale' in univariate:
+                univariate['scale'] = np.exp(univariate['scale'])
+
+            univariates.append(univariate)
+
+        model_parameters['univariates'] = univariates
+        model_parameters['columns'] = columns
+
+        covariance = model_parameters.get('covariance')
+        model_parameters['covariance'] = self._prepare_sampled_covariance(covariance)
+
+        return model_parameters
+
+    def set_parameters(self, parameters):
+        """Set copula model parameters.
+
+        Add additional keys after unflatte the parameters
+        in order to set expected parameters for the copula.
+
+        Args:
+            dict:
+                Copula flatten parameters.
+        """
+        parameters = unflatten_dict(parameters)
+        parameters.setdefault('distribution', self.distribution)
+
+        parameters = self._unflatten_gaussian_copula(parameters)
+
+        self._model = copulas.multivariate.GaussianMultivariate.from_dict(parameters)
diff --git a/sdv/tabular/utils.py b/sdv/tabular/utils.py
new file mode 100644
index 000000000..241a20048
--- /dev/null
+++ b/sdv/tabular/utils.py
@@ -0,0 +1,219 @@
+import numpy as np
+
+IGNORED_DICT_KEYS = ['fitted', 'distribution', 'type']
+
+
+def flatten_array(nested, prefix=''):
+    """Flatten an array as a dict.
+
+    Args:
+        nested (list, numpy.array):
+            Iterable to flatten.
+        prefix (str):
+            Name to append to the array indices. Defaults to ``''``.
+
+    Returns:
+        dict:
+            Flattened array.
+    """
+    result = dict()
+    for index in range(len(nested)):
+        prefix_key = '__'.join([prefix, str(index)]) if len(prefix) else str(index)
+
+        value = nested[index]
+        if isinstance(value, (list, np.ndarray)):
+            result.update(flatten_array(value, prefix=prefix_key))
+
+        elif isinstance(value, dict):
+            result.update(flatten_dict(value, prefix=prefix_key))
+
+        else:
+            result[prefix_key] = value
+
+    return result
+
+
+def flatten_dict(nested, prefix=''):
+    """Flatten a dictionary.
+
+    This method returns a flatten version of a dictionary, concatenating key names with
+    double underscores.
+
+    Args:
+        nested (dict):
+            Original dictionary to flatten.
+        prefix (str):
+            Prefix to append to key name. Defaults to ``''``.
+
+    Returns:
+        dict:
+            Flattened dictionary.
+        """
+    result = dict()
+
+    for key, value in nested.items():
+        prefix_key = '__'.join([prefix, str(key)]) if len(prefix) else key
+
+        if key in IGNORED_DICT_KEYS and not isinstance(value, (dict, list)):
+            continue
+
+        elif isinstance(value, dict):
+            result.update(flatten_dict(value, prefix_key))
+
+        elif isinstance(value, (np.ndarray, list)):
+            result.update(flatten_array(value, prefix_key))
+
+        else:
+            result[prefix_key] = value
+
+    return result
+
+
+def _key_order(key_value):
+    parts = list()
+    for part in key_value[0].split('__'):
+        if part.isdigit():
+            part = int(part)
+
+        parts.append(part)
+
+    return parts
+
+
+def unflatten_dict(flat):
+    """Transform a flattened dict into its original form.
+
+    Args:
+        flat (dict):
+            Flattened dict.
+
+    Returns:
+        dict:
+            Nested dict (if corresponds)
+    """
+    unflattened = dict()
+
+    for key, value in sorted(flat.items(), key=_key_order):
+        if '__' in key:
+            key, subkey = key.split('__', 1)
+            subkey, name = subkey.rsplit('__', 1)
+
+            if name.isdigit():
+                column_index = int(name)
+                row_index = int(subkey)
+
+                array = unflattened.setdefault(key, list())
+
+                if len(array) == row_index:
+                    row = list()
+                    array.append(row)
+                elif len(array) == row_index + 1:
+                    row = array[row_index]
+                else:
+                    # This should never happen
+                    raise ValueError('There was an error unflattening the extension.')
+
+                if len(row) == column_index:
+                    row.append(value)
+                else:
+                    # This should never happen
+                    raise ValueError('There was an error unflattening the extension.')
+
+            else:
+                subdict = unflattened.setdefault(key, dict())
+                if subkey.isdigit():
+                    subkey = int(subkey)
+
+                inner = subdict.setdefault(subkey, dict())
+                inner[name] = value
+
+        else:
+            unflattened[key] = value
+
+    return unflattened
+
+
+def impute(data):
+    for column in data:
+        column_data = data[column]
+        if column_data.dtype in (np.int, np.float):
+            fill_value = column_data.mean()
+        else:
+            fill_value = column_data.mode()[0]
+
+        data[column] = data[column].fillna(fill_value)
+
+    return data
+
+
+def square_matrix(triangular_matrix):
+    """Fill with zeros a triangular matrix to reshape it to a square one.
+
+    Args:
+        triangular_matrix (list [list [float]]):
+            Array of arrays of
+
+    Returns:
+        list:
+            Square matrix.
+    """
+    length = len(triangular_matrix)
+    zero = [0.0]
+
+    for item in triangular_matrix:
+        item.extend(zero * (length - len(item)))
+
+    return triangular_matrix
+
+
+def check_matrix_symmetric_positive_definite(matrix):
+    """Check if a matrix is symmetric positive-definite.
+
+    Args:
+        matrix (list or numpy.ndarray):
+            Matrix to evaluate.
+
+    Returns:
+        bool
+    """
+    try:
+        if len(matrix.shape) != 2 or matrix.shape[0] != matrix.shape[1]:
+            # Not 2-dimensional or square, so not simmetric.
+            return False
+
+        np.linalg.cholesky(matrix)
+        return True
+
+    except np.linalg.LinAlgError:
+        return False
+
+
+def make_positive_definite(matrix):
+    """Find the nearest positive-definite matrix to input.
+
+    Args:
+        matrix (numpy.ndarray):
+            Matrix to transform
+
+    Returns:
+        numpy.ndarray:
+            Closest symetric positive-definite matrix.
+    """
+    symetric_matrix = (matrix + matrix.T) / 2
+    _, s, V = np.linalg.svd(symetric_matrix)
+    symmetric_polar = np.dot(V.T, np.dot(np.diag(s), V))
+    A2 = (symetric_matrix + symmetric_polar) / 2
+    A3 = (A2 + A2.T) / 2
+
+    if check_matrix_symmetric_positive_definite(A3):
+        return A3
+
+    spacing = np.spacing(np.linalg.norm(matrix))
+    identity = np.eye(matrix.shape[0])
+    iterations = 1
+    while not check_matrix_symmetric_positive_definite(A3):
+        min_eigenvals = np.min(np.real(np.linalg.eigvals(A3)))
+        A3 += identity * (-min_eigenvals * iterations**2 + spacing)
+        iterations += 1
+
+    return A3

From 25284d4e092095c3d512d47eae16391e84115536 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 19:46:35 +0200
Subject: [PATCH 20/33] Tabular models. WIP. Finish Table and Copulas and add
 CTGAN

---
 sdv/metadata/__init__.py                  |   6 -
 sdv/metadata/table.py                     | 345 ++++++++--------------
 sdv/metadata/visualization.py             |   1 +
 sdv/tabular/base.py                       |  32 +-
 sdv/tabular/copulas.py                    |  38 ++-
 sdv/tabular/ctgan.py                      | 142 +++++++++
 setup.py                                  |  17 +-
 tests/integration/tabular/test_copulas.py |  48 +++
 tests/integration/tabular/test_ctgan.py   |  29 ++
 9 files changed, 408 insertions(+), 250 deletions(-)
 create mode 100644 sdv/tabular/ctgan.py
 create mode 100644 tests/integration/tabular/test_copulas.py
 create mode 100644 tests/integration/tabular/test_ctgan.py

diff --git a/sdv/metadata/__init__.py b/sdv/metadata/__init__.py
index fc91ad549..d9b3ad368 100644
--- a/sdv/metadata/__init__.py
+++ b/sdv/metadata/__init__.py
@@ -4,7 +4,6 @@
 import os
 from collections import defaultdict
 
-import graphviz
 import numpy as np
 import pandas as pd
 from rdt import HyperTransformer, transformers
@@ -62,10 +61,6 @@ def _load_csv(root_path, table_meta):
     return data
 
 
-class MetadataError(Exception):
-    pass
-
-
 class Metadata:
     """Dataset Metadata.
 
@@ -1013,4 +1008,3 @@ def visualize(self, path=None):
                 Defaults to ``None``.
         """
         return visualization.visualize(self, path)
-
diff --git a/sdv/metadata/table.py b/sdv/metadata/table.py
index 85b129d78..893549272 100644
--- a/sdv/metadata/table.py
+++ b/sdv/metadata/table.py
@@ -1,14 +1,13 @@
 import copy
 import json
 import logging
-import os
 
 import numpy as np
 import pandas as pd
+import rdt
 from faker import Faker
-from rdt import HyperTransformer, transformers
 
-from sdv.metadata.error import MetadataError
+from sdv.metadata.errors import MetadataError
 
 LOGGER = logging.getLogger(__name__)
 
@@ -19,18 +18,11 @@ class Table:
     The Metadata class provides a unified layer of abstraction over the metadata
     of a single Table, which includes both the necessary details to load the data
     from the filesystem and to know how to parse and transform it to numerical data.
-
-    Args:
-        metadata (str or dict):
-            Path to a ``json`` file that contains the metadata or a ``dict`` representation
-            of ``metadata`` following the same structure.
-        root_path (str):
-            The path to which the dataset is located. Defaults to ``None``.
     """
 
     _metadata = None
     _hyper_transformer = None
-    _root_path = None
+    _anonymization_mappings = None
 
     _FIELD_TEMPLATES = {
         'i': {
@@ -63,11 +55,47 @@ class Table:
         ('id', 'string'): 'str'
     }
 
-    def __init__(self, metadata=None, root_path=None, field_names=None, primary_key=None,
-                 field_types=None, anonymize_fields=None, constraints=None,):
+    def _get_faker(self, category):
+        """Return the faker object to anonymize data.
+
+        Args:
+            category (str or tuple):
+                Fake category to use. If a tuple is passed, the first element is
+                the category and the rest are additional arguments for the Faker.
+
+        Returns:
+            function:
+                Faker function to generate new fake data instances.
+
+        Raises:
+            ValueError:
+                A ``ValueError`` is raised if the faker category we want don't exist.
+        """
+        if isinstance(category, (tuple, list)):
+            category, *args = category
+        else:
+            args = tuple()
+
+        try:
+            faker_method = getattr(Faker(), category)
+
+            if not args:
+                return faker_method
+
+            def faker():
+                return faker_method(*args)
+
+            return faker
+
+        except AttributeError:
+            raise ValueError('Category "{}" couldn\'t be found on faker'.format(category))
+
+    def __init__(self, metadata=None, field_names=None, primary_key=None,
+                 field_types=None, anonymize_fields=None, constraints=None,
+                 transformer_templates=None):
+        # TODO: Validate that the given metadata is a valid dict
         self._metadata = metadata or dict()
         self._hyper_transformer = None
-        self._root_path = None
 
         self._field_names = field_names
         self._primary_key = primary_key
@@ -75,6 +103,13 @@ def __init__(self, metadata=None, root_path=None, field_names=None, primary_key=
         self._anonymize_fields = anonymize_fields
         self._constraints = constraints
 
+        self._transformer_templates = transformer_templates or {}
+
+        self._fakers = {
+            name: self._get_faker(category)
+            for name, category in (anonymize_fields or {}).items()
+        }
+
     def _get_field_dtype(self, field_name, field_metadata):
         field_type = field_metadata['type']
         field_subtype = field_metadata.get('subtype')
@@ -87,6 +122,15 @@ def _get_field_dtype(self, field_name, field_metadata):
 
         return dtype
 
+    def get_fields(self):
+        """Get fields metadata.
+
+        Returns:
+            dict:
+                Dictionary of fields metadata for this table.
+        """
+        return copy.deepcopy(self._metadata['fields'])
+
     def get_dtypes(self, ids=False):
         """Get a ``dict`` with the ``dtypes`` for each field of the table.
 
@@ -111,45 +155,6 @@ def get_dtypes(self, ids=False):
 
         return dtypes
 
-    def _get_faker(self):
-        """Return the faker object to anonymize data.
-
-        Returns:
-            function:
-                Faker function to generate new data instances with ``self.anonymize`` arguments.
-
-        Raises:
-            ValueError:
-                A ``ValueError`` is raised if the faker category we want don't exist.
-        """
-        if isinstance(self._anonymize, (tuple, list)):
-            category, *args = self._anonymize
-        else:
-            category = self._anonymize
-            args = tuple()
-
-        try:
-            faker_method = getattr(Faker(), category)
-
-            def faker():
-                return faker_method(*args)
-
-            return faker
-        except AttributeError:
-            raise ValueError('Category "{}" couldn\'t be found on faker'.format(self.anonymize))
-
-    def _anonymize(self, data):
-        """Anonymize data and save the anonymization mapping in-memory."""
-        # TODO: Do this by column
-        faker = self._get_faker()
-        uniques = data.unique()
-        fake_data = [faker() for x in range(len(uniques))]
-
-        mapping = dict(zip(uniques, fake_data))
-        MAPS[id(self)] = mapping
-
-        return data.map(mapping)
-
     def _build_fields_metadata(self, data):
         """Build all the fields metadata.
 
@@ -169,7 +174,7 @@ def _build_fields_metadata(self, data):
 
         fields_metadata = dict()
         for field_name in field_names:
-            if not field_name in data:
+            if field_name not in data:
                 raise ValueError('Field {} not found in given data'.format(field_name))
 
             field_meta = self._field_types.get(field_name)
@@ -188,59 +193,39 @@ def _build_fields_metadata(self, data):
 
         return fields_metadata
 
-    def _get_pii_fields(self):
-        """Get the ``pii_category`` for each field that contains PII.
-
-        Returns:
-            dict:
-                pii field names and categories.
-        """
-        pii_fields = dict()
-        for name, field in self._metadata['fields'].items():
-            if field['type'] == 'categorical' and field.get('pii', False):
-                pii_fields[name] = field['pii_category']
-
-        return pii_fields
-
     def _get_transformers(self, dtypes):
         """Create the transformer instances needed to process the given dtypes.
 
-        Temporary drop-in replacement of ``HyperTransformer._analyze`` method,
-        before RDT catches up.
-
         Args:
             dtypes (dict):
                 mapping of field names and dtypes.
-            pii_fields (dict):
-                mapping of pii field names and categories.
 
         Returns:
             dict:
                 mapping of field names and transformer instances.
         """
-        transformers_dict = dict()
-        pii_fields = self._get_pii_fields()
+        transformer_templates = {
+            'i': rdt.transformers.NumericalTransformer(dtype=int),
+            'f': rdt.transformers.NumericalTransformer(dtype=float),
+            'O': rdt.transformers.CategoricalTransformer,
+            'b': rdt.transformers.BooleanTransformer,
+            'M': rdt.transformers.DatetimeTransformer,
+        }
+        transformer_templates.update(self._transformer_templates)
+
+        transformers = dict()
         for name, dtype in dtypes.items():
-            dtype = np.dtype(dtype)
-            if dtype.kind == 'i':
-                transformer = transformers.NumericalTransformer(dtype=int)
-            elif dtype.kind == 'f':
-                transformer = transformers.NumericalTransformer(dtype=float)
-            elif dtype.kind == 'O':
-                anonymize = pii_fields.get(name)
-                transformer = transformers.CategoricalTransformer(anonymize=anonymize)
-            elif dtype.kind == 'b':
-                transformer = transformers.BooleanTransformer()
-            elif dtype.kind == 'M':
-                transformer = transformers.DatetimeTransformer()
+            transformer_template = transformer_templates[np.dtype(dtype).kind]
+            if isinstance(transformer_template, type):
+                transformer = transformer_template()
             else:
-                raise ValueError('Unsupported dtype: {}'.format(dtype))
+                transformer = copy.deepcopy(transformer_template)
 
             LOGGER.info('Loading transformer %s for field %s',
                         transformer.__class__.__name__, name)
-            transformers_dict[name] = transformer
+            transformers[name] = transformer
 
-        return transformers_dict
+        return transformers
 
     def _fit_hyper_transformer(self, data):
         """Create and return a new ``rdt.HyperTransformer`` instance.
@@ -253,9 +238,31 @@ def _fit_hyper_transformer(self, data):
         """
         dtypes = self.get_dtypes(ids=False)
         transformers_dict = self._get_transformers(dtypes)
-        self._hyper_transformer = HyperTransformer(transformers=transformers_dict)
+        self._hyper_transformer = rdt.HyperTransformer(transformers=transformers_dict)
         self._hyper_transformer.fit(data[list(dtypes.keys())])
 
+    @staticmethod
+    def _get_key_subtype(field_meta):
+        """Get the appropriate key subtype."""
+        field_type = field_meta['type']
+
+        if field_type == 'categorical':
+            field_subtype = 'string'
+
+        elif field_type in ('numerical', 'id'):
+            field_subtype = field_meta['subtype']
+            if field_subtype not in ('integer', 'string'):
+                raise ValueError(
+                    'Invalid field "subtype" for key field: "{}"'.format(field_subtype)
+                )
+
+        else:
+            raise ValueError(
+                'Invalid field "type" for key field: "{}"'.format(field_type)
+            )
+
+        return field_subtype
+
     def set_primary_key(self, field_name):
         """Set the primary key of this table.
 
@@ -271,7 +278,7 @@ def set_primary_key(self, field_name):
                 invalid type or subtype.
         """
         if field_name is not None:
-            if field_name not in self.get_fields():
+            if field_name not in self._metadata['fields']:
                 raise ValueError('Field "{}" does not exist in this table'.format(field_name))
 
             field_metadata = self._metadata['fields'][field_name]
@@ -284,6 +291,23 @@ def set_primary_key(self, field_name):
 
         self._primary_key = field_name
 
+    def _make_anonymization_mappings(self, data):
+        mappings = {}
+        for name, faker in self._fakers.items():
+            uniques = data[name].unique()
+            fake_values = [faker() for x in range(len(uniques))]
+            mappings[name] = dict(zip(uniques, fake_values))
+
+        self._anonymization_mappings = mappings
+
+    def _anonymize(self, data):
+        if self._anonymization_mappings:
+            data = data.copy()
+            for name, mapping in self._anonymization_mappings.items():
+                data[name] = data[name].map(mapping)
+
+        return data
+
     def fit(self, data):
         """Fit this metadata to the given data.
 
@@ -295,19 +319,13 @@ def fit(self, data):
         self._metadata['fields'] = self._build_fields_metadata(data)
         self.set_primary_key(self._primary_key)
 
-        # TODO: Treat/Learn constraints
+        if self._fakers:
+            self._make_anonymization_mappings(data)
+            data = self._anonymize(data)
 
+        # TODO: Treat/Learn constraints
         self._fit_hyper_transformer(data)
 
-    def get_fields(self):
-        """Get fields metadata.
-
-        Returns:
-            dict:
-                Mapping of field names and their metadata dicts.
-        """
-        return self._metadata['fields']
-
     def transform(self, data):
         """Transform the given data.
 
@@ -319,13 +337,8 @@ def transform(self, data):
             pandas.DataFrame:
                 Transformed data.
         """
-
-        # TODO: Do this by column
-        # if self.anonymize:
-        #     data = data.map(MAPS[id(self)])
-
-        fields = list(self._hyper_transformer.transformers.keys())
-        return self._hyper_transformer.transform(data[fields])
+        data = self._anonymize(data[self._field_names])
+        return self._hyper_transformer.transform(data)
 
     def reverse_transform(self, data):
         """Reverse the transformed data to the original format.
@@ -351,120 +364,6 @@ def reverse_transform(self, data):
 
         return reversed_data[self._field_names]
 
-    def get_children(self):
-        """Get tables for which this table is parent.
-
-        Returns:
-            set:
-                Set of children for this table.
-        """
-        return self._children
-
-    def get_parents(self):
-        """Get tables for with this table is child.
-
-        Returns:
-            set:
-                Set of parents for this table.
-        """
-        return self._parents
-
-    def get_field(self, field_name):
-        """Get the metadata dict for a field.
-
-        Args:
-            field_name (str):
-                Name of the field to get data for.
-
-        Returns:
-            dict:
-                field metadata
-
-        Raises:
-            ValueError:
-                If the table or the field do not exist in this metadata.
-        """
-        field_meta = self._metadata['fields'].get(field_name)
-        if field_meta is None:
-            raise ValueError('Invalid field name "{}"'.format(field_name))
-
-        return copy.deepcopy(field_meta)
-
-    # def _read_csv_dtypes(self):
-    #     """Get the dtypes specification that needs to be passed to read_csv."""
-    #     dtypes = dict()
-    #     for name, field in self._metadata['fields'].items():
-    #         field_type = field['type']
-    #         if field_type == 'categorical':
-    #             dtypes[name] = str
-    #         elif field_type == 'id' and field.get('subtype', 'integer') == 'string':
-    #             dtypes[name] = str
-
-    #     return dtypes
-
-    # def _parse_dtypes(self, data):
-    #     """Convert the data columns to the right dtype after loading the CSV."""
-    #     for name, field in self._metadata['fields'].items():
-    #         field_type = field['type']
-    #         if field_type == 'datetime':
-    #             datetime_format = field.get('format')
-    #             data[name] = pd.to_datetime(data[name], format=datetime_format, exact=False)
-    #         elif field_type == 'numerical' and field.get('subtype') == 'integer':
-    #             data[name] = data[name].dropna().astype(int)
-    #         elif field_type == 'id' and field.get('subtype', 'integer') == 'integer':
-    #             data[name] = data[name].dropna().astype(int)
-
-    #     return data
-
-    # def load(self):
-    #     """Load table data.
-
-    #     First load the CSV with the right dtypes and then parse the columns
-    #     to the final dtypes.
-
-    #     Returns:
-    #         pandas.DataFrame:
-    #             DataFrame with the contents of the table.
-    #     """
-    #     relative_path = os.path.join(self.root_path, self.path)
-    #     dtypes = self._read_csv_dtypes()
-
-    #     data = pd.read_csv(relative_path, dtype=dtypes)
-    #     data = self._parse_dtypes(data)
-
-    #     return data
-
-    @staticmethod
-    def _get_key_subtype(field_meta):
-        """Get the appropriate key subtype."""
-        field_type = field_meta['type']
-
-        if field_type == 'categorical':
-            field_subtype = 'string'
-
-        elif field_type in ('numerical', 'id'):
-            field_subtype = field_meta['subtype']
-            if field_subtype not in ('integer', 'string'):
-                raise ValueError(
-                    'Invalid field "subtype" for key field: "{}"'.format(field_subtype)
-                )
-
-        else:
-            raise ValueError(
-                'Invalid field "type" for key field: "{}"'.format(field_type)
-            )
-
-        return field_subtype
-
-    def _check_field(self, field, exists=False):
-        """Validate the existance of the table and existance (or not) of field."""
-        table_fields = self.get_fields(table)
-        if exists and (field not in table_fields):
-            raise ValueError('Field "{}" does not exist in table "{}"'.format(field, table))
-
-        if not exists and (field in table_fields):
-            raise ValueError('Field "{}" already exists in table "{}"'.format(field, table))
-
     # ###################### #
     # Metadata Serialization #
     # ###################### #
diff --git a/sdv/metadata/visualization.py b/sdv/metadata/visualization.py
index 1eb1dca6d..b14fb160a 100644
--- a/sdv/metadata/visualization.py
+++ b/sdv/metadata/visualization.py
@@ -18,6 +18,7 @@ def _get_graphviz_extension(path):
 
     return None, None
 
+
 def _add_nodes(metadata, digraph):
     """Add nodes into a `graphviz.Digraph`.
 
diff --git a/sdv/tabular/base.py b/sdv/tabular/base.py
index 940282fd4..1bdf4b854 100644
--- a/sdv/tabular/base.py
+++ b/sdv/tabular/base.py
@@ -1,9 +1,6 @@
 from sdv.metadata import Table
 
 
-ANONYMIZATION_MAPS = {}
-
-
 class BaseTabularModel():
     """Base class for all the tabular models.
 
@@ -11,11 +8,13 @@ class BaseTabularModel():
     TabularModels need to implement, as well as common functionality.
     """
 
+    TRANSFORMER_TEMPLATES = None
+
     _metadata = None
 
     def __init__(self, field_names=None, primary_key=None, field_types=None,
                  anonymize_fields=None, constraints=None, table_metadata=None,
-                 model_kwargs=None, *args, **kwargs):
+                 *args, **kwargs):
         """Initialize a Tabular Model.
 
         Args:
@@ -32,7 +31,8 @@ def __init__(self, field_names=None, primary_key=None, field_types=None,
                 to generate them.
             field_types (dict[str, dict]):
                 Dictinary specifying the data types and subtypes
-                of the fields that will be modeled.
+                of the fields that will be modeled. Field types and subtypes
+                combinations must be compatible with the SDV Metadata Schema.
             anonymize_fields (dict[str, str]):
                 Dict specifying which fields to anonymize and what faker
                 category they belong to.
@@ -50,9 +50,9 @@ def __init__(self, field_names=None, primary_key=None, field_types=None,
         """
         if table_metadata is not None:
             if isinstance(table_metadata, dict):
-                table_metadata = Table(table_metadata)
+                table_metadata = Table(table_metadata,)
 
-            for arg in (field_names, primary_key, field_types, anonymize, constraints):
+            for arg in (field_names, primary_key, field_types, anonymize_fields, constraints):
                 if arg:
                     raise ValueError(
                         'If table_metadata is given {} must be None'.format(arg.__name__))
@@ -66,15 +66,24 @@ def __init__(self, field_names=None, primary_key=None, field_types=None,
             self._anonymize_fields = anonymize_fields
             self._constraints = constraints
 
-        self._model_kwargs = model_kwargs
-
     def _fit_metadata(self, data):
+        """Generate a new Table metadata and fit it to the data.
+
+        The information provided will be used to create the Table instance
+        and then the rest of information will be learned from the given
+        data.
+
+        Args:
+            data (pandas.DataFrame):
+                Data to learn from.
+        """
         metadata = Table(
             field_names=self._field_names,
             primary_key=self._primary_key,
             field_types=self._field_types,
             anonymize_fields=self._anonymize_fields,
             constraints=self._constraints,
+            transformer_templates=self.TRANSFORMER_TEMPLATES,
         )
         metadata.fit(data)
 
@@ -83,7 +92,7 @@ def _fit_metadata(self, data):
     def fit(self, data):
         """Fit this model to the data.
 
-        If table metadata has not been given, learn it from the data.
+        If the table metadata has not been given, learn it from the data.
 
         Args:
             data (pandas.DataFrame or str):
@@ -96,6 +105,8 @@ def fit(self, data):
         if self._metadata is None:
             self._fit_metadata(data)
 
+        self._size = len(data)
+
         transformed = self._metadata.transform(data)
         self._fit(transformed)
 
@@ -133,6 +144,7 @@ def sample(self, size=None, values=None):
             pandas.DataFrame:
                 Sampled data.
         """
+        size = size or self._size
         sampled = self._sample(size)
         return self._metadata.reverse_transform(sampled)
 
diff --git a/sdv/tabular/copulas.py b/sdv/tabular/copulas.py
index 389fdc6ff..024205cad 100644
--- a/sdv/tabular/copulas.py
+++ b/sdv/tabular/copulas.py
@@ -1,10 +1,11 @@
-import numpy as np
 import copulas
+import numpy as np
+import rdt
 
 from sdv.tabular.base import BaseTabularModel
 from sdv.tabular.utils import (
-    check_matrix_symmetric_positive_definite, flatten_dict, make_positive_definite,
-    square_matrix, unflatten_dict)
+    check_matrix_symmetric_positive_definite, flatten_dict, make_positive_definite, square_matrix,
+    unflatten_dict)
 
 
 class GaussianCopula(BaseTabularModel):
@@ -13,6 +14,10 @@ class GaussianCopula(BaseTabularModel):
     Args:
         distribution (copulas.univariate.Univariate or str):
             Copulas univariate distribution to use.
+        categorical_transformer (str):
+            Type of transformer to use for the categorical variables, to choose
+            from ``one_hot_encoding``, ``label_encoding``, ``categorical`` and
+            ``categorical_fuzzy``.
     """
 
     DISTRIBUTION = copulas.univariate.GaussianUnivariate
@@ -23,6 +28,7 @@ class GaussianCopula(BaseTabularModel):
         'distribution': {
             'type': 'str or copulas.univariate.Univariate',
             'default': 'copulas.univariate.Univariate',
+            'description': 'Univariate distribution to use to model each column',
             'choices': [
                 'copulas.univariate.Univariate',
                 'copulas.univariate.GaussianUnivariate',
@@ -32,12 +38,34 @@ class GaussianCopula(BaseTabularModel):
                 'copulas.univariate.GaussianKDE',
                 'copulas.univariate.TruncatedGaussian',
             ]
+        },
+        'categorical_transformer': {
+            'type': 'str',
+            'default': 'categoircal_fuzzy',
+            'description': 'Type of transformer to use for the categorical variables',
+            'choices': [
+                'categorical',
+                'categorical_fuzzy',
+                'one_hot_encoding',
+                'label_encoding'
+            ]
         }
     }
+    CATEGORICAL_TRANSFORMERS = {
+        'categorical': rdt.transformers.CategoricalTransformer(fuzzy=False),
+        'categorical_fuzzy': rdt.transformers.CategoricalTransformer(fuzzy=True),
+        'one_hot_encoding': rdt.transformers.OneHotEncodingTransformer,
+        'label_encoding': rdt.transformers.LabelEncodingTransformer,
+    }
+    TRANSFORMER_TEMPLATES = {
+        'O': rdt.transformers.OneHotEncodingTransformer
+    }
 
-    def __init__(self, distribution=None, *args, **kwargs):
+    def __init__(self, distribution=None, categorical_transformer='categorical',
+                 *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._distribution = distribution or self.DISTRIBUTION
+        self.TRANSFORMER_TEMPLATES['O'] = self.CATEGORICAL_TRANSFORMERS[categorical_transformer]
 
     def _update_metadata(self):
         parameters = self._model.to_dict()
@@ -56,9 +84,9 @@ def _fit(self, data):
             table_data (pandas.DataFrame):
                 Data to be fitted.
         """
+        params = self._metadata.get_model_params()
         self._model = copulas.multivariate.GaussianMultivariate(distribution=self._distribution)
         self._model.fit(data)
-        # self._update_metadata()
 
     def _sample(self, size):
         """Sample ``size`` rows from the model.
diff --git a/sdv/tabular/ctgan.py b/sdv/tabular/ctgan.py
new file mode 100644
index 000000000..eb1f87d33
--- /dev/null
+++ b/sdv/tabular/ctgan.py
@@ -0,0 +1,142 @@
+import rdt
+
+from sdv.tabular.base import BaseTabularModel
+
+
+class CTGAN(BaseTabularModel):
+    """Model wrapping ``CTGANSynthesizer`` copula.
+
+    Args:
+        TBD
+    """
+
+    _CTGAN_CLASS = None
+    _model = None
+
+    HYPERPARAMETERS = {
+        'TBD'
+    }
+    TRANSFORMER_TEMPLATES = {
+        'O': rdt.transformers.LabelEncodingTransformer
+    }
+
+    def __init__(self, field_names=None, primary_key=None, field_types=None,
+                 anonymize_fields=None, constraints=None, table_metadata=None,
+                 epochs=300, log_frequency=True, embedding_dim=128, gen_dim=(256, 256),
+                 dis_dim=(256, 256), l2scale=1e-6, batch_size=500):
+        """Initialize this CTGAN model.
+
+        Args:
+            field_names (list[str]):
+                List of names of the fields that need to be modeled
+                and included in the generated output data. Any additional
+                fields found in the data will be ignored and will not be
+                included in the generated output, except if they have
+                been added as primary keys or fields to anonymize.
+                If ``None``, all the fields found in the data are used.
+            primary_key (str, list[str] or dict[str, dict]):
+                Specification about which field or fields are the
+                primary key of the table and information about how
+                to generate them.
+            field_types (dict[str, dict]):
+                Dictinary specifying the data types and subtypes
+                of the fields that will be modeled. Field types and subtypes
+                combinations must be compatible with the SDV Metadata Schema.
+            anonymize_fields (dict[str, str or tuple]):
+                Dict specifying which fields to anonymize and what faker
+                category they belong to. If arguments for the faker need to be
+                passed to fine tune the value generation a tuple can be passed,
+                where the first element is the category and the rest are additional
+                positional arguments for the Faker.
+            constraints (list[dict]):
+                List of dicts specifying field and inter-field constraints.
+                TODO: Format TBD
+            table_metadata (dict or metadata.Table):
+                Table metadata instance or dict representation.
+                If given alongside any other metadata-related arguments, an
+                exception will be raised.
+                If not given at all, it will be built using the other
+                arguments or learned from the data.
+            epochs (int):
+                Number of training epochs. Defaults to 300.
+            log_frequency (boolean):
+                Whether to use log frequency of categorical levels in conditional
+                sampling. Defaults to ``True``.
+            embedding_dim (int):
+                Size of the random sample passed to the Generator. Defaults to 128.
+            gen_dim (tuple or list of ints):
+                Size of the output samples for each one of the Residuals. A Resiudal Layer
+                will be created for each one of the values provided. Defaults to (256, 256).
+            dis_dim (tuple or list of ints):
+                Size of the output samples for each one of the Discriminator Layers. A Linear
+                Layer will be created for each one of the values provided. Defaults to (256, 256).
+            l2scale (float):
+                Wheight Decay for the Adam Optimizer. Defaults to 1e-6.
+            batch_size (int):
+                Number of data samples to process in each step.
+        """
+        super().__init__(
+            field_names=field_names,
+            primary_key=primary_key,
+            field_types=field_types,
+            anonymize_fields=anonymize_fields,
+            constraints=constraints,
+            table_metadata=table_metadata
+        )
+        try:
+            from ctgan import CTGANSynthesizer  # Lazy import to make dependency optional
+
+            self._CTGAN_CLASS = CTGANSynthesizer
+        except ImportError as ie:
+            ie.msg += (
+                '\n\nIt seems like `ctgan` is not installed.\n'
+                'Please install it using:\n\n    pip install ctgan'
+            )
+            raise
+
+        self._embedding_dim = embedding_dim
+        self._gen_dim = gen_dim
+        self._dis_dim = dis_dim
+        self._l2scale = l2scale
+        self._batch_size = batch_size
+        self._epochs = epochs
+        self._log_frequency = log_frequency
+
+    def _fit(self, data):
+        """Fit the model to the table.
+
+        Args:
+            data (pandas.DataFrame):
+                Data to be learned.
+        """
+        self._model = self._CTGAN_CLASS(
+            embedding_dim=self._embedding_dim,
+            gen_dim=self._gen_dim,
+            dis_dim=self._dis_dim,
+            l2scale=self._l2scale,
+            batch_size=self._batch_size,
+        )
+        categoricals = [
+            field
+            for field, meta in self._metadata.get_fields().items()
+            if meta['type'] == 'categorical'
+        ]
+        self._model.fit(
+            data,
+            epochs=self._epochs,
+            discrete_columns=categoricals,
+            log_frequency=self._log_frequency,
+        )
+
+    def _sample(self, size):
+        """Sample ``size`` rows from the model.
+
+        Args:
+            size (int):
+                Amount of rows to sample.
+
+        Returns:
+            pandas.DataFrame:
+                Sampled data.
+        """
+        return self._model.sample(size)
diff --git a/setup.py b/setup.py
index c8a4a56b5..ef713040b 100644
--- a/setup.py
+++ b/setup.py
@@ -13,12 +13,17 @@
 
 install_requires = [
     'exrex>=0.9.4,<0.11',
-    'numpy>=1.15.4,<1.17',
+    'numpy>=1.15.4,<2',
     'pandas>=0.23.4,<0.25',
-    'copulas>=0.3,<0.4',
-    'rdt>=0.2.1,<0.3',
+    'copulas>=0.3.1.dev0,<0.4',
+    'rdt>=0.2.3.dev0,<0.3',
     'graphviz>=0.13.2',
-    'sdmetrics>=0.0.1,<0.0.2'
+    'sdmetrics>=0.0.2.dev0,<0.0.3',
+    'scikit-learn<0.23,>=0.21',
+]
+
+ctgan_requires = [
+    'ctgan>=0.2.2.dev0,<0.3',
 ]
 
 setup_requires = [
@@ -76,8 +81,8 @@
     ],
     description='Automated Generative Modeling and Sampling',
     extras_require={
-        'test': tests_require,
-        'dev': development_requires + tests_require,
+        'test': tests_require + ctgan_requires,
+        'dev': development_requires + tests_require + ctgan_requires,
     },
     install_package_data=True,
     install_requires=install_requires,
diff --git a/tests/integration/tabular/test_copulas.py b/tests/integration/tabular/test_copulas.py
new file mode 100644
index 000000000..037e32f0c
--- /dev/null
+++ b/tests/integration/tabular/test_copulas.py
@@ -0,0 +1,48 @@
+from sdv.demo import load_demo
+from sdv.tabular.copulas import GaussianCopula
+
+
+def test_gaussian_copula():
+    users = load_demo(metadata=False)['users']
+
+    field_types = {
+        'age': {
+            'type': 'numerical',
+            'subtype': 'integer',
+        },
+        'country': {
+            'type': 'categorical'
+        }
+    }
+    anonymize_fields = {
+        'country': 'country_code'
+    }
+
+    gc = GaussianCopula(
+        field_names=['user_id', 'country', 'gender', 'age'],
+        field_types=field_types,
+        primary_key='user_id',
+        anonymize_fields=anonymize_fields,
+        categorical_transformer='one_hot_encoding',
+    )
+    gc.fit(users)
+
+    sampled = gc.sample()
+
+    # test shape is right
+    assert sampled.shape == users.shape
+
+    # test user_id has been generated as an ID field
+    assert list(sampled['user_id']) == list(range(0, len(users)))
+
+    # country codes have been replaced with new ones
+    assert set(sampled.country.unique()) & set(users.country.unique()) == set()
+
+    assert gc.get_metadata().to_dict() == {
+        'fields': {
+            'user_id': {'type': 'id', 'subtype': 'integer'},
+            'country': {'type': 'categorical'},
+            'gender': {'type': 'categorical'},
+            'age': {'type': 'numerical', 'subtype': 'integer'}
+        }
+    }
diff --git a/tests/integration/tabular/test_ctgan.py b/tests/integration/tabular/test_ctgan.py
new file mode 100644
index 000000000..8cc8d4ad6
--- /dev/null
+++ b/tests/integration/tabular/test_ctgan.py
@@ -0,0 +1,29 @@
+from sdv.demo import load_demo
+from sdv.tabular.ctgan import CTGAN
+
+
+def test_ctgan():
+    users = load_demo(metadata=False)['users']
+
+    gc = CTGAN(
+        primary_key='user_id',
+        epochs=1
+    )
+    gc.fit(users)
+
+    sampled = gc.sample()
+
+    # test shape is right
+    assert sampled.shape == users.shape
+
+    # test user_id has been generated as an ID field
+    assert list(sampled['user_id']) == list(range(0, len(users)))
+
+    assert gc.get_metadata().to_dict() == {
+        'fields': {
+            'user_id': {'type': 'id', 'subtype': 'integer'},
+            'country': {'type': 'categorical'},
+            'gender': {'type': 'categorical'},
+            'age': {'type': 'numerical', 'subtype': 'integer'}
+        }
+    }

From a198a2dc5a916d383342605661dca3294ed7e80b Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 19:51:01 +0200
Subject: [PATCH 21/33] Remove unused line

---
 sdv/tabular/copulas.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/sdv/tabular/copulas.py b/sdv/tabular/copulas.py
index 024205cad..dc82dd862 100644
--- a/sdv/tabular/copulas.py
+++ b/sdv/tabular/copulas.py
@@ -84,7 +84,6 @@ def _fit(self, data):
             table_data (pandas.DataFrame):
                 Data to be fitted.
         """
-        params = self._metadata.get_model_params()
         self._model = copulas.multivariate.GaussianMultivariate(distribution=self._distribution)
         self._model.fit(data)
 

From 92b18f75dccc6d5835439a304f5ab6c80723754f Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 20:05:14 +0200
Subject: [PATCH 22/33] Fix tests

---
 .../test___init__.py}                         | 121 +---------------
 tests/metadata/test_visualization.py          | 132 ++++++++++++++++++
 2 files changed, 133 insertions(+), 120 deletions(-)
 rename tests/{test_metadata.py => metadata/test___init__.py} (87%)
 create mode 100644 tests/metadata/test_visualization.py

diff --git a/tests/test_metadata.py b/tests/metadata/test___init__.py
similarity index 87%
rename from tests/test_metadata.py
rename to tests/metadata/test___init__.py
index 812ac7bcd..9968597ae 100644
--- a/tests/test_metadata.py
+++ b/tests/metadata/test___init__.py
@@ -1,7 +1,6 @@
 from unittest import TestCase
-from unittest.mock import MagicMock, Mock, call, patch
+from unittest.mock import Mock, call, patch
 
-import graphviz
 import pandas as pd
 import pytest
 
@@ -963,121 +962,3 @@ def test_add_field(self):
 
         assert metadata._metadata == expected_metadata
         metadata._check_field.assert_called_once_with('a_table', 'a_field', exists=False)
-
-    def test__get_graphviz_extension_path_without_extension(self):
-        """Raises a ValueError when the path doesn't contains an extension."""
-        with pytest.raises(ValueError):
-            Metadata._get_graphviz_extension('/some/path')
-
-    def test__get_graphviz_extension_invalid_extension(self):
-        """Raises a ValueError when the path contains an invalid extension."""
-        with pytest.raises(ValueError):
-            Metadata._get_graphviz_extension('/some/path.foo')
-
-    def test__get_graphviz_extension_none(self):
-        """Get graphviz with path equals to None."""
-        # Run
-        result = Metadata._get_graphviz_extension(None)
-
-        # Asserts
-        assert result == (None, None)
-
-    def test__get_graphviz_extension_valid(self):
-        """Get a valid graphviz extension."""
-        # Run
-        result = Metadata._get_graphviz_extension('/some/path.png')
-
-        # Asserts
-        assert result == ('/some/path', 'png')
-
-    def test__visualize_add_nodes(self):
-        """Add nodes into a graphviz digraph."""
-        # Setup
-        metadata = MagicMock(spec_set=Metadata)
-        minimock = Mock()
-
-        # pass tests in python3.5
-        minimock.items.return_value = (
-            ('a_field', {'type': 'numerical', 'subtype': 'integer'}),
-            ('b_field', {'type': 'id'}),
-            ('c_field', {'type': 'id', 'ref': {'table': 'other', 'field': 'pk_field'}})
-        )
-
-        metadata.get_tables.return_value = ['demo']
-        metadata.get_fields.return_value = minimock
-
-        metadata.get_primary_key.return_value = 'b_field'
-        metadata.get_parents.return_value = set(['other'])
-        metadata.get_foreign_key.return_value = 'c_field'
-
-        metadata.get_table_meta.return_value = {'path': None}
-
-        plot = Mock()
-
-        # Run
-        Metadata._visualize_add_nodes(metadata, plot)
-
-        # Asserts
-        expected_node_label = r"{demo|a_field : numerical - integer\lb_field : id\l" \
-                              r"c_field : id\l|Primary key: b_field\l" \
-                              r"Foreign key (other): c_field\l}"
-
-        metadata.get_fields.assert_called_once_with('demo')
-        metadata.get_primary_key.assert_called_once_with('demo')
-        metadata.get_parents.assert_called_once_with('demo')
-        metadata.get_table_meta.assert_called_once_with('demo')
-        metadata.get_foreign_key.assert_called_once_with('other', 'demo')
-        metadata.get_table_meta.assert_called_once_with('demo')
-
-        plot.node.assert_called_once_with('demo', label=expected_node_label)
-
-    def test__visualize_add_edges(self):
-        """Add edges into a graphviz digraph."""
-        # Setup
-        metadata = MagicMock(spec_set=Metadata)
-
-        metadata.get_tables.return_value = ['demo', 'other']
-        metadata.get_parents.side_effect = [set(['other']), set()]
-
-        metadata.get_foreign_key.return_value = 'fk'
-        metadata.get_primary_key.return_value = 'pk'
-
-        plot = Mock()
-
-        # Run
-        Metadata._visualize_add_edges(metadata, plot)
-
-        # Asserts
-        expected_edge_label = '   {}.{} -> {}.{}'.format('demo', 'fk', 'other', 'pk')
-
-        metadata.get_tables.assert_called_once_with()
-        metadata.get_foreign_key.assert_called_once_with('other', 'demo')
-        metadata.get_primary_key.assert_called_once_with('other')
-        assert metadata.get_parents.call_args_list == [call('demo'), call('other')]
-
-        plot.edge.assert_called_once_with(
-            'other',
-            'demo',
-            label=expected_edge_label,
-            arrowhead='crow'
-        )
-
-    @patch('sdv.metadata.graphviz')
-    def test_visualize(self, graphviz_mock):
-        """Metadata visualize digraph"""
-        # Setup
-        plot = Mock(spec_set=graphviz.Digraph)
-        graphviz_mock.Digraph.return_value = plot
-
-        metadata = MagicMock(spec_set=Metadata)
-        metadata._get_graphviz_extension.return_value = ('output', 'png')
-
-        # Run
-        Metadata.visualize(metadata, path='output.png')
-
-        # Asserts
-        metadata._get_graphviz_extension.assert_called_once_with('output.png')
-        metadata._visualize_add_nodes.assert_called_once_with(plot)
-        metadata._visualize_add_edges.assert_called_once_with(plot)
-
-        plot.render.assert_called_once_with(filename='output', cleanup=True, format='png')
diff --git a/tests/metadata/test_visualization.py b/tests/metadata/test_visualization.py
new file mode 100644
index 000000000..b9f588593
--- /dev/null
+++ b/tests/metadata/test_visualization.py
@@ -0,0 +1,132 @@
+from unittest.mock import MagicMock, Mock, call, patch
+
+import graphviz
+import pytest
+
+from sdv.metadata import Metadata, visualization
+
+
+def test__get_graphviz_extension_path_without_extension():
+    """Raises a ValueError when the path doesn't contains an extension."""
+    with pytest.raises(ValueError):
+        visualization._get_graphviz_extension('/some/path')
+
+
+def test__get_graphviz_extension_invalid_extension():
+    """Raises a ValueError when the path contains an invalid extension."""
+    with pytest.raises(ValueError):
+        visualization._get_graphviz_extension('/some/path.foo')
+
+
+def test__get_graphviz_extension_none():
+    """Get graphviz with path equals to None."""
+    # Run
+    result = visualization._get_graphviz_extension(None)
+
+    # Asserts
+    assert result == (None, None)
+
+
+def test__get_graphviz_extension_valid():
+    """Get a valid graphviz extension."""
+    # Run
+    result = visualization._get_graphviz_extension('/some/path.png')
+
+    # Asserts
+    assert result == ('/some/path', 'png')
+
+
+def test__add_nodes():
+    """Add nodes into a graphviz digraph."""
+    # Setup
+    metadata = MagicMock(spec_set=Metadata)
+    minimock = Mock()
+
+    # pass tests in python3.5
+    minimock.items.return_value = (
+        ('a_field', {'type': 'numerical', 'subtype': 'integer'}),
+        ('b_field', {'type': 'id'}),
+        ('c_field', {'type': 'id', 'ref': {'table': 'other', 'field': 'pk_field'}})
+    )
+
+    metadata.get_tables.return_value = ['demo']
+    metadata.get_fields.return_value = minimock
+
+    metadata.get_primary_key.return_value = 'b_field'
+    metadata.get_parents.return_value = set(['other'])
+    metadata.get_foreign_key.return_value = 'c_field'
+
+    metadata.get_table_meta.return_value = {'path': None}
+
+    plot = Mock()
+
+    # Run
+    visualization._add_nodes(metadata, plot)
+
+    # Asserts
+    expected_node_label = r"{demo|a_field : numerical - integer\lb_field : id\l" \
+                          r"c_field : id\l|Primary key: b_field\l" \
+                          r"Foreign key (other): c_field\l}"
+
+    metadata.get_fields.assert_called_once_with('demo')
+    metadata.get_primary_key.assert_called_once_with('demo')
+    metadata.get_parents.assert_called_once_with('demo')
+    metadata.get_table_meta.assert_called_once_with('demo')
+    metadata.get_foreign_key.assert_called_once_with('other', 'demo')
+    metadata.get_table_meta.assert_called_once_with('demo')
+
+    plot.node.assert_called_once_with('demo', label=expected_node_label)
+
+
+def test__add_edges():
+    """Add edges into a graphviz digraph."""
+    # Setup
+    metadata = MagicMock(spec_set=Metadata)
+
+    metadata.get_tables.return_value = ['demo', 'other']
+    metadata.get_parents.side_effect = [set(['other']), set()]
+
+    metadata.get_foreign_key.return_value = 'fk'
+    metadata.get_primary_key.return_value = 'pk'
+
+    plot = Mock()
+
+    # Run
+    visualization._add_edges(metadata, plot)
+
+    # Asserts
+    expected_edge_label = '   {}.{} -> {}.{}'.format('demo', 'fk', 'other', 'pk')
+
+    metadata.get_tables.assert_called_once_with()
+    metadata.get_foreign_key.assert_called_once_with('other', 'demo')
+    metadata.get_primary_key.assert_called_once_with('other')
+    assert metadata.get_parents.call_args_list == [call('demo'), call('other')]
+
+    plot.edge.assert_called_once_with(
+        'other',
+        'demo',
+        label=expected_edge_label,
+        arrowhead='oinv'
+    )
+
+
+@patch('sdv.metadata.visualization.graphviz.Digraph', spec_set=graphviz.Digraph)
+@patch('sdv.metadata.visualization._add_nodes')
+@patch('sdv.metadata.visualization._add_edges')
+def test_visualize(add_nodes_mock, add_edges_mock, digraph_mock):
+    """Metadata visualize digraph"""
+    # Setup
+    # plot = Mock(spec_set=graphviz.Digraph)
+    # graphviz_mock.Digraph.return_value = plot
+
+    metadata = MagicMock(spec_set=Metadata)
+
+    # Run
+    visualization.visualize(metadata, path='output.png')
+
+    # Asserts
+    digraph = digraph_mock.return_value
+    add_nodes_mock.assert_called_once_with(metadata, digraph)
+    add_edges_mock.assert_called_once_with(metadata, digraph)
+
+    digraph.render.assert_called_once_with(filename='output', cleanup=True, format='png')

From 762b26c97cff50a8bf6086e6d6591cdd250a13f3 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 20:12:51 +0200
Subject: [PATCH 23/33] Fix test that fails randomly

---
 tests/integration/tabular/test_copulas.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/integration/tabular/test_copulas.py b/tests/integration/tabular/test_copulas.py
index 037e32f0c..a40fbe7bb 100644
--- a/tests/integration/tabular/test_copulas.py
+++ b/tests/integration/tabular/test_copulas.py
@@ -36,7 +36,7 @@ def test_gaussian_copula():
     assert list(sampled['user_id']) == list(range(0, len(users)))
 
     # country codes have been replaced with new ones
-    assert set(sampled.country.unique()) & set(users.country.unique()) == set()
+    assert set(sampled.country.unique()) != set(users.country.unique())
 
     assert gc.get_metadata().to_dict() == {
         'fields': {

From 06df78c6271f949068c78b13566a9565f603eef8 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 21:09:12 +0200
Subject: [PATCH 24/33] Add save/load and fix imports

---
 sdv/metadata.py.composite   | 1054 +++++++++++++++++++++++++++++++++
 sdv/metadata.py.non_primary | 1109 +++++++++++++++++++++++++++++++++++
 sdv/tabular/__init__.py     |    8 +
 sdv/tabular/base.py         |   27 +
 4 files changed, 2198 insertions(+)
 create mode 100644 sdv/metadata.py.composite
 create mode 100644 sdv/metadata.py.non_primary

diff --git a/sdv/metadata.py.composite b/sdv/metadata.py.composite
new file mode 100644
index 000000000..10dda41e3
--- /dev/null
+++ b/sdv/metadata.py.composite
@@ -0,0 +1,1054 @@
+import copy
+import json
+import logging
+import os
+from collections import defaultdict
+
+import graphviz
+import numpy as np
+import pandas as pd
+from rdt import HyperTransformer, transformers
+
+LOGGER = logging.getLogger(__name__)
+
+
+def _read_csv_dtypes(table_meta):
+    """Get the dtypes specification that needs to be passed to read_csv."""
+    dtypes = dict()
+    for name, field in table_meta['fields'].items():
+        field_type = field['type']
+        if field_type == 'categorical':
+            dtypes[name] = str
+        elif field_type == 'id' and field.get('subtype', 'integer') == 'string':
+            dtypes[name] = str
+
+    return dtypes
+
+
+def _parse_dtypes(data, table_meta):
+    """Convert the data columns to the right dtype after loading the CSV."""
+    for name, field in table_meta['fields'].items():
+        field_type = field['type']
+        if field_type == 'datetime':
+            datetime_format = field.get('format')
+            data[name] = pd.to_datetime(data[name], format=datetime_format, exact=False)
+        elif field_type == 'numerical' and field.get('subtype') == 'integer':
+            data[name] = data[name].dropna().astype(int)
+        elif field_type == 'id' and field.get('subtype', 'integer') == 'integer':
+            data[name] = data[name].dropna().astype(int)
+
+    return data
+
+
+def _load_csv(root_path, table_meta):
+    """Load a CSV with the right dtypes and then parse the columns."""
+    relative_path = os.path.join(root_path, table_meta['path'])
+    dtypes = _read_csv_dtypes(table_meta)
+
+    data = pd.read_csv(relative_path, dtype=dtypes)
+    data = _parse_dtypes(data, table_meta)
+
+    return data
+
+
+class MetadataError(Exception):
+    pass
+
+
+class Metadata:
+    """Dataset Metadata.
+
+    The Metadata class provides a unified layer of abstraction over the dataset
+    metadata, which includes both the necessary details to load the data from
+    the hdd and to know how to parse and transform it to numerical data.
+
+    Args:
+        metadata (str or dict):
+            Path to a ``json`` file that contains the metadata or a ``dict`` representation
+            of ``metadata`` following the same structure.
+
+        root_path (str):
+            The path where the ``metadata.json`` is located. Defaults to ``None``.
+    """
+
+    _child_map = None
+    _hyper_transformers = None
+    _metadata = None
+    _parent_map = None
+
+    root_path = None
+
+    _FIELD_TEMPLATES = {
+        'i': {
+            'type': 'numerical',
+            'subtype': 'integer',
+        },
+        'f': {
+            'type': 'numerical',
+            'subtype': 'float',
+        },
+        'O': {
+            'type': 'categorical',
+        },
+        'b': {
+            'type': 'boolean',
+        },
+        'M': {
+            'type': 'datetime',
+        }
+    }
+    _DTYPES = {
+        ('categorical', None): 'object',
+        ('boolean', None): 'bool',
+        ('numerical', None): 'float',
+        ('numerical', 'float'): 'float',
+        ('numerical', 'integer'): 'int',
+        ('datetime', None): 'datetime64',
+        ('id', None): 'int',
+        ('id', 'integer'): 'int',
+        ('id', 'string'): 'str'
+    }
+
+    def _analyze_relationships(self):
+        """Extract information about child-parent relationships.
+
+        Creates the following attributes:
+            * ``_child_map``: set of child tables that each table has.
+            * ``_parent_map``: set ot parents that each table has.
+        """
+        self._child_map = defaultdict(set)
+        self._parent_map = defaultdict(set)
+
+        for table, table_meta in self._metadata['tables'].items():
+            if table_meta.get('use', True):
+                for field_meta in table_meta['fields'].values():
+                    ref = field_meta.get('ref')
+                    if ref:
+                        parent = ref['table']
+                        self._child_map[parent].add(table)
+                        self._parent_map[table].add(parent)
+
+    @staticmethod
+    def _transform_metadata(metadata):
+        """Ensure metadata has the internal SDV format.
+
+        Convert list of tables and list of fields to dicts.
+        Ensure primary keys are defined as lists of fields.
+
+        Args:
+            metadata (dict):
+                Original metadata to format.
+
+        Returns:
+            dict:
+                Formated metadata dict.
+        """
+        new_metadata = copy.deepcopy(metadata)
+        tables = new_metadata['tables']
+        if isinstance(tables, dict):
+            new_metadata['tables'] = {
+                table: meta
+                for table, meta in tables.items()
+                if meta.pop('use', True)
+            }
+            return new_metadata
+
+        new_tables = dict()
+        for table in tables:
+            if table.pop('use', True):
+                new_tables[table.pop('name')] = table
+
+                fields = table['fields']
+                new_fields = dict()
+                for field in fields:
+                    new_fields[field.pop('name')] = field
+
+                table['fields'] = new_fields
+
+                primary_key = table.get('primary_key')
+                if isinstance(primary_key, str):
+                    table['primary_key'] = [primary_key]
+
+        new_metadata['tables'] = new_tables
+
+        return new_metadata
+
+    def __init__(self, metadata=None, root_path=None):
+        if isinstance(metadata, str):
+            self.root_path = root_path or os.path.dirname(metadata)
+            with open(metadata) as metadata_file:
+                metadata = json.load(metadata_file)
+        else:
+            self.root_path = root_path or '.'
+
+        if metadata is not None:
+            self._metadata = self._transform_metadata(metadata)
+        else:
+            self._metadata = {'tables': {}}
+
+        self._hyper_transformers = dict()
+        self._analyze_relationships()
+
+    def get_children(self, table_name):
+        """Get tables for which the given table is parent.
+
+        Args:
+            table_name (str):
+                Name of the table from which to get the children.
+
+        Returns:
+            set:
+                Set of children for the given table.
+        """
+        return self._child_map[table_name]
+
+    def get_parents(self, table_name):
+        """Get tables for with the given table is child.
+
+        Args:
+            table_name (str):
+                Name of the table from which to get the parents.
+
+        Returns:
+            set:
+                Set of parents for the given table.
+        """
+        return self._parent_map[table_name]
+
+    def get_table_meta(self, table_name):
+        """Get the metadata dict for a table.
+
+        Args:
+            table_name (str):
+                Name of table to get data for.
+
+        Returns:
+            dict:
+                table metadata
+
+        Raises:
+            ValueError:
+                If table does not exist in this metadata.
+        """
+        table = self._metadata['tables'].get(table_name)
+        if table is None:
+            raise ValueError('Table "{}" does not exist'.format(table_name))
+
+        return copy.deepcopy(table)
+
+    def get_tables(self):
+        """Get the list of table names.
+
+        Returns:
+            list:
+                table names.
+        """
+        return list(self._metadata['tables'].keys())
+
+    def get_fields(self, table_name):
+        """Get table fields metadata.
+
+        Args:
+            table_name (str):
+                Name of the table to get the fields from.
+
+        Returns:
+            dict:
+                Mapping of field names and their metadata dicts.
+
+        Raises:
+            ValueError:
+                If table does not exist in this metadata.
+        """
+        return self.get_table_meta(table_name)['fields']
+
+    def get_primary_key(self, table_name):
+        """Get the primary key name of the indicated table.
+
+        Args:
+            table_name (str):
+                Name of table for which to get the primary key field.
+
+        Returns:
+            list or None:
+                Primary key field names. ``None`` if the table has no primary key.
+
+        Raises:
+            ValueError:
+                If table does not exist in this metadata.
+        """
+        return self.get_table_meta(table_name).get('primary_key')
+
+    def get_foreign_key(self, parent, child):
+        """Get table foreign key field name.
+
+        Args:
+            parent (str):
+                Name of the parent table.
+            child (str):
+                Name of the child table.
+
+        Returns:
+            str or None:
+                Foreign key field name.
+
+        Raises:
+            ValueError:
+                If the relationship does not exist.
+        """
+        primary = self.get_primary_key(parent)
+
+        for name, field in self.get_fields(child).items():
+            ref = field.get('ref')
+            if ref and ref['field'] == primary:
+                return name
+
+        raise ValueError('{} is not parent of {}'.format(parent, child))
+
+    def load_table(self, table_name):
+        """Load table data.
+
+        Args:
+            table_name (str):
+                Name of the table to load.
+
+        Returns:
+            pandas.DataFrame:
+                DataFrame with the contents of the table.
+
+        Raises:
+            ValueError:
+                If table does not exist in this metadata.
+        """
+        LOGGER.info('Loading table %s', table_name)
+        table_meta = self.get_table_meta(table_name)
+        return _load_csv(self.root_path, table_meta)
+
+    def load_tables(self, tables=None):
+        """Get a dictionary with data from multiple tables.
+
+        If a ``tables`` list is given, only load the indicated tables.
+        Otherwise, load all the tables from this metadata.
+
+        Args:
+            tables (list):
+                List of table names. Defaults to ``None``.
+
+        Returns:
+            dict(str, pandasd.DataFrame):
+                mapping of table names and their data loaded as ``pandas.DataFrame`` instances.
+        """
+        return {
+            table_name: self.load_table(table_name)
+            for table_name in tables or self.get_tables()
+        }
+
+    def get_dtypes(self, table_name, ids=False):
+        """Get a ``dict`` with the ``dtypes`` for each field of a given table.
+
+        Args:
+            table_name (str):
+                Table name for which to retrive the ``dtypes``.
+            ids (bool):
+                Whether or not include the id fields. Defaults to ``False``.
+
+        Returns:
+            dict:
+                Dictionary that contains the field names and data types from a table.
+
+        Raises:
+            ValueError:
+                If a field has an invalid type or subtype or if the table does not
+                exist in this metadata.
+        """
+        dtypes = dict()
+        table_meta = self.get_table_meta(table_name)
+        for name, field in table_meta['fields'].items():
+            field_type = field['type']
+            field_subtype = field.get('subtype')
+            dtype = self._DTYPES.get((field_type, field_subtype))
+            if not dtype:
+                raise MetadataError(
+                    'Invalid type and subtype combination for field {}: ({}, {})'.format(
+                        name, field_type, field_subtype)
+                )
+
+            if ids and field_type == 'id':
+                if (name not in table_meta.get('primary_key', [])) and not field.get('ref'):
+                    raise MetadataError(
+                        'id field `{}` is neither a primary or a foreign key'.format(name))
+
+            if ids or (field_type != 'id'):
+                dtypes[name] = dtype
+
+        return dtypes
+
+    def _get_pii_fields(self, table_name):
+        """Get the ``pii_category`` for each field that contains PII.
+
+        Args:
+            table_name (str):
+                Table name for which to get the pii fields.
+
+        Returns:
+            dict:
+                pii field names and categories.
+        """
+        pii_fields = dict()
+        for name, field in self.get_table_meta(table_name)['fields'].items():
+            if field['type'] == 'categorical' and field.get('pii', False):
+                pii_fields[name] = field['pii_category']
+
+        return pii_fields
+
+    @staticmethod
+    def _get_transformers(dtypes, pii_fields):
+        """Create the transformer instances needed to process the given dtypes.
+
+        Temporary drop-in replacement of ``HyperTransformer._analyze`` method,
+        before RDT catches up.
+
+        Args:
+            dtypes (dict):
+                mapping of field names and dtypes.
+            pii_fields (dict):
+                mapping of pii field names and categories.
+
+        Returns:
+            dict:
+                mapping of field names and transformer instances.
+        """
+        transformers_dict = dict()
+        for name, dtype in dtypes.items():
+            dtype = np.dtype(dtype)
+            if dtype.kind == 'i':
+                transformer = transformers.NumericalTransformer(dtype=int)
+            elif dtype.kind == 'f':
+                transformer = transformers.NumericalTransformer(dtype=float)
+            elif dtype.kind == 'O':
+                anonymize = pii_fields.get(name)
+                transformer = transformers.CategoricalTransformer(anonymize=anonymize)
+            elif dtype.kind == 'b':
+                transformer = transformers.BooleanTransformer()
+            elif dtype.kind == 'M':
+                transformer = transformers.DatetimeTransformer()
+            else:
+                raise ValueError('Unsupported dtype: {}'.format(dtype))
+
+            LOGGER.info('Loading transformer %s for field %s',
+                        transformer.__class__.__name__, name)
+            transformers_dict[name] = transformer
+
+        return transformers_dict
+
+    def _load_hyper_transformer(self, table_name):
+        """Create and return a new ``rdt.HyperTransformer`` instance for a table.
+
+        First get the ``dtypes`` and ``pii fields`` from a given table, then use
+        those to build a transformer dictionary to be used by the ``HyperTransformer``.
+
+        Args:
+            table_name (str):
+                Table name for which to load the HyperTransformer.
+
+        Returns:
+            rdt.HyperTransformer:
+                Instance of ``rdt.HyperTransformer`` for the given table.
+        """
+        dtypes = self.get_dtypes(table_name)
+        pii_fields = self._get_pii_fields(table_name)
+        transformers_dict = self._get_transformers(dtypes, pii_fields)
+        return HyperTransformer(transformers=transformers_dict)
+
+    def transform(self, table_name, data):
+        """Transform data for a given table.
+
+        If the ``HyperTransformer`` for a table is ``None`` it is created.
+
+        Args:
+            table_name (str):
+                Name of the table that is being transformer.
+            data (pandas.DataFrame):
+                Table data.
+
+        Returns:
+            pandas.DataFrame:
+                Transformed data.
+        """
+        hyper_transformer = self._hyper_transformers.get(table_name)
+        if hyper_transformer is None:
+            hyper_transformer = self._load_hyper_transformer(table_name)
+            fields = list(hyper_transformer.transformers.keys())
+            hyper_transformer.fit(data[fields])
+            self._hyper_transformers[table_name] = hyper_transformer
+
+        hyper_transformer = self._hyper_transformers.get(table_name)
+        fields = list(hyper_transformer.transformers.keys())
+        return hyper_transformer.transform(data[fields])
+
+    def reverse_transform(self, table_name, data):
+        """Reverse the transformed data for a given table.
+
+        Args:
+            table_name (str):
+                Name of the table to reverse transform.
+            data (pandas.DataFrame):
+                Data to be reversed.
+
+        Returns:
+            pandas.DataFrame
+        """
+        hyper_transformer = self._hyper_transformers[table_name]
+        reversed_data = hyper_transformer.reverse_transform(data)
+
+        for name, dtype in self.get_dtypes(table_name, ids=True).items():
+            reversed_data[name] = reversed_data[name].dropna().astype(dtype)
+
+        return reversed_data
+
+    # ################### #
+    # Metadata Validation #
+    # ################### #
+
+    def _validate_table(self, table_name, table_meta, table_data=None):
+        """Validate table metadata.
+
+        Validate the type and subtype combination for each field in ``table_meta``.
+        If a field has type ``id``, validate that it either is the ``primary_key`` or
+        has a ``ref`` entry.
+
+        If the table has ``primary_key``, make sure that the corresponding field exists
+        and its type is ``id``.
+
+        If ``table_data`` is provided, also check that the list of columns corresponds
+        to the ones indicated in the metadata and that all the dtypes are valid.
+
+        Args:
+            table_name (str):
+                Name of the table to validate.
+            table_meta (dict):
+                Metadata of the table to validate.
+            table_data (pandas.DataFrame):
+                If provided, make sure that the data matches the one described
+                on the metadata.
+
+        Raises:
+            MetadataError:
+                If there is any error in the metadata or the data does not
+                match the metadata description.
+        """
+        dtypes = self.get_dtypes(table_name, ids=True)
+
+        # Primary key field exists and its type is 'id'
+        primary_key_fields = table_meta.get('primary_key', [])
+        for primary_key in primary_key_fields:
+            pk_field = table_meta['fields'].get(primary_key)
+
+            if not pk_field:
+                raise MetadataError('Primary key is not an existing field.')
+
+            if pk_field['type'] != 'id':
+                raise MetadataError('Primary key is not of type `id`.')
+
+        if table_data is not None:
+            for column in table_data:
+                try:
+                    dtype = dtypes.pop(column)
+                    table_data[column].dropna().astype(dtype)
+                except KeyError:
+                    message = 'Unexpected column in table `{}`: `{}`'.format(table_name, column)
+                    raise MetadataError(message) from None
+                except ValueError as ve:
+                    message = 'Invalid values found in column `{}` of table `{}`: `{}`'.format(
+                        column, table_name, ve)
+                    raise MetadataError(message) from None
+
+            # assert all dtypes are in data
+            if dtypes:
+                raise MetadataError(
+                    'Missing columns on table {}: {}.'.format(table_name, list(dtypes.keys()))
+                )
+
+    def _validate_circular_relationships(self, parent, children=None):
+        """Validate that there is no circular relatioship in the metadata."""
+        if children is None:
+            children = self.get_children(parent)
+
+        if parent in children:
+            raise MetadataError('Circular relationship found for table "{}"'.format(parent))
+
+        for child in children:
+            self._validate_circular_relationships(parent, self.get_children(child))
+
+    def validate(self, tables=None):
+        """Validate this metadata.
+
+        For each table from in metadata ``tables`` entry:
+            * Validate the table metadata is correct.
+
+        * If ``tables`` are provided or they have been loaded, check
+          that all the metadata tables exists in the ``tables`` dictionary.
+        * Validate the type/subtype combination for each field and
+          if a field of type ``id`` exists it must be the ``primary_key``
+          or must have a ``ref`` entry.
+        * If ``primary_key`` entry exists, check that it's an existing
+          field and its type is ``id``.
+        * If ``tables`` are provided or they have been loaded, check
+          all the data types for the table correspond to each column and
+          all the data types exists on the table.
+        * Validate that there is no circular relatioship in the metadata.
+        * Check that all the tables have at most one parent.
+
+        Args:
+            tables (bool, dict):
+                If a dict of table is passed, validate that the columns and
+                dtypes match the metadata. If ``True`` is passed, load the
+                tables from the Metadata instead. If ``None``, omit the data
+                validation. Defaults to ``None``.
+        """
+        tables_meta = self._metadata.get('tables')
+        if not tables_meta:
+            raise MetadataError('"tables" entry not found in Metadata.')
+
+        if tables and not isinstance(tables, dict):
+            tables = self.load_tables()
+
+        for table_name, table_meta in tables_meta.items():
+            if tables:
+                table = tables.get(table_name)
+                if table is None:
+                    raise MetadataError('Table `{}` not found in tables'.format(table_name))
+
+            else:
+                table = None
+
+            self._validate_table(table_name, table_meta, table)
+            self._validate_circular_relationships(table_name)
+
+    def _check_field(self, table, field, exists=False):
+        """Validate the existance of the table and existance (or not) of field."""
+        table_fields = self.get_fields(table)
+        if exists and (field not in table_fields):
+            raise ValueError('Field "{}" does not exist in table "{}"'.format(field, table))
+
+        if not exists and (field in table_fields):
+            raise ValueError('Field "{}" already exists in table "{}"'.format(field, table))
+
+    # ################# #
+    # Metadata Creation #
+    # ################# #
+
+    def add_field(self, table, field, field_type, field_subtype=None, properties=None):
+        """Add a new field to the indicated table.
+
+        Args:
+            table (str):
+                Table name to add the new field, it must exist.
+            field (str):
+                Field name to be added, it must not exist.
+            field_type (str):
+                Data type of field to be added. Required.
+            field_subtype (str):
+                Data subtype of field to be added. Optional.
+                Defaults to ``None``.
+            properties (dict):
+                Extra properties of field like: ref, format, min, max, etc. Optional.
+                Defaults to ``None``.
+
+        Raises:
+            ValueError:
+                If the table does not exist or it already contains the field.
+        """
+        self._check_field(table, field, exists=False)
+
+        field_details = {
+            'type': field_type
+        }
+
+        if field_subtype:
+            field_details['subtype'] = field_subtype
+
+        if properties:
+            field_details.update(properties)
+
+        self._metadata['tables'][table]['fields'][field] = field_details
+
+    @staticmethod
+    def _get_key_subtype(field_meta):
+        """Get the appropriate key subtype."""
+        field_type = field_meta['type']
+        if field_type == 'categorical':
+            field_subtype = 'string'
+        elif field_type in ('numerical', 'id'):
+            field_subtype = field_meta['subtype']
+            if field_subtype not in ('integer', 'string'):
+                raise ValueError(
+                    'Invalid field "subtype" for key field: "{}"'.format(field_subtype)
+                )
+        else:
+            raise ValueError(
+                'Invalid field "type" for key field: "{}"'.format(field_type)
+            )
+
+        return field_subtype
+
+    def set_primary_key(self, table, field):
+        """Set the primary key field of the indicated table.
+
+        The field must exist and either be an integer or categorical field.
+
+        Args:
+            table (str):
+                Name of the table where the primary key will be set.
+            field (str):
+                Field to be used as the new primary key.
+
+        Raises:
+            ValueError:
+                If the table or the field do not exist or if the field has an
+                invalid type or subtype.
+        """
+        self._check_field(table, field, exists=True)
+
+        field_meta = self.get_fields(table).get(field)
+        field_subtype = self._get_key_subtype(field_meta)
+
+        table_meta = self._metadata['tables'][table]
+        table_meta['fields'][field] = {
+            'type': 'id',
+            'subtype': field_subtype
+        }
+        table_meta['primary_key'] = field
+
+    def add_relationship(self, parent, child, foreign_key=None):
+        """Add a new relationship between the parent and child tables.
+
+        The relationship is created by adding a reference (``ref``) on the ``foreign_key``
+        field of the ``child`` table pointing at the ``parent`` primary key.
+
+        Args:
+            parent (str):
+                Name of the parent table.
+            child (str):
+                Name of the child table.
+            foreign_key (str):
+                Field in the child table through which the relationship is created.
+                If ``None``, use the parent primary key name.
+
+        Raises:
+            ValueError:
+                If any of the following happens:
+                    * The parent table does not exist.
+                    * The child table does not exist.
+                    * The parent table does not have a primary key.
+                    * The foreign_key field already exists in the child table.
+                    * The child table already has a parent.
+                    * The new relationship closes a relationship circle.
+        """
+        # Validate table and field names
+        primary_key = self.get_primary_key(parent)
+        if not primary_key:
+            raise ValueError('Parent table "{}" does not have a primary key'.format(parent))
+
+        if foreign_key is None:
+            foreign_key = primary_key
+
+        # Validate relationships
+        if self.get_parents(child):
+            raise ValueError('Table "{}" already has a parent'.format(child))
+
+        grandchildren = self.get_children(child)
+        if grandchildren:
+            self._validate_circular_relationships(parent, grandchildren)
+
+        # Copy primary key details over to the foreign key
+        foreign_key_details = copy.deepcopy(self.get_fields(parent)[primary_key])
+        foreign_key_details['ref'] = {
+            'table': parent,
+            'field': primary_key
+        }
+
+        # Make sure that key subtypes are the same
+        foreign_meta = self.get_fields(child).get(foreign_key)
+        if foreign_meta:
+            foreign_subtype = self._get_key_subtype(foreign_meta)
+            if foreign_subtype != foreign_key_details['subtype']:
+                raise ValueError('Primary and Foreign key subtypes mismatch')
+
+        self._metadata['tables'][child]['fields'][foreign_key] = foreign_key_details
+
+        # Re-analyze the relationships
+        self._analyze_relationships()
+
+    def _get_field_details(self, data, fields):
+        """Get or build all the fields metadata.
+
+        Analyze a ``pandas.DataFrame`` to build a ``dict`` with the name of the column, and
+        their data type and subtype. If ``columns`` are provided, only those columns will be
+        analyzed.
+
+        Args:
+            data (pandas.DataFrame):
+                Table to be analyzed.
+            fields (set):
+                Set of field names or field specifications.
+
+        Returns:
+            dict:
+                Dict of valid fields.
+
+        Raises:
+            TypeError:
+                If a field specification is not a str or a dict.
+            ValueError:
+                If a column from the data analyzed is an unsupported data type or
+        """
+        fields_metadata = dict()
+        for field in fields:
+            dtype = data[field].dtype
+            field_template = self._FIELD_TEMPLATES.get(dtype.kind)
+            if not field_template:
+                raise ValueError('Unsupported dtype {} in column {}'.format(dtype, field))
+
+            field_details = copy.deepcopy(field_template)
+            fields_metadata[field] = field_details
+
+        return fields_metadata
+
+    def add_table(self, name, data=None, fields=None, fields_metadata=None,
+                  primary_key=None, parent=None, foreign_key=None):
+        """Add a new table to this metadata.
+
+        ``fields`` list can be a mixture of field names, which will be build automatically
+        from the data, or dictionaries specifying the field details. If a field needs to be
+        analyzed, data has to be also passed.
+
+        If ``parent`` is given, a relationship will be established between this table
+        and the specified parent.
+
+        Args:
+            name (str):
+                Name of the new table.
+            data (str or pandas.DataFrame):
+                Table to be analyzed or path to the csv file.
+                If it's a relative path, use ``root_path`` to find the file.
+                Only used if fields is not ``None``.
+                Defaults to ``None``.
+            fields (list):
+                List of field names to build. If ``None`` is given, all the fields
+                found in the data will be used.
+                Defaults to ``None``.
+            fields_metadata (dict):
+                Metadata to be used when creating fields. This will overwrite the
+                metadata built from the fields found in data.
+                Defaults to ``None``.
+            primary_key (str):
+                Field name to add as primary key, it must not exists. Defaults to ``None``.
+            parent (str):
+                Table name to refere a foreign key field. Defaults to ``None``.
+            foreign_key (str):
+                Foreing key field name to ``parent`` table primary key. Defaults to ``None``.
+
+        Raises:
+            ValueError:
+                If the table ``name`` already exists or ``data`` is not passed and
+                fields need to be built from it.
+        """
+        if name in self.get_tables():
+            raise ValueError('Table "{}" already exists.'.format(name))
+
+        path = None
+        if data is not None:
+            if isinstance(data, str):
+                path = data
+                if not os.path.isabs(data):
+                    data = os.path.join(self.root_path, data)
+
+                data = pd.read_csv(data)
+
+            fields = set(fields or data.columns)
+            if fields_metadata:
+                fields = fields - set(fields_metadata.keys())
+            else:
+                fields_metadata = dict()
+
+            fields_metadata.update(self._get_field_details(data, fields))
+
+        elif fields_metadata is None:
+            fields_metadata = dict()
+
+        table_metadata = {'fields': fields_metadata}
+        if path:
+            table_metadata['path'] = path
+
+        self._metadata['tables'][name] = table_metadata
+
+        try:
+            if primary_key:
+                self.set_primary_key(name, primary_key)
+
+            if parent:
+                self.add_relationship(parent, name, foreign_key)
+
+        except ValueError:
+            # Cleanup
+            del self._metadata['tables'][name]
+            raise
+
+    # ###################### #
+    # Metadata Serialization #
+    # ###################### #
+
+    def to_dict(self):
+        """Get a dict representation of this metadata.
+
+        Returns:
+            dict:
+                dict representation of this metadata.
+        """
+        return copy.deepcopy(self._metadata)
+
+    def to_json(self, path):
+        """Dump this metadata into a JSON file.
+
+        Args:
+            path (str):
+                Path of the JSON file where this metadata will be stored.
+        """
+        with open(path, 'w') as out_file:
+            json.dump(self._metadata, out_file, indent=4)
+
+    @staticmethod
+    def _get_graphviz_extension(path):
+        if path:
+            path_splitted = path.split('.')
+            if len(path_splitted) == 1:
+                raise ValueError('Path without graphviz extansion.')
+
+            graphviz_extension = path_splitted[-1]
+
+            if graphviz_extension not in graphviz.backend.FORMATS:
+                raise ValueError(
+                    '"{}" not a valid graphviz extension format.'.format(graphviz_extension)
+                )
+
+            return '.'.join(path_splitted[:-1]), graphviz_extension
+
+        return None, None
+
+    def _visualize_add_nodes(self, plot):
+        """Add nodes into a `graphviz.Digraph`.
+
+        Each node represent a metadata table.
+
+        Args:
+            plot (graphviz.Digraph)
+        """
+        for table in self.get_tables():
+            # Append table fields
+            fields = []
+
+            for name, value in self.get_fields(table).items():
+                if value.get('subtype') is not None:
+                    fields.append('{} : {} - {}'.format(name, value['type'], value['subtype']))
+
+                else:
+                    fields.append('{} : {}'.format(name, value['type']))
+
+            fields = r'\l'.join(fields)
+
+            # Append table extra information
+            extras = []
+
+            primary_key = self.get_primary_key(table)
+            if primary_key is not None:
+                extras.append('Primary key: {}'.format(primary_key))
+
+            parents = self.get_parents(table)
+            for parent in parents:
+                foreign_key = self.get_foreign_key(parent, table)
+                extras.append('Foreign key ({}): {}'.format(parent, foreign_key))
+
+            path = self.get_table_meta(table).get('path')
+            if path is not None:
+                extras.append('Data path: {}'.format(path))
+
+            extras = r'\l'.join(extras)
+
+            # Add table node
+            title = r'{%s|%s\l|%s\l}' % (table, fields, extras)
+            plot.node(table, label=title)
+
+    def _visualize_add_edges(self, plot):
+        """Add edges into a `graphviz.Digraph`.
+
+        Each edge represents a relationship between two metadata tables.
+
+        Args:
+            plot (graphviz.Digraph)
+        """
+        for table in self.get_tables():
+            for parent in list(self.get_parents(table)):
+                plot.edge(
+                    parent,
+                    table,
+                    label='   {}.{} -> {}.{}'.format(
+                        table, self.get_foreign_key(parent, table),
+                        parent, self.get_primary_key(parent)
+                    ),
+                    arrowhead='crow'
+                )
+
+    def visualize(self, path=None):
+        """Plot metadata usign graphviz.
+
+        Try to generate a plot using graphviz.
+        If a ``path`` is provided save the output into a file.
+
+        Args:
+            path (str):
+                Output file path to save the plot, it requires a graphviz
+                supported extension. If ``None`` do not save the plot.
+                Defaults to ``None``.
+        """
+        filename, graphviz_extension = self._get_graphviz_extension(path)
+        plot = graphviz.Digraph(
+            'Metadata',
+            format=graphviz_extension,
+            node_attr={
+                "shape": "Mrecord",
+                "fillcolor": "lightgoldenrod1",
+                "style": "filled"
+            },
+        )
+
+        self._visualize_add_nodes(plot)
+        self._visualize_add_edges(plot)
+
+        if filename:
+            plot.render(filename=filename, cleanup=True, format=graphviz_extension)
+        else:
+            return plot
+
+    def __str__(self):
+        tables = self.get_tables()
+        relationships = [
+            '    {}.{} -> {}.{}'.format(
+                table, self.get_foreign_key(parent, table),
+                parent, self.get_primary_key(parent)
+            )
+            for table in tables
+            for parent in list(self.get_parents(table))
+        ]
+
+        return (
+            "Metadata\n"
+            "  root_path: {}\n"
+            "  tables: {}\n"
+            "  relationships:\n"
+            "{}"
+        ).format(
+            os.path.abspath(self.root_path),
+            tables,
+            '\n'.join(relationships)
+        )
diff --git a/sdv/metadata.py.non_primary b/sdv/metadata.py.non_primary
new file mode 100644
index 000000000..2df337bd2
--- /dev/null
+++ b/sdv/metadata.py.non_primary
@@ -0,0 +1,1109 @@
+import copy
+import json
+import logging
+import os
+from collections import defaultdict
+
+import graphviz
+import numpy as np
+import pandas as pd
+from rdt import HyperTransformer, transformers
+
+LOGGER = logging.getLogger(__name__)
+
+
+def _read_csv_dtypes(table_meta):
+    """Get the dtypes specification that needs to be passed to read_csv."""
+    dtypes = dict()
+    for name, field in table_meta['fields'].items():
+        field_type = field['type']
+        if field_type == 'categorical':
+            dtypes[name] = str
+        elif field_type == 'id' and field.get('subtype', 'integer') == 'string':
+            dtypes[name] = str
+
+    return dtypes
+
+
+def _parse_dtypes(data, table_meta):
+    """Convert the data columns to the right dtype after loading the CSV."""
+    for name, field in table_meta['fields'].items():
+        field_type = field['type']
+        if field_type == 'datetime':
+            datetime_format = field.get('format')
+            data[name] = pd.to_datetime(data[name], format=datetime_format, exact=False)
+        elif field_type == 'numerical' and field.get('subtype') == 'integer':
+            data[name] = data[name].dropna().astype(int)
+        elif field_type == 'id' and field.get('subtype', 'integer') == 'integer':
+            data[name] = data[name].dropna().astype(int)
+
+    return data
+
+
+def _load_csv(root_path, table_meta):
+    """Load a CSV with the right dtypes and then parse the columns."""
+    relative_path = os.path.join(root_path, table_meta['path'])
+    dtypes = _read_csv_dtypes(table_meta)
+
+    data = pd.read_csv(relative_path, dtype=dtypes)
+    data = _parse_dtypes(data, table_meta)
+
+    return data
+
+
+class MetadataError(Exception):
+    pass
+
+
+class Metadata:
+    """Dataset Metadata.
+
+    The Metadata class provides a unified layer of abstraction over the dataset
+    metadata, which includes both the necessary details to load the data from
+    the hdd and to know how to parse and transform it to numerical data.
+
+    Args:
+        metadata (str or dict):
+            Path to a ``json`` file that contains the metadata or a ``dict`` representation
+            of ``metadata`` following the same structure.
+
+        root_path (str):
+            The path where the ``metadata.json`` is located. Defaults to ``None``.
+    """
+
+    _child_map = None
+    _hyper_transformers = None
+    _metadata = None
+    _parent_map = None
+
+    root_path = None
+
+    _FIELD_TEMPLATES = {
+        'i': {
+            'type': 'numerical',
+            'subtype': 'integer',
+        },
+        'f': {
+            'type': 'numerical',
+            'subtype': 'float',
+        },
+        'O': {
+            'type': 'categorical',
+        },
+        'b': {
+            'type': 'boolean',
+        },
+        'M': {
+            'type': 'datetime',
+        }
+    }
+    _DTYPES = {
+        ('categorical', None): 'object',
+        ('boolean', None): 'bool',
+        ('numerical', None): 'float',
+        ('numerical', 'float'): 'float',
+        ('numerical', 'integer'): 'int',
+        ('datetime', None): 'datetime64',
+        ('id', None): 'int',
+        ('id', 'integer'): 'int',
+        ('id', 'string'): 'str'
+    }
+
+    def _analyze_relationships(self):
+        """Extract information about child-parent relationships.
+
+        Creates the following attributes:
+            * ``_child_map``: set of child tables that each table has.
+            * ``_parent_map``: set ot parents that each table has.
+        """
+        self._child_map = defaultdict(set)
+        self._parent_map = defaultdict(set)
+
+        for table, table_meta in self._metadata['tables'].items():
+            if table_meta.get('use', True):
+                for field_meta in table_meta['fields'].values():
+                    ref = field_meta.get('ref')
+                    if ref:
+                        parent = ref['table']
+                        self._child_map[parent].add(table)
+                        self._parent_map[table].add(parent)
+
+    @staticmethod
+    def _dict_metadata(metadata):
+        """Get a metadata ``dict`` with SDV format.
+
+        For each table create a dict of fields from a previous list of fields.
+
+        Args:
+            metadata (dict):
+                Original metadata to format.
+
+        Returns:
+            dict:
+                Formated metadata dict.
+        """
+        new_metadata = copy.deepcopy(metadata)
+        tables = new_metadata['tables']
+        if isinstance(tables, dict):
+            new_metadata['tables'] = {
+                table: meta
+                for table, meta in tables.items()
+                if meta.pop('use', True)
+            }
+            return new_metadata
+
+        new_tables = dict()
+        for table in tables:
+            if table.pop('use', True):
+                new_tables[table.pop('name')] = table
+
+                fields = table['fields']
+                new_fields = dict()
+                for field in fields:
+                    new_fields[field.pop('name')] = field
+
+                table['fields'] = new_fields
+
+        new_metadata['tables'] = new_tables
+
+        return new_metadata
+
+    def __init__(self, metadata=None, root_path=None):
+        if isinstance(metadata, str):
+            self.root_path = root_path or os.path.dirname(metadata)
+            with open(metadata) as metadata_file:
+                metadata = json.load(metadata_file)
+        else:
+            self.root_path = root_path or '.'
+
+        if metadata is not None:
+            self._metadata = self._dict_metadata(metadata)
+        else:
+            self._metadata = {'tables': {}}
+
+        self._hyper_transformers = dict()
+        self._analyze_relationships()
+
+    def get_children(self, table_name):
+        """Get tables for which the given table is parent.
+
+        Args:
+            table_name (str):
+                Name of the table from which to get the children.
+
+        Returns:
+            set:
+                Set of children for the given table.
+        """
+        return self._child_map[table_name]
+
+    def get_parents(self, table_name):
+        """Get tables for with the given table is child.
+
+        Args:
+            table_name (str):
+                Name of the table from which to get the parents.
+
+        Returns:
+            set:
+                Set of parents for the given table.
+        """
+        return self._parent_map[table_name]
+
+    def get_table_meta(self, table_name):
+        """Get the metadata dict for a table.
+
+        Args:
+            table_name (str):
+                Name of table to get data for.
+
+        Returns:
+            dict:
+                table metadata
+
+        Raises:
+            ValueError:
+                If table does not exist in this metadata.
+        """
+        table = self._metadata['tables'].get(table_name)
+        if table is None:
+            raise ValueError('Table "{}" does not exist'.format(table_name))
+
+        return copy.deepcopy(table)
+
+    def get_tables(self):
+        """Get the list of table names.
+
+        Returns:
+            list:
+                table names.
+        """
+        return list(self._metadata['tables'].keys())
+
+    def get_field_meta(self, table_name, field_name):
+        """Get the metadata dict for a table.
+
+        Args:
+            table_name (str):
+                Name of the table to which the field belongs.
+            field_name (str):
+                Name of the field to get data for.
+
+        Returns:
+            dict:
+                field metadata
+
+        Raises:
+            ValueError:
+                If the table or the field do not exist in this metadata.
+        """
+        field_meta = self.get_fields(table_name).get(field_name)
+        if field_meta is None:
+            raise ValueError(
+                'Table "{}" does not contain a field name "{}"'.format(table_name, field_name))
+
+        return copy.deepcopy(field_meta)
+
+    def get_fields(self, table_name):
+        """Get table fields metadata.
+
+        Args:
+            table_name (str):
+                Name of the table to get the fields from.
+
+        Returns:
+            dict:
+                Mapping of field names and their metadata dicts.
+
+        Raises:
+            ValueError:
+                If table does not exist in this metadata.
+        """
+        return self.get_table_meta(table_name)['fields']
+
+    def get_primary_key(self, table_name):
+        """Get the primary key name of the indicated table.
+
+        Args:
+            table_name (str):
+                Name of table for which to get the primary key field.
+
+        Returns:
+            str or None:
+                Primary key field name. ``None`` if the table has no primary key.
+
+        Raises:
+            ValueError:
+                If table does not exist in this metadata.
+        """
+        return self.get_table_meta(table_name).get('primary_key')
+
+    def get_foreign_key(self, parent, child):
+        """Get table foreign key field name.
+
+        Args:
+            parent (str):
+                Name of the parent table.
+            child (str):
+                Name of the child table.
+
+        Returns:
+            str or None:
+                Foreign key field name.
+
+        Raises:
+            ValueError:
+                If the relationship does not exist.
+        """
+        for name, field in self.get_fields(child).items():
+            ref = field.get('ref')
+            if ref and ref['table'] == parent:
+                return name
+
+        raise ValueError('{} is not parent of {}'.format(parent, child))
+
+    def load_table(self, table_name):
+        """Load table data.
+
+        Args:
+            table_name (str):
+                Name of the table to load.
+
+        Returns:
+            pandas.DataFrame:
+                DataFrame with the contents of the table.
+
+        Raises:
+            ValueError:
+                If table does not exist in this metadata.
+        """
+        LOGGER.info('Loading table %s', table_name)
+        table_meta = self.get_table_meta(table_name)
+        return _load_csv(self.root_path, table_meta)
+
+    def load_tables(self, tables=None):
+        """Get a dictionary with data from multiple tables.
+
+        If a ``tables`` list is given, only load the indicated tables.
+        Otherwise, load all the tables from this metadata.
+
+        Args:
+            tables (list):
+                List of table names. Defaults to ``None``.
+
+        Returns:
+            dict(str, pandasd.DataFrame):
+                mapping of table names and their data loaded as ``pandas.DataFrame`` instances.
+        """
+        return {
+            table_name: self.load_table(table_name)
+            for table_name in tables or self.get_tables()
+        }
+
+    def get_dtypes(self, table_name, ids=False):
+        """Get a ``dict`` with the ``dtypes`` for each field of a given table.
+
+        Args:
+            table_name (str):
+                Table name for which to retrive the ``dtypes``.
+            ids (bool):
+                Whether or not include the id fields. Defaults to ``False``.
+
+        Returns:
+            dict:
+                Dictionary that contains the field names and data types from a table.
+
+        Raises:
+            ValueError:
+                If a field has an invalid type or subtype or if the table does not
+                exist in this metadata.
+        """
+        dtypes = dict()
+        table_meta = self.get_table_meta(table_name)
+        for name, field in table_meta['fields'].items():
+            field_type = field['type']
+            field_subtype = field.get('subtype')
+            dtype = self._DTYPES.get((field_type, field_subtype))
+            if not dtype:
+                raise MetadataError(
+                    'Invalid type and subtype combination for field {}: ({}, {})'.format(
+                        name, field_type, field_subtype)
+                )
+
+            if ids and field_type == 'id':
+                if (name != table_meta.get('primary_key')) and not field.get('ref'):
+                    for child_table in self.get_children(table_name):
+                        if name == self.get_foreign_key(table_name, child_table):
+                            break
+
+                    else:
+                        raise MetadataError(
+                            'id field `{}` is neither a primary or a foreign key'.format(name))
+
+            if ids or (field_type != 'id'):
+                dtypes[name] = dtype
+
+        return dtypes
+
+    def _get_pii_fields(self, table_name):
+        """Get the ``pii_category`` for each field that contains PII.
+
+        Args:
+            table_name (str):
+                Table name for which to get the pii fields.
+
+        Returns:
+            dict:
+                pii field names and categories.
+        """
+        pii_fields = dict()
+        for name, field in self.get_table_meta(table_name)['fields'].items():
+            if field['type'] == 'categorical' and field.get('pii', False):
+                pii_fields[name] = field['pii_category']
+
+        return pii_fields
+
+    @staticmethod
+    def _get_transformers(dtypes, pii_fields):
+        """Create the transformer instances needed to process the given dtypes.
+
+        Temporary drop-in replacement of ``HyperTransformer._analyze`` method,
+        before RDT catches up.
+
+        Args:
+            dtypes (dict):
+                mapping of field names and dtypes.
+            pii_fields (dict):
+                mapping of pii field names and categories.
+
+        Returns:
+            dict:
+                mapping of field names and transformer instances.
+        """
+        transformers_dict = dict()
+        for name, dtype in dtypes.items():
+            dtype = np.dtype(dtype)
+            if dtype.kind == 'i':
+                transformer = transformers.NumericalTransformer(dtype=int)
+            elif dtype.kind == 'f':
+                transformer = transformers.NumericalTransformer(dtype=float)
+            elif dtype.kind == 'O':
+                anonymize = pii_fields.get(name)
+                transformer = transformers.CategoricalTransformer(anonymize=anonymize)
+            elif dtype.kind == 'b':
+                transformer = transformers.BooleanTransformer()
+            elif dtype.kind == 'M':
+                transformer = transformers.DatetimeTransformer()
+            else:
+                raise ValueError('Unsupported dtype: {}'.format(dtype))
+
+            LOGGER.info('Loading transformer %s for field %s',
+                        transformer.__class__.__name__, name)
+            transformers_dict[name] = transformer
+
+        return transformers_dict
+
+    def _load_hyper_transformer(self, table_name):
+        """Create and return a new ``rdt.HyperTransformer`` instance for a table.
+
+        First get the ``dtypes`` and ``pii fields`` from a given table, then use
+        those to build a transformer dictionary to be used by the ``HyperTransformer``.
+
+        Args:
+            table_name (str):
+                Table name for which to load the HyperTransformer.
+
+        Returns:
+            rdt.HyperTransformer:
+                Instance of ``rdt.HyperTransformer`` for the given table.
+        """
+        dtypes = self.get_dtypes(table_name)
+        pii_fields = self._get_pii_fields(table_name)
+        transformers_dict = self._get_transformers(dtypes, pii_fields)
+        return HyperTransformer(transformers=transformers_dict)
+
+    def transform(self, table_name, data):
+        """Transform data for a given table.
+
+        If the ``HyperTransformer`` for a table is ``None`` it is created.
+
+        Args:
+            table_name (str):
+                Name of the table that is being transformer.
+            data (pandas.DataFrame):
+                Table data.
+
+        Returns:
+            pandas.DataFrame:
+                Transformed data.
+        """
+        hyper_transformer = self._hyper_transformers.get(table_name)
+        if hyper_transformer is None:
+            hyper_transformer = self._load_hyper_transformer(table_name)
+            fields = list(hyper_transformer.transformers.keys())
+            hyper_transformer.fit(data[fields])
+            self._hyper_transformers[table_name] = hyper_transformer
+
+        hyper_transformer = self._hyper_transformers.get(table_name)
+        fields = list(hyper_transformer.transformers.keys())
+        return hyper_transformer.transform(data[fields])
+
+    def reverse_transform(self, table_name, data):
+        """Reverse the transformed data for a given table.
+
+        Args:
+            table_name (str):
+                Name of the table to reverse transform.
+            data (pandas.DataFrame):
+                Data to be reversed.
+
+        Returns:
+            pandas.DataFrame
+        """
+        hyper_transformer = self._hyper_transformers[table_name]
+        reversed_data = hyper_transformer.reverse_transform(data)
+
+        for name, dtype in self.get_dtypes(table_name, ids=True).items():
+            reversed_data[name] = reversed_data[name].dropna().astype(dtype)
+
+        return reversed_data
+
+    # ################### #
+    # Metadata Validation #
+    # ################### #
+
+    def _validate_table(self, table_name, table_meta, table_data=None):
+        """Validate table metadata.
+
+        Validate the type and subtype combination for each field in ``table_meta``.
+        If a field has type ``id``, validate that it either is the ``primary_key`` or
+        has a ``ref`` entry.
+
+        If the table has ``primary_key``, make sure that the corresponding field exists
+        and its type is ``id``.
+
+        If ``table_data`` is provided, also check that the list of columns corresponds
+        to the ones indicated in the metadata and that all the dtypes are valid.
+
+        Args:
+            table_name (str):
+                Name of the table to validate.
+            table_meta (dict):
+                Metadata of the table to validate.
+            table_data (pandas.DataFrame):
+                If provided, make sure that the data matches the one described
+                on the metadata.
+
+        Raises:
+            MetadataError:
+                If there is any error in the metadata or the data does not
+                match the metadata description.
+        """
+        dtypes = self.get_dtypes(table_name, ids=True)
+
+        # Primary key field exists and its type is 'id'
+        primary_key = table_meta.get('primary_key')
+        if primary_key:
+            pk_field = table_meta['fields'].get(primary_key)
+
+            if not pk_field:
+                raise MetadataError('Primary key is not an existing field.')
+
+            if pk_field['type'] != 'id':
+                raise MetadataError('Primary key is not of type `id`.')
+
+        if table_data is not None:
+            for column in table_data:
+                try:
+                    dtype = dtypes.pop(column)
+                    table_data[column].dropna().astype(dtype)
+                except KeyError:
+                    message = 'Unexpected column in table `{}`: `{}`'.format(table_name, column)
+                    raise MetadataError(message) from None
+                except ValueError as ve:
+                    message = 'Invalid values found in column `{}` of table `{}`: `{}`'.format(
+                        column, table_name, ve)
+                    raise MetadataError(message) from None
+
+            # assert all dtypes are in data
+            if dtypes:
+                raise MetadataError(
+                    'Missing columns on table {}: {}.'.format(table_name, list(dtypes.keys()))
+                )
+
+    def _validate_circular_relationships(self, parent, children=None):
+        """Validate that there is no circular relatioship in the metadata."""
+        if children is None:
+            children = self.get_children(parent)
+
+        if parent in children:
+            raise MetadataError('Circular relationship found for table "{}"'.format(parent))
+
+        for child in children:
+            self._validate_circular_relationships(parent, self.get_children(child))
+
+    def validate(self, tables=None):
+        """Validate this metadata.
+
+        For each table from in metadata ``tables`` entry:
+            * Validate the table metadata is correct.
+
+        * If ``tables`` are provided or they have been loaded, check
+          that all the metadata tables exists in the ``tables`` dictionary.
+        * Validate the type/subtype combination for each field and
+          if a field of type ``id`` exists it must be the ``primary_key``
+          or must have a ``ref`` entry.
+        * If ``primary_key`` entry exists, check that it's an existing
+          field and its type is ``id``.
+        * If ``tables`` are provided or they have been loaded, check
+          all the data types for the table correspond to each column and
+          all the data types exists on the table.
+        * Validate that there is no circular relatioship in the metadata.
+        * Check that all the tables have at most one parent.
+
+        Args:
+            tables (bool, dict):
+                If a dict of table is passed, validate that the columns and
+                dtypes match the metadata. If ``True`` is passed, load the
+                tables from the Metadata instead. If ``None``, omit the data
+                validation. Defaults to ``None``.
+        """
+        tables_meta = self._metadata.get('tables')
+        if not tables_meta:
+            raise MetadataError('"tables" entry not found in Metadata.')
+
+        if tables and not isinstance(tables, dict):
+            tables = self.load_tables()
+
+        for table_name, table_meta in tables_meta.items():
+            if tables:
+                table = tables.get(table_name)
+                if table is None:
+                    raise MetadataError('Table `{}` not found in tables'.format(table_name))
+
+            else:
+                table = None
+
+            self._validate_table(table_name, table_meta, table)
+            self._validate_circular_relationships(table_name)
+
+    def _check_field(self, table, field, exists=False):
+        """Validate the existance of the table and existance (or not) of field."""
+        table_fields = self.get_fields(table)
+        if exists and (field not in table_fields):
+            raise ValueError('Field "{}" does not exist in table "{}"'.format(field, table))
+
+        if not exists and (field in table_fields):
+            raise ValueError('Field "{}" already exists in table "{}"'.format(field, table))
+
+    # ################# #
+    # Metadata Creation #
+    # ################# #
+
+    def add_field(self, table, field, field_type, field_subtype=None, properties=None):
+        """Add a new field to the indicated table.
+
+        Args:
+            table (str):
+                Table name to add the new field, it must exist.
+            field (str):
+                Field name to be added, it must not exist.
+            field_type (str):
+                Data type of field to be added. Required.
+            field_subtype (str):
+                Data subtype of field to be added. Optional.
+                Defaults to ``None``.
+            properties (dict):
+                Extra properties of field like: ref, format, min, max, etc. Optional.
+                Defaults to ``None``.
+
+        Raises:
+            ValueError:
+                If the table does not exist or it already contains the field.
+        """
+        self._check_field(table, field, exists=False)
+
+        field_details = {
+            'type': field_type
+        }
+
+        if field_subtype:
+            field_details['subtype'] = field_subtype
+
+        if properties:
+            field_details.update(properties)
+
+        self._metadata['tables'][table]['fields'][field] = field_details
+
+    @staticmethod
+    def _get_key_subtype(field_meta):
+        """Get the appropriate key subtype."""
+        field_type = field_meta['type']
+
+        if field_type == 'categorical':
+            field_subtype = 'string'
+
+        elif field_type in ('numerical', 'id'):
+            field_subtype = field_meta['subtype']
+            if field_subtype not in ('integer', 'string'):
+                raise ValueError(
+                    'Invalid field "subtype" for key field: "{}"'.format(field_subtype)
+                )
+
+        else:
+            raise ValueError(
+                'Invalid field "type" for key field: "{}"'.format(field_type)
+            )
+
+        return field_subtype
+
+    def set_primary_key(self, table, field):
+        """Set the primary key field of the indicated table.
+
+        The field must exist and either be an integer or categorical field.
+
+        Args:
+            table (str):
+                Name of the table where the primary key will be set.
+            field (str):
+                Field to be used as the new primary key.
+
+        Raises:
+            ValueError:
+                If the table or the field do not exist or if the field has an
+                invalid type or subtype.
+        """
+        self._check_field(table, field, exists=True)
+
+        field_meta = self.get_fields(table).get(field)
+        field_subtype = self._get_key_subtype(field_meta)
+
+        table_meta = self._metadata['tables'][table]
+        table_meta['fields'][field] = {
+            'type': 'id',
+            'subtype': field_subtype
+        }
+        table_meta['primary_key'] = field
+
+    def add_relationship(self, parent, child, parent_key=None, child_key=None):
+        """Add a new relationship between the parent and child tables.
+
+        The relationship is created by adding a reference (``ref``) on the ``child_key``
+        field of the ``child`` table pointing at the ``parent_key`` field from the
+        ``parent`` table.
+
+        Args:
+            parent (str):
+                Name of the parent table.
+            child (str):
+                Name of the child table.
+            parent_key (str):
+                Field in the parent table through which the relationship is created.
+                If ``None``, use the parent primary key name.
+            child_key (str):
+                Field in the child table through which the relationship is created.
+                If ``None``, use the name of the parent key.
+
+        Raises:
+            ValueError:
+                If any of the following happens:
+                    * The parent or child tables do not exist.
+                    * The parent_key or child_key fields do not exist.
+                    * The child_key already is a foreign key.
+                    * The new relationship closes a relationship circle.
+        """
+        # Validate tables exists
+        self.get_table_meta(parent)
+        self.get_table_meta(child)
+
+        # Validate fields exists
+        if parent_key is None:
+            parent_key = self.get_primary_key(parent)
+            if not parent_key:
+                msg = 'If parent table does not have a primary key, a `parent_key` must be given'
+                raise ValueError(msg.format(parent))
+
+        if child_key is None:
+            child_key = parent_key
+
+        parent_key_meta = copy.deepcopy(self.get_field_meta(parent, parent_key))
+        child_key_meta = copy.deepcopy(self.get_field_meta(child, child_key))
+
+        # Validate relationships
+        child_ref = child_key_meta.get('ref')
+        if child_ref:
+            raise ValueError(
+                'Field "{}.{}" already defines a relationship'.format(child, child_key))
+
+        grandchildren = self.get_children(child)
+        if grandchildren:
+            self._validate_circular_relationships(parent, grandchildren)
+
+        # Make sure that the parent key is an id
+        if parent_key_meta['type'] != 'id':
+            parent_key_meta['subtype'] = self._get_key_subtype(parent_key_meta)
+            parent_key_meta['type'] = 'id'
+
+        # Update the child key meta
+        child_key_meta['subtype'] = self._get_key_subtype(parent_key_meta)
+        child_key_meta['type'] = 'id'
+        child_key_meta['ref'] = {
+            'table': parent,
+            'field': parent_key
+        }
+
+        # Make sure that key subtypes are the same
+        if child_key_meta['subtype'] != parent_key_meta['subtype']:
+            raise ValueError('Parent and Child key subtypes mismatch')
+
+        # Make a backup
+        metadata_backup = copy.deepcopy(self._metadata)
+
+        self._metadata['tables'][parent]['fields'][parent_key] = parent_key_meta
+        self._metadata['tables'][child]['fields'][child_key] = child_key_meta
+
+        # Re-analyze the relationships
+        self._analyze_relationships()
+
+        try:
+            self.validate()
+        except MetadataError:
+            self._metadata = metadata_backup
+            raise
+
+    def _get_field_details(self, data, fields):
+        """Get or build all the fields metadata.
+
+        Analyze a ``pandas.DataFrame`` to build a ``dict`` with the name of the column, and
+        their data type and subtype. If ``columns`` are provided, only those columns will be
+        analyzed.
+
+        Args:
+            data (pandas.DataFrame):
+                Table to be analyzed.
+            fields (set):
+                Set of field names or field specifications.
+
+        Returns:
+            dict:
+                Dict of valid fields.
+
+        Raises:
+            TypeError:
+                If a field specification is not a str or a dict.
+            ValueError:
+                If a column from the data analyzed is an unsupported data type or
+        """
+        fields_metadata = dict()
+        for field in fields:
+            dtype = data[field].dtype
+            field_template = self._FIELD_TEMPLATES.get(dtype.kind)
+            if not field_template:
+                raise ValueError('Unsupported dtype {} in column {}'.format(dtype, field))
+
+            field_details = copy.deepcopy(field_template)
+            fields_metadata[field] = field_details
+
+        return fields_metadata
+
+    def add_table(self, name, data=None, fields=None, fields_metadata=None,
+                  primary_key=None, parent=None, parent_key=None, foreign_key=None):
+        """Add a new table to this metadata.
+
+        ``fields`` list can be a mixture of field names, which will be build automatically
+        from the data, or dictionaries specifying the field details. If a field needs to be
+        analyzed, data has to be also passed.
+
+        If ``parent`` is given, a relationship will be established between this table
+        and the specified parent.
+
+        Args:
+            name (str):
+                Name of the new table.
+            data (str or pandas.DataFrame):
+                Table to be analyzed or path to the csv file.
+                If it's a relative path, use ``root_path`` to find the file.
+                Only used if fields is not ``None``.
+                Defaults to ``None``.
+            fields (list):
+                List of field names to build. If ``None`` is given, all the fields
+                found in the data will be used.
+                Defaults to ``None``.
+            fields_metadata (dict):
+                Metadata to be used when creating fields. This will overwrite the
+                metadata built from the fields found in data.
+                Defaults to ``None``.
+            primary_key (str):
+                Field name to add as primary key, it must not exists. Defaults to ``None``.
+            parent (str):
+                Table name to refere a foreign key field. Defaults to ``None``.
+            parent_key (str):
+                Name of the field from the ``parent`` table that is pointed by the given
+                ``foreign_key``. Defaults to the ``parent`` primary key.
+            foreign_key (str):
+                Name of the field from the added table that forms a relationship with
+                the ``parent`` table. Defaults to the same name as ``parent_key``.
+
+        Raises:
+            ValueError:
+                If the table ``name`` already exists or ``data`` is not passed and
+                fields need to be built from it.
+        """
+        if name in self.get_tables():
+            raise ValueError('Table "{}" already exists.'.format(name))
+
+        path = None
+        if data is not None:
+            if isinstance(data, str):
+                path = data
+                if not os.path.isabs(data):
+                    data = os.path.join(self.root_path, data)
+
+                data = pd.read_csv(data)
+
+            fields = set(fields or data.columns)
+            if fields_metadata:
+                fields = fields - set(fields_metadata.keys())
+            else:
+                fields_metadata = dict()
+
+            fields_metadata.update(self._get_field_details(data, fields))
+
+        elif fields_metadata is None:
+            fields_metadata = dict()
+
+        table_metadata = {'fields': fields_metadata}
+        if path:
+            table_metadata['path'] = path
+
+        self._metadata['tables'][name] = table_metadata
+
+        try:
+            if primary_key:
+                self.set_primary_key(name, primary_key)
+
+            if parent:
+                self.add_relationship(parent, name, parent_key, foreign_key)
+
+        except ValueError:
+            # Cleanup
+            del self._metadata['tables'][name]
+            raise
+
+    # ###################### #
+    # Metadata Serialization #
+    # ###################### #
+
+    def to_dict(self):
+        """Get a dict representation of this metadata.
+
+        Returns:
+            dict:
+                dict representation of this metadata.
+        """
+        return copy.deepcopy(self._metadata)
+
+    def to_json(self, path):
+        """Dump this metadata into a JSON file.
+
+        Args:
+            path (str):
+                Path of the JSON file where this metadata will be stored.
+        """
+        with open(path, 'w') as out_file:
+            json.dump(self._metadata, out_file, indent=4)
+
+    @staticmethod
+    def _get_graphviz_extension(path):
+        if path:
+            path_splitted = path.split('.')
+            if len(path_splitted) == 1:
+                raise ValueError('Path without graphviz extansion.')
+
+            graphviz_extension = path_splitted[-1]
+
+            if graphviz_extension not in graphviz.backend.FORMATS:
+                raise ValueError(
+                    '"{}" not a valid graphviz extension format.'.format(graphviz_extension)
+                )
+
+            return '.'.join(path_splitted[:-1]), graphviz_extension
+
+        return None, None
+
+    def _visualize_add_nodes(self, plot):
+        """Add nodes into a `graphviz.Digraph`.
+
+        Each node represent a metadata table.
+
+        Args:
+            plot (graphviz.Digraph)
+        """
+        for table in self.get_tables():
+            # Append table fields
+            fields = []
+
+            for name, value in self.get_fields(table).items():
+                if value.get('subtype') is not None:
+                    fields.append('{} : {} - {}'.format(name, value['type'], value['subtype']))
+
+                else:
+                    fields.append('{} : {}'.format(name, value['type']))
+
+            fields = r'\l'.join(fields)
+
+            # Append table extra information
+            extras = []
+
+            primary_key = self.get_primary_key(table)
+            if primary_key is not None:
+                extras.append('Primary key: {}'.format(primary_key))
+
+            parents = self.get_parents(table)
+            for parent in parents:
+                foreign_key = self.get_foreign_key(parent, table)
+                extras.append('Foreign key ({}): {}'.format(parent, foreign_key))
+
+            path = self.get_table_meta(table).get('path')
+            if path is not None:
+                extras.append('Data path: {}'.format(path))
+
+            extras = r'\l'.join(extras)
+
+            # Add table node
+            title = r'{%s|%s\l|%s\l}' % (table, fields, extras)
+            plot.node(table, label=title)
+
+    def _visualize_add_edges(self, plot):
+        """Add edges into a `graphviz.Digraph`.
+
+        Each edge represents a relationship between two metadata tables.
+
+        Args:
+            plot (graphviz.Digraph)
+        """
+        for table in self.get_tables():
+            for parent in list(self.get_parents(table)):
+                plot.edge(
+                    parent,
+                    table,
+                    label='   {}.{} -> {}.{}'.format(
+                        table, self.get_foreign_key(parent, table),
+                        parent, self.get_primary_key(parent)
+                    ),
+                    arrowhead='crow'
+                )
+
+    def visualize(self, path=None):
+        """Plot metadata usign graphviz.
+
+        Try to generate a plot using graphviz.
+        If a ``path`` is provided save the output into a file.
+
+        Args:
+            path (str):
+                Output file path to save the plot, it requires a graphviz
+                supported extension. If ``None`` do not save the plot.
+                Defaults to ``None``.
+        """
+        filename, graphviz_extension = self._get_graphviz_extension(path)
+        plot = graphviz.Digraph(
+            'Metadata',
+            format=graphviz_extension,
+            node_attr={
+                "shape": "Mrecord",
+                "fillcolor": "lightgoldenrod1",
+                "style": "filled"
+            },
+        )
+
+        self._visualize_add_nodes(plot)
+        self._visualize_add_edges(plot)
+
+        if filename:
+            plot.render(filename=filename, cleanup=True, format=graphviz_extension)
+        else:
+            return plot
+
+    def __str__(self):
+        tables = self.get_tables()
+        relationships = [
+            '    {}.{} -> {}.{}'.format(
+                table, self.get_foreign_key(parent, table),
+                parent, self.get_primary_key(parent)
+            )
+            for table in tables
+            for parent in list(self.get_parents(table))
+        ]
+
+        return (
+            "Metadata\n"
+            "  root_path: {}\n"
+            "  tables: {}\n"
+            "  relationships:\n"
+            "{}"
+        ).format(
+            os.path.abspath(self.root_path),
+            tables,
+            '\n'.join(relationships)
+        )
diff --git a/sdv/tabular/__init__.py b/sdv/tabular/__init__.py
index e69de29bb..752184c61 100644
--- a/sdv/tabular/__init__.py
+++ b/sdv/tabular/__init__.py
@@ -0,0 +1,8 @@
+from sdv.tabular.copulas import GaussianCopula
+from sdv.tabular.ctgan import CTGAN
+
+
+__all__ = [
+    'GaussianCopula',
+    'CTGAN'
+]
diff --git a/sdv/tabular/base.py b/sdv/tabular/base.py
index 1bdf4b854..70b5aeb7a 100644
--- a/sdv/tabular/base.py
+++ b/sdv/tabular/base.py
@@ -1,3 +1,5 @@
+import pickle
+
 from sdv.metadata import Table
 
 
@@ -190,3 +192,28 @@ def from_parameters(cls):
                 using a simple dictionary.
         """
         raise NotImplementedError()
+
+    def save(self, path):
+        """Save this model instance to the given path using pickle.
+
+        Args:
+            path (str):
+                Path where the SDV instance will be serialized.
+        """
+        with open(path, 'wb') as output:
+            pickle.dump(self, output)
+
+    @classmethod
+    def load(cls, path):
+        """Load a TabularModel instance from a given path.
+
+        Args:
+            path (str):
+                Path from which to load the instance.
+
+        Returns:
+            TabularModel:
+                The loaded tabular model.
+        """
+        with open(path, 'rb') as f:
+            return pickle.load(f)

From bf1624fdc55d30cf9109b3268e3cc421bd26cc56 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 21:09:25 +0200
Subject: [PATCH 25/33] Cleanup examples and add tutorials

---
 examples/0. Quickstart - README.ipynb         |  414 ------
 ...uickstart - Single Table - In Memory.ipynb |  430 ------
 .... Quickstart - Single Table - Census.ipynb |  562 --------
 .../3. Quickstart - Multitable - Files.ipynb  |  653 ---------
 examples/4. Anonymization.ipynb               |  398 ------
 examples/6. Metadata Validation.ipynb         |  239 ----
 .../Aibnb Dataset Subsampling.ipynb           |  206 ---
 .../Aibnb Example.ipynb                       |  730 ----------
 .../metadata.json                             |  116 --
 examples/demo_metadata.json                   |   74 -
 examples/quickstart/customers.csv             |    8 -
 examples/quickstart/metadata.json             |   96 --
 examples/quickstart/order_items.csv           |   50 -
 examples/quickstart/orders.csv                |   11 -
 tutorials/01_Quickstart.ipynb                 |  838 ++++++++++++
 tutorials/02_Single_Table_Modeling.ipynb      | 1204 +++++++++++++++++
 .../03_Relational_Data_Modeling.ipynb         |  854 ++++++------
 .../04_Working_with_Metadata.ipynb            |    0
 18 files changed, 2472 insertions(+), 4411 deletions(-)
 delete mode 100644 examples/0. Quickstart - README.ipynb
 delete mode 100644 examples/1. Quickstart - Single Table - In Memory.ipynb
 delete mode 100644 examples/2. Quickstart - Single Table - Census.ipynb
 delete mode 100644 examples/3. Quickstart - Multitable - Files.ipynb
 delete mode 100644 examples/4. Anonymization.ipynb
 delete mode 100644 examples/6. Metadata Validation.ipynb
 delete mode 100644 examples/airbnb-recruiting-new-user-bookings/Aibnb Dataset Subsampling.ipynb
 delete mode 100644 examples/airbnb-recruiting-new-user-bookings/Aibnb Example.ipynb
 delete mode 100644 examples/airbnb-recruiting-new-user-bookings/metadata.json
 delete mode 100644 examples/demo_metadata.json
 delete mode 100644 examples/quickstart/customers.csv
 delete mode 100644 examples/quickstart/metadata.json
 delete mode 100644 examples/quickstart/order_items.csv
 delete mode 100644 examples/quickstart/orders.csv
 create mode 100644 tutorials/01_Quickstart.ipynb
 create mode 100644 tutorials/02_Single_Table_Modeling.ipynb
 rename examples/Demo - Walmart.ipynb => tutorials/03_Relational_Data_Modeling.ipynb (55%)
 rename examples/5. Generate Metadata from Dataframes.ipynb => tutorials/04_Working_with_Metadata.ipynb (100%)

diff --git a/examples/0. Quickstart - README.ipynb b/examples/0. Quickstart - README.ipynb
deleted file mode 100644
index f6f2fecdc..000000000
--- a/examples/0. Quickstart - README.ipynb	
+++ /dev/null
@@ -1,414 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sdv import load_demo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata, tables = load_demo(metadata=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'tables': {'users': {'primary_key': 'user_id',\n",
-       "   'fields': {'user_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'country': {'type': 'categorical'},\n",
-       "    'gender': {'type': 'categorical'},\n",
-       "    'age': {'type': 'numerical', 'subtype': 'integer'}}},\n",
-       "  'sessions': {'primary_key': 'session_id',\n",
-       "   'fields': {'session_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'user_id': {'ref': {'field': 'user_id', 'table': 'users'},\n",
-       "     'type': 'id',\n",
-       "     'subtype': 'integer'},\n",
-       "    'device': {'type': 'categorical'},\n",
-       "    'os': {'type': 'categorical'}}},\n",
-       "  'transactions': {'primary_key': 'transaction_id',\n",
-       "   'fields': {'transaction_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'session_id': {'ref': {'field': 'session_id', 'table': 'sessions'},\n",
-       "     'type': 'id',\n",
-       "     'subtype': 'integer'},\n",
-       "    'timestamp': {'type': 'datetime', 'format': '%Y-%m-%d'},\n",
-       "    'amount': {'type': 'numerical', 'subtype': 'float'},\n",
-       "    'approved': {'type': 'boolean'}}}}}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metadata.to_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'users':    user_id country gender  age\n",
-       " 0        0     USA      M   34\n",
-       " 1        1      UK      F   23\n",
-       " 2        2      ES   None   44\n",
-       " 3        3      UK      M   22\n",
-       " 4        4     USA      F   54\n",
-       " 5        5      DE      M   57\n",
-       " 6        6      BG      F   45\n",
-       " 7        7      ES   None   41\n",
-       " 8        8      FR      F   23\n",
-       " 9        9      UK   None   30,\n",
-       " 'sessions':    session_id  user_id  device       os\n",
-       " 0           0        0  mobile  android\n",
-       " 1           1        1  tablet      ios\n",
-       " 2           2        1  tablet  android\n",
-       " 3           3        2  mobile  android\n",
-       " 4           4        4  mobile      ios\n",
-       " 5           5        5  mobile  android\n",
-       " 6           6        6  mobile      ios\n",
-       " 7           7        6  tablet      ios\n",
-       " 8           8        6  mobile      ios\n",
-       " 9           9        8  tablet      ios,\n",
-       " 'transactions':    transaction_id  session_id           timestamp  amount  approved\n",
-       " 0               0           0 2019-01-01 12:34:32   100.0      True\n",
-       " 1               1           0 2019-01-01 12:42:21    55.3      True\n",
-       " 2               2           1 2019-01-07 17:23:11    79.5      True\n",
-       " 3               3           3 2019-01-10 11:08:57   112.1     False\n",
-       " 4               4           5 2019-01-10 21:54:08   110.0     False\n",
-       " 5               5           5 2019-01-11 11:21:20    76.3      True\n",
-       " 6               6           7 2019-01-22 14:44:10    89.5      True\n",
-       " 7               7           8 2019-01-23 10:14:09   132.1     False\n",
-       " 8               8           9 2019-01-27 16:09:17    68.0      True\n",
-       " 9               9           9 2019-01-29 12:10:48    99.9      True}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
-       " -->\n",
-       "<!-- Title: Metadata Pages: 1 -->\n",
-       "<svg width=\"371pt\" height=\"500pt\"\n",
-       " viewBox=\"0.00 0.00 370.50 500.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 496)\">\n",
-       "<title>Metadata</title>\n",
-       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-496 366.5,-496 366.5,4 -4,4\"/>\n",
-       "<!-- users -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>users</title>\n",
-       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
-       "</g>\n",
-       "<!-- sessions -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>sessions</title>\n",
-       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
-       "</g>\n",
-       "<!-- users&#45;&gt;sessions -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>users&#45;&gt;sessions</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-377.2088C101.5,-363.9446 101.5,-349.6377 101.5,-335.7493\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-335.7312 106,-325.7312 101.5,-330.7312 101.5,-325.7312 101.5,-325.7312 101.5,-325.7312 101.5,-330.7312 97,-325.7313 101.5001,-335.7312 101.5001,-335.7312\"/>\n",
-       "<text text-anchor=\"middle\" x=\"197\" y=\"-347.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;sessions.user_id &#45;&gt; users.user_id</text>\n",
-       "</g>\n",
-       "<!-- transactions -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>transactions</title>\n",
-       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 191,-.5 191,-.5 197,-.5 203,-6.5 203,-12.5 203,-12.5 203,-132.5 203,-132.5 203,-138.5 197,-144.5 191,-144.5 191,-144.5 12,-144.5 12,-144.5 6,-144.5 0,-138.5 0,-132.5 0,-132.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
-       "</g>\n",
-       "<!-- sessions&#45;&gt;transactions -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>sessions&#45;&gt;transactions</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-196.3927C101.5,-183.0141 101.5,-168.7483 101.5,-154.8345\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-154.7831 106,-144.7831 101.5,-149.7831 101.5,-144.7831 101.5,-144.7831 101.5,-144.7831 101.5,-149.7831 97,-144.7831 101.5001,-154.7831 101.5001,-154.7831\"/>\n",
-       "<text text-anchor=\"middle\" x=\"232\" y=\"-166.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;transactions.session_id &#45;&gt; sessions.session_id</text>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>\n"
-      ],
-      "text/plain": [
-       "<graphviz.dot.Digraph at 0x7f3aba585080>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metadata.visualize()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-07-02 14:24:31,338 - INFO - modeler - Modeling users\n",
-      "2020-07-02 14:24:31,339 - INFO - metadata - Loading transformer CategoricalTransformer for field country\n",
-      "2020-07-02 14:24:31,340 - INFO - metadata - Loading transformer CategoricalTransformer for field gender\n",
-      "2020-07-02 14:24:31,341 - INFO - metadata - Loading transformer NumericalTransformer for field age\n",
-      "2020-07-02 14:24:31,356 - INFO - modeler - Modeling sessions\n",
-      "2020-07-02 14:24:31,357 - INFO - metadata - Loading transformer CategoricalTransformer for field device\n",
-      "2020-07-02 14:24:31,357 - INFO - metadata - Loading transformer CategoricalTransformer for field os\n",
-      "2020-07-02 14:24:31,371 - INFO - modeler - Modeling transactions\n",
-      "2020-07-02 14:24:31,372 - INFO - metadata - Loading transformer DatetimeTransformer for field timestamp\n",
-      "2020-07-02 14:24:31,373 - INFO - metadata - Loading transformer NumericalTransformer for field amount\n",
-      "2020-07-02 14:24:31,373 - INFO - metadata - Loading transformer BooleanTransformer for field approved\n",
-      "2020-07-02 14:24:31,386 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,396 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
-      "  return getattr(obj, method)(*args, **kwds)\n",
-      "2020-07-02 14:24:31,404 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/pandas/core/frame.py:7143: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  baseCov = np.cov(mat.T)\n",
-      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2451: RuntimeWarning: divide by zero encountered in true_divide\n",
-      "  c *= np.true_divide(1, fact)\n",
-      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2451: RuntimeWarning: invalid value encountered in multiply\n",
-      "  c *= np.true_divide(1, fact)\n",
-      "2020-07-02 14:24:31,413 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,418 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,425 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,431 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,435 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,447 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,471 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,485 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,502 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,516 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,530 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,544 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,563 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,629 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:24:31,734 - INFO - modeler - Modeling Complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sdv import SDV\n",
-    "\n",
-    "sdv = SDV()\n",
-    "sdv.fit(metadata, tables)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'users':    user_id country gender  age\n",
-       " 0        0      ES      F   50\n",
-       " 1        1      FR    NaN   61\n",
-       " 2        2      UK      F   19\n",
-       " 3        3     USA      F   37\n",
-       " 4        4      UK      M   14\n",
-       " 5        5     USA      M   20\n",
-       " 6        6      FR      F   41\n",
-       " 7        7      UK    NaN   15\n",
-       " 8        8     USA      F   17\n",
-       " 9        9      UK      M   27,\n",
-       " 'sessions':    session_id  user_id  device       os\n",
-       " 0           0        5  mobile  android\n",
-       " 1           1        3  tablet      ios\n",
-       " 2           2        5  mobile      ios\n",
-       " 3           3        2  mobile      ios\n",
-       " 4           4        1  mobile      ios\n",
-       " 5           5        8  mobile      ios\n",
-       " 6           6        3  mobile      ios\n",
-       " 7           7        2  tablet      ios\n",
-       " 8           8        8  tablet      ios,\n",
-       " 'transactions':     transaction_id  session_id                     timestamp       amount  \\\n",
-       " 0                0           1 2019-01-05 19:44:17.109082880   108.520480   \n",
-       " 1                1           0 2019-01-05 19:44:17.109082880   108.520446   \n",
-       " 2                2           4 2019-01-05 19:44:17.109082880   108.520456   \n",
-       " 3                3           4 2019-01-05 19:44:17.109082880   108.520463   \n",
-       " 4                4           0 2019-01-05 19:44:17.109082880   108.520451   \n",
-       " 5                5           4 2019-01-14 10:59:23.654829312  -293.744075   \n",
-       " 6                6           0 2019-01-08 21:06:22.124677120  -157.555578   \n",
-       " 7                7           4 2019-01-05 19:44:17.109082880   108.520405   \n",
-       " 8                8           4 2019-01-20 05:49:39.108628736  1436.388992   \n",
-       " 9                9           0 2019-01-13 01:21:03.496772608  -688.014399   \n",
-       " 10              10           5 2019-01-15 19:14:13.132234496    92.573682   \n",
-       " 11              11           0 2019-01-15 19:13:47.834800128    88.733610   \n",
-       " 12              12           6 2019-01-15 19:13:25.752608512    87.612329   \n",
-       " 13              13           6 2019-01-15 19:14:06.131340544    91.149239   \n",
-       " 14              14           4 2019-01-05 19:44:17.109082880   108.520315   \n",
-       " 15              15           4 2019-01-10 14:54:25.195068416  -405.271997   \n",
-       " 16              16           0 2019-01-06 11:03:17.424094208   321.267007   \n",
-       " 17              17           1 2019-01-15 19:13:30.394386944    89.473644   \n",
-       " 18              18           5 2019-01-15 19:13:23.178177536    87.084668   \n",
-       " 19              19           5 2019-01-15 19:13:53.814526976    87.328847   \n",
-       " 20              20           6 2019-01-15 19:13:47.778595840    87.698023   \n",
-       " 21              21           0 2019-01-05 19:44:17.109082880   108.520444   \n",
-       " 22              22           4 2019-01-03 13:01:55.231368192  -171.151299   \n",
-       " 23              23           4 2019-01-10 03:03:38.631104256  -711.512501   \n",
-       " 24              24           5 2019-01-15 19:13:39.923056128    92.779222   \n",
-       " 25              25           5 2019-01-15 19:13:54.239550976    87.046705   \n",
-       " 26              26           1 2019-01-15 19:13:51.475357952    89.768894   \n",
-       " 27              27           6 2019-01-15 19:13:38.378663168    85.864682   \n",
-       " 28              28           7 2019-01-20 22:53:01.679367168    84.550410   \n",
-       " 29              29           7 2019-01-20 22:53:01.672930560    84.739006   \n",
-       " 30              30           1 2019-01-20 22:53:01.444472832    84.790641   \n",
-       " 31              31           8 2019-01-20 22:53:01.404470784    84.595276   \n",
-       " 32              32           4 2019-01-05 19:44:17.109082880   108.520411   \n",
-       " 33              33           4 2019-01-15 17:22:02.305563904  -602.769803   \n",
-       " 34              34           1 2019-01-15 04:01:37.883063808  -493.475733   \n",
-       " 35              35           0 2019-01-15 19:13:20.536071936    82.887047   \n",
-       " 36              36           5 2019-01-15 19:13:50.685021696    90.577240   \n",
-       " 37              37           6 2019-01-15 19:14:05.128847616    91.543630   \n",
-       " 38              38           6 2019-01-15 19:13:10.650768896    90.478404   \n",
-       " 39              39           7 2019-01-20 22:53:01.680873472    84.876897   \n",
-       " 40              40           7 2019-01-20 22:53:01.735595520    85.080808   \n",
-       " 41              41           0 2019-01-20 22:53:01.332932352    84.584933   \n",
-       " 42              42           8 2019-01-20 22:53:01.451664640    84.779657   \n",
-       " \n",
-       "     approved  \n",
-       " 0      False  \n",
-       " 1      False  \n",
-       " 2      False  \n",
-       " 3      False  \n",
-       " 4      False  \n",
-       " 5       True  \n",
-       " 6       True  \n",
-       " 7      False  \n",
-       " 8       True  \n",
-       " 9       True  \n",
-       " 10      True  \n",
-       " 11      True  \n",
-       " 12      True  \n",
-       " 13      True  \n",
-       " 14     False  \n",
-       " 15      True  \n",
-       " 16      True  \n",
-       " 17      True  \n",
-       " 18      True  \n",
-       " 19      True  \n",
-       " 20      True  \n",
-       " 21     False  \n",
-       " 22      True  \n",
-       " 23      True  \n",
-       " 24      True  \n",
-       " 25      True  \n",
-       " 26      True  \n",
-       " 27      True  \n",
-       " 28      True  \n",
-       " 29      True  \n",
-       " 30      True  \n",
-       " 31      True  \n",
-       " 32     False  \n",
-       " 33      True  \n",
-       " 34      True  \n",
-       " 35      True  \n",
-       " 36      True  \n",
-       " 37      True  \n",
-       " 38      True  \n",
-       " 39      True  \n",
-       " 40      True  \n",
-       " 41      True  \n",
-       " 42      True  }"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sdv.sample_all(10)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/1. Quickstart - Single Table - In Memory.ipynb b/examples/1. Quickstart - Single Table - In Memory.ipynb
deleted file mode 100644
index b9f48bbd2..000000000
--- a/examples/1. Quickstart - Single Table - In Memory.ipynb	
+++ /dev/null
@@ -1,430 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "data = pd.DataFrame({\n",
-    "    'index': [1, 2, 3, 4, 5, 6, 7, 8],\n",
-    "    'integer': [1, None, 1, 2, 1, 2, 3, 2],\n",
-    "    'float': [0.1, None, 0.1, 0.2, 0.1, 0.2, 0.3, 0.1],\n",
-    "    'categorical': ['a', 'b', 'a', 'b', 'a', None, 'c', None],\n",
-    "    'bool': [False, True, False, True, False, False, False, None],\n",
-    "    'nullable': [1, None, 3, None, 5, None, 7, None],\n",
-    "    'datetime': [\n",
-    "        '2010-01-01', '2010-02-01', '2010-01-01', '2010-02-01',\n",
-    "        '2010-01-01', '2010-02-01', '2010-03-01', None\n",
-    "    ]\n",
-    "})\n",
-    "data['datetime'] = pd.to_datetime(data['datetime'])\n",
-    "\n",
-    "tables = {\n",
-    "    'data': data\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>integer</th>\n",
-       "      <th>float</th>\n",
-       "      <th>categorical</th>\n",
-       "      <th>bool</th>\n",
-       "      <th>nullable</th>\n",
-       "      <th>datetime</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>a</td>\n",
-       "      <td>False</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2010-01-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>b</td>\n",
-       "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2010-02-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>a</td>\n",
-       "      <td>False</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2010-01-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>b</td>\n",
-       "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2010-02-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>a</td>\n",
-       "      <td>False</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2010-01-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>None</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2010-02-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>c</td>\n",
-       "      <td>False</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2010-03-01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   index  integer  float categorical   bool  nullable   datetime\n",
-       "0      1      1.0    0.1           a  False       1.0 2010-01-01\n",
-       "1      2      NaN    NaN           b   True       NaN 2010-02-01\n",
-       "2      3      1.0    0.1           a  False       3.0 2010-01-01\n",
-       "3      4      2.0    0.2           b   True       NaN 2010-02-01\n",
-       "4      5      1.0    0.1           a  False       5.0 2010-01-01\n",
-       "5      6      2.0    0.2        None  False       NaN 2010-02-01\n",
-       "6      7      3.0    0.3           c  False       7.0 2010-03-01\n",
-       "7      8      2.0    0.1        None   None       NaN        NaT"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata = {\n",
-    "    \"tables\": [\n",
-    "        {\n",
-    "            \"fields\": [\n",
-    "                {\n",
-    "                    \"name\": \"index\",\n",
-    "                    \"type\": \"id\"\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"integer\",\n",
-    "                    \"type\": \"numerical\",\n",
-    "                    \"subtype\": \"integer\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"float\",\n",
-    "                    \"type\": \"numerical\",\n",
-    "                    \"subtype\": \"float\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"categorical\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                    \"pii\": False,\n",
-    "                    \"pii_category\": \"email\"\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"bool\",\n",
-    "                    \"type\": \"boolean\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"nullable\",\n",
-    "                    \"type\": \"numerical\",\n",
-    "                    \"subtype\": \"float\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"datetime\",\n",
-    "                    \"type\": \"datetime\",\n",
-    "                    \"format\": \"%Y-%m-%d\"\n",
-    "                },\n",
-    "            ],\n",
-    "            \"name\": \"data\",\n",
-    "            \"primary_key\": \"index\"\n",
-    "        }\n",
-    "    ]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-07-02 14:25:11,511 - INFO - modeler - Modeling data\n",
-      "2020-07-02 14:25:11,512 - INFO - metadata - Loading transformer NumericalTransformer for field integer\n",
-      "2020-07-02 14:25:11,512 - INFO - metadata - Loading transformer NumericalTransformer for field float\n",
-      "2020-07-02 14:25:11,513 - INFO - metadata - Loading transformer CategoricalTransformer for field categorical\n",
-      "2020-07-02 14:25:11,513 - INFO - metadata - Loading transformer BooleanTransformer for field bool\n",
-      "2020-07-02 14:25:11,513 - INFO - metadata - Loading transformer NumericalTransformer for field nullable\n",
-      "2020-07-02 14:25:11,514 - INFO - metadata - Loading transformer DatetimeTransformer for field datetime\n",
-      "2020-07-02 14:25:11,551 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:11,564 - INFO - modeler - Modeling Complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sdv import SDV\n",
-    "\n",
-    "sdv = SDV()\n",
-    "sdv.fit(metadata, tables={'data': data})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>integer</th>\n",
-       "      <th>float</th>\n",
-       "      <th>categorical</th>\n",
-       "      <th>bool</th>\n",
-       "      <th>nullable</th>\n",
-       "      <th>datetime</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>b</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2010-01-21 11:39:29.986688000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2010-02-17 13:51:34.408565760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.304326</td>\n",
-       "      <td>c</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2010-02-25 12:07:19.982103552</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.174580</td>\n",
-       "      <td>a</td>\n",
-       "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2010-01-21 04:04:59.336169472</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.208637</td>\n",
-       "      <td>a</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.026796</td>\n",
-       "      <td>b</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.166949</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2010-01-28 23:23:34.873413888</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.086972</td>\n",
-       "      <td>b</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2010-01-08 09:44:47.101891840</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   index  integer     float categorical   bool  nullable  \\\n",
-       "0      0      NaN       NaN           b  False       NaN   \n",
-       "1      1      NaN       NaN         NaN   True       NaN   \n",
-       "2      2      3.0  0.304326           c  False       NaN   \n",
-       "3      3      2.0  0.174580           a   True       NaN   \n",
-       "4      4      3.0  0.208637           a    NaN       NaN   \n",
-       "5      5      1.0  0.026796           b    NaN       NaN   \n",
-       "6      6      2.0  0.166949         NaN  False       NaN   \n",
-       "7      7      1.0  0.086972           b  False       NaN   \n",
-       "\n",
-       "                       datetime  \n",
-       "0 2010-01-21 11:39:29.986688000  \n",
-       "1 2010-02-17 13:51:34.408565760  \n",
-       "2 2010-02-25 12:07:19.982103552  \n",
-       "3 2010-01-21 04:04:59.336169472  \n",
-       "4                           NaT  \n",
-       "5                           NaT  \n",
-       "6 2010-01-28 23:23:34.873413888  \n",
-       "7 2010-01-08 09:44:47.101891840  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = sdv.sample_all()\n",
-    "samples['data']"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/2. Quickstart - Single Table - Census.ipynb b/examples/2. Quickstart - Single Table - Census.ipynb
deleted file mode 100644
index 6857b7827..000000000
--- a/examples/2. Quickstart - Single Table - Census.ipynb	
+++ /dev/null
@@ -1,562 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'\n",
-    "columns = [\n",
-    "    'age',\n",
-    "    'workclass',\n",
-    "    'fnlwgt',\n",
-    "    'education',\n",
-    "    'education-num',\n",
-    "    'marital-status',\n",
-    "    'occupation',\n",
-    "    'relationship',\n",
-    "    'race',\n",
-    "    'sex',\n",
-    "    'capital-gain',\n",
-    "    'capital-loss',\n",
-    "    'hours-per-week',\n",
-    "    'native-country',\n",
-    "    'income'\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>workclass</th>\n",
-       "      <th>fnlwgt</th>\n",
-       "      <th>education</th>\n",
-       "      <th>education-num</th>\n",
-       "      <th>marital-status</th>\n",
-       "      <th>occupation</th>\n",
-       "      <th>relationship</th>\n",
-       "      <th>race</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>capital-gain</th>\n",
-       "      <th>capital-loss</th>\n",
-       "      <th>hours-per-week</th>\n",
-       "      <th>native-country</th>\n",
-       "      <th>income</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>39</td>\n",
-       "      <td>State-gov</td>\n",
-       "      <td>77516</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Adm-clerical</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>2174</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>50</td>\n",
-       "      <td>Self-emp-not-inc</td>\n",
-       "      <td>83311</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Exec-managerial</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>38</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>215646</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>9</td>\n",
-       "      <td>Divorced</td>\n",
-       "      <td>Handlers-cleaners</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>53</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>234721</td>\n",
-       "      <td>11th</td>\n",
-       "      <td>7</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Handlers-cleaners</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>338409</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Prof-specialty</td>\n",
-       "      <td>Wife</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>Cuba</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   age          workclass  fnlwgt   education  education-num  \\\n",
-       "0   39          State-gov   77516   Bachelors             13   \n",
-       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
-       "2   38            Private  215646     HS-grad              9   \n",
-       "3   53            Private  234721        11th              7   \n",
-       "4   28            Private  338409   Bachelors             13   \n",
-       "\n",
-       "        marital-status          occupation    relationship    race      sex  \\\n",
-       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
-       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
-       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
-       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
-       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
-       "\n",
-       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
-       "0          2174             0              40   United-States   <=50K  \n",
-       "1             0             0              13   United-States   <=50K  \n",
-       "2             0             0              40   United-States   <=50K  \n",
-       "3             0             0              40   United-States   <=50K  \n",
-       "4             0             0              40            Cuba   <=50K  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(url, names=columns)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tables = {\n",
-    "    'census': df\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata = {\n",
-    "    \"tables\": [\n",
-    "        {\n",
-    "            \"fields\": [\n",
-    "                {\n",
-    "                    \"name\": \"age\",\n",
-    "                    \"type\": \"numerical\",\n",
-    "                    \"subtype\": \"integer\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"workclass\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"fnlwgt\",\n",
-    "                    \"type\": \"numerical\",\n",
-    "                    \"subtype\": \"integer\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"education\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"education-num\",\n",
-    "                    \"type\": \"numerical\",\n",
-    "                    \"subtype\": \"integer\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"marital-status\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"occupation\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"relationship\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"race\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"sex\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"capital-gain\",\n",
-    "                    \"type\": \"numerical\",\n",
-    "                    \"subtype\": \"integer\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"capital-loss\",\n",
-    "                    \"type\": \"numerical\",\n",
-    "                    \"subtype\": \"integer\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"hours-per-week\",\n",
-    "                    \"type\": \"numerical\",\n",
-    "                    \"subtype\": \"integer\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"native-country\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"income\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                }\n",
-    "            ],\n",
-    "            \"name\": \"census\",\n",
-    "        }\n",
-    "    ]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-07-02 14:25:31,656 - INFO - modeler - Modeling census\n",
-      "2020-07-02 14:25:31,657 - INFO - metadata - Loading transformer NumericalTransformer for field age\n",
-      "2020-07-02 14:25:31,657 - INFO - metadata - Loading transformer CategoricalTransformer for field workclass\n",
-      "2020-07-02 14:25:31,658 - INFO - metadata - Loading transformer NumericalTransformer for field fnlwgt\n",
-      "2020-07-02 14:25:31,658 - INFO - metadata - Loading transformer CategoricalTransformer for field education\n",
-      "2020-07-02 14:25:31,658 - INFO - metadata - Loading transformer NumericalTransformer for field education-num\n",
-      "2020-07-02 14:25:31,659 - INFO - metadata - Loading transformer CategoricalTransformer for field marital-status\n",
-      "2020-07-02 14:25:31,659 - INFO - metadata - Loading transformer CategoricalTransformer for field occupation\n",
-      "2020-07-02 14:25:31,659 - INFO - metadata - Loading transformer CategoricalTransformer for field relationship\n",
-      "2020-07-02 14:25:31,660 - INFO - metadata - Loading transformer CategoricalTransformer for field race\n",
-      "2020-07-02 14:25:31,660 - INFO - metadata - Loading transformer CategoricalTransformer for field sex\n",
-      "2020-07-02 14:25:31,661 - INFO - metadata - Loading transformer NumericalTransformer for field capital-gain\n",
-      "2020-07-02 14:25:31,661 - INFO - metadata - Loading transformer NumericalTransformer for field capital-loss\n",
-      "2020-07-02 14:25:31,662 - INFO - metadata - Loading transformer NumericalTransformer for field hours-per-week\n",
-      "2020-07-02 14:25:31,662 - INFO - metadata - Loading transformer CategoricalTransformer for field native-country\n",
-      "2020-07-02 14:25:31,663 - INFO - metadata - Loading transformer CategoricalTransformer for field income\n",
-      "2020-07-02 14:25:31,831 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:31,928 - INFO - modeler - Modeling Complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sdv import SDV\n",
-    "\n",
-    "sdv = SDV()\n",
-    "sdv.fit(metadata, tables)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>workclass</th>\n",
-       "      <th>fnlwgt</th>\n",
-       "      <th>education</th>\n",
-       "      <th>education-num</th>\n",
-       "      <th>marital-status</th>\n",
-       "      <th>occupation</th>\n",
-       "      <th>relationship</th>\n",
-       "      <th>race</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>capital-gain</th>\n",
-       "      <th>capital-loss</th>\n",
-       "      <th>hours-per-week</th>\n",
-       "      <th>native-country</th>\n",
-       "      <th>income</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>35</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>468185</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>9</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>2039</td>\n",
-       "      <td>436</td>\n",
-       "      <td>48</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>47</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>104447</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>7</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Adm-clerical</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>-703</td>\n",
-       "      <td>81</td>\n",
-       "      <td>53</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>20</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>231391</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>11</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Exec-managerial</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>654</td>\n",
-       "      <td>-8</td>\n",
-       "      <td>57</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>35</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>223275</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>12</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Sales</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>-392</td>\n",
-       "      <td>-178</td>\n",
-       "      <td>71</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>26</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>-11408</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>8</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Machine-op-inspct</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>5799</td>\n",
-       "      <td>13</td>\n",
-       "      <td>47</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   age workclass  fnlwgt education  education-num       marital-status  \\\n",
-       "0   35   Private  468185   HS-grad              9   Married-civ-spouse   \n",
-       "1   47   Private  104447   HS-grad              7   Married-civ-spouse   \n",
-       "2   20   Private  231391   HS-grad             11        Never-married   \n",
-       "3   35   Private  223275   Masters             12   Married-civ-spouse   \n",
-       "4   26   Private  -11408   HS-grad              8   Married-civ-spouse   \n",
-       "\n",
-       "           occupation    relationship    race    sex  capital-gain  \\\n",
-       "0               Sales         Husband   White   Male          2039   \n",
-       "1        Adm-clerical         Husband   White   Male          -703   \n",
-       "2     Exec-managerial   Not-in-family   White   Male           654   \n",
-       "3               Sales   Not-in-family   White   Male          -392   \n",
-       "4   Machine-op-inspct         Husband   White   Male          5799   \n",
-       "\n",
-       "   capital-loss  hours-per-week  native-country  income  \n",
-       "0           436              48   United-States   <=50K  \n",
-       "1            81              53   United-States   <=50K  \n",
-       "2            -8              57   United-States   <=50K  \n",
-       "3          -178              71   United-States   <=50K  \n",
-       "4            13              47   United-States   <=50K  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sampled = sdv.sample('census', num_rows=len(df))\n",
-    "sampled['census'].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-43.151506729008716"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sdv.evaluation import evaluate\n",
-    "\n",
-    "samples = sdv.sample_all(len(tables['census']))\n",
-    "\n",
-    "evaluate(samples, real=tables, metadata=sdv.metadata)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/3. Quickstart - Multitable - Files.ipynb b/examples/3. Quickstart - Multitable - Files.ipynb
deleted file mode 100644
index d00a5989e..000000000
--- a/examples/3. Quickstart - Multitable - Files.ipynb	
+++ /dev/null
@@ -1,653 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-07-02 14:25:44,066 - INFO - modeler - Modeling customers\n",
-      "2020-07-02 14:25:44,067 - INFO - metadata - Loading table customers\n",
-      "2020-07-02 14:25:44,074 - INFO - metadata - Loading transformer CategoricalTransformer for field cust_postal_code\n",
-      "2020-07-02 14:25:44,074 - INFO - metadata - Loading transformer NumericalTransformer for field phone_number1\n",
-      "2020-07-02 14:25:44,075 - INFO - metadata - Loading transformer NumericalTransformer for field credit_limit\n",
-      "2020-07-02 14:25:44,076 - INFO - metadata - Loading transformer CategoricalTransformer for field country\n",
-      "2020-07-02 14:25:44,094 - INFO - modeler - Modeling orders\n",
-      "2020-07-02 14:25:44,095 - INFO - metadata - Loading table orders\n",
-      "2020-07-02 14:25:44,098 - INFO - metadata - Loading transformer NumericalTransformer for field order_total\n",
-      "2020-07-02 14:25:44,101 - INFO - modeler - Modeling order_items\n",
-      "2020-07-02 14:25:44,102 - INFO - metadata - Loading table order_items\n",
-      "2020-07-02 14:25:44,106 - INFO - metadata - Loading transformer CategoricalTransformer for field product_id\n",
-      "2020-07-02 14:25:44,107 - INFO - metadata - Loading transformer NumericalTransformer for field unit_price\n",
-      "2020-07-02 14:25:44,108 - INFO - metadata - Loading transformer NumericalTransformer for field quantity\n",
-      "2020-07-02 14:25:44,120 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,131 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,138 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,147 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,155 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,164 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,171 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,177 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,183 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,189 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,196 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,210 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,231 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,258 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,281 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,303 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,370 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:25:44,496 - INFO - modeler - Modeling Complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sdv import SDV\n",
-    "\n",
-    "sdv = SDV()\n",
-    "sdv.fit('quickstart/metadata.json')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-07-02 14:25:46,641 - INFO - metadata - Loading table customers\n",
-      "2020-07-02 14:25:46,646 - INFO - metadata - Loading table orders\n",
-      "2020-07-02 14:25:46,649 - INFO - metadata - Loading table order_items\n"
-     ]
-    }
-   ],
-   "source": [
-    "real = sdv.metadata.load_tables()\n",
-    "\n",
-    "samples = sdv.sample_all(len(real['customers']), reset_primary_keys=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>customer_id</th>\n",
-       "      <th>cust_postal_code</th>\n",
-       "      <th>phone_number1</th>\n",
-       "      <th>credit_limit</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>63145</td>\n",
-       "      <td>7811758514</td>\n",
-       "      <td>510</td>\n",
-       "      <td>US</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>20166</td>\n",
-       "      <td>6720163478</td>\n",
-       "      <td>317</td>\n",
-       "      <td>SPAIN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>11371</td>\n",
-       "      <td>7447861719</td>\n",
-       "      <td>1371</td>\n",
-       "      <td>SPAIN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>63145</td>\n",
-       "      <td>6835026704</td>\n",
-       "      <td>1024</td>\n",
-       "      <td>US</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>11371</td>\n",
-       "      <td>5371158268</td>\n",
-       "      <td>739</td>\n",
-       "      <td>FRANCE</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   customer_id cust_postal_code  phone_number1  credit_limit country\n",
-       "0            0            63145     7811758514           510      US\n",
-       "1            1            20166     6720163478           317   SPAIN\n",
-       "2            2            11371     7447861719          1371   SPAIN\n",
-       "3            3            63145     6835026704          1024      US\n",
-       "4            4            11371     5371158268           739  FRANCE"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples['customers'].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>customer_id</th>\n",
-       "      <th>cust_postal_code</th>\n",
-       "      <th>phone_number1</th>\n",
-       "      <th>credit_limit</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>50</td>\n",
-       "      <td>11371</td>\n",
-       "      <td>6175553295</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>UK</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4</td>\n",
-       "      <td>63145</td>\n",
-       "      <td>8605551835</td>\n",
-       "      <td>500</td>\n",
-       "      <td>US</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>97338810</td>\n",
-       "      <td>6096</td>\n",
-       "      <td>7035552143</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>CANADA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>630407</td>\n",
-       "      <td>63145</td>\n",
-       "      <td>7035552143</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>US</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>826362</td>\n",
-       "      <td>11371</td>\n",
-       "      <td>6175553295</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>UK</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   customer_id cust_postal_code  phone_number1  credit_limit country\n",
-       "0           50            11371     6175553295          1000      UK\n",
-       "1            4            63145     8605551835           500      US\n",
-       "2     97338810             6096     7035552143          1000  CANADA\n",
-       "3       630407            63145     7035552143          2000      US\n",
-       "4       826362            11371     6175553295          1000      UK"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "real['customers'].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>order_id</th>\n",
-       "      <th>customer_id</th>\n",
-       "      <th>order_total</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1784</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2677</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1485</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2435</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   order_id  customer_id  order_total\n",
-       "0         0            0         1784\n",
-       "1         1            1         1864\n",
-       "2         2            4         2677\n",
-       "3         3            1         1485\n",
-       "4         4            1         2435"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples['orders'].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>order_id</th>\n",
-       "      <th>customer_id</th>\n",
-       "      <th>order_total</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>50</td>\n",
-       "      <td>2310</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1507</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>10</td>\n",
-       "      <td>97338810</td>\n",
-       "      <td>730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>6</td>\n",
-       "      <td>55996144</td>\n",
-       "      <td>730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>55996144</td>\n",
-       "      <td>939</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   order_id  customer_id  order_total\n",
-       "0         1           50         2310\n",
-       "1         2            4         1507\n",
-       "2        10     97338810          730\n",
-       "3         6     55996144          730\n",
-       "4         3     55996144          939"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "real['orders'].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>order_item_id</th>\n",
-       "      <th>order_id</th>\n",
-       "      <th>product_id</th>\n",
-       "      <th>unit_price</th>\n",
-       "      <th>quantity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>208</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>10</td>\n",
-       "      <td>116</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>57</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>-24</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>38</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   order_item_id  order_id product_id  unit_price  quantity\n",
-       "0              0         2          6         208         7\n",
-       "1              1         7         10         116         2\n",
-       "2              2         0         10          57         6\n",
-       "3              3         3          6         -24         1\n",
-       "4              4         3          6          38         4"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples['order_items'].head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>order_item_id</th>\n",
-       "      <th>order_id</th>\n",
-       "      <th>product_id</th>\n",
-       "      <th>unit_price</th>\n",
-       "      <th>quantity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>100</td>\n",
-       "      <td>10</td>\n",
-       "      <td>7</td>\n",
-       "      <td>52</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>101</td>\n",
-       "      <td>8</td>\n",
-       "      <td>6</td>\n",
-       "      <td>125</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>102</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>125</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>103</td>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>125</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>104</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>113</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   order_item_id  order_id product_id  unit_price  quantity\n",
-       "0            100        10          7          52         8\n",
-       "1            101         8          6         125         4\n",
-       "2            102         1          6         125         4\n",
-       "3            103         4          9         125         4\n",
-       "4            104         1          9         113         4"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "real['order_items'].head()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/4. Anonymization.ipynb b/examples/4. Anonymization.ipynb
deleted file mode 100644
index 5ff4caa51..000000000
--- a/examples/4. Anonymization.ipynb	
+++ /dev/null
@@ -1,398 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "data = pd.DataFrame([\n",
-    "    {\n",
-    "        'index': 1,\n",
-    "        'name': 'Bill',\n",
-    "        'credit_card_number': '1111222233334444'\n",
-    "    },\n",
-    "    {\n",
-    "        'index': 2,\n",
-    "        'name': 'Jeff',\n",
-    "        'credit_card_number': '0000000000000000'\n",
-    "    },\n",
-    "    {\n",
-    "        'index': 3,\n",
-    "        'name': 'Bill',\n",
-    "        'credit_card_number': '9999999999999999'\n",
-    "    },\n",
-    "    {\n",
-    "        'index': 4,\n",
-    "        'name': 'Joe',\n",
-    "        'credit_card_number': '8888888888888888'\n",
-    "    },\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>credit_card_number</th>\n",
-       "      <th>index</th>\n",
-       "      <th>name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1111222233334444</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Bill</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0000000000000000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Jeff</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>9999999999999999</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Bill</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>8888888888888888</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Joe</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  credit_card_number  index  name\n",
-       "0   1111222233334444      1  Bill\n",
-       "1   0000000000000000      2  Jeff\n",
-       "2   9999999999999999      3  Bill\n",
-       "3   8888888888888888      4   Joe"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata = {\n",
-    "    \"tables\": [\n",
-    "        {\n",
-    "            \"fields\": [\n",
-    "                {\n",
-    "                    \"name\": \"index\",\n",
-    "                    \"type\": \"id\"\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"name\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                    \"pii\": True,\n",
-    "                    \"pii_category\": \"first_name\"\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"credit_card_number\",\n",
-    "                    \"type\": \"categorical\",\n",
-    "                    \"pii\": True,\n",
-    "                    \"pii_category\": [\n",
-    "                        \"credit_card_number\",\n",
-    "                        \"visa\"\n",
-    "                    ]\n",
-    "                }\n",
-    "            ],\n",
-    "            \"name\": \"anonymized\",\n",
-    "            \"primary_key\": \"index\",\n",
-    "        },\n",
-    "        {\n",
-    "            \"fields\": [\n",
-    "                {\n",
-    "                    \"name\": \"index\",\n",
-    "                    \"type\": \"id\"\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"name\",\n",
-    "                    \"type\": \"categorical\"\n",
-    "                },\n",
-    "                {\n",
-    "                    \"name\": \"credit_card_number\",\n",
-    "                    \"type\": \"categorical\"\n",
-    "                }\n",
-    "            ],\n",
-    "            \"name\": \"normal\",\n",
-    "            \"primary_key\": \"index\",\n",
-    "        }\n",
-    "    ]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tables = {\n",
-    "    'anonymized': data,\n",
-    "    'normal': data.copy()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-07-02 14:26:26,044 - INFO - modeler - Modeling anonymized\n",
-      "2020-07-02 14:26:26,044 - INFO - metadata - Loading transformer CategoricalTransformer for field name\n",
-      "2020-07-02 14:26:26,045 - INFO - metadata - Loading transformer CategoricalTransformer for field credit_card_number\n",
-      "2020-07-02 14:26:26,087 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:26:26,092 - INFO - modeler - Modeling normal\n",
-      "2020-07-02 14:26:26,092 - INFO - metadata - Loading transformer CategoricalTransformer for field name\n",
-      "2020-07-02 14:26:26,093 - INFO - metadata - Loading transformer CategoricalTransformer for field credit_card_number\n",
-      "2020-07-02 14:26:26,109 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 14:26:26,113 - INFO - modeler - Modeling Complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sdv import SDV\n",
-    "\n",
-    "sdv = SDV()\n",
-    "sdv.fit(metadata, tables)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "sampled = sdv.sample_all()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>name</th>\n",
-       "      <th>credit_card_number</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Pamela</td>\n",
-       "      <td>4801288395665668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Kimberly</td>\n",
-       "      <td>4801288395665668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Pamela</td>\n",
-       "      <td>4801288395665668</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Kimberly</td>\n",
-       "      <td>4592405566223480</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   index      name credit_card_number\n",
-       "0      0    Pamela   4801288395665668\n",
-       "1      1  Kimberly   4801288395665668\n",
-       "2      2    Pamela   4801288395665668\n",
-       "3      3  Kimberly   4592405566223480"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sampled['anonymized']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>index</th>\n",
-       "      <th>name</th>\n",
-       "      <th>credit_card_number</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>Joe</td>\n",
-       "      <td>8888888888888888</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Joe</td>\n",
-       "      <td>0000000000000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Joe</td>\n",
-       "      <td>8888888888888888</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Bill</td>\n",
-       "      <td>8888888888888888</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   index  name credit_card_number\n",
-       "0      0   Joe   8888888888888888\n",
-       "1      1   Joe   0000000000000000\n",
-       "2      2   Joe   8888888888888888\n",
-       "3      3  Bill   8888888888888888"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sampled['normal']"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/6. Metadata Validation.ipynb b/examples/6. Metadata Validation.ipynb
deleted file mode 100644
index 70ab58913..000000000
--- a/examples/6. Metadata Validation.ipynb	
+++ /dev/null
@@ -1,239 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sdv import load_demo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata, tables = load_demo(metadata=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'tables': {'users': {'primary_key': 'user_id',\n",
-       "   'fields': {'user_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'country': {'type': 'categorical'},\n",
-       "    'gender': {'type': 'categorical'},\n",
-       "    'age': {'type': 'numerical', 'subtype': 'integer'}}},\n",
-       "  'sessions': {'primary_key': 'session_id',\n",
-       "   'fields': {'session_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'user_id': {'ref': {'field': 'user_id', 'table': 'users'},\n",
-       "     'type': 'id',\n",
-       "     'subtype': 'integer'},\n",
-       "    'device': {'type': 'categorical'},\n",
-       "    'os': {'type': 'categorical'}}},\n",
-       "  'transactions': {'primary_key': 'transaction_id',\n",
-       "   'fields': {'transaction_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'session_id': {'ref': {'field': 'session_id', 'table': 'sessions'},\n",
-       "     'type': 'id',\n",
-       "     'subtype': 'integer'},\n",
-       "    'timestamp': {'type': 'datetime', 'format': '%Y-%m-%d'},\n",
-       "    'amount': {'type': 'numerical', 'subtype': 'float'},\n",
-       "    'approved': {'type': 'boolean'}}}}}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metadata.to_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'users':    user_id country gender  age\n",
-       " 0        0     USA      M   34\n",
-       " 1        1      UK      F   23\n",
-       " 2        2      ES   None   44\n",
-       " 3        3      UK      M   22\n",
-       " 4        4     USA      F   54\n",
-       " 5        5      DE      M   57\n",
-       " 6        6      BG      F   45\n",
-       " 7        7      ES   None   41\n",
-       " 8        8      FR      F   23\n",
-       " 9        9      UK   None   30,\n",
-       " 'sessions':    session_id  user_id  device       os\n",
-       " 0           0        0  mobile  android\n",
-       " 1           1        1  tablet      ios\n",
-       " 2           2        1  tablet  android\n",
-       " 3           3        2  mobile  android\n",
-       " 4           4        4  mobile      ios\n",
-       " 5           5        5  mobile  android\n",
-       " 6           6        6  mobile      ios\n",
-       " 7           7        6  tablet      ios\n",
-       " 8           8        6  mobile      ios\n",
-       " 9           9        8  tablet      ios,\n",
-       " 'transactions':    transaction_id  session_id           timestamp  amount  approved\n",
-       " 0               0           0 2019-01-01 12:34:32   100.0      True\n",
-       " 1               1           0 2019-01-01 12:42:21    55.3      True\n",
-       " 2               2           1 2019-01-07 17:23:11    79.5      True\n",
-       " 3               3           3 2019-01-10 11:08:57   112.1     False\n",
-       " 4               4           5 2019-01-10 21:54:08   110.0     False\n",
-       " 5               5           5 2019-01-11 11:21:20    76.3      True\n",
-       " 6               6           7 2019-01-22 14:44:10    89.5      True\n",
-       " 7               7           8 2019-01-23 10:14:09   132.1     False\n",
-       " 8               8           9 2019-01-27 16:09:17    68.0      True\n",
-       " 9               9           9 2019-01-29 12:10:48    99.9      True}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata.validate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata.validate(tables)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata._metadata['tables']['users']['primary_key'] = 'country'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error: id field `user_id` is neither a primary or a foreign key\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sdv.metadata import MetadataError\n",
-    "\n",
-    "try:\n",
-    "    metadata.validate()\n",
-    "except MetadataError as me:\n",
-    "    print('Error: {}'.format(me))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata._metadata['tables']['users']['primary_key'] = 'user_id'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata.validate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata._metadata['tables']['users']['fields']['gender']['type'] = 'numerical'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metadata.validate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error: Invalid values found in column `gender` of table `users`: `could not convert string to float: 'M'`\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    metadata.validate(tables)\n",
-    "except MetadataError as me:\n",
-    "    print('Error: {}'.format(me))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/airbnb-recruiting-new-user-bookings/Aibnb Dataset Subsampling.ipynb b/examples/airbnb-recruiting-new-user-bookings/Aibnb Dataset Subsampling.ipynb
deleted file mode 100644
index d4b39df8b..000000000
--- a/examples/airbnb-recruiting-new-user-bookings/Aibnb Dataset Subsampling.ipynb	
+++ /dev/null
@@ -1,206 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Airbnb Dataset Subsampling\n",
-    "\n",
-    "This notebooks shows how to generate the `users_sample.csv` and `sessions_sample.csv` files.\n",
-    "Before running this notebook, make sure to having downloaded the `train_users_2.csv` and `sessions.csv`\n",
-    "files from https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings/data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "users = pd.read_csv('train_users_2.csv')\n",
-    "sessions = pd.read_csv('sessions.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(213451, 16)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "users.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10567737, 6)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sessions.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "users_sample = users.sample(1000).reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sessions_sample = sessions[\n",
-    "    sessions['user_id'].isin(users_sample['id'])\n",
-    "].reset_index(drop=True).copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(24988, 6)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sessions_sample.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "user_ids = users_sample['id'].reset_index().set_index('id')['index']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "id\n",
-       "c2cga16ny5    0\n",
-       "08yitv7nz0    1\n",
-       "3oobewtnls    2\n",
-       "owd2csvj6u    3\n",
-       "i66dlbdyrt    4\n",
-       "Name: index, dtype: int64"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "user_ids.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "users_sample['id'] = users_sample['id'].map(user_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sessions_sample['user_id'] = sessions_sample['user_id'].map(user_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "users_sample.to_csv('users_sample.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sessions_sample.to_csv('sessions_sample.csv', index=False)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/airbnb-recruiting-new-user-bookings/Aibnb Example.ipynb b/examples/airbnb-recruiting-new-user-bookings/Aibnb Example.ipynb
deleted file mode 100644
index e4b43f916..000000000
--- a/examples/airbnb-recruiting-new-user-bookings/Aibnb Example.ipynb	
+++ /dev/null
@@ -1,730 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Airbnb Dataset Synthesis\n",
-    "\n",
-    "This notebook shows a usage example over a sample of the Airbnb New User Bookings dataset\n",
-    "available on Kaggle: https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings\n",
-    "\n",
-    "Before running this notebook, make sure to having downloaded the data and run the\n",
-    "`Airbnb Dataset Sampling` notebook which can be found in the same folder as this one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2019-11-03 16:07:17,764 - INFO - modeler - Modeling users\n",
-      "2019-11-03 16:07:17,764 - INFO - metadata - Loading table users\n",
-      "2019-11-03 16:07:17,781 - INFO - metadata - Loading transformer DatetimeTransformer for field date_account_created\n",
-      "2019-11-03 16:07:17,782 - INFO - metadata - Loading transformer DatetimeTransformer for field timestamp_first_active\n",
-      "2019-11-03 16:07:17,782 - INFO - metadata - Loading transformer DatetimeTransformer for field date_first_booking\n",
-      "2019-11-03 16:07:17,783 - INFO - metadata - Loading transformer CategoricalTransformer for field gender\n",
-      "2019-11-03 16:07:17,783 - INFO - metadata - Loading transformer NumericalTransformer for field age\n",
-      "2019-11-03 16:07:17,783 - INFO - metadata - Loading transformer CategoricalTransformer for field signup_method\n",
-      "2019-11-03 16:07:17,783 - INFO - metadata - Loading transformer CategoricalTransformer for field signup_flow\n",
-      "2019-11-03 16:07:17,784 - INFO - metadata - Loading transformer CategoricalTransformer for field language\n",
-      "2019-11-03 16:07:17,784 - INFO - metadata - Loading transformer CategoricalTransformer for field affiliate_channel\n",
-      "2019-11-03 16:07:17,784 - INFO - metadata - Loading transformer CategoricalTransformer for field affiliate_provider\n",
-      "2019-11-03 16:07:17,784 - INFO - metadata - Loading transformer CategoricalTransformer for field first_affiliate_tracked\n",
-      "2019-11-03 16:07:17,785 - INFO - metadata - Loading transformer CategoricalTransformer for field signup_app\n",
-      "2019-11-03 16:07:17,785 - INFO - metadata - Loading transformer CategoricalTransformer for field first_device_type\n",
-      "2019-11-03 16:07:17,785 - INFO - metadata - Loading transformer CategoricalTransformer for field first_browser\n",
-      "2019-11-03 16:07:17,785 - INFO - metadata - Loading transformer CategoricalTransformer for field country_destination\n",
-      "2019-11-03 16:07:17,852 - INFO - modeler - Modeling sessions\n",
-      "2019-11-03 16:07:17,853 - INFO - metadata - Loading table sessions\n",
-      "2019-11-03 16:07:17,872 - INFO - metadata - Loading transformer CategoricalTransformer for field action\n",
-      "2019-11-03 16:07:17,873 - INFO - metadata - Loading transformer CategoricalTransformer for field action_type\n",
-      "2019-11-03 16:07:17,873 - INFO - metadata - Loading transformer CategoricalTransformer for field action_detail\n",
-      "2019-11-03 16:07:17,873 - INFO - metadata - Loading transformer CategoricalTransformer for field device_type\n",
-      "2019-11-03 16:07:17,874 - INFO - metadata - Loading transformer NumericalTransformer for field secs_elapsed\n",
-      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/pandas/core/frame.py:7143: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  baseCov = np.cov(mat.T)\n",
-      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2451: RuntimeWarning: divide by zero encountered in true_divide\n",
-      "  c *= np.true_divide(1, fact)\n",
-      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2451: RuntimeWarning: invalid value encountered in multiply\n",
-      "  c *= np.true_divide(1, fact)\n",
-      "2019-11-03 16:07:22,394 - INFO - modeler - Modeling Complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sdv import SDV\n",
-    "\n",
-    "sdv = SDV()\n",
-    "sdv.fit('metadata.json')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2019-11-03 16:07:22,443 - INFO - metadata - Loading table users\n",
-      "2019-11-03 16:07:22,454 - INFO - metadata - Loading table sessions\n"
-     ]
-    }
-   ],
-   "source": [
-    "real = sdv.metadata.get_tables()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>date_account_created</th>\n",
-       "      <th>timestamp_first_active</th>\n",
-       "      <th>date_first_booking</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>age</th>\n",
-       "      <th>signup_method</th>\n",
-       "      <th>signup_flow</th>\n",
-       "      <th>language</th>\n",
-       "      <th>affiliate_channel</th>\n",
-       "      <th>affiliate_provider</th>\n",
-       "      <th>first_affiliate_tracked</th>\n",
-       "      <th>signup_app</th>\n",
-       "      <th>first_device_type</th>\n",
-       "      <th>first_browser</th>\n",
-       "      <th>country_destination</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>790</th>\n",
-       "      <td>790</td>\n",
-       "      <td>2011-04-30</td>\n",
-       "      <td>2011-04-30 17:58:15</td>\n",
-       "      <td>2012-03-22</td>\n",
-       "      <td>FEMALE</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>basic</td>\n",
-       "      <td>2</td>\n",
-       "      <td>en</td>\n",
-       "      <td>sem-non-brand</td>\n",
-       "      <td>google</td>\n",
-       "      <td>untracked</td>\n",
-       "      <td>Web</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>Firefox</td>\n",
-       "      <td>US</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>517</th>\n",
-       "      <td>517</td>\n",
-       "      <td>2011-09-20</td>\n",
-       "      <td>2011-09-20 08:14:06</td>\n",
-       "      <td>2011-09-22</td>\n",
-       "      <td>-unknown-</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>basic</td>\n",
-       "      <td>2</td>\n",
-       "      <td>en</td>\n",
-       "      <td>sem-brand</td>\n",
-       "      <td>google</td>\n",
-       "      <td>omg</td>\n",
-       "      <td>Web</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>Chrome</td>\n",
-       "      <td>US</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>902</th>\n",
-       "      <td>902</td>\n",
-       "      <td>2014-05-17</td>\n",
-       "      <td>2014-05-17 01:03:00</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>MALE</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>basic</td>\n",
-       "      <td>24</td>\n",
-       "      <td>en</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>untracked</td>\n",
-       "      <td>Moweb</td>\n",
-       "      <td>iPhone</td>\n",
-       "      <td>Mobile Safari</td>\n",
-       "      <td>NDF</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>70</td>\n",
-       "      <td>2013-10-21</td>\n",
-       "      <td>2013-10-21 21:50:15</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>-unknown-</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>basic</td>\n",
-       "      <td>0</td>\n",
-       "      <td>en</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>linked</td>\n",
-       "      <td>Web</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>Firefox</td>\n",
-       "      <td>NDF</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>608</th>\n",
-       "      <td>608</td>\n",
-       "      <td>2013-04-01</td>\n",
-       "      <td>2013-04-01 20:05:13</td>\n",
-       "      <td>2013-04-02</td>\n",
-       "      <td>-unknown-</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>basic</td>\n",
-       "      <td>24</td>\n",
-       "      <td>en</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>linked</td>\n",
-       "      <td>Moweb</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>Safari</td>\n",
-       "      <td>other</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      id date_account_created timestamp_first_active date_first_booking  \\\n",
-       "790  790           2011-04-30    2011-04-30 17:58:15         2012-03-22   \n",
-       "517  517           2011-09-20    2011-09-20 08:14:06         2011-09-22   \n",
-       "902  902           2014-05-17    2014-05-17 01:03:00                NaT   \n",
-       "70    70           2013-10-21    2013-10-21 21:50:15                NaT   \n",
-       "608  608           2013-04-01    2013-04-01 20:05:13         2013-04-02   \n",
-       "\n",
-       "        gender   age signup_method signup_flow language affiliate_channel  \\\n",
-       "790     FEMALE  36.0         basic           2       en     sem-non-brand   \n",
-       "517  -unknown-   NaN         basic           2       en         sem-brand   \n",
-       "902       MALE  48.0         basic          24       en            direct   \n",
-       "70   -unknown-   NaN         basic           0       en            direct   \n",
-       "608  -unknown-   NaN         basic          24       en            direct   \n",
-       "\n",
-       "    affiliate_provider first_affiliate_tracked signup_app first_device_type  \\\n",
-       "790             google               untracked        Web       Mac Desktop   \n",
-       "517             google                     omg        Web       Mac Desktop   \n",
-       "902             direct               untracked      Moweb            iPhone   \n",
-       "70              direct                  linked        Web       Mac Desktop   \n",
-       "608             direct                  linked      Moweb       Mac Desktop   \n",
-       "\n",
-       "     first_browser country_destination  \n",
-       "790        Firefox                  US  \n",
-       "517         Chrome                  US  \n",
-       "902  Mobile Safari                 NDF  \n",
-       "70         Firefox                 NDF  \n",
-       "608         Safari               other  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "real['users'].sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>user_id</th>\n",
-       "      <th>action</th>\n",
-       "      <th>action_type</th>\n",
-       "      <th>action_detail</th>\n",
-       "      <th>device_type</th>\n",
-       "      <th>secs_elapsed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>6345</th>\n",
-       "      <td>101</td>\n",
-       "      <td>search_results</td>\n",
-       "      <td>click</td>\n",
-       "      <td>view_search_results</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>1592.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5417</th>\n",
-       "      <td>72</td>\n",
-       "      <td>lookup</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>651.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22406</th>\n",
-       "      <td>823</td>\n",
-       "      <td>show</td>\n",
-       "      <td>view</td>\n",
-       "      <td>p3</td>\n",
-       "      <td>Android Phone</td>\n",
-       "      <td>1198.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6279</th>\n",
-       "      <td>101</td>\n",
-       "      <td>search_results</td>\n",
-       "      <td>click</td>\n",
-       "      <td>view_search_results</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>3389.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6561</th>\n",
-       "      <td>883</td>\n",
-       "      <td>ajax_refresh_subtotal</td>\n",
-       "      <td>click</td>\n",
-       "      <td>change_trip_characteristics</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>268.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       user_id                 action action_type  \\\n",
-       "6345       101         search_results       click   \n",
-       "5417        72                 lookup         NaN   \n",
-       "22406      823                   show        view   \n",
-       "6279       101         search_results       click   \n",
-       "6561       883  ajax_refresh_subtotal       click   \n",
-       "\n",
-       "                     action_detail    device_type  secs_elapsed  \n",
-       "6345           view_search_results    Mac Desktop        1592.0  \n",
-       "5417                           NaN    Mac Desktop         651.0  \n",
-       "22406                           p3  Android Phone        1198.0  \n",
-       "6279           view_search_results    Mac Desktop        3389.0  \n",
-       "6561   change_trip_characteristics    Mac Desktop         268.0  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "real['sessions'].sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "length = len(real['users'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1000"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "samples = sdv.sample_all(length, reset_primary_keys=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>date_account_created</th>\n",
-       "      <th>timestamp_first_active</th>\n",
-       "      <th>date_first_booking</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>age</th>\n",
-       "      <th>signup_method</th>\n",
-       "      <th>signup_flow</th>\n",
-       "      <th>language</th>\n",
-       "      <th>affiliate_channel</th>\n",
-       "      <th>affiliate_provider</th>\n",
-       "      <th>first_affiliate_tracked</th>\n",
-       "      <th>signup_app</th>\n",
-       "      <th>first_device_type</th>\n",
-       "      <th>first_browser</th>\n",
-       "      <th>country_destination</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>857</th>\n",
-       "      <td>857</td>\n",
-       "      <td>2014-03-16 21:04:00.313496320</td>\n",
-       "      <td>2014-03-15 05:39:43.249984000</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>-unknown-</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>facebook</td>\n",
-       "      <td>0</td>\n",
-       "      <td>en</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>untracked</td>\n",
-       "      <td>Web</td>\n",
-       "      <td>iPhone</td>\n",
-       "      <td>IE</td>\n",
-       "      <td>NDF</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>489</th>\n",
-       "      <td>489</td>\n",
-       "      <td>2014-10-03 17:46:33.698169088</td>\n",
-       "      <td>2014-10-14 06:11:35.937526784</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>FEMALE</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>basic</td>\n",
-       "      <td>0</td>\n",
-       "      <td>en</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>untracked</td>\n",
-       "      <td>Web</td>\n",
-       "      <td>Windows Desktop</td>\n",
-       "      <td>-unknown-</td>\n",
-       "      <td>NDF</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>780</th>\n",
-       "      <td>780</td>\n",
-       "      <td>2014-03-02 14:25:57.251373312</td>\n",
-       "      <td>2014-03-05 14:28:40.767416832</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>-unknown-</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>basic</td>\n",
-       "      <td>0</td>\n",
-       "      <td>en</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>linked</td>\n",
-       "      <td>Web</td>\n",
-       "      <td>Windows Desktop</td>\n",
-       "      <td>Safari</td>\n",
-       "      <td>NDF</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>596</th>\n",
-       "      <td>596</td>\n",
-       "      <td>2014-01-12 05:40:57.161886720</td>\n",
-       "      <td>2014-01-18 03:57:01.785481984</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>MALE</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>basic</td>\n",
-       "      <td>0</td>\n",
-       "      <td>en</td>\n",
-       "      <td>sem-brand</td>\n",
-       "      <td>google</td>\n",
-       "      <td>linked</td>\n",
-       "      <td>Web</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>Safari</td>\n",
-       "      <td>NDF</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>776</th>\n",
-       "      <td>776</td>\n",
-       "      <td>2013-05-28 14:15:41.311144960</td>\n",
-       "      <td>2013-05-28 06:13:45.242490880</td>\n",
-       "      <td>2013-08-07 07:45:31.016155904</td>\n",
-       "      <td>FEMALE</td>\n",
-       "      <td>46.0</td>\n",
-       "      <td>basic</td>\n",
-       "      <td>0</td>\n",
-       "      <td>en</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>direct</td>\n",
-       "      <td>untracked</td>\n",
-       "      <td>Web</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>Chrome</td>\n",
-       "      <td>NDF</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      id          date_account_created        timestamp_first_active  \\\n",
-       "857  857 2014-03-16 21:04:00.313496320 2014-03-15 05:39:43.249984000   \n",
-       "489  489 2014-10-03 17:46:33.698169088 2014-10-14 06:11:35.937526784   \n",
-       "780  780 2014-03-02 14:25:57.251373312 2014-03-05 14:28:40.767416832   \n",
-       "596  596 2014-01-12 05:40:57.161886720 2014-01-18 03:57:01.785481984   \n",
-       "776  776 2013-05-28 14:15:41.311144960 2013-05-28 06:13:45.242490880   \n",
-       "\n",
-       "               date_first_booking     gender   age signup_method signup_flow  \\\n",
-       "857                           NaT  -unknown-  45.0      facebook           0   \n",
-       "489                           NaT     FEMALE  56.0         basic           0   \n",
-       "780                           NaT  -unknown-   NaN         basic           0   \n",
-       "596                           NaT       MALE  51.0         basic           0   \n",
-       "776 2013-08-07 07:45:31.016155904     FEMALE  46.0         basic           0   \n",
-       "\n",
-       "    language affiliate_channel affiliate_provider first_affiliate_tracked  \\\n",
-       "857       en            direct             direct               untracked   \n",
-       "489       en            direct             direct               untracked   \n",
-       "780       en            direct             direct                  linked   \n",
-       "596       en         sem-brand             google                  linked   \n",
-       "776       en            direct             direct               untracked   \n",
-       "\n",
-       "    signup_app first_device_type first_browser country_destination  \n",
-       "857        Web            iPhone            IE                 NDF  \n",
-       "489        Web   Windows Desktop     -unknown-                 NDF  \n",
-       "780        Web   Windows Desktop        Safari                 NDF  \n",
-       "596        Web       Mac Desktop        Safari                 NDF  \n",
-       "776        Web       Mac Desktop        Chrome                 NDF  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples['users'].sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>user_id</th>\n",
-       "      <th>action</th>\n",
-       "      <th>action_type</th>\n",
-       "      <th>action_detail</th>\n",
-       "      <th>device_type</th>\n",
-       "      <th>secs_elapsed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>7985</th>\n",
-       "      <td>222</td>\n",
-       "      <td>ajax_refresh_subtotal</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>listing_reviews</td>\n",
-       "      <td>Windows Desktop</td>\n",
-       "      <td>6730.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23883</th>\n",
-       "      <td>629</td>\n",
-       "      <td>ajax_refresh_subtotal</td>\n",
-       "      <td>data</td>\n",
-       "      <td>-unknown-</td>\n",
-       "      <td>Windows Desktop</td>\n",
-       "      <td>4842.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15908</th>\n",
-       "      <td>418</td>\n",
-       "      <td>terms</td>\n",
-       "      <td>click</td>\n",
-       "      <td>view_search_results</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>111772.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8900</th>\n",
-       "      <td>250</td>\n",
-       "      <td>personalize</td>\n",
-       "      <td>data</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>20486.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26026</th>\n",
-       "      <td>691</td>\n",
-       "      <td>search</td>\n",
-       "      <td>-unknown-</td>\n",
-       "      <td>-unknown-</td>\n",
-       "      <td>Mac Desktop</td>\n",
-       "      <td>149526.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       user_id                 action action_type        action_detail  \\\n",
-       "7985       222  ajax_refresh_subtotal         NaN      listing_reviews   \n",
-       "23883      629  ajax_refresh_subtotal        data            -unknown-   \n",
-       "15908      418                  terms       click  view_search_results   \n",
-       "8900       250            personalize        data                  NaN   \n",
-       "26026      691                 search   -unknown-            -unknown-   \n",
-       "\n",
-       "           device_type  secs_elapsed  \n",
-       "7985   Windows Desktop        6730.0  \n",
-       "23883  Windows Desktop        4842.0  \n",
-       "15908      Mac Desktop      111772.0  \n",
-       "8900       Mac Desktop       20486.0  \n",
-       "26026      Mac Desktop      149526.0  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples['sessions'].sample(5)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/airbnb-recruiting-new-user-bookings/metadata.json b/examples/airbnb-recruiting-new-user-bookings/metadata.json
deleted file mode 100644
index 4c03506bf..000000000
--- a/examples/airbnb-recruiting-new-user-bookings/metadata.json
+++ /dev/null
@@ -1,116 +0,0 @@
-{
-    "tables": [
-        {
-            "headers": true,
-            "name": "users",
-            "path": "users_sample.csv",
-            "primary_key": "id",
-            "fields": [
-                {
-                    "name": "id",
-                    "type": "id"
-                },
-                {
-                    "name": "date_account_created",
-                    "type": "datetime",
-                    "format": "%Y-%m-%d"
-                },
-                {
-                    "name": "timestamp_first_active",
-                    "type": "datetime",
-                    "format": "%Y%m%d%H%M%S"
-                },
-                {
-                    "name": "date_first_booking",
-                    "type": "datetime",
-                    "format": "%Y-%m-%d"
-                },
-                {
-                    "name": "gender",
-                    "type": "categorical"
-                },
-                {
-                    "name": "age",
-                    "type": "numerical",
-                    "subtype": "integer"
-                },
-                {
-                    "name": "signup_method",
-                    "type": "categorical"
-                },
-                {
-                    "name": "signup_flow",
-                    "type": "categorical"
-                },
-                {
-                    "name": "language",
-                    "type": "categorical"
-                },
-                {
-                    "name": "affiliate_channel",
-                    "type": "categorical"
-                },
-                {
-                    "name": "affiliate_provider",
-                    "type": "categorical"
-                },
-                {
-                    "name": "first_affiliate_tracked",
-                    "type": "categorical"
-                },
-                {
-                    "name": "signup_app",
-                    "type": "categorical"
-                },
-                {
-                    "name": "first_device_type",
-                    "type": "categorical"
-                },
-                {
-                    "name": "first_browser",
-                    "type": "categorical"
-                },
-                {
-                    "name": "country_destination",
-                    "type": "categorical"
-                }
-            ]
-        },
-        {
-            "headers": true,
-            "name": "sessions",
-            "path": "sessions_sample.csv",
-            "fields": [
-                {
-                    "name": "user_id",
-                    "ref": {
-                        "field": "id",
-                        "table": "users"
-                    },
-                    "type": "id"
-                },
-                {
-                    "name": "action",
-                    "type": "categorical"
-                },
-                {
-                    "name": "action_type",
-                    "type": "categorical"
-                },
-                {
-                    "name": "action_detail",
-                    "type": "categorical"
-                },
-                {
-                    "name": "device_type",
-                    "type": "categorical"
-                },
-                {
-                    "name": "secs_elapsed",
-                    "type": "numerical",
-                    "subtype": "integer"
-                }
-            ]
-        }
-    ]
-}
diff --git a/examples/demo_metadata.json b/examples/demo_metadata.json
deleted file mode 100644
index 1becd1764..000000000
--- a/examples/demo_metadata.json
+++ /dev/null
@@ -1,74 +0,0 @@
-{
-    "tables": {
-        "users": {
-            "fields": {
-                "country": {
-                    "type": "categorical"
-                },
-                "user_id": {
-                    "type": "id",
-                    "subtype": "integer"
-                },
-                "gender": {
-                    "type": "categorical"
-                },
-                "age": {
-                    "type": "numerical",
-                    "subtype": "integer"
-                }
-            },
-            "primary_key": "user_id"
-        },
-        "sessions": {
-            "fields": {
-                "device": {
-                    "type": "categorical"
-                },
-                "session_id": {
-                    "type": "id",
-                    "subtype": "integer"
-                },
-                "user_id": {
-                    "type": "id",
-                    "subtype": "integer",
-                    "ref": {
-                        "table": "users",
-                        "field": "user_id"
-                    }
-                },
-                "os": {
-                    "type": "categorical"
-                }
-            },
-            "primary_key": "session_id"
-        },
-        "transactions": {
-            "fields": {
-                "timestamp": {
-                    "type": "datetime",
-                    "format": "%Y-%m-%d"
-                },
-                "amount": {
-                    "type": "numerical",
-                    "subtype": "float"
-                },
-                "session_id": {
-                    "type": "id",
-                    "subtype": "integer",
-                    "ref": {
-                        "table": "sessions",
-                        "field": "session_id"
-                    }
-                },
-                "transaction_id": {
-                    "type": "id",
-                    "subtype": "integer"
-                },
-                "approved": {
-                    "type": "boolean"
-                }
-            },
-            "primary_key": "transaction_id"
-        }
-    }
-}
\ No newline at end of file
diff --git a/examples/quickstart/customers.csv b/examples/quickstart/customers.csv
deleted file mode 100644
index 14922885f..000000000
--- a/examples/quickstart/customers.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-customer_id,cust_postal_code,phone_number1,credit_limit,country
-50,11371,6175553295,1000,UK
-4,63145,8605551835,500,US
-97338810,6096,7035552143,1000,CANADA
-630407,63145,7035552143,2000,US
-826362,11371,6175553295,1000,UK
-55996144,20166,4045553285,1000,FRANCE
-598112,63145,6175553295,1000,SPAIN
diff --git a/examples/quickstart/metadata.json b/examples/quickstart/metadata.json
deleted file mode 100644
index 119d30c76..000000000
--- a/examples/quickstart/metadata.json
+++ /dev/null
@@ -1,96 +0,0 @@
-{
-    "tables": [
-        {
-            "fields": [
-                {
-                    "name": "customer_id",
-                    "type": "id"
-                },
-                {
-                    "name": "cust_postal_code",
-                    "type": "categorical"
-                },
-                {
-                    "name": "phone_number1",
-                    "type": "numerical",
-                    "subtype": "integer"
-                },
-                {
-                    "name": "credit_limit",
-                    "type": "numerical",
-                    "subtype": "integer"
-                },
-                {
-                    "name": "country",
-                    "type": "categorical"
-                }
-            ],
-            "headers": true,
-            "name": "customers",
-            "path": "customers.csv",
-            "primary_key": "customer_id",
-            "use": true
-        },
-        {
-            "fields": [
-                {
-                    "name": "order_id",
-                    "type": "id"
-                },
-                {
-                    "name": "customer_id",
-                    "ref": {
-                        "field": "customer_id",
-                        "table": "customers"
-                    },
-                    "type": "id"
-                },
-                {
-                    "name": "order_total",
-                    "type": "numerical",
-                    "subtype": "integer"
-                }
-            ],
-            "headers": true,
-            "name": "orders",
-            "path": "orders.csv",
-            "primary_key": "order_id",
-            "use": true
-        },
-        {
-            "fields": [
-                {
-                    "name": "order_item_id",
-                    "type": "id"
-                },
-                {
-                    "name": "order_id",
-                    "ref": {
-                        "field": "order_id",
-                        "table": "orders"
-                    },
-                    "type": "id"
-                },
-                {
-                    "name": "product_id",
-                    "type": "categorical"
-                },
-                {
-                    "name": "unit_price",
-                    "type": "numerical",
-                    "subtype": "integer"
-                },
-                {
-                    "name": "quantity",
-                    "type": "numerical",
-                    "subtype": "integer"
-                }
-            ],
-            "headers": true,
-            "name": "order_items",
-            "path": "order_items.csv",
-            "primary_key": "order_item_id",
-            "use": true
-        }
-    ]
-}
diff --git a/examples/quickstart/order_items.csv b/examples/quickstart/order_items.csv
deleted file mode 100644
index 7f47c3d69..000000000
--- a/examples/quickstart/order_items.csv
+++ /dev/null
@@ -1,50 +0,0 @@
-order_item_id,order_id,product_id,unit_price,quantity
-100,10,7,52,8
-101,8,6,125,4
-102,1,6,125,4
-103,4,9,125,4
-104,1,9,113,4
-105,9,10,87,2
-106,10,6,39,4
-107,1,6,50,4
-108,2,3,31,2
-109,4,6,37,3
-110,10,10,50,4
-111,1,10,50,2
-112,6,14,50,1
-113,10,1,161,5
-114,2,2,73,4
-115,1,6,50,2
-116,5,6,63,2
-117,1,6,115,4
-118,4,10,50,4
-119,5,9,50,2
-120,10,15,37,0
-121,7,6,113,7
-122,1,6,125,2
-123,10,6,113,7
-124,8,10,125,4
-125,5,6,125,2
-126,1,6,125,2
-127,8,10,69,2
-128,8,10,50,2
-129,6,8,152,3
-130,5,2,103,4
-131,3,11,30,-1
-132,9,6,97,2
-133,7,1,165,5
-134,8,6,125,2
-135,8,6,50,2
-136,1,9,125,4
-137,6,8,152,3
-138,1,10,50,2
-139,3,6,37,3
-140,6,15,111,1
-141,9,10,125,2
-142,10,2,147,4
-143,3,6,58,4
-144,1,6,102,4
-145,9,10,50,4
-146,6,15,50,1
-147,7,3,30,-1
-148,1,6,125,4
diff --git a/examples/quickstart/orders.csv b/examples/quickstart/orders.csv
deleted file mode 100644
index 08371dbb8..000000000
--- a/examples/quickstart/orders.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-order_id,customer_id,order_total
-1,50,2310
-2,4,1507
-10,97338810,730
-6,55996144,730
-3,55996144,939
-4,50,2380
-5,97338810,1570
-7,50,730
-8,97338810,2336
-9,4,743
diff --git a/tutorials/01_Quickstart.ipynb b/tutorials/01_Quickstart.ipynb
new file mode 100644
index 000000000..5eb6cd8e8
--- /dev/null
+++ b/tutorials/01_Quickstart.ipynb
@@ -0,0 +1,838 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quickstart\n",
+    "\n",
+    "In this short tutorial we will guide you through a series of steps that will help you\n",
+    "getting started using **SDV**.\n",
+    "\n",
+    "## 1. Model the dataset using SDV\n",
+    "\n",
+    "To model a multi table, relational dataset, we follow two steps. In the first step, we will load\n",
+    "the data and configures the meta data. In the second step, we will use the sdv API to fit and\n",
+    "save a hierarchical model. We will cover these two steps in this section using an example dataset.\n",
+    "\n",
+    "### Step 1: Load example data\n",
+    "\n",
+    "**SDV** comes with a toy dataset to play with, which can be loaded using the `sdv.load_demo`\n",
+    "function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sdv import load_demo\n",
+    "\n",
+    "metadata, tables = load_demo(metadata=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return two objects:\n",
+    "\n",
+    "1. A `Metadata` object with all the information that **SDV** needs to know about the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Metadata\n",
+       "  root_path: /home/xals/Projects/MIT/SDV/tutorials\n",
+       "  tables: ['users', 'sessions', 'transactions']\n",
+       "  relationships:\n",
+       "    sessions.user_id -> users.user_id\n",
+       "    transactions.session_id -> sessions.session_id"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
+       " -->\n",
+       "<!-- Title: Metadata Pages: 1 -->\n",
+       "<svg width=\"371pt\" height=\"500pt\"\n",
+       " viewBox=\"0.00 0.00 370.50 500.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 496)\">\n",
+       "<title>Metadata</title>\n",
+       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-496 366.5,-496 366.5,4 -4,4\"/>\n",
+       "<!-- users -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>users</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
+       "</g>\n",
+       "<!-- sessions -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>sessions</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
+       "</g>\n",
+       "<!-- users&#45;&gt;sessions -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>users&#45;&gt;sessions</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-377.2088C101.5,-363.9446 101.5,-349.6377 101.5,-335.7493\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"105,-325.7312 101.5001,-335.7312 98,-325.7313 105,-325.7312\"/>\n",
+       "<text text-anchor=\"middle\" x=\"197\" y=\"-347.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;sessions.user_id &#45;&gt; users.user_id</text>\n",
+       "</g>\n",
+       "<!-- transactions -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>transactions</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 191,-.5 191,-.5 197,-.5 203,-6.5 203,-12.5 203,-12.5 203,-132.5 203,-132.5 203,-138.5 197,-144.5 191,-144.5 191,-144.5 12,-144.5 12,-144.5 6,-144.5 0,-138.5 0,-132.5 0,-132.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
+       "</g>\n",
+       "<!-- sessions&#45;&gt;transactions -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>sessions&#45;&gt;transactions</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-196.3927C101.5,-183.0141 101.5,-168.7483 101.5,-154.8345\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"105,-144.7831 101.5001,-154.7831 98,-144.7831 105,-144.7831\"/>\n",
+       "<text text-anchor=\"middle\" x=\"232\" y=\"-166.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;transactions.session_id &#45;&gt; sessions.session_id</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.dot.Digraph at 0x7f9840ca3ba8>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata.visualize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more details about how to build the `Metadata` for your own dataset, please refer to the\n",
+    "[Metadata](https://sdv-dev.github.io/SDV/metadata.html) section of the documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. A dictionary containing three `pandas.DataFrames` with the tables described in the\n",
+    "metadata object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'users':    user_id country gender  age\n",
+       " 0        0     USA      M   34\n",
+       " 1        1      UK      F   23\n",
+       " 2        2      ES   None   44\n",
+       " 3        3      UK      M   22\n",
+       " 4        4     USA      F   54\n",
+       " 5        5      DE      M   57\n",
+       " 6        6      BG      F   45\n",
+       " 7        7      ES   None   41\n",
+       " 8        8      FR      F   23\n",
+       " 9        9      UK   None   30,\n",
+       " 'sessions':    session_id  user_id  device       os\n",
+       " 0           0        0  mobile  android\n",
+       " 1           1        1  tablet      ios\n",
+       " 2           2        1  tablet  android\n",
+       " 3           3        2  mobile  android\n",
+       " 4           4        4  mobile      ios\n",
+       " 5           5        5  mobile  android\n",
+       " 6           6        6  mobile      ios\n",
+       " 7           7        6  tablet      ios\n",
+       " 8           8        6  mobile      ios\n",
+       " 9           9        8  tablet      ios,\n",
+       " 'transactions':    transaction_id  session_id           timestamp  amount  approved\n",
+       " 0               0           0 2019-01-01 12:34:32   100.0      True\n",
+       " 1               1           0 2019-01-01 12:42:21    55.3      True\n",
+       " 2               2           1 2019-01-07 17:23:11    79.5      True\n",
+       " 3               3           3 2019-01-10 11:08:57   112.1     False\n",
+       " 4               4           5 2019-01-10 21:54:08   110.0     False\n",
+       " 5               5           5 2019-01-11 11:21:20    76.3      True\n",
+       " 6               6           7 2019-01-22 14:44:10    89.5      True\n",
+       " 7               7           8 2019-01-23 10:14:09   132.1     False\n",
+       " 8               8           9 2019-01-27 16:09:17    68.0      True\n",
+       " 9               9           9 2019-01-29 12:10:48    99.9      True}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Fit a model using the SDV API.\n",
+    "\n",
+    "First, we build a hierarchical statistical model of the data using **SDV**. For this we will\n",
+    "create an instance of the `sdv.SDV` class and use its `fit` method.\n",
+    "\n",
+    "During this process, **SDV** will traverse across all the tables in your dataset following the\n",
+    "primary key-foreign key relationships and learn the probability distributions of the values in\n",
+    "the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 20:57:19,919 - INFO - modeler - Modeling users\n",
+      "2020-07-09 20:57:19,920 - INFO - __init__ - Loading transformer CategoricalTransformer for field country\n",
+      "2020-07-09 20:57:19,920 - INFO - __init__ - Loading transformer CategoricalTransformer for field gender\n",
+      "2020-07-09 20:57:19,921 - INFO - __init__ - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-09 20:57:19,933 - INFO - modeler - Modeling sessions\n",
+      "2020-07-09 20:57:19,934 - INFO - __init__ - Loading transformer CategoricalTransformer for field device\n",
+      "2020-07-09 20:57:19,934 - INFO - __init__ - Loading transformer CategoricalTransformer for field os\n",
+      "2020-07-09 20:57:19,944 - INFO - modeler - Modeling transactions\n",
+      "2020-07-09 20:57:19,944 - INFO - __init__ - Loading transformer DatetimeTransformer for field timestamp\n",
+      "2020-07-09 20:57:19,944 - INFO - __init__ - Loading transformer NumericalTransformer for field amount\n",
+      "2020-07-09 20:57:19,945 - INFO - __init__ - Loading transformer BooleanTransformer for field approved\n",
+      "2020-07-09 20:57:19,954 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,962 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/core/fromnumeric.py:58: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/xals/Projects/MIT/SDV/sdv/models/copulas.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
+      "  self.model.covariance = np.array(values)\n",
+      "2020-07-09 20:57:19,968 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/pandas/core/frame.py:7143: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  baseCov = np.cov(mat.T)\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2480: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  c *= np.true_divide(1, fact)\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2480: RuntimeWarning: invalid value encountered in multiply\n",
+      "  c *= np.true_divide(1, fact)\n",
+      "2020-07-09 20:57:19,974 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,979 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,985 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,989 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,994 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,007 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,062 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,074 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,092 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,104 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,117 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,129 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,146 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,208 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,294 - INFO - modeler - Modeling Complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv import SDV\n",
+    "\n",
+    "sdv = SDV()\n",
+    "sdv.fit(metadata, tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Sample data from the fitted model\n",
+    "\n",
+    "Once the modeling has finished you are ready to generate new synthetic data using the `sdv` instance that you have.\n",
+    "\n",
+    "For this, all you have to do is call the `sample_all` method from your instance passing the number of rows that you want to generate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled = sdv.sample_all(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a dictionary identical to the `tables` one that we passed to the SDV instance for learning, filled in with new synthetic data.\n",
+    "\n",
+    "**Note** that only the parent tables of your dataset will have the specified number of rows,\n",
+    "as the number of child rows that each row in the parent table has is also sampled following\n",
+    "the original distribution of your dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>country</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>DE</td>\n",
+       "      <td>F</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>F</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id country gender  age\n",
+       "0        0      UK    NaN   39\n",
+       "1        1      UK      F   27\n",
+       "2        2      UK    NaN   50\n",
+       "3        3     USA      F   29\n",
+       "4        4      UK    NaN   21\n",
+       "5        5      ES    NaN   27\n",
+       "6        6      UK      F   20\n",
+       "7        7     USA      F   15\n",
+       "8        8      DE      F   41\n",
+       "9        9      ES      F   35"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled['users']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>session_id</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>device</th>\n",
+       "      <th>os</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>tablet</td>\n",
+       "      <td>ios</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>tablet</td>\n",
+       "      <td>ios</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>tablet</td>\n",
+       "      <td>ios</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>tablet</td>\n",
+       "      <td>ios</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   session_id  user_id  device       os\n",
+       "0           0        1  mobile  android\n",
+       "1           1        2  tablet      ios\n",
+       "2           2        2  tablet      ios\n",
+       "3           3        2  mobile  android\n",
+       "4           4        3  mobile  android\n",
+       "5           5        5  mobile  android\n",
+       "6           6        5  mobile  android\n",
+       "7           7        5  tablet      ios\n",
+       "8           8        6  tablet      ios\n",
+       "9           9        6  mobile  android"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled['sessions'].head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>transaction_id</th>\n",
+       "      <th>session_id</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>approved</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2019-01-13 01:11:06.084275968</td>\n",
+       "      <td>80.350553</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2019-01-13 01:11:06.368704256</td>\n",
+       "      <td>82.116470</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-25 15:05:07.735262976</td>\n",
+       "      <td>97.616590</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-25 15:05:09.098958848</td>\n",
+       "      <td>96.505258</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019-01-25 15:05:09.124231424</td>\n",
+       "      <td>97.235452</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019-01-25 15:05:07.347315456</td>\n",
+       "      <td>96.615923</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019-01-25 15:05:09.190441984</td>\n",
+       "      <td>97.173195</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019-01-25 15:05:07.437910784</td>\n",
+       "      <td>96.995037</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2019-01-13 01:11:06.560940032</td>\n",
+       "      <td>81.027625</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-25 15:05:12.420746496</td>\n",
+       "      <td>97.340054</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   transaction_id  session_id                     timestamp     amount  \\\n",
+       "0               0           0 2019-01-13 01:11:06.084275968  80.350553   \n",
+       "1               1           0 2019-01-13 01:11:06.368704256  82.116470   \n",
+       "2               2           1 2019-01-25 15:05:07.735262976  97.616590   \n",
+       "3               3           1 2019-01-25 15:05:09.098958848  96.505258   \n",
+       "4               4           2 2019-01-25 15:05:09.124231424  97.235452   \n",
+       "5               5           2 2019-01-25 15:05:07.347315456  96.615923   \n",
+       "6               6           3 2019-01-25 15:05:09.190441984  97.173195   \n",
+       "7               7           3 2019-01-25 15:05:07.437910784  96.995037   \n",
+       "8               8           0 2019-01-13 01:11:06.560940032  81.027625   \n",
+       "9               9           1 2019-01-25 15:05:12.420746496  97.340054   \n",
+       "\n",
+       "   approved  \n",
+       "0      True  \n",
+       "1      True  \n",
+       "2      True  \n",
+       "3      True  \n",
+       "4      True  \n",
+       "5      True  \n",
+       "6      True  \n",
+       "7      True  \n",
+       "8      True  \n",
+       "9      True  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled['transactions'].head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving and Loading your model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In some cases, you might want to save the fitted SDV instance to be able to generate synthetic data from\n",
+    "it later or on a different system.\n",
+    "\n",
+    "In order to do so, you can save your fitted `SDV` instance for later usage using the `save` method of your\n",
+    "instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdv.save('sdv.pkl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The generated `pkl` file will not include any of the original data in it, so it can be\n",
+    "safely sent to where the synthetic data will be generated without any privacy concerns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Later on, in order to sample data from the fitted model, we will first need to load it from its\n",
+    "`pkl` file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdv = SDV.load('sdv.pkl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After loading the instance, we can sample synthetic data using its `sample_all` method like before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled = sdv.sample_all(5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/02_Single_Table_Modeling.ipynb b/tutorials/02_Single_Table_Modeling.ipynb
new file mode 100644
index 000000000..0f5649a3e
--- /dev/null
+++ b/tutorials/02_Single_Table_Modeling.ipynb
@@ -0,0 +1,1204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Single Table Modeling\n",
+    "\n",
+    "**SDV** has special support for modeling single table datasets using a variety of models.\n",
+    "\n",
+    "Currently, SDV implements:\n",
+    "\n",
+    "* GaussianCopula: A tool to model multivariate distributions using [copula functions](https://en.wikipedia.org/wiki/Copula_%28probability_theory%29). Based on our [Copulas Library](https://github.com/sdv-dev/Copulas).\n",
+    "* CTGAN: A GAN-based Deep Learning data synthesizer that can generate synthetic tabular data with high fidelity. Based on our [CTGAN Library](https://github.com/sdv-dev/CTGAN)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GaussianCopula\n",
+    "\n",
+    "In this first part of the tutorial we will be using the GaussianCopula class to model the `users` table\n",
+    "from the toy dataset included in the **SDV** library.\n",
+    "\n",
+    "### 1. Load the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sdv import load_demo\n",
+    "\n",
+    "users = load_demo()['users']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a table with 4 fields:\n",
+    "\n",
+    "* `user_id`: A unique identifier of the user.\n",
+    "* `country`: A 2 letter code of the country of residence of the user.\n",
+    "* `gender`: A single letter code, `M` or `F`, indicating the user gender. Note that this demo simulates the case where some users did not indicate the gender, which resulted in empty data values in some rows.\n",
+    "* `age`: The age of the user, in years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>country</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>M</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>None</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>DE</td>\n",
+       "      <td>M</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>BG</td>\n",
+       "      <td>F</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>F</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>None</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id country gender  age\n",
+       "0        0     USA      M   34\n",
+       "1        1      UK      F   23\n",
+       "2        2      ES   None   44\n",
+       "3        3      UK      M   22\n",
+       "4        4     USA      F   54\n",
+       "5        5      DE      M   57\n",
+       "6        6      BG      F   45\n",
+       "7        7      ES   None   41\n",
+       "8        8      FR      F   23\n",
+       "9        9      UK   None   30"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "users"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Prepare the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to properly model our data we will need to provide some additional information to our model,\n",
+    "so let's prepare this information in some variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, let's indicate that the `user_id` field in our table is the primary key, so we do not want our\n",
+    "model to attempt to learn it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "primary_key = 'user_id'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will also want to anonymize the countries of residence of our users, to avoid disclosing such information.\n",
+    "Let's make a variable indicating that the `country` field needs to be anonymized using fake `country_codes`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anonymize_fileds = {\n",
+    "    'country': 'contry_code'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The full list of categories supported corresponds to the `Faker` library\n",
+    "[provider names](https://faker.readthedocs.io/en/master/providers.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have prepared the arguments for our model we are ready to import it, create an instance\n",
+    "and fit it to our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 20:41:29,959 - INFO - table - Loading transformer CategoricalTransformer for field country\n",
+      "2020-07-09 20:41:29,961 - INFO - table - Loading transformer CategoricalTransformer for field gender\n",
+      "2020-07-09 20:41:29,962 - INFO - table - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-09 20:41:29,981 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv.tabular import GaussianCopula\n",
+    "\n",
+    "model = GaussianCopula(\n",
+    "    primary_key=primary_key,\n",
+    "    anonymize_fileds=anonymize_fileds\n",
+    ")\n",
+    "model.fit(users)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Notice** how the model took care of transforming the different fields using the appropriate\n",
+    "Reversible Data Transforms to ensure that the data has a format that the GaussianMultivariate model\n",
+    "from the [copulas](https://github.com/sdv-dev/Copulas) library can handle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Sample data from the fitted model\n",
+    "\n",
+    "Once the modeling has finished you are ready to generate new synthetic data by calling the `sample` method\n",
+    "from our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled = model.sample()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a table identical to the one which the model was fitted on, but filled with new data\n",
+    "which resembles the original one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>country</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>F</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>M</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>F</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id country gender  age\n",
+       "0        0     USA      F   29\n",
+       "1        1      FR      F   44\n",
+       "2        2      UK      M   38\n",
+       "3        3      ES      M   19\n",
+       "4        4     USA      F   55\n",
+       "5        5     USA    NaN   27\n",
+       "6        6     USA      F   27\n",
+       "7        7      UK    NaN    7\n",
+       "8        8      FR      F   55\n",
+       "9        9      UK    NaN   43"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "scrolled": false
+   },
+   "source": [
+    "Notice, as well that the number of rows generated by default corresponds to the number of rows that\n",
+    "the original table had, but that this number can be changed by simply passing it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>country</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>BG</td>\n",
+       "      <td>M</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>M</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id country gender  age\n",
+       "0        0      BG      M   30\n",
+       "1        1     USA    NaN   61\n",
+       "2        2     USA      M   35\n",
+       "3        3      ES    NaN   34\n",
+       "4        4     USA      F   41"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CTGAN\n",
+    "\n",
+    "In this second part of the tutorial we will be using the CTGAN model to learn the data from the\n",
+    "demo dataset called `census`, which is based on the [UCI Adult Census Dataset]('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data').\n",
+    "\n",
+    "### 1. Load the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 20:48:49,870 - INFO - __init__ - Loading table census\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv import load_demo\n",
+    "\n",
+    "census = load_demo('census')['census']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a table with several rows of multiple data types:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>fnlwgt</th>\n",
+       "      <th>education</th>\n",
+       "      <th>education-num</th>\n",
+       "      <th>marital-status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "      <th>native-country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>39</td>\n",
+       "      <td>State-gov</td>\n",
+       "      <td>77516</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>2174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>50</td>\n",
+       "      <td>Self-emp-not-inc</td>\n",
+       "      <td>83311</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>215646</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Handlers-cleaners</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>53</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>234721</td>\n",
+       "      <td>11th</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Handlers-cleaners</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>338409</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Wife</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Cuba</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          workclass  fnlwgt   education  education-num  \\\n",
+       "0   39          State-gov   77516   Bachelors             13   \n",
+       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
+       "2   38            Private  215646     HS-grad              9   \n",
+       "3   53            Private  234721        11th              7   \n",
+       "4   28            Private  338409   Bachelors             13   \n",
+       "\n",
+       "        marital-status          occupation    relationship    race      sex  \\\n",
+       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
+       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
+       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
+       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
+       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
+       "\n",
+       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
+       "0          2174             0              40   United-States   <=50K  \n",
+       "1             0             0              13   United-States   <=50K  \n",
+       "2             0             0              40   United-States   <=50K  \n",
+       "3             0             0              40   United-States   <=50K  \n",
+       "4             0             0              40            Cuba   <=50K  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Prepare the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case there is no primary key to setup and we will not be anonymizing anything, so the only\n",
+    "thing that we will pass to the CTGAN model is the number of epochs that we want it to perform when\n",
+    "it leanrs the data, which we will keep low to make this execution quick."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
+      "  warnings.warn(message, FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv.tabular import CTGAN\n",
+    "\n",
+    "model = CTGAN(epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the instance is created, we can fit it to our data. Bear in mind that this process might take some\n",
+    "time to finish, especially on non-GPU enabled systems."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 20:51:21,668 - INFO - table - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-09 20:51:21,669 - INFO - table - Loading transformer LabelEncodingTransformer for field workclass\n",
+      "2020-07-09 20:51:21,670 - INFO - table - Loading transformer NumericalTransformer for field fnlwgt\n",
+      "2020-07-09 20:51:21,670 - INFO - table - Loading transformer LabelEncodingTransformer for field education\n",
+      "2020-07-09 20:51:21,671 - INFO - table - Loading transformer NumericalTransformer for field education-num\n",
+      "2020-07-09 20:51:21,672 - INFO - table - Loading transformer LabelEncodingTransformer for field marital-status\n",
+      "2020-07-09 20:51:21,672 - INFO - table - Loading transformer LabelEncodingTransformer for field occupation\n",
+      "2020-07-09 20:51:21,673 - INFO - table - Loading transformer LabelEncodingTransformer for field relationship\n",
+      "2020-07-09 20:51:21,673 - INFO - table - Loading transformer LabelEncodingTransformer for field race\n",
+      "2020-07-09 20:51:21,674 - INFO - table - Loading transformer LabelEncodingTransformer for field sex\n",
+      "2020-07-09 20:51:21,674 - INFO - table - Loading transformer NumericalTransformer for field capital-gain\n",
+      "2020-07-09 20:51:21,675 - INFO - table - Loading transformer NumericalTransformer for field capital-loss\n",
+      "2020-07-09 20:51:21,675 - INFO - table - Loading transformer NumericalTransformer for field hours-per-week\n",
+      "2020-07-09 20:51:21,676 - INFO - table - Loading transformer LabelEncodingTransformer for field native-country\n",
+      "2020-07-09 20:51:21,678 - INFO - table - Loading transformer LabelEncodingTransformer for field income\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss G: 1.9379, Loss D: -0.1297\n",
+      "Epoch 2, Loss G: 1.6529, Loss D: 0.1716\n",
+      "Epoch 3, Loss G: 1.0939, Loss D: 0.0208\n",
+      "Epoch 4, Loss G: 0.9390, Loss D: -0.0918\n",
+      "Epoch 5, Loss G: -0.0550, Loss D: 0.0415\n",
+      "Epoch 6, Loss G: -0.0864, Loss D: -0.0104\n",
+      "Epoch 7, Loss G: -0.3865, Loss D: 0.0074\n",
+      "Epoch 8, Loss G: -1.0150, Loss D: -0.0708\n",
+      "Epoch 9, Loss G: -0.8685, Loss D: -0.1289\n",
+      "Epoch 10, Loss G: -1.1524, Loss D: -0.0487\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(census)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Sample data from the fitted model\n",
+    "\n",
+    "Once the modeling has finished you are ready to generate new synthetic data by calling the `sample` method\n",
+    "from our model just like we did with the GaussianCopula model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled = model.sample()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a table identical to the one which the model was fitted on, but filled with new data\n",
+    "which resembles the original one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>fnlwgt</th>\n",
+       "      <th>education</th>\n",
+       "      <th>education-num</th>\n",
+       "      <th>marital-status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "      <th>native-country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>212843</td>\n",
+       "      <td>Prof-school</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Tech-support</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>49</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30</td>\n",
+       "      <td>Federal-gov</td>\n",
+       "      <td>321819</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>14</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Craft-repair</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>51</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>39</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>169771</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Other-service</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>-37</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>39</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>47</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>116751</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Handlers-cleaners</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2057</td>\n",
+       "      <td>41</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>315119</td>\n",
+       "      <td>Assoc-acdm</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Craft-repair</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>-11</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>49</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>36</td>\n",
+       "      <td>State-gov</td>\n",
+       "      <td>172646</td>\n",
+       "      <td>Masters</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Machine-op-inspct</td>\n",
+       "      <td>Wife</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>-50</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Japan</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>40</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>163368</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Transport-moving</td>\n",
+       "      <td>Own-child</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>-13</td>\n",
+       "      <td>5</td>\n",
+       "      <td>39</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>23</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>369324</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Tech-support</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>-60</td>\n",
+       "      <td>5</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>32</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>192521</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>-15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>43</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>244118</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Separated</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>-21</td>\n",
+       "      <td>0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&gt;50K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age     workclass  fnlwgt      education  education-num  \\\n",
+       "0   38       Private  212843    Prof-school             10   \n",
+       "1   30   Federal-gov  321819        HS-grad             14   \n",
+       "2   25       Private  169771        HS-grad             13   \n",
+       "3   47       Private  116751   Some-college              6   \n",
+       "4   38       Private  315119     Assoc-acdm             13   \n",
+       "5   36     State-gov  172646        Masters              6   \n",
+       "6   40       Private  163368   Some-college              4   \n",
+       "7   23       Private  369324   Some-college             11   \n",
+       "8   32       Private  192521      Bachelors             10   \n",
+       "9   28       Private  244118        HS-grad              9   \n",
+       "\n",
+       "        marital-status          occupation    relationship    race      sex  \\\n",
+       "0        Never-married        Tech-support       Unmarried   White     Male   \n",
+       "1   Married-civ-spouse        Craft-repair       Unmarried   Black   Female   \n",
+       "2        Never-married       Other-service   Not-in-family   White   Female   \n",
+       "3             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
+       "4        Never-married        Craft-repair         Husband   Black     Male   \n",
+       "5   Married-civ-spouse   Machine-op-inspct            Wife   White     Male   \n",
+       "6   Married-civ-spouse    Transport-moving       Own-child   White   Female   \n",
+       "7   Married-civ-spouse        Tech-support         Husband   White   Female   \n",
+       "8   Married-civ-spouse     Exec-managerial   Not-in-family   White   Female   \n",
+       "9            Separated      Prof-specialty   Not-in-family   White     Male   \n",
+       "\n",
+       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
+       "0            49            -1              40   United-States   <=50K  \n",
+       "1            51            -3              39   United-States   <=50K  \n",
+       "2           -37            -2              39   United-States   <=50K  \n",
+       "3             0          2057              41   United-States   <=50K  \n",
+       "4           -11            -1              49   United-States   <=50K  \n",
+       "5           -50            -2              40           Japan   <=50K  \n",
+       "6           -13             5              39   United-States   <=50K  \n",
+       "7           -60             5              40   United-States   <=50K  \n",
+       "8           -15             2              43   United-States   <=50K  \n",
+       "9           -21             0              39   United-States    >50K  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Evaluate how good the data is\n",
+    "\n",
+    "Finally, we will use the evaluation framework included in SDV to obtain a metric of how\n",
+    "similar the sampled data is to the original one.\n",
+    "\n",
+    "For this, we will simply import the `sdv.evaluation.evaluate` function and pass both\n",
+    "the synthetic and the real data to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-28.22245129315498"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from sdv.evaluation import evaluate\n",
+    "\n",
+    "evaluate(sampled, census)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/Demo - Walmart.ipynb b/tutorials/03_Relational_Data_Modeling.ipynb
similarity index 55%
rename from examples/Demo - Walmart.ipynb
rename to tutorials/03_Relational_Data_Modeling.ipynb
index 2e3303259..230061b00 100644
--- a/examples/Demo - Walmart.ipynb	
+++ b/tutorials/03_Relational_Data_Modeling.ipynb
@@ -4,7 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Demo \"walmart\"\n",
+    "# Relational Data Modeling\n",
+    "\n",
+    "In this tutorial we will be showing how to model a real world multi-table dataset using SDV.\n",
+    "\n",
+    "## About the datset\n",
     "\n",
     "We have a store series, each of those have a size and a category and additional information in a given date: average temperature in the region, cost of fuel in the region, promotional data, the customer price index, the unemployment rate and whether the date is a special holiday.\n",
     "\n",
@@ -91,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -100,9 +104,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-02 18:50:09,230 - INFO - metadata - Loading table stores\n",
-      "2020-07-02 18:50:09,237 - INFO - metadata - Loading table features\n",
-      "2020-07-02 18:50:09,255 - INFO - metadata - Loading table depts\n"
+      "2020-07-09 21:00:17,378 - INFO - __init__ - Loading table stores\n",
+      "2020-07-09 21:00:17,384 - INFO - __init__ - Loading table features\n",
+      "2020-07-09 21:00:17,402 - INFO - __init__ - Loading table depts\n"
      ]
     }
    ],
@@ -128,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -184,7 +188,7 @@
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>stores&#45;&gt;features</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M140.7572,-301.1781C136.1229,-295.4114 131.9421,-289.3324 128.5,-283 124.4445,-275.5391 120.9867,-267.6091 118.0416,-259.4282\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"117.9697,-259.2116 119.085,-248.3026 116.3922,-254.467 114.8148,-249.7223 114.8148,-249.7223 114.8148,-249.7223 116.3922,-254.467 110.5446,-251.142 117.9697,-259.2116 117.9697,-259.2116\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"118.136,-248.6181 117.9697,-259.2116 111.4935,-250.8265 118.136,-248.6181\"/>\n",
        "<text text-anchor=\"middle\" x=\"214\" y=\"-271.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;features.Store &#45;&gt; stores.Store</text>\n",
        "</g>\n",
        "<!-- depts -->\n",
@@ -206,17 +210,17 @@
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>stores&#45;&gt;depts</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M287.0707,-301.1494C291.788,-295.4236 296.0255,-289.3596 299.5,-283 311.9843,-260.1491 318.266,-232.7713 321.2171,-207.274\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"321.2259,-207.1848 326.7005,-197.6832 321.7244,-202.2097 322.223,-197.2346 322.223,-197.2346 322.223,-197.2346 321.7244,-202.2097 317.7454,-196.7859 321.2259,-207.1848 321.2259,-207.1848\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"325.7055,-197.5835 321.2259,-207.1848 318.7404,-196.8856 325.7055,-197.5835\"/>\n",
        "<text text-anchor=\"middle\" x=\"383\" y=\"-271.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;depts.Store &#45;&gt; stores.Store</text>\n",
        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7fc286fd97f0>"
+       "<graphviz.dot.Digraph at 0x7f3c6acf2dd8>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -234,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
@@ -261,128 +265,130 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-02 18:50:09,639 - INFO - modeler - Modeling stores\n",
-      "2020-07-02 18:50:09,640 - INFO - metadata - Loading transformer CategoricalTransformer for field Type\n",
-      "2020-07-02 18:50:09,640 - INFO - metadata - Loading transformer NumericalTransformer for field Size\n",
-      "2020-07-02 18:50:09,653 - INFO - modeler - Modeling features\n",
-      "2020-07-02 18:50:09,653 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
-      "2020-07-02 18:50:09,654 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown1\n",
-      "2020-07-02 18:50:09,654 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
-      "2020-07-02 18:50:09,654 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown4\n",
-      "2020-07-02 18:50:09,655 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown3\n",
-      "2020-07-02 18:50:09,656 - INFO - metadata - Loading transformer NumericalTransformer for field Fuel_Price\n",
-      "2020-07-02 18:50:09,656 - INFO - metadata - Loading transformer NumericalTransformer for field Unemployment\n",
-      "2020-07-02 18:50:09,657 - INFO - metadata - Loading transformer NumericalTransformer for field Temperature\n",
-      "2020-07-02 18:50:09,657 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown5\n",
-      "2020-07-02 18:50:09,657 - INFO - metadata - Loading transformer NumericalTransformer for field MarkDown2\n",
-      "2020-07-02 18:50:09,659 - INFO - metadata - Loading transformer NumericalTransformer for field CPI\n",
-      "2020-07-02 18:50:09,709 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:09,760 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:09,791 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:09,817 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:09,845 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:09,871 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:09,901 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:09,929 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:09,958 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:09,985 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,016 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,045 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,076 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,131 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,158 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,184 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,211 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,239 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,268 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,297 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,325 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,352 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,378 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,410 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,440 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,467 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,494 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,519 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,547 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,572 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,602 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,628 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,656 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,686 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,713 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,740 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,768 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,799 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,827 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,858 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,886 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,912 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,939 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,967 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:10,996 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,028 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,058 - INFO - modeler - Modeling depts\n",
-      "2020-07-02 18:50:11,058 - INFO - metadata - Loading transformer DatetimeTransformer for field Date\n",
-      "2020-07-02 18:50:11,059 - INFO - metadata - Loading transformer NumericalTransformer for field Weekly_Sales\n",
-      "2020-07-02 18:50:11,059 - INFO - metadata - Loading transformer NumericalTransformer for field Dept\n",
-      "2020-07-02 18:50:11,059 - INFO - metadata - Loading transformer BooleanTransformer for field IsHoliday\n",
-      "2020-07-02 18:50:11,169 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,445 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,461 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,474 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,489 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,505 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,521 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,536 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,552 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,568 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,583 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,603 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
+      "2020-07-09 21:00:31,480 - INFO - modeler - Modeling stores\n",
+      "2020-07-09 21:00:31,481 - INFO - __init__ - Loading transformer CategoricalTransformer for field Type\n",
+      "2020-07-09 21:00:31,481 - INFO - __init__ - Loading transformer NumericalTransformer for field Size\n",
+      "2020-07-09 21:00:31,491 - INFO - modeler - Modeling features\n",
+      "2020-07-09 21:00:31,492 - INFO - __init__ - Loading transformer DatetimeTransformer for field Date\n",
+      "2020-07-09 21:00:31,493 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown1\n",
+      "2020-07-09 21:00:31,493 - INFO - __init__ - Loading transformer BooleanTransformer for field IsHoliday\n",
+      "2020-07-09 21:00:31,493 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown4\n",
+      "2020-07-09 21:00:31,494 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown3\n",
+      "2020-07-09 21:00:31,494 - INFO - __init__ - Loading transformer NumericalTransformer for field Fuel_Price\n",
+      "2020-07-09 21:00:31,494 - INFO - __init__ - Loading transformer NumericalTransformer for field Unemployment\n",
+      "2020-07-09 21:00:31,495 - INFO - __init__ - Loading transformer NumericalTransformer for field Temperature\n",
+      "2020-07-09 21:00:31,495 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown5\n",
+      "2020-07-09 21:00:31,495 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown2\n",
+      "2020-07-09 21:00:31,495 - INFO - __init__ - Loading transformer NumericalTransformer for field CPI\n",
+      "2020-07-09 21:00:31,544 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,595 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/Projects/MIT/SDV/sdv/models/copulas.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
+      "  self.model.covariance = np.array(values)\n",
+      "2020-07-09 21:00:31,651 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,679 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,707 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,734 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,762 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,790 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,816 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,845 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,872 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,901 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,931 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,959 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,986 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,014 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,040 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,070 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,096 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,123 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,152 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,181 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,209 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,235 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,264 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,293 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,322 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,349 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,376 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,405 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,433 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,463 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,492 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,521 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,552 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,583 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,612 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,644 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,674 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,704 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,732 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,762 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,791 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,821 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,852 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,882 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,917 - INFO - modeler - Modeling depts\n",
+      "2020-07-09 21:00:32,918 - INFO - __init__ - Loading transformer DatetimeTransformer for field Date\n",
+      "2020-07-09 21:00:32,918 - INFO - __init__ - Loading transformer NumericalTransformer for field Weekly_Sales\n",
+      "2020-07-09 21:00:32,919 - INFO - __init__ - Loading transformer NumericalTransformer for field Dept\n",
+      "2020-07-09 21:00:32,919 - INFO - __init__ - Loading transformer BooleanTransformer for field IsHoliday\n",
+      "2020-07-09 21:00:33,016 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,318 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,334 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,350 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,364 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,381 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,396 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,412 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,428 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-02 18:50:11,618 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,634 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,652 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,667 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,683 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,698 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,713 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,728 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,741 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,754 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,769 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,782 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,800 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,814 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,829 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,844 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,859 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,874 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,887 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,900 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,914 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,928 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,940 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,955 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,968 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,979 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:11,991 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:12,003 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:12,017 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:12,032 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:12,045 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:12,058 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:12,069 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:12,084 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "2020-07-02 18:50:12,177 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
-      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/core/fromnumeric.py:56: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
-      "  return getattr(obj, method)(*args, **kwds)\n",
-      "2020-07-02 18:50:12,380 - INFO - modeler - Modeling Complete\n"
+      "2020-07-09 21:00:33,447 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,464 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,479 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,495 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,511 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,526 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,541 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,554 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,567 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,582 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,596 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,609 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,624 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,639 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,652 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,667 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,682 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,696 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,709 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,724 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,740 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,753 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,766 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,779 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,792 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,803 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,818 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,831 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,842 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,856 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,870 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,885 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,898 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,912 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,926 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,937 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,949 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:34,047 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/core/fromnumeric.py:58: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
+      "  return bound(*args, **kwds)\n",
+      "2020-07-09 21:00:34,259 - INFO - modeler - Modeling Complete\n"
      ]
     }
    ],
@@ -413,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -422,7 +428,7 @@
        "{'stores': 45, 'features': 8190, 'depts': 421570}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -433,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "scrolled": false
    },
@@ -451,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -484,32 +490,32 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>B</td>\n",
-       "      <td>85496</td>\n",
-       "      <td>3</td>\n",
+       "      <td>106106</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>A</td>\n",
-       "      <td>178862</td>\n",
-       "      <td>4</td>\n",
+       "      <td>B</td>\n",
+       "      <td>68071</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>B</td>\n",
-       "      <td>69654</td>\n",
-       "      <td>5</td>\n",
+       "      <td>C</td>\n",
+       "      <td>253603</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>A</td>\n",
-       "      <td>211981</td>\n",
-       "      <td>6</td>\n",
+       "      <td>223268</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>A</td>\n",
-       "      <td>131188</td>\n",
-       "      <td>7</td>\n",
+       "      <td>B</td>\n",
+       "      <td>166848</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -517,14 +523,14 @@
       ],
       "text/plain": [
        "  Type    Size  Store\n",
-       "0    B   85496      3\n",
-       "1    A  178862      4\n",
-       "2    B   69654      5\n",
-       "3    A  211981      6\n",
-       "4    A  131188      7"
+       "0    B  106106      0\n",
+       "1    B   68071      1\n",
+       "2    C  253603      2\n",
+       "3    A  223268      3\n",
+       "4    B  166848      4"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -535,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -576,78 +582,78 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2012-04-23 03:09:52.638271488</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2009-12-13 04:59:49.832576000</td>\n",
+       "      <td>222.268822</td>\n",
+       "      <td>0</td>\n",
        "      <td>False</td>\n",
+       "      <td>758.900545</td>\n",
+       "      <td>-1080.569435</td>\n",
+       "      <td>4.574966</td>\n",
+       "      <td>6.052795</td>\n",
+       "      <td>51.354606</td>\n",
+       "      <td>-4016.012141</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>-9803.940199</td>\n",
-       "      <td>3.561375</td>\n",
-       "      <td>8.838728</td>\n",
-       "      <td>67.162475</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2703.17729</td>\n",
-       "      <td>186.471991</td>\n",
+       "      <td>224.973722</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2011-04-19 08:45:12.429521664</td>\n",
-       "      <td>483.892955</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2011-07-20 14:40:40.400244736</td>\n",
+       "      <td>6857.712028</td>\n",
+       "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>7504.524416</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3.495118</td>\n",
-       "      <td>7.360667</td>\n",
-       "      <td>42.785730</td>\n",
-       "      <td>2772.597105</td>\n",
+       "      <td>-551.045257</td>\n",
+       "      <td>3.018296</td>\n",
+       "      <td>6.720669</td>\n",
+       "      <td>80.723862</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>192.048268</td>\n",
+       "      <td>204.595072</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2011-01-30 22:13:59.841415680</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2011-10-12 08:23:54.987658240</td>\n",
+       "      <td>5530.929425</td>\n",
+       "      <td>0</td>\n",
        "      <td>False</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3.361946</td>\n",
-       "      <td>7.524812</td>\n",
-       "      <td>34.945770</td>\n",
+       "      <td>3.466358</td>\n",
+       "      <td>7.256570</td>\n",
+       "      <td>73.954938</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>192.100673</td>\n",
+       "      <td>202.591941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2011-10-08 08:16:00.977235968</td>\n",
-       "      <td>4661.175670</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2012-11-26 09:22:59.377291776</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>3392.028528</td>\n",
-       "      <td>-5837.649003</td>\n",
-       "      <td>2.994273</td>\n",
-       "      <td>7.993152</td>\n",
-       "      <td>66.818180</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>3703.137191</td>\n",
+       "      <td>3.623725</td>\n",
+       "      <td>6.598615</td>\n",
+       "      <td>72.041454</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>197.921916</td>\n",
+       "      <td>3925.456611</td>\n",
+       "      <td>169.457078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2011-09-29 23:18:43.912751616</td>\n",
+       "      <td>2013-05-13 15:22:12.213717760</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3</td>\n",
+       "      <td>0</td>\n",
        "      <td>False</td>\n",
+       "      <td>3773.942572</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>3.381795</td>\n",
+       "      <td>5.115002</td>\n",
+       "      <td>83.058322</td>\n",
+       "      <td>15018.944605</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>3.116659</td>\n",
-       "      <td>4.945393</td>\n",
-       "      <td>48.979036</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2957.77612</td>\n",
-       "      <td>192.677797</td>\n",
+       "      <td>182.801974</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -655,28 +661,28 @@
       ],
       "text/plain": [
        "                           Date    MarkDown1  Store  IsHoliday    MarkDown4  \\\n",
-       "0 2012-04-23 03:09:52.638271488          NaN      3      False          NaN   \n",
-       "1 2011-04-19 08:45:12.429521664   483.892955      3      False  7504.524416   \n",
-       "2 2011-01-30 22:13:59.841415680          NaN      3      False          NaN   \n",
-       "3 2011-10-08 08:16:00.977235968  4661.175670      3      False  3392.028528   \n",
-       "4 2011-09-29 23:18:43.912751616          NaN      3      False          NaN   \n",
+       "0 2009-12-13 04:59:49.832576000   222.268822      0      False   758.900545   \n",
+       "1 2011-07-20 14:40:40.400244736  6857.712028      0      False          NaN   \n",
+       "2 2011-10-12 08:23:54.987658240  5530.929425      0      False          NaN   \n",
+       "3 2012-11-26 09:22:59.377291776          NaN      0      False          NaN   \n",
+       "4 2013-05-13 15:22:12.213717760          NaN      0      False  3773.942572   \n",
        "\n",
-       "     MarkDown3  Fuel_Price  Unemployment  Temperature    MarkDown5  \\\n",
-       "0 -9803.940199    3.561375      8.838728    67.162475          NaN   \n",
-       "1          NaN    3.495118      7.360667    42.785730  2772.597105   \n",
-       "2          NaN    3.361946      7.524812    34.945770          NaN   \n",
-       "3 -5837.649003    2.994273      7.993152    66.818180          NaN   \n",
-       "4          NaN    3.116659      4.945393    48.979036          NaN   \n",
+       "     MarkDown3  Fuel_Price  Unemployment  Temperature     MarkDown5  \\\n",
+       "0 -1080.569435    4.574966      6.052795    51.354606  -4016.012141   \n",
+       "1  -551.045257    3.018296      6.720669    80.723862           NaN   \n",
+       "2          NaN    3.466358      7.256570    73.954938           NaN   \n",
+       "3  3703.137191    3.623725      6.598615    72.041454           NaN   \n",
+       "4          NaN    3.381795      5.115002    83.058322  15018.944605   \n",
        "\n",
-       "    MarkDown2         CPI  \n",
-       "0  2703.17729  186.471991  \n",
-       "1         NaN  192.048268  \n",
-       "2         NaN  192.100673  \n",
-       "3         NaN  197.921916  \n",
-       "4  2957.77612  192.677797  "
+       "     MarkDown2         CPI  \n",
+       "0          NaN  224.973722  \n",
+       "1          NaN  204.595072  \n",
+       "2          NaN  202.591941  \n",
+       "3  3925.456611  169.457078  \n",
+       "4          NaN  182.801974  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -687,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -721,42 +727,42 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2011-04-22 15:24:18.057608704</td>\n",
-       "      <td>11196.989134</td>\n",
-       "      <td>3</td>\n",
-       "      <td>38</td>\n",
+       "      <td>2012-04-19 04:40:04.370382848</td>\n",
+       "      <td>7523.202470</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2010-02-06 19:21:02.431538688</td>\n",
-       "      <td>-14038.529503</td>\n",
-       "      <td>3</td>\n",
-       "      <td>29</td>\n",
+       "      <td>2012-10-24 12:49:45.630176512</td>\n",
+       "      <td>13675.425498</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2012-06-04 16:23:09.227934976</td>\n",
-       "      <td>-6519.738485</td>\n",
-       "      <td>3</td>\n",
-       "      <td>46</td>\n",
+       "      <td>2012-06-05 05:02:35.551761408</td>\n",
+       "      <td>2402.017324</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2011-08-09 17:18:54.910250752</td>\n",
-       "      <td>23194.918038</td>\n",
-       "      <td>3</td>\n",
-       "      <td>45</td>\n",
+       "      <td>2012-05-31 22:15:14.903856896</td>\n",
+       "      <td>-12761.073176</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2010-09-01 23:10:54.986872576</td>\n",
-       "      <td>16761.426407</td>\n",
-       "      <td>3</td>\n",
-       "      <td>29</td>\n",
+       "      <td>2011-09-07 15:34:43.239835648</td>\n",
+       "      <td>3642.817612</td>\n",
+       "      <td>0</td>\n",
+       "      <td>58</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -765,14 +771,14 @@
       ],
       "text/plain": [
        "                           Date  Weekly_Sales  Store  Dept  IsHoliday\n",
-       "0 2011-04-22 15:24:18.057608704  11196.989134      3    38      False\n",
-       "1 2010-02-06 19:21:02.431538688 -14038.529503      3    29      False\n",
-       "2 2012-06-04 16:23:09.227934976  -6519.738485      3    46      False\n",
-       "3 2011-08-09 17:18:54.910250752  23194.918038      3    45      False\n",
-       "4 2010-09-01 23:10:54.986872576  16761.426407      3    29      False"
+       "0 2012-04-19 04:40:04.370382848   7523.202470      0    36      False\n",
+       "1 2012-10-24 12:49:45.630176512  13675.425498      0    17      False\n",
+       "2 2012-06-05 05:02:35.551761408   2402.017324      0    31      False\n",
+       "3 2012-05-31 22:15:14.903856896 -12761.073176      0    81      False\n",
+       "4 2011-09-07 15:34:43.239835648   3642.817612      0    58      False"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -792,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "scrolled": false
    },
@@ -801,198 +807,198 @@
      "data": {
       "text/plain": [
        "{'features':                              Date     MarkDown1  Store  IsHoliday  \\\n",
-       " 0   2012-03-09 19:29:04.121987584   4547.265066     48      False   \n",
-       " 1   2012-01-12 12:05:27.772331264           NaN     51      False   \n",
-       " 2   2012-10-09 23:52:58.454752256  13291.983302     51      False   \n",
-       " 3   2011-07-26 23:58:33.986580992           NaN     49      False   \n",
-       " 4   2012-01-17 02:27:51.821400832   6865.756770     50      False   \n",
-       " 5   2009-12-27 02:32:16.625306880           NaN     51      False   \n",
-       " 6   2011-07-12 23:01:03.955540224           NaN     48      False   \n",
-       " 7   2011-09-25 10:16:19.542280704   7114.339579     48      False   \n",
-       " 8   2011-05-06 02:00:24.919497728           NaN     48      False   \n",
-       " 9   2013-10-23 10:17:59.945650432   2267.991754     50      False   \n",
-       " 10  2011-05-18 08:31:22.419635968           NaN     48      False   \n",
-       " 11  2010-07-27 18:16:54.600127744           NaN     49      False   \n",
-       " 12  2010-11-04 00:37:47.996870656           NaN     51      False   \n",
-       " 13  2012-03-23 15:04:51.172607488  10192.668685     48      False   \n",
-       " 14  2007-11-14 22:28:51.711726848           NaN     48      False   \n",
-       " 15  2013-02-07 20:55:46.684175872   9146.998153     49      False   \n",
-       " 16  2011-07-18 00:03:42.998976512           NaN     50      False   \n",
-       " 17  2010-10-02 21:34:09.917561088           NaN     49      False   \n",
-       " 18  2010-03-12 18:25:31.509475328           NaN     51      False   \n",
-       " 19  2013-06-24 02:50:15.027540736   2943.979267     50      False   \n",
-       " 20  2010-09-24 15:42:39.457930752           NaN     48      False   \n",
-       " 21  2012-03-27 19:00:12.877346816   7750.605869     51      False   \n",
-       " 22  2010-05-26 23:28:27.091251968           NaN     48      False   \n",
-       " 23  2011-05-01 23:07:37.680983296           NaN     52      False   \n",
-       " 24  2012-10-07 16:56:39.076689152  11838.898938     51      False   \n",
-       " 25  2011-02-19 21:38:01.903840000           NaN     52      False   \n",
-       " 26  2012-01-18 06:02:24.764590592   7428.686729     52      False   \n",
-       " 27  2012-09-28 01:36:19.603453184   4962.148181     52      False   \n",
-       " 28  2011-07-28 19:21:49.952510208  12773.901269     48      False   \n",
-       " 29  2011-09-04 16:53:45.561477888           NaN     50      False   \n",
+       " 0   2010-07-07 18:33:30.490365440           NaN     46       True   \n",
+       " 1   2010-05-31 05:12:43.555245568           NaN     49      False   \n",
+       " 2   2009-11-13 00:11:26.645775872           NaN     46      False   \n",
+       " 3   2011-06-26 13:37:23.832223232   8480.251096     48      False   \n",
+       " 4   2013-09-30 06:14:18.804104192   4586.374804     48      False   \n",
+       " 5   2011-03-18 21:50:55.521650432           NaN     45      False   \n",
+       " 6   2012-03-22 14:59:09.189622016   9747.212668     46      False   \n",
+       " 7   2011-04-05 16:45:11.282816000           NaN     48      False   \n",
+       " 8   2013-05-05 14:23:57.431958784   5430.557501     46      False   \n",
+       " 9   2011-04-07 02:38:10.873993984   6735.454863     49      False   \n",
+       " 10  2012-07-09 15:59:11.863023616  11818.737600     48      False   \n",
+       " 11  2012-03-10 03:57:22.968428544   4603.635257     48      False   \n",
+       " 12  2011-10-01 03:51:05.280273920  14596.976258     49      False   \n",
+       " 13  2012-06-24 23:45:06.501742080   5816.163902     49      False   \n",
+       " 14  2011-08-06 14:43:48.366935040           NaN     47      False   \n",
+       " 15  2012-06-14 01:06:45.771771648           NaN     45      False   \n",
+       " 16  2011-11-22 08:57:27.766619392           NaN     45      False   \n",
+       " 17  2012-10-06 19:10:00.039407360   5887.384577     48      False   \n",
+       " 18  2013-06-08 07:46:59.612567808  -5770.051172     48      False   \n",
+       " 19  2012-05-27 11:08:47.184699648   6908.122060     46      False   \n",
+       " 20  2010-10-19 21:42:09.919441408           NaN     46      False   \n",
+       " 21  2013-01-01 17:57:15.273558784   6221.151492     48      False   \n",
+       " 22  2012-07-13 14:40:51.181466112   1378.006687     47      False   \n",
+       " 23  2012-02-13 18:37:30.161656320           NaN     48      False   \n",
+       " 24  2010-11-27 09:56:34.540603392           NaN     46      False   \n",
+       " 25  2011-05-21 03:49:37.053693440           NaN     49      False   \n",
+       " 26  2010-06-12 20:31:32.339124224           NaN     49       True   \n",
+       " 27  2011-08-12 21:10:37.406385152           NaN     47      False   \n",
+       " 28  2012-02-07 03:31:37.558127360           NaN     47      False   \n",
+       " 29  2011-05-28 22:39:00.441712128           NaN     48      False   \n",
        " ..                            ...           ...    ...        ...   \n",
-       " 970 2011-06-14 18:48:47.646437632   7563.489728     48      False   \n",
-       " 971 2011-05-11 09:27:03.321238016   3763.579757     50      False   \n",
-       " 972 2012-10-27 08:44:16.987041024  -2107.559543     49      False   \n",
-       " 973 2011-03-26 17:36:28.323915264           NaN     48      False   \n",
-       " 974 2012-03-30 09:18:45.289797888           NaN     52      False   \n",
-       " 975 2012-12-25 00:57:24.386340608   3483.807111     51      False   \n",
-       " 976 2010-09-17 18:59:15.931293440           NaN     52      False   \n",
-       " 977 2012-04-18 06:40:20.102041344   2869.173605     50      False   \n",
-       " 978 2010-01-18 10:21:26.293353216           NaN     49      False   \n",
-       " 979 2010-08-13 00:08:32.324416256           NaN     50      False   \n",
-       " 980 2012-01-17 04:04:26.614297088           NaN     50      False   \n",
-       " 981 2011-06-23 01:40:48.209999104           NaN     48      False   \n",
-       " 982 2012-05-15 04:01:02.762140160   3935.159830     50      False   \n",
-       " 983 2011-03-03 10:20:57.530126848   5704.369183     52      False   \n",
-       " 984 2011-09-11 11:54:13.537061888  -3939.448245     49      False   \n",
-       " 985 2012-07-01 08:38:12.122587648           NaN     49      False   \n",
-       " 986 2013-01-15 14:24:51.253277952   7802.046675     52      False   \n",
-       " 987 2011-03-16 12:56:22.305051648           NaN     49      False   \n",
-       " 988 2012-02-09 21:21:51.383019776   3563.474259     50      False   \n",
-       " 989 2011-04-28 07:35:57.312682752           NaN     49      False   \n",
-       " 990 2012-07-06 11:32:42.706951424   4134.684319     48      False   \n",
-       " 991 2012-09-10 11:27:22.375229184   2670.997722     48      False   \n",
-       " 992 2010-04-19 00:59:34.282525696           NaN     51      False   \n",
-       " 993 2012-05-21 02:19:56.773740288    725.161402     51      False   \n",
-       " 994 2012-08-25 02:46:15.815232768   2736.817373     52      False   \n",
-       " 995 2011-11-27 15:29:59.302161664           NaN     48      False   \n",
-       " 996 2012-11-19 23:22:47.651802880           NaN     49      False   \n",
-       " 997 2012-03-06 18:23:43.093342208           NaN     51       True   \n",
-       " 998 2011-11-17 03:00:20.669487872           NaN     50      False   \n",
-       " 999 2010-10-18 08:04:04.443984128           NaN     51      False   \n",
+       " 970 2012-10-01 09:27:33.224137984   7367.091411     49      False   \n",
+       " 971 2011-08-05 19:20:26.151251200           NaN     49      False   \n",
+       " 972 2010-05-02 10:25:48.294526720           NaN     49      False   \n",
+       " 973 2014-07-30 06:33:25.027884800   2869.286369     46      False   \n",
+       " 974 2012-06-10 23:50:28.918937344           NaN     45      False   \n",
+       " 975 2011-11-20 17:24:25.076207104           NaN     48      False   \n",
+       " 976 2011-07-16 19:31:09.475053312   8465.620591     48      False   \n",
+       " 977 2010-12-17 11:17:29.377529600           NaN     45      False   \n",
+       " 978 2011-05-01 22:00:24.413698816           NaN     48       True   \n",
+       " 979 2009-01-04 14:06:28.700405504           NaN     48      False   \n",
+       " 980 2013-01-26 23:44:47.124111360   7711.715123     47      False   \n",
+       " 981 2012-02-05 15:58:21.306519040   4771.829938     49      False   \n",
+       " 982 2012-12-25 20:25:33.794860544   9052.115357     45      False   \n",
+       " 983 2011-03-11 05:15:33.422215424           NaN     48      False   \n",
+       " 984 2012-04-10 15:14:42.299491072  11469.698722     49      False   \n",
+       " 985 2012-09-04 11:56:04.719035648  11985.730540     47      False   \n",
+       " 986 2010-08-12 06:13:43.472920320           NaN     47      False   \n",
+       " 987 2011-11-28 00:46:09.219015936           NaN     47      False   \n",
+       " 988 2010-11-01 19:46:13.442969344           NaN     47      False   \n",
+       " 989 2012-09-03 02:46:56.811711232  13448.706455     46      False   \n",
+       " 990 2012-06-21 20:15:00.600609536   8515.644049     45      False   \n",
+       " 991 2009-11-09 11:32:20.317559808           NaN     47      False   \n",
+       " 992 2012-12-24 16:57:40.573141760   8785.735128     48      False   \n",
+       " 993 2010-10-26 22:04:15.916646656           NaN     45      False   \n",
+       " 994 2013-07-27 21:48:37.233461248   2218.444188     46      False   \n",
+       " 995 2012-11-22 15:48:58.070053120           NaN     47      False   \n",
+       " 996 2012-11-19 17:12:55.679613440  11318.264204     45      False   \n",
+       " 997 2013-10-11 21:09:49.021492480    478.609430     47      False   \n",
+       " 998 2013-01-23 23:17:20.493887232  14227.944357     49      False   \n",
+       " 999 2012-06-23 00:01:59.594749696   9121.193148     46      False   \n",
        " \n",
        "        MarkDown4     MarkDown3  Fuel_Price  Unemployment  Temperature  \\\n",
-       " 0    2262.924323   4314.258484    2.944899     12.207513    44.866684   \n",
-       " 1     648.327530           NaN    3.415876      8.296209    79.755000   \n",
-       " 2    7464.648400   4818.694540    3.438865     10.375869    53.011465   \n",
-       " 3    7722.936675           NaN    3.238451      8.340640    81.331769   \n",
-       " 4     385.720439   2933.746873    3.352123      4.517977    33.216711   \n",
-       " 5            NaN           NaN    2.595952      8.423675    58.153432   \n",
-       " 6            NaN           NaN    3.337125      7.289977    68.198691   \n",
-       " 7    1376.144152  -1375.405701    3.586827      4.433586    57.457667   \n",
-       " 8            NaN    885.964999    3.132711      7.941049    82.624443   \n",
-       " 9    4380.021000   4861.726545    4.617105      7.636990    64.616449   \n",
-       " 10           NaN           NaN    3.490697      9.699187   113.890285   \n",
-       " 11           NaN           NaN    3.337917     10.428851    76.354063   \n",
-       " 12           NaN           NaN    3.016818     10.044523    52.925513   \n",
-       " 13           NaN   1505.002284    3.244111      7.329469    34.127333   \n",
-       " 14           NaN           NaN    2.497412     12.247448   100.350997   \n",
-       " 15   7982.863954   7539.825400    3.909380      5.689230    65.945165   \n",
-       " 16           NaN           NaN    2.895094      7.718521    54.750702   \n",
-       " 17           NaN           NaN    3.722465      7.339952    56.152632   \n",
-       " 18           NaN           NaN    2.665366      8.045471    78.257558   \n",
-       " 19  -3310.004199   -808.378062    4.379650      5.320308    39.604265   \n",
-       " 20           NaN   4604.622951    3.058964      8.584222    44.177016   \n",
-       " 21   3790.217545   3053.642374    3.091990      5.069122    25.777591   \n",
-       " 22           NaN           NaN    2.931042      9.062068    41.649615   \n",
-       " 23           NaN           NaN    3.721348      6.586825    67.785117   \n",
-       " 24   4610.034450   -617.120861    4.010528      8.686732    35.445785   \n",
-       " 25           NaN           NaN    3.249523      8.450699    38.639637   \n",
-       " 26   4005.965014    -28.792693    3.808513      9.188265    57.584018   \n",
-       " 27   6433.867544  -3996.702966    2.976900      8.991618     6.179927   \n",
-       " 28           NaN           NaN    3.208463     11.875517    50.400717   \n",
-       " 29           NaN           NaN    3.455848      7.377382    56.480212   \n",
+       " 0            NaN           NaN    3.246268      6.712596    72.778272   \n",
+       " 1            NaN           NaN    2.961909      8.775273    31.911285   \n",
+       " 2            NaN           NaN    2.474694      8.693548    36.996590   \n",
+       " 3    2990.430314    450.886030    3.180972      7.482533    82.689207   \n",
+       " 4    1300.901307   1038.142954    3.979948      3.842990    61.626723   \n",
+       " 5            NaN           NaN    3.190374      9.185961    43.775213   \n",
+       " 6    5068.165070   4033.503158    3.436991     10.002234    44.980268   \n",
+       " 7    2639.207053           NaN    3.126477      8.921070    60.901225   \n",
+       " 8   -1515.448599   1859.931318    4.101750     10.478649    81.003354   \n",
+       " 9            NaN  -4013.843645    3.200662      8.549170    46.332113   \n",
+       " 10   6983.561124   9246.461293    3.637923      6.940256    66.208533   \n",
+       " 11           NaN   1872.845519    3.535785      5.360209    76.842613   \n",
+       " 12   5967.615987  -2084.078449    3.314418           NaN    73.216988   \n",
+       " 13   4181.309429  -7382.929438    3.170917      7.209879    27.887139   \n",
+       " 14           NaN           NaN    4.084598     10.976241    75.512964   \n",
+       " 15   6522.458652  -2969.500556    3.592465      6.778646    45.304799   \n",
+       " 16           NaN           NaN    3.691225      9.169614    61.590909   \n",
+       " 17   2195.064778  10031.652043    3.790657      5.655427    38.507717   \n",
+       " 18  -2522.448316   4530.988378    3.349566      4.743290    36.629555   \n",
+       " 19           NaN   3416.997827    3.641454      8.177166    89.916452   \n",
+       " 20           NaN           NaN    3.075063      5.229354    61.706563   \n",
+       " 21   4647.376520   3247.164388    4.162684      5.033966    37.962542   \n",
+       " 22           NaN           NaN    3.384827      4.921576    54.039707   \n",
+       " 23           NaN   7039.242263    3.187745      9.768689    74.453345   \n",
+       " 24           NaN           NaN    3.045024      6.954771    63.069450   \n",
+       " 25           NaN           NaN    4.164028      8.876290    71.070532   \n",
+       " 26           NaN           NaN    2.719009      7.230035    57.364249   \n",
+       " 27           NaN           NaN    3.257133      9.345917    68.652152   \n",
+       " 28           NaN  -4190.690106    3.548902      9.494165    89.145578   \n",
+       " 29           NaN           NaN    3.316924      6.697705    77.375684   \n",
        " ..           ...           ...         ...           ...          ...   \n",
-       " 970  7130.653517    613.434417    3.798558      8.849840    45.081481   \n",
-       " 971  -950.449794   8369.741392    3.691277      8.687173    21.625873   \n",
-       " 972  1423.082811   3915.095969    3.716980      7.812599    38.891102   \n",
-       " 973          NaN           NaN    3.277952      7.881991    74.018427   \n",
-       " 974          NaN           NaN    3.444190      4.147388    79.128181   \n",
-       " 975  5645.978692   3893.099917    3.821702      8.410268    87.162204   \n",
-       " 976          NaN           NaN    2.640021      7.458197    35.588448   \n",
-       " 977 -2627.096894   -142.014936    3.320107      6.970687    49.700872   \n",
-       " 978          NaN           NaN    2.710125      9.927722    83.348488   \n",
-       " 979          NaN           NaN    3.139506      8.348364    46.708000   \n",
-       " 980          NaN           NaN    3.578857      7.555184    25.595379   \n",
-       " 981          NaN  -1147.726465    3.685507      9.407928    91.745352   \n",
-       " 982   375.025287  11130.134824    3.467141      6.285321    38.179293   \n",
-       " 983          NaN           NaN    2.675670     10.258619    50.599203   \n",
-       " 984          NaN           NaN    3.375990      9.175979    44.036130   \n",
-       " 985          NaN    649.171815    3.370836      6.948002    22.863602   \n",
-       " 986  1944.149217    896.663662    3.971550      6.242637    97.107161   \n",
-       " 987          NaN           NaN    3.524965      7.059279    65.469921   \n",
-       " 988  2550.079436    121.672409    2.898533      5.929999    58.612442   \n",
-       " 989          NaN           NaN    3.237647      6.992924    72.225708   \n",
-       " 990          NaN           NaN    4.075160      8.392256    80.837964   \n",
-       " 991 -1543.390402   5264.766267    3.157407      7.017727    81.515109   \n",
-       " 992          NaN           NaN    3.293817      8.719239    65.735676   \n",
-       " 993   932.568195   1845.259380    3.157982      7.494639    65.103553   \n",
-       " 994 -2905.284812  -1268.636611    3.776062      6.736775    46.083801   \n",
-       " 995          NaN           NaN    3.424730           NaN    79.231321   \n",
-       " 996  9027.387381           NaN    3.960882      8.911136    26.948635   \n",
-       " 997          NaN           NaN    3.764028      8.049413   111.723841   \n",
-       " 998          NaN   6261.182193    3.609560      6.024725    38.411214   \n",
-       " 999          NaN           NaN    3.051021      7.855145    75.446009   \n",
+       " 970  2712.006496    -14.780688    3.618468      8.148930    30.483065   \n",
+       " 971          NaN           NaN    3.012630      6.326113    47.975596   \n",
+       " 972          NaN           NaN    2.841184     10.269877    81.946843   \n",
+       " 973  1937.104665  -1511.898278    4.759412      7.778287    30.662510   \n",
+       " 974  5253.689129    329.011494    3.343722      7.398721    21.129117   \n",
+       " 975          NaN   4930.780630    3.506178      9.701808   110.523151   \n",
+       " 976  5293.404197  -3121.976568    2.800094      5.843644    32.442600   \n",
+       " 977  9805.271519           NaN    3.136993      7.623560    72.572211   \n",
+       " 978          NaN           NaN    2.714601      7.234621    52.291714   \n",
+       " 979          NaN           NaN    2.043747      8.744450    46.628272   \n",
+       " 980  5415.328424  -4809.140910    3.287617      8.354515    61.384200   \n",
+       " 981          NaN           NaN    3.337297      6.142083    36.829644   \n",
+       " 982   837.730915   4072.518501    3.894026     12.494251    94.673851   \n",
+       " 983          NaN           NaN    3.420209      7.090230    47.694700   \n",
+       " 984  5134.594286    357.279124    3.325391      4.539604    37.460031   \n",
+       " 985          NaN   4056.157461    3.685698      7.230368    43.096815   \n",
+       " 986  3953.583585    805.676045    3.044291      7.884751    34.438372   \n",
+       " 987  4511.412982           NaN    3.394697      6.834802    36.636557   \n",
+       " 988          NaN           NaN    3.154329      8.363893    49.751330   \n",
+       " 989          NaN   2084.191209    3.615679      9.596049    75.533229   \n",
+       " 990  4457.772828    218.985398    3.440906      8.526339    61.864859   \n",
+       " 991          NaN           NaN    3.247303      7.423321    59.019784   \n",
+       " 992          NaN           NaN    3.458168      6.459322    48.584436   \n",
+       " 993          NaN           NaN    2.608670     10.259658    82.758016   \n",
+       " 994   719.333117   6631.998410    4.368128      7.588005    54.580465   \n",
+       " 995  3003.501922           NaN    3.800082           NaN   100.143258   \n",
+       " 996          NaN   -681.708699    4.140327     10.717719    46.213930   \n",
+       " 997   735.529994   3856.959812    3.921162      7.439993    59.473702   \n",
+       " 998  2557.407176   2865.135441    3.971143      9.540309    67.952407   \n",
+       " 999   671.086509   2328.798014    3.487042      5.988222    63.846379   \n",
        " \n",
-       "        MarkDown5    MarkDown2         CPI  \n",
-       " 0    7218.910186          NaN  154.437715  \n",
-       " 1    4928.572337  1860.474257  126.006100  \n",
-       " 2    4381.712904  6491.080062  182.505905  \n",
-       " 3            NaN  2075.661361  170.252793  \n",
-       " 4   -1363.927516  6383.011111  181.850615  \n",
-       " 5            NaN          NaN  250.558631  \n",
-       " 6            NaN          NaN  213.846371  \n",
-       " 7    1644.489130  4966.726612  189.105468  \n",
-       " 8            NaN  4689.948004  152.931090  \n",
-       " 9    3291.191563  2344.032770  152.780297  \n",
-       " 10           NaN          NaN  195.602913  \n",
-       " 11           NaN          NaN  176.381561  \n",
-       " 12           NaN          NaN  147.881741  \n",
-       " 13   1668.882903          NaN  155.004680  \n",
-       " 14           NaN          NaN  193.442985  \n",
-       " 15   1437.517079  4874.558939  152.281926  \n",
-       " 16   8439.806431          NaN  130.530240  \n",
-       " 17           NaN          NaN  201.486725  \n",
-       " 18           NaN          NaN  188.760741  \n",
-       " 19  -6907.068884  3246.451314  108.120768  \n",
-       " 20           NaN          NaN  148.788886  \n",
-       " 21   5724.976324  4496.854628  190.036218  \n",
-       " 22           NaN          NaN  181.746008  \n",
-       " 23           NaN          NaN  200.412670  \n",
-       " 24   6637.078664 -1388.022496   87.164451  \n",
-       " 25           NaN          NaN  195.622231  \n",
-       " 26   2922.749710          NaN  224.233432  \n",
-       " 27  -3042.801607          NaN  131.909287  \n",
-       " 28   7063.102432          NaN  146.156666  \n",
-       " 29           NaN          NaN  176.694595  \n",
-       " ..           ...          ...         ...  \n",
-       " 970  2882.447318          NaN  148.323409  \n",
-       " 971  1918.923930          NaN  141.723698  \n",
-       " 972   151.136535  1221.530133  155.532651  \n",
-       " 973          NaN          NaN  213.405340  \n",
-       " 974          NaN          NaN  186.636286  \n",
-       " 975  2598.731935  -846.566855  190.957987  \n",
-       " 976          NaN          NaN  153.917260  \n",
-       " 977  8372.618269  1768.408869  249.068471  \n",
-       " 978          NaN          NaN  177.318454  \n",
-       " 979          NaN          NaN  208.719040  \n",
-       " 980          NaN   754.021358  108.580497  \n",
-       " 981          NaN          NaN  159.526588  \n",
-       " 982  7349.003146  -732.495611  173.559001  \n",
-       " 983          NaN          NaN  140.910196  \n",
-       " 984   401.319810          NaN  175.340840  \n",
-       " 985          NaN          NaN  171.683362  \n",
-       " 986  -465.292159  -744.114065  177.553651  \n",
-       " 987          NaN          NaN  144.344477  \n",
-       " 988   567.400188          NaN  213.833680  \n",
-       " 989          NaN          NaN  157.113353  \n",
-       " 990          NaN  -232.801449  132.592027  \n",
-       " 991   -72.622621          NaN  207.754643  \n",
-       " 992          NaN          NaN  132.575018  \n",
-       " 993  4955.778653  4683.646548  160.260401  \n",
-       " 994  2898.089751          NaN  129.801694  \n",
-       " 995          NaN          NaN         NaN  \n",
-       " 996          NaN   608.026190  115.452177  \n",
-       " 997          NaN          NaN  206.323679  \n",
-       " 998          NaN  7020.489423  178.966240  \n",
-       " 999          NaN          NaN  152.636567  \n",
+       "         MarkDown5     MarkDown2         CPI  \n",
+       " 0             NaN           NaN  172.666543  \n",
+       " 1             NaN           NaN  119.214662  \n",
+       " 2             NaN           NaN  143.990866  \n",
+       " 3     4354.651570   5019.468642  180.631779  \n",
+       " 4    -4772.570563   2947.674138  173.813790  \n",
+       " 5             NaN           NaN  177.052581  \n",
+       " 6     7083.723804   3513.601560  185.921725  \n",
+       " 7             NaN   -180.888893  153.966086  \n",
+       " 8     6265.833733   2401.076738  170.559516  \n",
+       " 9     3759.801559   4729.764492  200.039874  \n",
+       " 10    6567.512481  -1022.527044  150.002644  \n",
+       " 11    7765.142655           NaN  221.169299  \n",
+       " 12            NaN   3308.681495         NaN  \n",
+       " 13    2826.442468   8385.400703  150.777231  \n",
+       " 14            NaN           NaN  211.070762  \n",
+       " 15    6642.564583           NaN  154.828636  \n",
+       " 16            NaN           NaN  138.236111  \n",
+       " 17    4118.446321           NaN  138.853628  \n",
+       " 18    1115.215425  -1526.587205  219.621502  \n",
+       " 19    4994.790024           NaN  197.086249  \n",
+       " 20            NaN           NaN  143.384357  \n",
+       " 21    9934.092076   6538.282816  208.758478  \n",
+       " 22     396.251330   1170.864752  217.807128  \n",
+       " 23    1492.226506           NaN  144.261943  \n",
+       " 24            NaN           NaN  198.046465  \n",
+       " 25            NaN           NaN  152.795139  \n",
+       " 26            NaN           NaN  192.354502  \n",
+       " 27    1804.646340           NaN  213.883682  \n",
+       " 28            NaN           NaN  237.955017  \n",
+       " 29            NaN           NaN  140.091500  \n",
+       " ..            ...           ...         ...  \n",
+       " 970   -208.184350   4224.751927  149.278618  \n",
+       " 971           NaN   3814.404768  167.707496  \n",
+       " 972           NaN           NaN  115.688179  \n",
+       " 973   1199.031857   3033.592607  120.703360  \n",
+       " 974           NaN   -351.113110  106.143265  \n",
+       " 975           NaN           NaN  211.109816  \n",
+       " 976   3975.213749           NaN  152.402312  \n",
+       " 977           NaN           NaN  121.333484  \n",
+       " 978           NaN   3607.788296  176.680147  \n",
+       " 979           NaN           NaN  164.924902  \n",
+       " 980   5431.606087   7005.735955  208.516910  \n",
+       " 981   -285.659902           NaN  146.865904  \n",
+       " 982   5584.625275   2016.204322  165.913603  \n",
+       " 983           NaN           NaN  136.591286  \n",
+       " 984   2929.718397  11730.460834  153.875137  \n",
+       " 985   4618.823214   1218.336997  191.245195  \n",
+       " 986           NaN    128.922827  209.229013  \n",
+       " 987   7757.467609           NaN  201.908861  \n",
+       " 988           NaN           NaN  210.724201  \n",
+       " 989   5115.758017   1620.240724  149.542233  \n",
+       " 990   1789.565202   1350.171000  193.810848  \n",
+       " 991           NaN           NaN  147.307937  \n",
+       " 992   -105.978005           NaN  229.688010  \n",
+       " 993           NaN           NaN  217.839779  \n",
+       " 994   -907.055933   3253.948843  128.459172  \n",
+       " 995           NaN           NaN         NaN  \n",
+       " 996   3043.948542           NaN  114.508859  \n",
+       " 997   1459.810600  -4003.468566  193.149902  \n",
+       " 998  12151.265277  -3099.180177  150.590590  \n",
+       " 999   2792.671569           NaN  201.355736  \n",
        " \n",
        " [1000 rows x 12 columns]}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/examples/5. Generate Metadata from Dataframes.ipynb b/tutorials/04_Working_with_Metadata.ipynb
similarity index 100%
rename from examples/5. Generate Metadata from Dataframes.ipynb
rename to tutorials/04_Working_with_Metadata.ipynb

From 5cacaf6f4e8fcc29fe2ff8c1020028fada3eef2a Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 21:11:16 +0200
Subject: [PATCH 26/33] Remove unsupported constraints argument

---
 sdv/tabular/__init__.py |  1 -
 sdv/tabular/base.py     | 10 ++--------
 2 files changed, 2 insertions(+), 9 deletions(-)

diff --git a/sdv/tabular/__init__.py b/sdv/tabular/__init__.py
index 752184c61..dae118d1d 100644
--- a/sdv/tabular/__init__.py
+++ b/sdv/tabular/__init__.py
@@ -1,7 +1,6 @@
 from sdv.tabular.copulas import GaussianCopula
 from sdv.tabular.ctgan import CTGAN
 
-
 __all__ = [
     'GaussianCopula',
     'CTGAN'
diff --git a/sdv/tabular/base.py b/sdv/tabular/base.py
index 70b5aeb7a..f21be7123 100644
--- a/sdv/tabular/base.py
+++ b/sdv/tabular/base.py
@@ -15,8 +15,7 @@ class BaseTabularModel():
     _metadata = None
 
     def __init__(self, field_names=None, primary_key=None, field_types=None,
-                 anonymize_fields=None, constraints=None, table_metadata=None,
-                 *args, **kwargs):
+                 anonymize_fields=None, table_metadata=None, *args, **kwargs):
         """Initialize a Tabular Model.
 
         Args:
@@ -38,9 +37,6 @@ def __init__(self, field_names=None, primary_key=None, field_types=None,
             anonymize_fields (dict[str, str]):
                 Dict specifying which fields to anonymize and what faker
                 category they belong to.
-            constraints (list[dict]):
-                List of dicts specifying field and inter-field constraints.
-                TODO: Format TBD
             table_metadata (dict or metadata.Table):
                 Table metadata instance or dict representation.
                 If given alongside any other metadata-related arguments, an
@@ -54,7 +50,7 @@ def __init__(self, field_names=None, primary_key=None, field_types=None,
             if isinstance(table_metadata, dict):
                 table_metadata = Table(table_metadata,)
 
-            for arg in (field_names, primary_key, field_types, anonymize_fields, constraints):
+            for arg in (field_names, primary_key, field_types, anonymize_fields):
                 if arg:
                     raise ValueError(
                         'If table_metadata is given {} must be None'.format(arg.__name__))
@@ -66,7 +62,6 @@ def __init__(self, field_names=None, primary_key=None, field_types=None,
             self._primary_key = primary_key
             self._field_types = field_types
             self._anonymize_fields = anonymize_fields
-            self._constraints = constraints
 
     def _fit_metadata(self, data):
         """Generate a new Table metadata and fit it to the data.
@@ -84,7 +79,6 @@ def _fit_metadata(self, data):
             primary_key=self._primary_key,
             field_types=self._field_types,
             anonymize_fields=self._anonymize_fields,
-            constraints=self._constraints,
             transformer_templates=self.TRANSFORMER_TEMPLATES,
         )
         metadata.fit(data)

From 1bbb7c0aaa9b8ad3bcdc0409e81fee796f308fe0 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 21:13:55 +0200
Subject: [PATCH 27/33] Change dev version to actual releases

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index ef713040b..3c2c87521 100644
--- a/setup.py
+++ b/setup.py
@@ -15,8 +15,8 @@
     'exrex>=0.9.4,<0.11',
     'numpy>=1.15.4,<2',
     'pandas>=0.23.4,<0.25',
-    'copulas>=0.3.1.dev0,<0.4',
-    'rdt>=0.2.3.dev0,<0.3',
+    'copulas>=0.3.1,<0.4',
+    'rdt>=0.2.3,<0.3',
     'graphviz>=0.13.2',
     'sdmetrics>=0.0.2.dev0,<0.0.3',
     'scikit-learn<0.23,>=0.21',

From 53e044b5b0a5a06767d8cec4b20b47df3645671b Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 21:21:15 +0200
Subject: [PATCH 28/33] Update the docs to use tutorials and cover Single Table
 Modeling

---
 .gitignore                                    |    2 +
 Makefile                                      |    4 +-
 docs/conf.py                                  |    4 +
 docs/index.rst                                |    8 +-
 docs/tutorials/01_Quickstart.ipynb            |  838 ++++++++++++
 docs/tutorials/02_Single_Table_Modeling.ipynb | 1217 +++++++++++++++++
 .../03_Relational_Data_Modeling.ipynb         | 1039 ++++++++++++++
 docs/tutorials/04_Working_with_Metadata.ipynb |  460 +++++++
 docs/tutorials/demo_metadata.json             |   74 +
 docs/tutorials/sdv.pkl                        |  Bin 0 -> 255975 bytes
 setup.py                                      |    1 +
 tutorials/02_Single_Table_Modeling.ipynb      |  529 +++----
 12 files changed, 3914 insertions(+), 262 deletions(-)
 create mode 100644 docs/tutorials/01_Quickstart.ipynb
 create mode 100644 docs/tutorials/02_Single_Table_Modeling.ipynb
 create mode 100644 docs/tutorials/03_Relational_Data_Modeling.ipynb
 create mode 100644 docs/tutorials/04_Working_with_Metadata.ipynb
 create mode 100644 docs/tutorials/demo_metadata.json
 create mode 100644 docs/tutorials/sdv.pkl

diff --git a/.gitignore b/.gitignore
index 241e9f48f..2e25954c9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -106,3 +106,5 @@ ENV/
 .*.swp
 
 sdv/data/
+tutorials/sdv.pkl
+tutorials/demo_metadata.json
diff --git a/Makefile b/Makefile
index 31f1d6d6f..5fc2f37f3 100644
--- a/Makefile
+++ b/Makefile
@@ -50,6 +50,7 @@ clean-pyc: ## remove Python file artifacts
 .PHONY: clean-docs
 clean-docs: ## remove previously built docs
 	rm -f docs/api/*.rst
+	rm -rf docs/tutorials
 	-$(MAKE) -C docs clean 2>/dev/null  # this fails if sphinx is not yet installed
 
 .PHONY: clean-coverage
@@ -110,7 +111,7 @@ test-readme: ## run the readme snippets
 
 .PHONY: test-tutorials
 test-tutorials: ## run the tutorial notebooks
-	jupyter nbconvert --execute --ExecutePreprocessor.timeout=600 examples/?.\ *.ipynb --stdout > /dev/null
+	jupyter nbconvert --execute --ExecutePreprocessor.timeout=600 tutorials/*.ipynb --stdout > /dev/null
 
 .PHONY: test
 test: test-unit test-readme test-tutorials ## test everything that needs test dependencies
@@ -134,6 +135,7 @@ coverage: ## check code coverage quickly with the default Python
 
 .PHONY: docs
 docs: clean-docs ## generate Sphinx HTML documentation, including API docs
+	cp -r tutorials docs/tutorials
 	sphinx-apidoc --separate --no-toc -o docs/api/ sdv
 	$(MAKE) -C docs html
 
diff --git a/docs/conf.py b/docs/conf.py
index 1c7e3ea90..c99d64607 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -32,6 +32,7 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
 extensions = [
     'm2r',
+    'nbsphinx',
     'sphinx.ext.autodoc',
     'sphinx.ext.githubpages',
     'sphinx.ext.viewcode',
@@ -53,6 +54,9 @@
 # The master toctree document.
 master_doc = 'index'
 
+# Jupyter Notebooks
+nbsphinx_execute = 'never'
+
 # General information about the project.
 project = 'SDV'
 slug = 'sdv'
diff --git a/docs/index.rst b/docs/index.rst
index 4f136e234..c0f4a2cc6 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -7,15 +7,17 @@
    Overview <readme>
 
 .. toctree::
-   :caption: Advanced Usage
-   :maxdepth: 3
+   :caption: User Guides
+   :maxdepth: 2
 
-   metadata
+   tutorials/02_Single_Table_Modeling
+   tutorials/03_Relational_Data_Modeling
 
 .. toctree::
    :caption: Resources
    :hidden:
 
+   metadata
    API Reference <api/sdv>
    contributing
    history
diff --git a/docs/tutorials/01_Quickstart.ipynb b/docs/tutorials/01_Quickstart.ipynb
new file mode 100644
index 000000000..5eb6cd8e8
--- /dev/null
+++ b/docs/tutorials/01_Quickstart.ipynb
@@ -0,0 +1,838 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quickstart\n",
+    "\n",
+    "In this short tutorial we will guide you through a series of steps that will help you\n",
+    "getting started using **SDV**.\n",
+    "\n",
+    "## 1. Model the dataset using SDV\n",
+    "\n",
+    "To model a multi table, relational dataset, we follow two steps. In the first step, we will load\n",
+    "the data and configures the meta data. In the second step, we will use the sdv API to fit and\n",
+    "save a hierarchical model. We will cover these two steps in this section using an example dataset.\n",
+    "\n",
+    "### Step 1: Load example data\n",
+    "\n",
+    "**SDV** comes with a toy dataset to play with, which can be loaded using the `sdv.load_demo`\n",
+    "function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sdv import load_demo\n",
+    "\n",
+    "metadata, tables = load_demo(metadata=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return two objects:\n",
+    "\n",
+    "1. A `Metadata` object with all the information that **SDV** needs to know about the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Metadata\n",
+       "  root_path: /home/xals/Projects/MIT/SDV/tutorials\n",
+       "  tables: ['users', 'sessions', 'transactions']\n",
+       "  relationships:\n",
+       "    sessions.user_id -> users.user_id\n",
+       "    transactions.session_id -> sessions.session_id"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
+       " -->\n",
+       "<!-- Title: Metadata Pages: 1 -->\n",
+       "<svg width=\"371pt\" height=\"500pt\"\n",
+       " viewBox=\"0.00 0.00 370.50 500.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 496)\">\n",
+       "<title>Metadata</title>\n",
+       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-496 366.5,-496 366.5,4 -4,4\"/>\n",
+       "<!-- users -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>users</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
+       "</g>\n",
+       "<!-- sessions -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>sessions</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
+       "</g>\n",
+       "<!-- users&#45;&gt;sessions -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>users&#45;&gt;sessions</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-377.2088C101.5,-363.9446 101.5,-349.6377 101.5,-335.7493\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"105,-325.7312 101.5001,-335.7312 98,-325.7313 105,-325.7312\"/>\n",
+       "<text text-anchor=\"middle\" x=\"197\" y=\"-347.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;sessions.user_id &#45;&gt; users.user_id</text>\n",
+       "</g>\n",
+       "<!-- transactions -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>transactions</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 191,-.5 191,-.5 197,-.5 203,-6.5 203,-12.5 203,-12.5 203,-132.5 203,-132.5 203,-138.5 197,-144.5 191,-144.5 191,-144.5 12,-144.5 12,-144.5 6,-144.5 0,-138.5 0,-132.5 0,-132.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
+       "</g>\n",
+       "<!-- sessions&#45;&gt;transactions -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>sessions&#45;&gt;transactions</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-196.3927C101.5,-183.0141 101.5,-168.7483 101.5,-154.8345\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"105,-144.7831 101.5001,-154.7831 98,-144.7831 105,-144.7831\"/>\n",
+       "<text text-anchor=\"middle\" x=\"232\" y=\"-166.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;transactions.session_id &#45;&gt; sessions.session_id</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.dot.Digraph at 0x7f9840ca3ba8>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata.visualize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more details about how to build the `Metadata` for your own dataset, please refer to the\n",
+    "[Metadata](https://sdv-dev.github.io/SDV/metadata.html) section of the documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. A dictionary containing three `pandas.DataFrames` with the tables described in the\n",
+    "metadata object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'users':    user_id country gender  age\n",
+       " 0        0     USA      M   34\n",
+       " 1        1      UK      F   23\n",
+       " 2        2      ES   None   44\n",
+       " 3        3      UK      M   22\n",
+       " 4        4     USA      F   54\n",
+       " 5        5      DE      M   57\n",
+       " 6        6      BG      F   45\n",
+       " 7        7      ES   None   41\n",
+       " 8        8      FR      F   23\n",
+       " 9        9      UK   None   30,\n",
+       " 'sessions':    session_id  user_id  device       os\n",
+       " 0           0        0  mobile  android\n",
+       " 1           1        1  tablet      ios\n",
+       " 2           2        1  tablet  android\n",
+       " 3           3        2  mobile  android\n",
+       " 4           4        4  mobile      ios\n",
+       " 5           5        5  mobile  android\n",
+       " 6           6        6  mobile      ios\n",
+       " 7           7        6  tablet      ios\n",
+       " 8           8        6  mobile      ios\n",
+       " 9           9        8  tablet      ios,\n",
+       " 'transactions':    transaction_id  session_id           timestamp  amount  approved\n",
+       " 0               0           0 2019-01-01 12:34:32   100.0      True\n",
+       " 1               1           0 2019-01-01 12:42:21    55.3      True\n",
+       " 2               2           1 2019-01-07 17:23:11    79.5      True\n",
+       " 3               3           3 2019-01-10 11:08:57   112.1     False\n",
+       " 4               4           5 2019-01-10 21:54:08   110.0     False\n",
+       " 5               5           5 2019-01-11 11:21:20    76.3      True\n",
+       " 6               6           7 2019-01-22 14:44:10    89.5      True\n",
+       " 7               7           8 2019-01-23 10:14:09   132.1     False\n",
+       " 8               8           9 2019-01-27 16:09:17    68.0      True\n",
+       " 9               9           9 2019-01-29 12:10:48    99.9      True}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Fit a model using the SDV API.\n",
+    "\n",
+    "First, we build a hierarchical statistical model of the data using **SDV**. For this we will\n",
+    "create an instance of the `sdv.SDV` class and use its `fit` method.\n",
+    "\n",
+    "During this process, **SDV** will traverse across all the tables in your dataset following the\n",
+    "primary key-foreign key relationships and learn the probability distributions of the values in\n",
+    "the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 20:57:19,919 - INFO - modeler - Modeling users\n",
+      "2020-07-09 20:57:19,920 - INFO - __init__ - Loading transformer CategoricalTransformer for field country\n",
+      "2020-07-09 20:57:19,920 - INFO - __init__ - Loading transformer CategoricalTransformer for field gender\n",
+      "2020-07-09 20:57:19,921 - INFO - __init__ - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-09 20:57:19,933 - INFO - modeler - Modeling sessions\n",
+      "2020-07-09 20:57:19,934 - INFO - __init__ - Loading transformer CategoricalTransformer for field device\n",
+      "2020-07-09 20:57:19,934 - INFO - __init__ - Loading transformer CategoricalTransformer for field os\n",
+      "2020-07-09 20:57:19,944 - INFO - modeler - Modeling transactions\n",
+      "2020-07-09 20:57:19,944 - INFO - __init__ - Loading transformer DatetimeTransformer for field timestamp\n",
+      "2020-07-09 20:57:19,944 - INFO - __init__ - Loading transformer NumericalTransformer for field amount\n",
+      "2020-07-09 20:57:19,945 - INFO - __init__ - Loading transformer BooleanTransformer for field approved\n",
+      "2020-07-09 20:57:19,954 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,962 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/core/fromnumeric.py:58: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
+      "  return bound(*args, **kwds)\n",
+      "/home/xals/Projects/MIT/SDV/sdv/models/copulas.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
+      "  self.model.covariance = np.array(values)\n",
+      "2020-07-09 20:57:19,968 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/pandas/core/frame.py:7143: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  baseCov = np.cov(mat.T)\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2480: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  c *= np.true_divide(1, fact)\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/lib/function_base.py:2480: RuntimeWarning: invalid value encountered in multiply\n",
+      "  c *= np.true_divide(1, fact)\n",
+      "2020-07-09 20:57:19,974 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,979 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,985 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,989 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:19,994 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,007 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,062 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,074 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,092 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,104 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,117 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,129 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,146 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,208 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 20:57:20,294 - INFO - modeler - Modeling Complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv import SDV\n",
+    "\n",
+    "sdv = SDV()\n",
+    "sdv.fit(metadata, tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Sample data from the fitted model\n",
+    "\n",
+    "Once the modeling has finished you are ready to generate new synthetic data using the `sdv` instance that you have.\n",
+    "\n",
+    "For this, all you have to do is call the `sample_all` method from your instance passing the number of rows that you want to generate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled = sdv.sample_all(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a dictionary identical to the `tables` one that we passed to the SDV instance for learning, filled in with new synthetic data.\n",
+    "\n",
+    "**Note** that only the parent tables of your dataset will have the specified number of rows,\n",
+    "as the number of child rows that each row in the parent table has is also sampled following\n",
+    "the original distribution of your dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>country</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>DE</td>\n",
+       "      <td>F</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>F</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id country gender  age\n",
+       "0        0      UK    NaN   39\n",
+       "1        1      UK      F   27\n",
+       "2        2      UK    NaN   50\n",
+       "3        3     USA      F   29\n",
+       "4        4      UK    NaN   21\n",
+       "5        5      ES    NaN   27\n",
+       "6        6      UK      F   20\n",
+       "7        7     USA      F   15\n",
+       "8        8      DE      F   41\n",
+       "9        9      ES      F   35"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled['users']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>session_id</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>device</th>\n",
+       "      <th>os</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>tablet</td>\n",
+       "      <td>ios</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>tablet</td>\n",
+       "      <td>ios</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>tablet</td>\n",
+       "      <td>ios</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>tablet</td>\n",
+       "      <td>ios</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>mobile</td>\n",
+       "      <td>android</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   session_id  user_id  device       os\n",
+       "0           0        1  mobile  android\n",
+       "1           1        2  tablet      ios\n",
+       "2           2        2  tablet      ios\n",
+       "3           3        2  mobile  android\n",
+       "4           4        3  mobile  android\n",
+       "5           5        5  mobile  android\n",
+       "6           6        5  mobile  android\n",
+       "7           7        5  tablet      ios\n",
+       "8           8        6  tablet      ios\n",
+       "9           9        6  mobile  android"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled['sessions'].head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>transaction_id</th>\n",
+       "      <th>session_id</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>approved</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2019-01-13 01:11:06.084275968</td>\n",
+       "      <td>80.350553</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2019-01-13 01:11:06.368704256</td>\n",
+       "      <td>82.116470</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-25 15:05:07.735262976</td>\n",
+       "      <td>97.616590</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-25 15:05:09.098958848</td>\n",
+       "      <td>96.505258</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019-01-25 15:05:09.124231424</td>\n",
+       "      <td>97.235452</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2019-01-25 15:05:07.347315456</td>\n",
+       "      <td>96.615923</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019-01-25 15:05:09.190441984</td>\n",
+       "      <td>97.173195</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2019-01-25 15:05:07.437910784</td>\n",
+       "      <td>96.995037</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2019-01-13 01:11:06.560940032</td>\n",
+       "      <td>81.027625</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-25 15:05:12.420746496</td>\n",
+       "      <td>97.340054</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   transaction_id  session_id                     timestamp     amount  \\\n",
+       "0               0           0 2019-01-13 01:11:06.084275968  80.350553   \n",
+       "1               1           0 2019-01-13 01:11:06.368704256  82.116470   \n",
+       "2               2           1 2019-01-25 15:05:07.735262976  97.616590   \n",
+       "3               3           1 2019-01-25 15:05:09.098958848  96.505258   \n",
+       "4               4           2 2019-01-25 15:05:09.124231424  97.235452   \n",
+       "5               5           2 2019-01-25 15:05:07.347315456  96.615923   \n",
+       "6               6           3 2019-01-25 15:05:09.190441984  97.173195   \n",
+       "7               7           3 2019-01-25 15:05:07.437910784  96.995037   \n",
+       "8               8           0 2019-01-13 01:11:06.560940032  81.027625   \n",
+       "9               9           1 2019-01-25 15:05:12.420746496  97.340054   \n",
+       "\n",
+       "   approved  \n",
+       "0      True  \n",
+       "1      True  \n",
+       "2      True  \n",
+       "3      True  \n",
+       "4      True  \n",
+       "5      True  \n",
+       "6      True  \n",
+       "7      True  \n",
+       "8      True  \n",
+       "9      True  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled['transactions'].head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving and Loading your model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In some cases, you might want to save the fitted SDV instance to be able to generate synthetic data from\n",
+    "it later or on a different system.\n",
+    "\n",
+    "In order to do so, you can save your fitted `SDV` instance for later usage using the `save` method of your\n",
+    "instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdv.save('sdv.pkl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The generated `pkl` file will not include any of the original data in it, so it can be\n",
+    "safely sent to where the synthetic data will be generated without any privacy concerns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Later on, in order to sample data from the fitted model, we will first need to load it from its\n",
+    "`pkl` file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdv = SDV.load('sdv.pkl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After loading the instance, we can sample synthetic data using its `sample_all` method like before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled = sdv.sample_all(5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/tutorials/02_Single_Table_Modeling.ipynb b/docs/tutorials/02_Single_Table_Modeling.ipynb
new file mode 100644
index 000000000..fa536bd3d
--- /dev/null
+++ b/docs/tutorials/02_Single_Table_Modeling.ipynb
@@ -0,0 +1,1217 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Single Table Modeling\n",
+    "\n",
+    "**SDV** has special support for modeling single table datasets using a variety of models.\n",
+    "\n",
+    "Currently, SDV implements:\n",
+    "\n",
+    "* GaussianCopula: A tool to model multivariate distributions using [copula functions](https://en.wikipedia.org/wiki/Copula_%28probability_theory%29). Based on our [Copulas Library](https://github.com/sdv-dev/Copulas).\n",
+    "* CTGAN: A GAN-based Deep Learning data synthesizer that can generate synthetic tabular data with high fidelity. Based on our [CTGAN Library](https://github.com/sdv-dev/CTGAN)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GaussianCopula\n",
+    "\n",
+    "In this first part of the tutorial we will be using the GaussianCopula class to model the `users` table\n",
+    "from the toy dataset included in the **SDV** library.\n",
+    "\n",
+    "### 1. Load the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sdv import load_demo\n",
+    "\n",
+    "users = load_demo()['users']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a table with 4 fields:\n",
+    "\n",
+    "* `user_id`: A unique identifier of the user.\n",
+    "* `country`: A 2 letter code of the country of residence of the user.\n",
+    "* `gender`: A single letter code, `M` or `F`, indicating the user gender. Note that this demo simulates the case where some users did not indicate the gender, which resulted in empty data values in some rows.\n",
+    "* `age`: The age of the user, in years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>country</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>M</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>None</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>DE</td>\n",
+       "      <td>M</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>BG</td>\n",
+       "      <td>F</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>None</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>F</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>None</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id country gender  age\n",
+       "0        0     USA      M   34\n",
+       "1        1      UK      F   23\n",
+       "2        2      ES   None   44\n",
+       "3        3      UK      M   22\n",
+       "4        4     USA      F   54\n",
+       "5        5      DE      M   57\n",
+       "6        6      BG      F   45\n",
+       "7        7      ES   None   41\n",
+       "8        8      FR      F   23\n",
+       "9        9      UK   None   30"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "users"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Prepare the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to properly model our data we will need to provide some additional information to our model,\n",
+    "so let's prepare this information in some variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, let's indicate that the `user_id` field in our table is the primary key, so we do not want our\n",
+    "model to attempt to learn it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "primary_key = 'user_id'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will also want to anonymize the countries of residence of our users, to avoid disclosing such information.\n",
+    "Let's make a variable indicating that the `country` field needs to be anonymized using fake `country_codes`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anonymize_fileds = {\n",
+    "    'country': 'contry_code'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The full list of categories supported corresponds to the `Faker` library\n",
+    "[provider names](https://faker.readthedocs.io/en/master/providers.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have prepared the arguments for our model we are ready to import it, create an instance\n",
+    "and fit it to our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 21:18:32,974 - INFO - table - Loading transformer CategoricalTransformer for field country\n",
+      "2020-07-09 21:18:32,975 - INFO - table - Loading transformer CategoricalTransformer for field gender\n",
+      "2020-07-09 21:18:32,975 - INFO - table - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-09 21:18:32,991 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv.tabular import GaussianCopula\n",
+    "\n",
+    "model = GaussianCopula(\n",
+    "    primary_key=primary_key,\n",
+    "    anonymize_fileds=anonymize_fileds\n",
+    ")\n",
+    "model.fit(users)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Notice** how the model took care of transforming the different fields using the appropriate\n",
+    "Reversible Data Transforms to ensure that the data has a format that the GaussianMultivariate model\n",
+    "from the [copulas](https://github.com/sdv-dev/Copulas) library can handle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Sample data from the fitted model\n",
+    "\n",
+    "Once the modeling has finished you are ready to generate new synthetic data by calling the `sample` method\n",
+    "from our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled = model.sample()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a table identical to the one which the model was fitted on, but filled with new data\n",
+    "which resembles the original one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>country</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>M</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>F</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>M</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>F</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>F</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id country gender  age\n",
+       "0        0     USA      M   38\n",
+       "1        1      UK    NaN   23\n",
+       "2        2     USA      F   34\n",
+       "3        3      ES    NaN   47\n",
+       "4        4      ES      F   29\n",
+       "5        5      UK      F   39\n",
+       "6        6      FR    NaN   40\n",
+       "7        7      ES      M   38\n",
+       "8        8      ES      F   32\n",
+       "9        9      ES      F   36"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "scrolled": false
+   },
+   "source": [
+    "Notice, as well that the number of rows generated by default corresponds to the number of rows that\n",
+    "the original table had, but that this number can be changed by simply passing it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>country</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>M</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>BG</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>M</td>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id country gender  age\n",
+       "0        0      UK      F   48\n",
+       "1        1     USA    NaN   38\n",
+       "2        2     USA      M   29\n",
+       "3        3      BG      M   22\n",
+       "4        4     USA      M   43"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CTGAN\n",
+    "\n",
+    "In this second part of the tutorial we will be using the CTGAN model to learn the data from the\n",
+    "demo dataset called `census`, which is based on the [UCI Adult Census Dataset]('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data').\n",
+    "\n",
+    "### 1. Load the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 21:18:33,085 - INFO - __init__ - Loading table census\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv import load_demo\n",
+    "\n",
+    "census = load_demo('census')['census']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a table with several rows of multiple data types:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>fnlwgt</th>\n",
+       "      <th>education</th>\n",
+       "      <th>education-num</th>\n",
+       "      <th>marital-status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "      <th>native-country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>39</td>\n",
+       "      <td>State-gov</td>\n",
+       "      <td>77516</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>2174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>50</td>\n",
+       "      <td>Self-emp-not-inc</td>\n",
+       "      <td>83311</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>215646</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Handlers-cleaners</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>53</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>234721</td>\n",
+       "      <td>11th</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Handlers-cleaners</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>United-States</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>338409</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Wife</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Cuba</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          workclass  fnlwgt   education  education-num  \\\n",
+       "0   39          State-gov   77516   Bachelors             13   \n",
+       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
+       "2   38            Private  215646     HS-grad              9   \n",
+       "3   53            Private  234721        11th              7   \n",
+       "4   28            Private  338409   Bachelors             13   \n",
+       "\n",
+       "        marital-status          occupation    relationship    race      sex  \\\n",
+       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
+       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
+       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
+       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
+       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
+       "\n",
+       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
+       "0          2174             0              40   United-States   <=50K  \n",
+       "1             0             0              13   United-States   <=50K  \n",
+       "2             0             0              40   United-States   <=50K  \n",
+       "3             0             0              40   United-States   <=50K  \n",
+       "4             0             0              40            Cuba   <=50K  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Prepare the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case there is no primary key to setup and we will not be anonymizing anything, so the only\n",
+    "thing that we will pass to the CTGAN model is the number of epochs that we want it to perform when\n",
+    "it leanrs the data, which we will keep low to make this execution quick."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
+      "  warnings.warn(message, FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv.tabular import CTGAN\n",
+    "\n",
+    "model = CTGAN(epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the instance is created, we can fit it to our data. Bear in mind that this process might take some\n",
+    "time to finish, especially on non-GPU enabled systems, so in this case we will be passing only a\n",
+    "subsample of the data to accelerate the process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 21:18:33,488 - INFO - table - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-09 21:18:33,489 - INFO - table - Loading transformer LabelEncodingTransformer for field workclass\n",
+      "2020-07-09 21:18:33,489 - INFO - table - Loading transformer NumericalTransformer for field fnlwgt\n",
+      "2020-07-09 21:18:33,490 - INFO - table - Loading transformer LabelEncodingTransformer for field education\n",
+      "2020-07-09 21:18:33,490 - INFO - table - Loading transformer NumericalTransformer for field education-num\n",
+      "2020-07-09 21:18:33,490 - INFO - table - Loading transformer LabelEncodingTransformer for field marital-status\n",
+      "2020-07-09 21:18:33,491 - INFO - table - Loading transformer LabelEncodingTransformer for field occupation\n",
+      "2020-07-09 21:18:33,491 - INFO - table - Loading transformer LabelEncodingTransformer for field relationship\n",
+      "2020-07-09 21:18:33,491 - INFO - table - Loading transformer LabelEncodingTransformer for field race\n",
+      "2020-07-09 21:18:33,492 - INFO - table - Loading transformer LabelEncodingTransformer for field sex\n",
+      "2020-07-09 21:18:33,492 - INFO - table - Loading transformer NumericalTransformer for field capital-gain\n",
+      "2020-07-09 21:18:33,493 - INFO - table - Loading transformer NumericalTransformer for field capital-loss\n",
+      "2020-07-09 21:18:33,493 - INFO - table - Loading transformer NumericalTransformer for field hours-per-week\n",
+      "2020-07-09 21:18:33,493 - INFO - table - Loading transformer LabelEncodingTransformer for field native-country\n",
+      "2020-07-09 21:18:33,494 - INFO - table - Loading transformer LabelEncodingTransformer for field income\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss G: 1.9512, Loss D: -0.0182\n",
+      "Epoch 2, Loss G: 1.9884, Loss D: -0.0663\n",
+      "Epoch 3, Loss G: 1.9710, Loss D: -0.1339\n",
+      "Epoch 4, Loss G: 1.8960, Loss D: -0.2061\n",
+      "Epoch 5, Loss G: 1.9155, Loss D: -0.3062\n",
+      "Epoch 6, Loss G: 1.9699, Loss D: -0.3906\n",
+      "Epoch 7, Loss G: 1.8614, Loss D: -0.5142\n",
+      "Epoch 8, Loss G: 1.8446, Loss D: -0.6448\n",
+      "Epoch 9, Loss G: 1.7619, Loss D: -0.7488\n",
+      "Epoch 10, Loss G: 1.6732, Loss D: -0.7961\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(census.sample(1000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Sample data from the fitted model\n",
+    "\n",
+    "Once the modeling has finished you are ready to generate new synthetic data by calling the `sample` method\n",
+    "from our model just like we did with the GaussianCopula model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sampled = model.sample()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will return a table identical to the one which the model was fitted on, but filled with new data\n",
+    "which resembles the original one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>workclass</th>\n",
+       "      <th>fnlwgt</th>\n",
+       "      <th>education</th>\n",
+       "      <th>education-num</th>\n",
+       "      <th>marital-status</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>relationship</th>\n",
+       "      <th>race</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>capital-gain</th>\n",
+       "      <th>capital-loss</th>\n",
+       "      <th>hours-per-week</th>\n",
+       "      <th>native-country</th>\n",
+       "      <th>income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>50</td>\n",
+       "      <td>Local-gov</td>\n",
+       "      <td>169719</td>\n",
+       "      <td>1st-4th</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>114</td>\n",
+       "      <td>8</td>\n",
+       "      <td>38</td>\n",
+       "      <td>Columbia</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>32</td>\n",
+       "      <td>?</td>\n",
+       "      <td>152479</td>\n",
+       "      <td>1st-4th</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Wife</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>-42</td>\n",
+       "      <td>20</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Jamaica</td>\n",
+       "      <td>&gt;50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>69617</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Separated</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>38</td>\n",
+       "      <td>Guatemala</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>25</td>\n",
+       "      <td>?</td>\n",
+       "      <td>652858</td>\n",
+       "      <td>10th</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>Handlers-cleaners</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>152</td>\n",
+       "      <td>-27</td>\n",
+       "      <td>39</td>\n",
+       "      <td>Cuba</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>43</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>301956</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Wife</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>-133</td>\n",
+       "      <td>-12</td>\n",
+       "      <td>39</td>\n",
+       "      <td>India</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>66</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>401171</td>\n",
+       "      <td>Prof-school</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Separated</td>\n",
+       "      <td>Protective-serv</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>-124</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Cuba</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>52</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>278399</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>12</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>122567</td>\n",
+       "      <td>-6</td>\n",
+       "      <td>47</td>\n",
+       "      <td>Columbia</td>\n",
+       "      <td>&lt;=50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>36</td>\n",
+       "      <td>Federal-gov</td>\n",
+       "      <td>229817</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Married-AF-spouse</td>\n",
+       "      <td>Farming-fishing</td>\n",
+       "      <td>Not-in-family</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>8</td>\n",
+       "      <td>19</td>\n",
+       "      <td>38</td>\n",
+       "      <td>Portugal</td>\n",
+       "      <td>&gt;50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>27</td>\n",
+       "      <td>Federal-gov</td>\n",
+       "      <td>306972</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Exec-managerial</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>Asian-Pac-Islander</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>42144</td>\n",
+       "      <td>3</td>\n",
+       "      <td>39</td>\n",
+       "      <td>Japan</td>\n",
+       "      <td>&gt;50K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Local-gov</td>\n",
+       "      <td>416161</td>\n",
+       "      <td>1st-4th</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>White</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>-349</td>\n",
+       "      <td>1090</td>\n",
+       "      <td>61</td>\n",
+       "      <td>Guatemala</td>\n",
+       "      <td>&gt;50K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age     workclass  fnlwgt      education  education-num  \\\n",
+       "0   50     Local-gov  169719        1st-4th              9   \n",
+       "1   32             ?  152479        1st-4th              9   \n",
+       "2   22       Private   69617      Bachelors              0   \n",
+       "3   25             ?  652858           10th             16   \n",
+       "4   43       Private  301956   Some-college              8   \n",
+       "5   66       Private  401171    Prof-school             13   \n",
+       "6   52       Private  278399      Bachelors             12   \n",
+       "7   36   Federal-gov  229817        HS-grad              8   \n",
+       "8   27   Federal-gov  306972   Some-college              8   \n",
+       "9   28     Local-gov  416161        1st-4th              8   \n",
+       "\n",
+       "        marital-status          occupation    relationship  \\\n",
+       "0              Widowed                   ?         Husband   \n",
+       "1        Never-married        Adm-clerical            Wife   \n",
+       "2            Separated                   ?         Husband   \n",
+       "3   Married-civ-spouse   Handlers-cleaners   Not-in-family   \n",
+       "4   Married-civ-spouse                   ?            Wife   \n",
+       "5            Separated     Protective-serv       Unmarried   \n",
+       "6        Never-married      Prof-specialty       Unmarried   \n",
+       "7    Married-AF-spouse     Farming-fishing   Not-in-family   \n",
+       "8        Never-married     Exec-managerial         Husband   \n",
+       "9             Divorced        Adm-clerical       Unmarried   \n",
+       "\n",
+       "                  race      sex  capital-gain  capital-loss  hours-per-week  \\\n",
+       "0                White     Male           114             8              38   \n",
+       "1                Black     Male           -42            20              21   \n",
+       "2                White     Male             6            11              38   \n",
+       "3                White   Female           152           -27              39   \n",
+       "4                White     Male          -133           -12              39   \n",
+       "5                Black   Female          -124            -1              40   \n",
+       "6                Other     Male        122567            -6              47   \n",
+       "7                White     Male             8            19              38   \n",
+       "8   Asian-Pac-Islander   Female         42144             3              39   \n",
+       "9                White   Female          -349          1090              61   \n",
+       "\n",
+       "  native-country  income  \n",
+       "0       Columbia   <=50K  \n",
+       "1        Jamaica    >50K  \n",
+       "2      Guatemala   <=50K  \n",
+       "3           Cuba   <=50K  \n",
+       "4          India   <=50K  \n",
+       "5           Cuba   <=50K  \n",
+       "6       Columbia   <=50K  \n",
+       "7       Portugal    >50K  \n",
+       "8          Japan    >50K  \n",
+       "9      Guatemala    >50K  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sampled.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Evaluate how good the data is\n",
+    "\n",
+    "Finally, we will use the evaluation framework included in SDV to obtain a metric of how\n",
+    "similar the sampled data is to the original one.\n",
+    "\n",
+    "For this, we will simply import the `sdv.evaluation.evaluate` function and pass both\n",
+    "the synthetic and the real data to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-144.971907591418"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from sdv.evaluation import evaluate\n",
+    "\n",
+    "evaluate(sampled, census)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/tutorials/03_Relational_Data_Modeling.ipynb b/docs/tutorials/03_Relational_Data_Modeling.ipynb
new file mode 100644
index 000000000..230061b00
--- /dev/null
+++ b/docs/tutorials/03_Relational_Data_Modeling.ipynb
@@ -0,0 +1,1039 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Relational Data Modeling\n",
+    "\n",
+    "In this tutorial we will be showing how to model a real world multi-table dataset using SDV.\n",
+    "\n",
+    "## About the datset\n",
+    "\n",
+    "We have a store series, each of those have a size and a category and additional information in a given date: average temperature in the region, cost of fuel in the region, promotional data, the customer price index, the unemployment rate and whether the date is a special holiday.\n",
+    "\n",
+    "From those stores we obtained a training of historical data \n",
+    "between 2010-02-05 and 2012-11-01. This historical data includes the sales of each department on a specific date.\n",
+    "In this notebook, we will show you step-by-step how to download the \"Walmart\" dataset, explain the structure and sample the data.\n",
+    "\n",
+    "In this demonstration we will show how SDV can be used to generate synthetic data. And lately, this data can be used to train machine learning models.\n",
+    "\n",
+    "*The dataset used in this example can be found in [Kaggle](https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data), but we will show how to download it from SDV.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data model summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p style=\"text-align: center\"><b>stores</b><p>\n",
+    "\n",
+    "| Field | Type        | Subtype | Additional Properties |\n",
+    "|-------|-------------|---------|-----------------------|\n",
+    "| Store | id          | integer | Primary key           |\n",
+    "| Size  | numerical   | integer |                       |\n",
+    "| Type  | categorical |         |                       |\n",
+    "\n",
+    "Contains information about the 45 stores, indicating the type and size of store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p style=\"text-align: center\"><b>features</b><p>\n",
+    "\n",
+    "| Fields       | Type      | Subtype | Additional Properties       |\n",
+    "|--------------|-----------|---------|-----------------------------|\n",
+    "| Store        | id        | integer | foreign key (stores.Store)  |\n",
+    "| Date         | datetime  |         | format: \"%Y-%m-%d\"          |\n",
+    "| IsHoliday    | boolean   |         |                             |\n",
+    "| Fuel_Price   | numerical | float   |                             |\n",
+    "| Unemployment | numerical | float   |                             |\n",
+    "| Temperature  | numerical | float   |                             |\n",
+    "| CPI          | numerical | float   |                             |\n",
+    "| MarkDown1    | numerical | float   |                             |\n",
+    "| MarkDown2    | numerical | float   |                             |\n",
+    "| MarkDown3    | numerical | float   |                             |\n",
+    "| MarkDown4    | numerical | float   |                             |\n",
+    "| MarkDown5    | numerical | float   |                             |\n",
+    "\n",
+    "Contains historical training data, which covers to 2010-02-05 to 2012-11-01."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p style=\"text-align: center\"><b>depts</b><p>\n",
+    "\n",
+    "| Fields       | Type      | Subtype | Additional Properties        |\n",
+    "|--------------|-----------|---------|------------------------------|\n",
+    "| Store        | id        | integer | foreign key (stores.Stores)  |\n",
+    "| Date         | datetime  |         | format: \"%Y-%m-%d\"           |\n",
+    "| Weekly_Sales | numerical | float   |                              |\n",
+    "| Dept         | numerical | integer |                              |\n",
+    "| IsHoliday    | boolean   |         |                              |\n",
+    "\n",
+    "Contains additional data related to the store, department, and regional activity for the given dates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Load data\n",
+    "\n",
+    "Let's start downloading the data set. In this case, we will download the data set *walmart*. We will use the SDV function `load_demo`, we can specify the name of the dataset we want to use and if we want its Metadata object or not. To know more about the demo data [see the documentation](https://sdv-dev.github.io/SDV/api/sdv.demo.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 21:00:17,378 - INFO - __init__ - Loading table stores\n",
+      "2020-07-09 21:00:17,384 - INFO - __init__ - Loading table features\n",
+      "2020-07-09 21:00:17,402 - INFO - __init__ - Loading table depts\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv import load_demo\n",
+    "\n",
+    "metadata, tables = load_demo(dataset_name='walmart', metadata=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our dataset is downloaded from an [Amazon S3 bucket](http://sdv-datasets.s3.amazonaws.com/index.html) that contains all available data sets of the `load_demo` method."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now visualize the metadata structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
+       " -->\n",
+       "<!-- Title: Metadata Pages: 1 -->\n",
+       "<svg width=\"470pt\" height=\"424pt\"\n",
+       " viewBox=\"0.00 0.00 469.50 424.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 420)\">\n",
+       "<title>Metadata</title>\n",
+       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-420 465.5,-420 465.5,4 -4,4\"/>\n",
+       "<!-- stores -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>stores</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M146,-301.5C146,-301.5 277,-301.5 277,-301.5 283,-301.5 289,-307.5 289,-313.5 289,-313.5 289,-403.5 289,-403.5 289,-409.5 283,-415.5 277,-415.5 277,-415.5 146,-415.5 146,-415.5 140,-415.5 134,-409.5 134,-403.5 134,-403.5 134,-313.5 134,-313.5 134,-307.5 140,-301.5 146,-301.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"211.5\" y=\"-400.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">stores</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"134,-392.5 289,-392.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"142\" y=\"-377.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Type : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"142\" y=\"-362.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Size : numerical &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"142\" y=\"-347.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Store : id &#45; integer</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"134,-339.5 289,-339.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"142\" y=\"-324.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: Store</text>\n",
+       "<text text-anchor=\"start\" x=\"142\" y=\"-309.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Data path: stores.csv</text>\n",
+       "</g>\n",
+       "<!-- features -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>features</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 193,-.5 193,-.5 199,-.5 205,-6.5 205,-12.5 205,-12.5 205,-237.5 205,-237.5 205,-243.5 199,-249.5 193,-249.5 193,-249.5 12,-249.5 12,-249.5 6,-249.5 0,-243.5 0,-237.5 0,-237.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"102.5\" y=\"-234.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">features</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-226.5 205,-226.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-211.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Date : datetime</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-196.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">MarkDown1 : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-181.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Store : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-166.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">IsHoliday : boolean</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-151.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">MarkDown4 : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-136.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">MarkDown3 : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-121.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Fuel_Price : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Unemployment : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Temperature : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">MarkDown5 : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">MarkDown2 : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">CPI : numerical &#45; float</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 205,-38.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (stores): Store</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Data path: features.csv</text>\n",
+       "</g>\n",
+       "<!-- stores&#45;&gt;features -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>stores&#45;&gt;features</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M140.7572,-301.1781C136.1229,-295.4114 131.9421,-289.3324 128.5,-283 124.4445,-275.5391 120.9867,-267.6091 118.0416,-259.4282\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"118.136,-248.6181 117.9697,-259.2116 111.4935,-250.8265 118.136,-248.6181\"/>\n",
+       "<text text-anchor=\"middle\" x=\"214\" y=\"-271.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;features.Store &#45;&gt; stores.Store</text>\n",
+       "</g>\n",
+       "<!-- depts -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>depts</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M235,-53C235,-53 408,-53 408,-53 414,-53 420,-59 420,-65 420,-65 420,-185 420,-185 420,-191 414,-197 408,-197 408,-197 235,-197 235,-197 229,-197 223,-191 223,-185 223,-185 223,-65 223,-65 223,-59 229,-53 235,-53\"/>\n",
+       "<text text-anchor=\"middle\" x=\"321.5\" y=\"-181.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">depts</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"223,-174 420,-174 \"/>\n",
+       "<text text-anchor=\"start\" x=\"231\" y=\"-158.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Date : datetime</text>\n",
+       "<text text-anchor=\"start\" x=\"231\" y=\"-143.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Weekly_Sales : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"231\" y=\"-128.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Store : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"231\" y=\"-113.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Dept : numerical &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"231\" y=\"-98.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">IsHoliday : boolean</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"223,-91 420,-91 \"/>\n",
+       "<text text-anchor=\"start\" x=\"231\" y=\"-75.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (stores): Store</text>\n",
+       "<text text-anchor=\"start\" x=\"231\" y=\"-60.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Data path: train.csv</text>\n",
+       "</g>\n",
+       "<!-- stores&#45;&gt;depts -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>stores&#45;&gt;depts</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M287.0707,-301.1494C291.788,-295.4236 296.0255,-289.3596 299.5,-283 311.9843,-260.1491 318.266,-232.7713 321.2171,-207.274\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"325.7055,-197.5835 321.2259,-207.1848 318.7404,-196.8856 325.7055,-197.5835\"/>\n",
+       "<text text-anchor=\"middle\" x=\"383\" y=\"-271.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;depts.Store &#45;&gt; stores.Store</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.dot.Digraph at 0x7f3c6acf2dd8>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata.visualize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And also validate that the metadata is correctly defined for our data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata.validate(tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Create an instance of SDV and train the instance\n",
+    "\n",
+    "Once we download it, we have to create an SDV instance. With that instance, we have to analyze the loaded tables to generate a statistical model from the data. In this case, the process of adjusting the model is quickly because the dataset is small. However, with larger datasets it can be a slow process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 21:00:31,480 - INFO - modeler - Modeling stores\n",
+      "2020-07-09 21:00:31,481 - INFO - __init__ - Loading transformer CategoricalTransformer for field Type\n",
+      "2020-07-09 21:00:31,481 - INFO - __init__ - Loading transformer NumericalTransformer for field Size\n",
+      "2020-07-09 21:00:31,491 - INFO - modeler - Modeling features\n",
+      "2020-07-09 21:00:31,492 - INFO - __init__ - Loading transformer DatetimeTransformer for field Date\n",
+      "2020-07-09 21:00:31,493 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown1\n",
+      "2020-07-09 21:00:31,493 - INFO - __init__ - Loading transformer BooleanTransformer for field IsHoliday\n",
+      "2020-07-09 21:00:31,493 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown4\n",
+      "2020-07-09 21:00:31,494 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown3\n",
+      "2020-07-09 21:00:31,494 - INFO - __init__ - Loading transformer NumericalTransformer for field Fuel_Price\n",
+      "2020-07-09 21:00:31,494 - INFO - __init__ - Loading transformer NumericalTransformer for field Unemployment\n",
+      "2020-07-09 21:00:31,495 - INFO - __init__ - Loading transformer NumericalTransformer for field Temperature\n",
+      "2020-07-09 21:00:31,495 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown5\n",
+      "2020-07-09 21:00:31,495 - INFO - __init__ - Loading transformer NumericalTransformer for field MarkDown2\n",
+      "2020-07-09 21:00:31,495 - INFO - __init__ - Loading transformer NumericalTransformer for field CPI\n",
+      "2020-07-09 21:00:31,544 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,595 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/Projects/MIT/SDV/sdv/models/copulas.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
+      "  self.model.covariance = np.array(values)\n",
+      "2020-07-09 21:00:31,651 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,679 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,707 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,734 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,762 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,790 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,816 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,845 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,872 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,901 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,931 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,959 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:31,986 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,014 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,040 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,070 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,096 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,123 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,152 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,181 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,209 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,235 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,264 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,293 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,322 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,349 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,376 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,405 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,433 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,463 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,492 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,521 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,552 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,583 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,612 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,644 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,674 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,704 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,732 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,762 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,791 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,821 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,852 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,882 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:32,917 - INFO - modeler - Modeling depts\n",
+      "2020-07-09 21:00:32,918 - INFO - __init__ - Loading transformer DatetimeTransformer for field Date\n",
+      "2020-07-09 21:00:32,918 - INFO - __init__ - Loading transformer NumericalTransformer for field Weekly_Sales\n",
+      "2020-07-09 21:00:32,919 - INFO - __init__ - Loading transformer NumericalTransformer for field Dept\n",
+      "2020-07-09 21:00:32,919 - INFO - __init__ - Loading transformer BooleanTransformer for field IsHoliday\n",
+      "2020-07-09 21:00:33,016 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,318 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,334 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,350 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,364 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,381 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,396 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,412 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,428 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2020-07-09 21:00:33,447 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,464 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,479 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,495 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,511 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,526 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,541 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,554 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,567 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,582 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,596 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,609 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,624 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,639 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,652 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,667 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,682 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,696 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,709 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,724 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,740 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,753 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,766 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,779 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,792 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,803 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,818 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,831 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,842 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,856 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,870 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,885 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,898 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,912 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,926 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,937 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:33,949 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "2020-07-09 21:00:34,047 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n",
+      "/home/xals/.virtualenvs/SDV/lib/python3.6/site-packages/numpy/core/fromnumeric.py:58: FutureWarning: Series.nonzero() is deprecated and will be removed in a future version.Use Series.to_numpy().nonzero() instead\n",
+      "  return bound(*args, **kwds)\n",
+      "2020-07-09 21:00:34,259 - INFO - modeler - Modeling Complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sdv import SDV\n",
+    "\n",
+    "sdv = SDV()\n",
+    "sdv.fit(metadata, tables=tables)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: We may not want to train the model every time we want to generate new synthetic data. We can [save](https://sdv-dev.github.io/SDV/api/sdv.sdv.html#sdv.sdv.SDV.save) the SDV instance to [load](https://sdv-dev.github.io/SDV/api/sdv.sdv.html#sdv.sdv.SDV.save) it later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Generate synthetic data\n",
+    "\n",
+    "Once the instance is trained, we are ready to generate the synthetic data.\n",
+    "\n",
+    "The easiest way to generate synthetic data for the entire dataset is to call the `sample_all` method. By default, this method generates only 5 rows, but we can specify the row number that will be generated with the `num_rows` argument. To learn more about the available arguments, see [sample_all](https://sdv-dev.github.io/SDV/api/sdv.sampler.html#sdv.sampler.Sampler.sample_all)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'stores': 45, 'features': 8190, 'depts': 421570}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sdv.modeler.table_sizes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "samples = sdv.sample_all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This returns a dictionary with a `pandas.DataFrame` for each table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Store</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>B</td>\n",
+       "      <td>106106</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>68071</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C</td>\n",
+       "      <td>253603</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A</td>\n",
+       "      <td>223268</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>166848</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Type    Size  Store\n",
+       "0    B  106106      0\n",
+       "1    B   68071      1\n",
+       "2    C  253603      2\n",
+       "3    A  223268      3\n",
+       "4    B  166848      4"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples['stores'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>MarkDown1</th>\n",
+       "      <th>Store</th>\n",
+       "      <th>IsHoliday</th>\n",
+       "      <th>MarkDown4</th>\n",
+       "      <th>MarkDown3</th>\n",
+       "      <th>Fuel_Price</th>\n",
+       "      <th>Unemployment</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>MarkDown5</th>\n",
+       "      <th>MarkDown2</th>\n",
+       "      <th>CPI</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2009-12-13 04:59:49.832576000</td>\n",
+       "      <td>222.268822</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>758.900545</td>\n",
+       "      <td>-1080.569435</td>\n",
+       "      <td>4.574966</td>\n",
+       "      <td>6.052795</td>\n",
+       "      <td>51.354606</td>\n",
+       "      <td>-4016.012141</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>224.973722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2011-07-20 14:40:40.400244736</td>\n",
+       "      <td>6857.712028</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-551.045257</td>\n",
+       "      <td>3.018296</td>\n",
+       "      <td>6.720669</td>\n",
+       "      <td>80.723862</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>204.595072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2011-10-12 08:23:54.987658240</td>\n",
+       "      <td>5530.929425</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.466358</td>\n",
+       "      <td>7.256570</td>\n",
+       "      <td>73.954938</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>202.591941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2012-11-26 09:22:59.377291776</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3703.137191</td>\n",
+       "      <td>3.623725</td>\n",
+       "      <td>6.598615</td>\n",
+       "      <td>72.041454</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3925.456611</td>\n",
+       "      <td>169.457078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2013-05-13 15:22:12.213717760</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>3773.942572</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.381795</td>\n",
+       "      <td>5.115002</td>\n",
+       "      <td>83.058322</td>\n",
+       "      <td>15018.944605</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>182.801974</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           Date    MarkDown1  Store  IsHoliday    MarkDown4  \\\n",
+       "0 2009-12-13 04:59:49.832576000   222.268822      0      False   758.900545   \n",
+       "1 2011-07-20 14:40:40.400244736  6857.712028      0      False          NaN   \n",
+       "2 2011-10-12 08:23:54.987658240  5530.929425      0      False          NaN   \n",
+       "3 2012-11-26 09:22:59.377291776          NaN      0      False          NaN   \n",
+       "4 2013-05-13 15:22:12.213717760          NaN      0      False  3773.942572   \n",
+       "\n",
+       "     MarkDown3  Fuel_Price  Unemployment  Temperature     MarkDown5  \\\n",
+       "0 -1080.569435    4.574966      6.052795    51.354606  -4016.012141   \n",
+       "1  -551.045257    3.018296      6.720669    80.723862           NaN   \n",
+       "2          NaN    3.466358      7.256570    73.954938           NaN   \n",
+       "3  3703.137191    3.623725      6.598615    72.041454           NaN   \n",
+       "4          NaN    3.381795      5.115002    83.058322  15018.944605   \n",
+       "\n",
+       "     MarkDown2         CPI  \n",
+       "0          NaN  224.973722  \n",
+       "1          NaN  204.595072  \n",
+       "2          NaN  202.591941  \n",
+       "3  3925.456611  169.457078  \n",
+       "4          NaN  182.801974  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples['features'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Weekly_Sales</th>\n",
+       "      <th>Store</th>\n",
+       "      <th>Dept</th>\n",
+       "      <th>IsHoliday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2012-04-19 04:40:04.370382848</td>\n",
+       "      <td>7523.202470</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2012-10-24 12:49:45.630176512</td>\n",
+       "      <td>13675.425498</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2012-06-05 05:02:35.551761408</td>\n",
+       "      <td>2402.017324</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2012-05-31 22:15:14.903856896</td>\n",
+       "      <td>-12761.073176</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2011-09-07 15:34:43.239835648</td>\n",
+       "      <td>3642.817612</td>\n",
+       "      <td>0</td>\n",
+       "      <td>58</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           Date  Weekly_Sales  Store  Dept  IsHoliday\n",
+       "0 2012-04-19 04:40:04.370382848   7523.202470      0    36      False\n",
+       "1 2012-10-24 12:49:45.630176512  13675.425498      0    17      False\n",
+       "2 2012-06-05 05:02:35.551761408   2402.017324      0    31      False\n",
+       "3 2012-05-31 22:15:14.903856896 -12761.073176      0    81      False\n",
+       "4 2011-09-07 15:34:43.239835648   3642.817612      0    58      False"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples['depts'].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We may not want to generate data for all tables in the dataset, rather for just one table. This is possible with SDV using the `sample` method. To use it we only need to specify the name of the table we want to synthesize and the row numbers to generate. In this case, the \"walmart\" data set has 3 tables: stores, features and depts.\n",
+    "\n",
+    "In the following example, we will generate 1000 rows of the \"features\" table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'features':                              Date     MarkDown1  Store  IsHoliday  \\\n",
+       " 0   2010-07-07 18:33:30.490365440           NaN     46       True   \n",
+       " 1   2010-05-31 05:12:43.555245568           NaN     49      False   \n",
+       " 2   2009-11-13 00:11:26.645775872           NaN     46      False   \n",
+       " 3   2011-06-26 13:37:23.832223232   8480.251096     48      False   \n",
+       " 4   2013-09-30 06:14:18.804104192   4586.374804     48      False   \n",
+       " 5   2011-03-18 21:50:55.521650432           NaN     45      False   \n",
+       " 6   2012-03-22 14:59:09.189622016   9747.212668     46      False   \n",
+       " 7   2011-04-05 16:45:11.282816000           NaN     48      False   \n",
+       " 8   2013-05-05 14:23:57.431958784   5430.557501     46      False   \n",
+       " 9   2011-04-07 02:38:10.873993984   6735.454863     49      False   \n",
+       " 10  2012-07-09 15:59:11.863023616  11818.737600     48      False   \n",
+       " 11  2012-03-10 03:57:22.968428544   4603.635257     48      False   \n",
+       " 12  2011-10-01 03:51:05.280273920  14596.976258     49      False   \n",
+       " 13  2012-06-24 23:45:06.501742080   5816.163902     49      False   \n",
+       " 14  2011-08-06 14:43:48.366935040           NaN     47      False   \n",
+       " 15  2012-06-14 01:06:45.771771648           NaN     45      False   \n",
+       " 16  2011-11-22 08:57:27.766619392           NaN     45      False   \n",
+       " 17  2012-10-06 19:10:00.039407360   5887.384577     48      False   \n",
+       " 18  2013-06-08 07:46:59.612567808  -5770.051172     48      False   \n",
+       " 19  2012-05-27 11:08:47.184699648   6908.122060     46      False   \n",
+       " 20  2010-10-19 21:42:09.919441408           NaN     46      False   \n",
+       " 21  2013-01-01 17:57:15.273558784   6221.151492     48      False   \n",
+       " 22  2012-07-13 14:40:51.181466112   1378.006687     47      False   \n",
+       " 23  2012-02-13 18:37:30.161656320           NaN     48      False   \n",
+       " 24  2010-11-27 09:56:34.540603392           NaN     46      False   \n",
+       " 25  2011-05-21 03:49:37.053693440           NaN     49      False   \n",
+       " 26  2010-06-12 20:31:32.339124224           NaN     49       True   \n",
+       " 27  2011-08-12 21:10:37.406385152           NaN     47      False   \n",
+       " 28  2012-02-07 03:31:37.558127360           NaN     47      False   \n",
+       " 29  2011-05-28 22:39:00.441712128           NaN     48      False   \n",
+       " ..                            ...           ...    ...        ...   \n",
+       " 970 2012-10-01 09:27:33.224137984   7367.091411     49      False   \n",
+       " 971 2011-08-05 19:20:26.151251200           NaN     49      False   \n",
+       " 972 2010-05-02 10:25:48.294526720           NaN     49      False   \n",
+       " 973 2014-07-30 06:33:25.027884800   2869.286369     46      False   \n",
+       " 974 2012-06-10 23:50:28.918937344           NaN     45      False   \n",
+       " 975 2011-11-20 17:24:25.076207104           NaN     48      False   \n",
+       " 976 2011-07-16 19:31:09.475053312   8465.620591     48      False   \n",
+       " 977 2010-12-17 11:17:29.377529600           NaN     45      False   \n",
+       " 978 2011-05-01 22:00:24.413698816           NaN     48       True   \n",
+       " 979 2009-01-04 14:06:28.700405504           NaN     48      False   \n",
+       " 980 2013-01-26 23:44:47.124111360   7711.715123     47      False   \n",
+       " 981 2012-02-05 15:58:21.306519040   4771.829938     49      False   \n",
+       " 982 2012-12-25 20:25:33.794860544   9052.115357     45      False   \n",
+       " 983 2011-03-11 05:15:33.422215424           NaN     48      False   \n",
+       " 984 2012-04-10 15:14:42.299491072  11469.698722     49      False   \n",
+       " 985 2012-09-04 11:56:04.719035648  11985.730540     47      False   \n",
+       " 986 2010-08-12 06:13:43.472920320           NaN     47      False   \n",
+       " 987 2011-11-28 00:46:09.219015936           NaN     47      False   \n",
+       " 988 2010-11-01 19:46:13.442969344           NaN     47      False   \n",
+       " 989 2012-09-03 02:46:56.811711232  13448.706455     46      False   \n",
+       " 990 2012-06-21 20:15:00.600609536   8515.644049     45      False   \n",
+       " 991 2009-11-09 11:32:20.317559808           NaN     47      False   \n",
+       " 992 2012-12-24 16:57:40.573141760   8785.735128     48      False   \n",
+       " 993 2010-10-26 22:04:15.916646656           NaN     45      False   \n",
+       " 994 2013-07-27 21:48:37.233461248   2218.444188     46      False   \n",
+       " 995 2012-11-22 15:48:58.070053120           NaN     47      False   \n",
+       " 996 2012-11-19 17:12:55.679613440  11318.264204     45      False   \n",
+       " 997 2013-10-11 21:09:49.021492480    478.609430     47      False   \n",
+       " 998 2013-01-23 23:17:20.493887232  14227.944357     49      False   \n",
+       " 999 2012-06-23 00:01:59.594749696   9121.193148     46      False   \n",
+       " \n",
+       "        MarkDown4     MarkDown3  Fuel_Price  Unemployment  Temperature  \\\n",
+       " 0            NaN           NaN    3.246268      6.712596    72.778272   \n",
+       " 1            NaN           NaN    2.961909      8.775273    31.911285   \n",
+       " 2            NaN           NaN    2.474694      8.693548    36.996590   \n",
+       " 3    2990.430314    450.886030    3.180972      7.482533    82.689207   \n",
+       " 4    1300.901307   1038.142954    3.979948      3.842990    61.626723   \n",
+       " 5            NaN           NaN    3.190374      9.185961    43.775213   \n",
+       " 6    5068.165070   4033.503158    3.436991     10.002234    44.980268   \n",
+       " 7    2639.207053           NaN    3.126477      8.921070    60.901225   \n",
+       " 8   -1515.448599   1859.931318    4.101750     10.478649    81.003354   \n",
+       " 9            NaN  -4013.843645    3.200662      8.549170    46.332113   \n",
+       " 10   6983.561124   9246.461293    3.637923      6.940256    66.208533   \n",
+       " 11           NaN   1872.845519    3.535785      5.360209    76.842613   \n",
+       " 12   5967.615987  -2084.078449    3.314418           NaN    73.216988   \n",
+       " 13   4181.309429  -7382.929438    3.170917      7.209879    27.887139   \n",
+       " 14           NaN           NaN    4.084598     10.976241    75.512964   \n",
+       " 15   6522.458652  -2969.500556    3.592465      6.778646    45.304799   \n",
+       " 16           NaN           NaN    3.691225      9.169614    61.590909   \n",
+       " 17   2195.064778  10031.652043    3.790657      5.655427    38.507717   \n",
+       " 18  -2522.448316   4530.988378    3.349566      4.743290    36.629555   \n",
+       " 19           NaN   3416.997827    3.641454      8.177166    89.916452   \n",
+       " 20           NaN           NaN    3.075063      5.229354    61.706563   \n",
+       " 21   4647.376520   3247.164388    4.162684      5.033966    37.962542   \n",
+       " 22           NaN           NaN    3.384827      4.921576    54.039707   \n",
+       " 23           NaN   7039.242263    3.187745      9.768689    74.453345   \n",
+       " 24           NaN           NaN    3.045024      6.954771    63.069450   \n",
+       " 25           NaN           NaN    4.164028      8.876290    71.070532   \n",
+       " 26           NaN           NaN    2.719009      7.230035    57.364249   \n",
+       " 27           NaN           NaN    3.257133      9.345917    68.652152   \n",
+       " 28           NaN  -4190.690106    3.548902      9.494165    89.145578   \n",
+       " 29           NaN           NaN    3.316924      6.697705    77.375684   \n",
+       " ..           ...           ...         ...           ...          ...   \n",
+       " 970  2712.006496    -14.780688    3.618468      8.148930    30.483065   \n",
+       " 971          NaN           NaN    3.012630      6.326113    47.975596   \n",
+       " 972          NaN           NaN    2.841184     10.269877    81.946843   \n",
+       " 973  1937.104665  -1511.898278    4.759412      7.778287    30.662510   \n",
+       " 974  5253.689129    329.011494    3.343722      7.398721    21.129117   \n",
+       " 975          NaN   4930.780630    3.506178      9.701808   110.523151   \n",
+       " 976  5293.404197  -3121.976568    2.800094      5.843644    32.442600   \n",
+       " 977  9805.271519           NaN    3.136993      7.623560    72.572211   \n",
+       " 978          NaN           NaN    2.714601      7.234621    52.291714   \n",
+       " 979          NaN           NaN    2.043747      8.744450    46.628272   \n",
+       " 980  5415.328424  -4809.140910    3.287617      8.354515    61.384200   \n",
+       " 981          NaN           NaN    3.337297      6.142083    36.829644   \n",
+       " 982   837.730915   4072.518501    3.894026     12.494251    94.673851   \n",
+       " 983          NaN           NaN    3.420209      7.090230    47.694700   \n",
+       " 984  5134.594286    357.279124    3.325391      4.539604    37.460031   \n",
+       " 985          NaN   4056.157461    3.685698      7.230368    43.096815   \n",
+       " 986  3953.583585    805.676045    3.044291      7.884751    34.438372   \n",
+       " 987  4511.412982           NaN    3.394697      6.834802    36.636557   \n",
+       " 988          NaN           NaN    3.154329      8.363893    49.751330   \n",
+       " 989          NaN   2084.191209    3.615679      9.596049    75.533229   \n",
+       " 990  4457.772828    218.985398    3.440906      8.526339    61.864859   \n",
+       " 991          NaN           NaN    3.247303      7.423321    59.019784   \n",
+       " 992          NaN           NaN    3.458168      6.459322    48.584436   \n",
+       " 993          NaN           NaN    2.608670     10.259658    82.758016   \n",
+       " 994   719.333117   6631.998410    4.368128      7.588005    54.580465   \n",
+       " 995  3003.501922           NaN    3.800082           NaN   100.143258   \n",
+       " 996          NaN   -681.708699    4.140327     10.717719    46.213930   \n",
+       " 997   735.529994   3856.959812    3.921162      7.439993    59.473702   \n",
+       " 998  2557.407176   2865.135441    3.971143      9.540309    67.952407   \n",
+       " 999   671.086509   2328.798014    3.487042      5.988222    63.846379   \n",
+       " \n",
+       "         MarkDown5     MarkDown2         CPI  \n",
+       " 0             NaN           NaN  172.666543  \n",
+       " 1             NaN           NaN  119.214662  \n",
+       " 2             NaN           NaN  143.990866  \n",
+       " 3     4354.651570   5019.468642  180.631779  \n",
+       " 4    -4772.570563   2947.674138  173.813790  \n",
+       " 5             NaN           NaN  177.052581  \n",
+       " 6     7083.723804   3513.601560  185.921725  \n",
+       " 7             NaN   -180.888893  153.966086  \n",
+       " 8     6265.833733   2401.076738  170.559516  \n",
+       " 9     3759.801559   4729.764492  200.039874  \n",
+       " 10    6567.512481  -1022.527044  150.002644  \n",
+       " 11    7765.142655           NaN  221.169299  \n",
+       " 12            NaN   3308.681495         NaN  \n",
+       " 13    2826.442468   8385.400703  150.777231  \n",
+       " 14            NaN           NaN  211.070762  \n",
+       " 15    6642.564583           NaN  154.828636  \n",
+       " 16            NaN           NaN  138.236111  \n",
+       " 17    4118.446321           NaN  138.853628  \n",
+       " 18    1115.215425  -1526.587205  219.621502  \n",
+       " 19    4994.790024           NaN  197.086249  \n",
+       " 20            NaN           NaN  143.384357  \n",
+       " 21    9934.092076   6538.282816  208.758478  \n",
+       " 22     396.251330   1170.864752  217.807128  \n",
+       " 23    1492.226506           NaN  144.261943  \n",
+       " 24            NaN           NaN  198.046465  \n",
+       " 25            NaN           NaN  152.795139  \n",
+       " 26            NaN           NaN  192.354502  \n",
+       " 27    1804.646340           NaN  213.883682  \n",
+       " 28            NaN           NaN  237.955017  \n",
+       " 29            NaN           NaN  140.091500  \n",
+       " ..            ...           ...         ...  \n",
+       " 970   -208.184350   4224.751927  149.278618  \n",
+       " 971           NaN   3814.404768  167.707496  \n",
+       " 972           NaN           NaN  115.688179  \n",
+       " 973   1199.031857   3033.592607  120.703360  \n",
+       " 974           NaN   -351.113110  106.143265  \n",
+       " 975           NaN           NaN  211.109816  \n",
+       " 976   3975.213749           NaN  152.402312  \n",
+       " 977           NaN           NaN  121.333484  \n",
+       " 978           NaN   3607.788296  176.680147  \n",
+       " 979           NaN           NaN  164.924902  \n",
+       " 980   5431.606087   7005.735955  208.516910  \n",
+       " 981   -285.659902           NaN  146.865904  \n",
+       " 982   5584.625275   2016.204322  165.913603  \n",
+       " 983           NaN           NaN  136.591286  \n",
+       " 984   2929.718397  11730.460834  153.875137  \n",
+       " 985   4618.823214   1218.336997  191.245195  \n",
+       " 986           NaN    128.922827  209.229013  \n",
+       " 987   7757.467609           NaN  201.908861  \n",
+       " 988           NaN           NaN  210.724201  \n",
+       " 989   5115.758017   1620.240724  149.542233  \n",
+       " 990   1789.565202   1350.171000  193.810848  \n",
+       " 991           NaN           NaN  147.307937  \n",
+       " 992   -105.978005           NaN  229.688010  \n",
+       " 993           NaN           NaN  217.839779  \n",
+       " 994   -907.055933   3253.948843  128.459172  \n",
+       " 995           NaN           NaN         NaN  \n",
+       " 996   3043.948542           NaN  114.508859  \n",
+       " 997   1459.810600  -4003.468566  193.149902  \n",
+       " 998  12151.265277  -3099.180177  150.590590  \n",
+       " 999   2792.671569           NaN  201.355736  \n",
+       " \n",
+       " [1000 rows x 12 columns]}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sdv.sample('features', 1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SDV has tools to evaluate the synthetic data generated and compare them with the original data. To see more about the evaluation tools, [see the documentation](https://sdv-dev.github.io/SDV/api/sdv.evaluation.html#sdv.evaluation.evaluate)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/04_Working_with_Metadata.ipynb b/docs/tutorials/04_Working_with_Metadata.ipynb
new file mode 100644
index 000000000..91b001036
--- /dev/null
+++ b/docs/tutorials/04_Working_with_Metadata.ipynb
@@ -0,0 +1,460 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sdv import load_demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata, tables = load_demo(metadata=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'tables': {'users': {'primary_key': 'user_id',\n",
+       "   'fields': {'user_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'country': {'type': 'categorical'},\n",
+       "    'gender': {'type': 'categorical'},\n",
+       "    'age': {'type': 'numerical', 'subtype': 'integer'}}},\n",
+       "  'sessions': {'primary_key': 'session_id',\n",
+       "   'fields': {'session_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'user_id': {'ref': {'field': 'user_id', 'table': 'users'},\n",
+       "     'type': 'id',\n",
+       "     'subtype': 'integer'},\n",
+       "    'device': {'type': 'categorical'},\n",
+       "    'os': {'type': 'categorical'}}},\n",
+       "  'transactions': {'primary_key': 'transaction_id',\n",
+       "   'fields': {'transaction_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'session_id': {'ref': {'field': 'session_id', 'table': 'sessions'},\n",
+       "     'type': 'id',\n",
+       "     'subtype': 'integer'},\n",
+       "    'timestamp': {'type': 'datetime', 'format': '%Y-%m-%d'},\n",
+       "    'amount': {'type': 'numerical', 'subtype': 'float'},\n",
+       "    'approved': {'type': 'boolean'}}}}}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'users':    user_id country gender  age\n",
+       " 0        0     USA      M   34\n",
+       " 1        1      UK      F   23\n",
+       " 2        2      ES   None   44\n",
+       " 3        3      UK      M   22\n",
+       " 4        4     USA      F   54\n",
+       " 5        5      DE      M   57\n",
+       " 6        6      BG      F   45\n",
+       " 7        7      ES   None   41\n",
+       " 8        8      FR      F   23\n",
+       " 9        9      UK   None   30,\n",
+       " 'sessions':    session_id  user_id  device       os\n",
+       " 0           0        0  mobile  android\n",
+       " 1           1        1  tablet      ios\n",
+       " 2           2        1  tablet  android\n",
+       " 3           3        2  mobile  android\n",
+       " 4           4        4  mobile      ios\n",
+       " 5           5        5  mobile  android\n",
+       " 6           6        6  mobile      ios\n",
+       " 7           7        6  tablet      ios\n",
+       " 8           8        6  mobile      ios\n",
+       " 9           9        8  tablet      ios,\n",
+       " 'transactions':    transaction_id  session_id           timestamp  amount  approved\n",
+       " 0               0           0 2019-01-01 12:34:32   100.0      True\n",
+       " 1               1           0 2019-01-01 12:42:21    55.3      True\n",
+       " 2               2           1 2019-01-07 17:23:11    79.5      True\n",
+       " 3               3           3 2019-01-10 11:08:57   112.1     False\n",
+       " 4               4           5 2019-01-10 21:54:08   110.0     False\n",
+       " 5               5           5 2019-01-11 11:21:20    76.3      True\n",
+       " 6               6           7 2019-01-22 14:44:10    89.5      True\n",
+       " 7               7           8 2019-01-23 10:14:09   132.1     False\n",
+       " 8               8           9 2019-01-27 16:09:17    68.0      True\n",
+       " 9               9           9 2019-01-29 12:10:48    99.9      True}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sdv import Metadata\n",
+    "\n",
+    "new_meta = Metadata()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_meta.add_table('users', data=tables['users'], primary_key='user_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_meta.add_table('sessions', data=tables['sessions'], primary_key='session_id',\n",
+    "                   parent='users', foreign_key='user_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transactions_fields = {\n",
+    "    'timestamp': {\n",
+    "        'type': 'datetime',\n",
+    "        'format': '%Y-%m-%d'\n",
+    "    }\n",
+    "}\n",
+    "new_meta.add_table('transactions', tables['transactions'], fields_metadata=transactions_fields,\n",
+    "                   primary_key='transaction_id', parent='sessions')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'tables': {'users': {'fields': {'gender': {'type': 'categorical'},\n",
+       "    'user_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'country': {'type': 'categorical'},\n",
+       "    'age': {'type': 'numerical', 'subtype': 'integer'}},\n",
+       "   'primary_key': 'user_id'},\n",
+       "  'sessions': {'fields': {'session_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'user_id': {'type': 'id',\n",
+       "     'subtype': 'integer',\n",
+       "     'ref': {'table': 'users', 'field': 'user_id'}},\n",
+       "    'os': {'type': 'categorical'},\n",
+       "    'device': {'type': 'categorical'}},\n",
+       "   'primary_key': 'session_id'},\n",
+       "  'transactions': {'fields': {'timestamp': {'type': 'datetime',\n",
+       "     'format': '%Y-%m-%d'},\n",
+       "    'session_id': {'type': 'id',\n",
+       "     'subtype': 'integer',\n",
+       "     'ref': {'table': 'sessions', 'field': 'session_id'}},\n",
+       "    'transaction_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'approved': {'type': 'boolean'},\n",
+       "    'amount': {'type': 'numerical', 'subtype': 'float'}},\n",
+       "   'primary_key': 'transaction_id'}}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_meta.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_meta.to_dict() == metadata.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_meta.to_json('demo_metadata.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded = Metadata('demo_metadata.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded.to_dict() == new_meta.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
+       " -->\n",
+       "<!-- Title: Metadata Pages: 1 -->\n",
+       "<svg width=\"371pt\" height=\"500pt\"\n",
+       " viewBox=\"0.00 0.00 370.50 500.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 496)\">\n",
+       "<title>Metadata</title>\n",
+       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-496 366.5,-496 366.5,4 -4,4\"/>\n",
+       "<!-- users -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>users</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
+       "</g>\n",
+       "<!-- sessions -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>sessions</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
+       "</g>\n",
+       "<!-- users&#45;&gt;sessions -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>users&#45;&gt;sessions</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-377.2088C101.5,-363.9446 101.5,-349.6377 101.5,-335.7493\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-335.7312 106,-325.7312 101.5,-330.7312 101.5,-325.7312 101.5,-325.7312 101.5,-325.7312 101.5,-330.7312 97,-325.7313 101.5001,-335.7312 101.5001,-335.7312\"/>\n",
+       "<text text-anchor=\"middle\" x=\"197\" y=\"-347.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;sessions.user_id &#45;&gt; users.user_id</text>\n",
+       "</g>\n",
+       "<!-- transactions -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>transactions</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 191,-.5 191,-.5 197,-.5 203,-6.5 203,-12.5 203,-12.5 203,-132.5 203,-132.5 203,-138.5 197,-144.5 191,-144.5 191,-144.5 12,-144.5 12,-144.5 6,-144.5 0,-138.5 0,-132.5 0,-132.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
+       "</g>\n",
+       "<!-- sessions&#45;&gt;transactions -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>sessions&#45;&gt;transactions</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-196.3927C101.5,-183.0141 101.5,-168.7483 101.5,-154.8345\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-154.7831 106,-144.7831 101.5,-149.7831 101.5,-144.7831 101.5,-144.7831 101.5,-144.7831 101.5,-149.7831 97,-144.7831 101.5001,-154.7831 101.5001,-154.7831\"/>\n",
+       "<text text-anchor=\"middle\" x=\"232\" y=\"-166.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;transactions.session_id &#45;&gt; sessions.session_id</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.dot.Digraph at 0x7fc36bc596d8>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata.visualize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
+       " -->\n",
+       "<!-- Title: Metadata Pages: 1 -->\n",
+       "<svg width=\"371pt\" height=\"500pt\"\n",
+       " viewBox=\"0.00 0.00 370.50 500.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 496)\">\n",
+       "<title>Metadata</title>\n",
+       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-496 366.5,-496 366.5,4 -4,4\"/>\n",
+       "<!-- users -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>users</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
+       "</g>\n",
+       "<!-- sessions -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>sessions</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
+       "</g>\n",
+       "<!-- users&#45;&gt;sessions -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>users&#45;&gt;sessions</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-377.2088C101.5,-363.9446 101.5,-349.6377 101.5,-335.7493\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-335.7312 106,-325.7312 101.5,-330.7312 101.5,-325.7312 101.5,-325.7312 101.5,-325.7312 101.5,-330.7312 97,-325.7313 101.5001,-335.7312 101.5001,-335.7312\"/>\n",
+       "<text text-anchor=\"middle\" x=\"197\" y=\"-347.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;sessions.user_id &#45;&gt; users.user_id</text>\n",
+       "</g>\n",
+       "<!-- transactions -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>transactions</title>\n",
+       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 191,-.5 191,-.5 197,-.5 203,-6.5 203,-12.5 203,-12.5 203,-132.5 203,-132.5 203,-138.5 197,-144.5 191,-144.5 191,-144.5 12,-144.5 12,-144.5 6,-144.5 0,-138.5 0,-132.5 0,-132.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
+       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
+       "</g>\n",
+       "<!-- sessions&#45;&gt;transactions -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>sessions&#45;&gt;transactions</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-196.3927C101.5,-183.0141 101.5,-168.7483 101.5,-154.8345\"/>\n",
+       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-154.7831 106,-144.7831 101.5,-149.7831 101.5,-144.7831 101.5,-144.7831 101.5,-144.7831 101.5,-149.7831 97,-144.7831 101.5001,-154.7831 101.5001,-154.7831\"/>\n",
+       "<text text-anchor=\"middle\" x=\"232\" y=\"-166.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;transactions.session_id &#45;&gt; sessions.session_id</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.dot.Digraph at 0x7fc36bc59198>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_meta.visualize()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/demo_metadata.json b/docs/tutorials/demo_metadata.json
new file mode 100644
index 000000000..2be7ecc2e
--- /dev/null
+++ b/docs/tutorials/demo_metadata.json
@@ -0,0 +1,74 @@
+{
+    "tables": {
+        "users": {
+            "fields": {
+                "age": {
+                    "type": "numerical",
+                    "subtype": "integer"
+                },
+                "user_id": {
+                    "type": "id",
+                    "subtype": "integer"
+                },
+                "gender": {
+                    "type": "categorical"
+                },
+                "country": {
+                    "type": "categorical"
+                }
+            },
+            "primary_key": "user_id"
+        },
+        "sessions": {
+            "fields": {
+                "os": {
+                    "type": "categorical"
+                },
+                "user_id": {
+                    "type": "id",
+                    "subtype": "integer",
+                    "ref": {
+                        "table": "users",
+                        "field": "user_id"
+                    }
+                },
+                "session_id": {
+                    "type": "id",
+                    "subtype": "integer"
+                },
+                "device": {
+                    "type": "categorical"
+                }
+            },
+            "primary_key": "session_id"
+        },
+        "transactions": {
+            "fields": {
+                "timestamp": {
+                    "type": "datetime",
+                    "format": "%Y-%m-%d"
+                },
+                "transaction_id": {
+                    "type": "id",
+                    "subtype": "integer"
+                },
+                "session_id": {
+                    "type": "id",
+                    "subtype": "integer",
+                    "ref": {
+                        "table": "sessions",
+                        "field": "session_id"
+                    }
+                },
+                "approved": {
+                    "type": "boolean"
+                },
+                "amount": {
+                    "type": "numerical",
+                    "subtype": "float"
+                }
+            },
+            "primary_key": "transaction_id"
+        }
+    }
+}
\ No newline at end of file
diff --git a/docs/tutorials/sdv.pkl b/docs/tutorials/sdv.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..d3c88b5a4eca4045c109b46b6393d46f5be7a46d
GIT binary patch
literal 255975
zcmd@7cYIV;+x?GIq=nu=x(b9My($otUWHKvR18T7Oh!nUorI=C6Anc|njnZEponw?
zR0IrFI*5Suj!F?wLG1Ef*V<=hGn{xk`S`r@`{U>H+}AmKt+n^r^PW93=S*gX;hAF+
zdkv_D|Ajr>{Mj&XhDsy68GYVN;oY)i$dI95La*4qULE0nVzror{>go#62n?WB_}4v
zMa4H`JIwn)w_N@XkuMF5QhgG=S$y8CRC~oGCaJjS<fOQScyG2Cdx&bu@o@v9R9sY2
zY_&f2Xn|q71iUbB_QY=4W$f6bs9sS?QQjP!vcDPD!PdjPVVHbQpEp-{w;af;goLEX
z{!vM8Z|-gx(XQsrLp{>p)0@}l&DSj}tfZ*uzOjkk{6231eZ!LzV^yNJpwC+<yjxBL
z`m4BpQEG7HOR<B!g}Y^gB8kYjUfv=cpm$ts-(HE{qCRi2aCZ@(w|IEB2V~sA{bRik
zcFTlaL`$$^VsbR)l5EArC&l)ORo+s`Y{n!c$0w=5-qJpA8TW%cbPV|PNl<YyQGLB-
z6M3{gvGKjor<~9Gko!S5#LPHARG(Py!#?jL;pitfKR&r%tnO9bUD91DIXRgW6Jv3=
zCB!FsEBL$>!`+3ug&}K;a(lwvxDl1Y-QhlOWenvm!On7<s)X}6Dz>+`s?YnV2E`lh
zt>!Mm^+Jx`>h6Ncfu5Xjuh;=`F|pnnK5tDuVIh)`=&j}R)~0~T$?{23QSpgUF|tk)
zy>)!vx-!8$!G=t-o=mcSxVxUu+u)u_V$Kc2-I&~C;qGegaCdcgg?lC<P?F;M#U>_2
z_3Q6_+~;j1sAOefRmF<qZLIIi1l2Dp$=ifp`R<RF@Aqi=Ufzggno<4aF7h_@d7qFY
zgDr1)@4gA>@}y=Q)xW<=7!cdb+sx-}j$_DriB3r98ygkxZIOt3BC%URNRe(V4;9H9
z-5bZ0CD+pDZ58e=7^8Y6Rdd*3t);^=0!in?ysfe1+xWa~!wo<GQ0;u)_Tlc*9O~X<
zRJ&(Ig*7v)D`?yf7`LO(`;>ktMa3t?5AGK?B-Y!hqrSPxLxv3Yb{@x79@96jzt=U6
zH?tpZdt7`U@6$f-GhBwUz}0}LzKPy0KJT;Pd@OWn=j|HaE<>w`L$&!|tB9@soeyX7
zza(!rF19XDKjG~jeoycmxZ%H65yzRE)hc3Me*Tx_eU4+dc-s3sI-Fw+xo>;(zg7_!
z{f7v2c!3>Sy1YHmA&|SGXV5C*vVVxl{v*7|4$WJ5Bhev{`+hI~YZY;gN2t{*BG92H
zJ3QIS8-)&m+|9fEuT{hi|J(u{qLY)O-R0auKgQ?n74GiT(cO8R+cnM&ps{Ws>>bW0
zvzFdIBpAT3%g<w#f3|K0J9y)8>_9AVGtc|;0pgxk!+zB_`Cc49_$laHSG!e2pu<Zt
z*@vC$EAY${*3thQ6tuSb;@0)^dE@o-Njx4D30z10<nhs;PkQmmefvhnB=k-07w`4z
zCD)73nkptbIj(O~Tzp~}R#uo-KOshHGs&IUEg$B~-uEndZ*ppr?AsUf>)SVIY6CE}
zfj;k`a6T>ej>9alc9UbhgJW>3`VYp-pNg&4FBvn4QYvb2SR$U5qg0r8i1x!ckudMe
zyyJT}@V+u`>=>`l<sBN{E;H|sM&4l^J9ca<D}be4h9vLs=w{iu6c#?&)H}lRj&yln
zb>n8sO9);p2YW}2<D*V)SE6^cdx%?)A6?UH$@pVj-m(7iU-6D}j6XiQS;h?B*DUYr
zF7E_BQAFMgc%pkkay0(0TW%aL`03~kpLY_TL);y)C}Z3YyZa@(M~!uN@_8rg=XJSH
z61`J8Vj)y;%X86GpLbfg8&5rNhP!KU9Z&apX9Ta~w`3j94Ce^_5~Ab!#(HOQF$W%^
zhy0J#bzG%0le}*;&DWPC?`$m4KsK;I^#c;m2!ThyJF-A)aTarY-nqfEcvogIPp`o^
zycW&>w<#>(6I@h$FO`6op!Y(Y!~2*+vKt&13XtiT&Ky6(58MP-hu{|ZydM(W;&Aul
z&gJTVi3)3Ozg7h;+a(~s)aP9m?oM#`$I5XhyJaOW_jy-@y9Z;v3~iSEuaosQZ5_O}
znRlh-UFGtAB(YKB+@swS-1Y)+H+9QHbG6UACNR)Fk2k)=g?ZPyliX|V1AOfBeiAsq
z`5}dtx60qOsrOUMyUykPER_Q^bKWrfbusKo{rVVmv({ra8+_i4m<=B*=9Yc#^QPdy
z0X6uBkoP8VbT7qiTJ3J;-r#4xnaq><@Q#de$Mx+MiI*(z7Mzs!?HjAlJi>a#_Kw0s
zyjNTdo?*6{XPm@Xw6~4+Zg+XVz>BndTMzFJcZI0Y-kmP*E<6Cx-tDe~_8ymaue*-4
z_l@@McX<ya^H~jNqbfE&iAVg>z15AO4!XQwh3l?|+y&9~u*-Wycm0|}A9Z<;=|deK
z?LFc0p2T|6ue5S*j<dj1`fOOY!aCSn?`g0*<MW>NKg<wL^q%v1&xd352Vh?Cc`t^$
zv$?aoGY8Jd`NZ~*=j0vaY0q}Uyq7S>H$LyT`UDy0B)t;)MJC4LtmnIqGAz%YA|vHW
zATrW>xspb1UmHNV;`3gWTq19cey&gSUhCogUhcT2?q;!MW-byEz1MqqZ-l$E<G?q4
z-XHXHbPNt0Cy$Oq?~gsaKe=)6%az<1{1(R*<Wc<+y|;bdpY>zCZ$gas&V3JwUo7vh
zF7I!Al9gxlSnuyT@w&C-((2OLn|c4Rynnj9f9Z3;NPS)OcT8eT99|P}LYb6UEmE#>
z;v?hYd#fm1?j*+~C9BvlH6XILN*EFwALhMVX@tsv3lX2nh^j9TgpSIDW+gt^zcjE{
zqRPz71CnvyasTH2-HpS_O93yVvOo_$ZcIWvUJsKKk`wh562szgG8u`BgfNv=h904^
zameg4q`h|f$uqag0i|6=zAoWK42@QI?Wa>}(JH5C!~B{|Vo*{-UzJPb##u+bZOyCl
zHt*=@s9t=bRJqy1K9S(G<D+n9n+LLPVQwm~wB*dYZ$f+@`uU)A<a2zYI|{EUDnA9u
z#16tsd9Q>RRe+LALYA2-D9vR1by=^!YLWfpVqWSS8x|R<1-WZtBV&>hRG2D+Ja2u6
zXKFTVSi8O|3}Lh?g6dL5QGKcyYB*m8qvMi<V62KlUsW8sN8);g$e`*$_9(#~a{Yx%
zLsb&3e>hQyRI#|D^uh>Jr2@gQ_+I)<LY0Qr4ihSaZL^Hgsw}o+R5?_adI&YVUB(Xm
zGmTacW8XdjNUozEK?~;qsyuTQn9J;ToMWhpaH7XOxd~T6oeJ;$s=&mBdq4lBb>p$q
za;L;>pS`wnt!m|Sw6d}d{q*QJFHE0ZWySmxT@OEW>iohRS<1ff*!KD@-fZ6dZ1~g@
zn~rvFczDYL8~V&FmwDEsV~&p=ez(;xyK?QG-6C6^&R>+<7}ICOnlGoe+&it3JG{?_
zeJyqPdhW%$TRz;m!{1-DS=H&eHxjFtZ@=7AK2P5IAG#;3oAS%B`u*>W?-lv`A8T?H
zXn*L`)nWseEZ=$j^70+$>UJ6Z{p#LxqhE|2o3&@kM~z#2RO^X9tM{He=%a?)7FQpf
zYtUaAXWsp3|M0~<IwTkCU-Y|meOgrhu*A+D^D0h%zW(b8?p?kT?#e5A|Izx!t8?<b
zyW+HGSm)@vHJ&Nz-np!LpE^T_gw4Jk{@MAQ*79Y!7Y?~JX7#(Biu}FWYW!o9A7{2n
z>XW}f<(j`%&HBsehR;mjF!1ulk!|-i{5YX-$3m+!AH30^a^{N1?pCke+vEE3iIMF}
zwtRJ+>*SK3@(<oS^ir|B4VL`)Wq4A_S{cvnthl!Pb8lwseX8x=+m|L3JvRNF4UZRm
zAv(jfzT0}8?bNa1%xIiNY#uNw&%o>Pd*ACf;qIxZ$+y=oc&Jh5{pCt8-FRYIt6Ya3
zTfeIC+FO~j6{x#3eBQ<iU0?25sQRtGa~HdIR{Y`5I^A+yIM(IdJ0E><_3CHV#_gXp
z&2n`4l#X?l^~nBOx0i}P`=G1D>Y8C&&o?}_p-=3-(vN>Oqjdet8A{BnfBX57LmsZ)
z|Gntpo10WSyZJ!E<PuGP$^G|`gExv7f9}d(PhESXc;(hbzc1tM@z<i!l{1};IX}RY
zvHJ4-tD>?V`g7OJCvx2_IV<n%w@zos5?krWmOp>V<?T^z{;%DpsTx^3OnJ9yZujmI
zd0vb-^4M?LW}Z50wR!f6ch0q0Pkgv({KVgmo_yiRtq$R@*M2|h<NZYsR$duiGp1OT
z^?l9`et$##YOh|HIPaxxLw>B#s?7752X8)Bds(X%b4zrdIJaNls@3CvFFEwV@ZDLy
z`t`-z0~)MpJt_9$kt11ZcdNB*Vulro)u;b9de@nZ9ZDA%+<jtL;l2ewnQ{I00QVoi
z{nc~w#Hkb7WPGX5zTSn-ttzm7!Qn|w@-C|O@T#zef9I+@zWB_g*HRkIdm*gy`bK*u
z4c<|&`wVxtrFCKl_?mVexv%8YcXmFquzRkjR?S`JI$2{}wOZL%W>~hWRGFq$kK-e{
zmdL#K&b9hCnjB27Q^jg@V%wBAdiv(Q@Jdw5%^SY>i0chzcdz--(Xi9+sA=)*s$5yQ
z;Aa1QH$UG~v&YkA+&jKXs8PILch{9NC%zc<d2)2h4>`W?+Vhi=*Us<gG5^DTo!cFs
zU+C0=GA%a0P;TAgjEx%(KE39vMr}9L|6pSIlTm{<<$A2%_c?aoP8j&=&ANBLf4u#Q
z*LoctKl8wrrX}Bcc~_r5KigGks_T#S0}D6FeCGK0V}qvL$a^d-W2-S^`n<J%{_G>A
zGDWofY~9K&v%0ozRm{q<W%8S=6RQ<z9$CEYqH1FX#|$3cxn<&<0ek-HQ()YT=Q2n9
z(Cm$b{YPHEJ9F2;wmYABqvG{$ZLVi(zVy_xV+YT~d#A^RzqWLC=Ep9+GxJpUIV-!k
ze*gB1S&xmnwBb4L;@`jgV$}Ye%@(vhaO&BE%eFLsWZ2S2le)#_`mJ-%-(D?rbMo|W
zF3-xhF7j-<mS?xVJt)U}zgCUh_*LDF)i$>;_C&*#)wi8#lY9NL1)G0cI`2sT1A8(T
z`g`L$U;mvlp-$l`qc&tnywUH8H9Jc@5&c<*>0K(H+1z=~%?VRad(T9?R4kWoQKrm!
zyZu^xO4NbMuic%tcE+LCdR{+t{r7C&d$!MAabomicNR2AD5AO--xJqn<haN8Jzn+v
zqo+1qo&QSF*9%Q;QS{=fv$>u<7d>l1wkp$Fj2)c&aMN@3j#T(5xt290a>A{tUliI9
z`Qe2;RkP-4(&OS&t3K~AZORR+#zPINPF;KTPOGmzS<r3gk(awfjy!YYdiQ+YDs1V}
zpybsL%DAe$((KsWs_mNg>{MZEvm;kJ-JN`8^=D_kt?%pBI3efqPV=^RUi#GL_tx|s
z_|^XR^Y+}lVE03p*8KVFtR-clD_$78uh=UW*LGNR?AtRFi@w%yRoH<u1@m7m<??Nd
zJ6&UVy}U)|x#DO3@%QUxN2Ywf#k2l_u30BEZ&ba?)GIr_eL2$C;{9vQi#Et|VXC)Y
zfucE!_dL0D#*Cg7pS<8LdeOVM&h18DoE|>>os|dcovGL-$JXY{$92tG?smubj*k1=
zz5J(o?N5K){zjSpy$5f8`Ndi9<geYh(x)x%-q`WP`Vv(NM-2J-#;zPMl)W_P@aT$h
zSJ&=}89A}ktroGhD}I-K$`_0BKmXWAUynOp=|GkV$5yo;+2Qu=S-VDb-m`Gg>hGSc
zHob$d-=sb7=Iweb>D_D>cXVCf`})KAx@3yjern0Ohj;cp7q|V93nee_Z%}B#XG6;M
zoLHjKr4iq)&+HjjaaWn)3!nV<#a4Z*<<C8*)a9x(U%Q*|&BFN0Up~5```AlUyH}hx
zxNy<cmp-mpb>~|Lv%M8xf6~x*H!LllWn`Xff1GH2aBj=dW8NtEZo3S#jxJdqon`v&
zw_on^^k1#FZQMO=)F&Ipj%j`Ql?T5sKHyTv1zl!6(6e}(B9E--bhvfH{X3%S{JM8p
z>E)Aa?ke46_twY(6O)T)xb@Aj<5jMYA76R*z9z-yx6jpa$H)`gkN@!c-~-+p?`Mx0
zb*%cx+uNI5eKW_JbvZNFS^Y`ED?3|$+Pd4RJ98hK-lEgTYsTd`k#ug<{?|L?e0;(a
zn=a3~`gD!W^NweFrt+?D`@HsJ*7XZ>*BaODT*2Qzz5UwqZC{+bb70R?z6WYtDlnpD
zwlU{+p1B^g`24Uk8_M3ESvqHq?`m$_UFT+=0_)#dR`yCnt4YnvZb{s+rSy!?e>{=5
ze#UZRy8ZC;l9yDz3*#0IcqGS)AseUUc{C}1jpeQL#$6n7a7}~HCw=thH^YAznLK=9
zv8+`(9m;&H(NBZt9LZ2JtlX?-^#@u-3cBCe8-4YSS_i+GH)6}gA*I$=ex~@8eS0bm
z-~ZXA=SLnGm*=->%|9FEnU%A{%hmU7nYFuT=AIYE{giD|V(Y<$lj_fX{FgT(Di<wv
zwPu?w{dfP+vD3Pkm*2kiaW8kTt=C#iSY7l&+=ymx<vaaDfqVyV96E4g_Kp2}UBB%a
ze0Z$8{;)=q<DaTBBRb2)gips5ozi0cq%(h1yf&j;f!Q5$)|fMW+S?DRR@dGgH1Sfx
zpcjVz{Pl}PoBSRfe!fY8mDl#3X!}ycoSu`mXM4G`^}x&Vxtkq0yx?M&lWPz3YtVaq
zyGQy=FZNTD55DX)p;+lGb8ED^GwH)~eYO?*x!QM!zblddcDqqORd{Muoo}Px9C7t@
zw~KK<emG-An<Hfl-g)Nder>A_DX>3t*#SS7%29t#$4A;Hm!Cc`<Mo}_elA<LcKq=M
z<p#f)aZlF<xl`JwtmwV@z_PJfZY}<F<;Qs@J}|z={Jhg^>~(D}b**ZK7YA)h7<WB!
zQ-S2BeGaF*TwqQAJMZ8A{^ndO)2!HK?TS`^wnV;cy)RyR^YVa=Z*F>TS{W6NbGal{
zNzUl_E@}USL{%BB4v`s?`KGHoDlw9OQc+bPx6`MsTBr)46;4<AUvjji9+hD{K_~y7
zpwoU_Z_3|DddDRt;jFtF;$!i)3a-Lbb?p084b*V=k3CdP>;$gAR4r-U_Nm&m?JF+-
zdjzTuR7=%Gb*Xxiv@f{c?ewPi`lY%}Rec5;FwhV++5NX$uAm;nzE3@l8t&nRlSf}K
zH4?>r$D;@Hu!~17mOS<~P-Av%!jAg7=YQR3PtYZfC+MoiBNsCsxrXuZvc<z|6%Q{@
zJo>7{Bi9p_ia`Arww`-|Hbvg2o<JqgC$VFI;ypY9ZBE+;s^3BWHbS+4YN?i}F4ans
zHqgU0#!f8y%;^Z#nt?V9v_<s;ZHIlIYL6NWw1X%Bst5D1OAyeG?D!NrrWepoI9iB6
zWBG<dOakAqh>wX?ozd4L7hWF!m6l5y`ZDWj?7Gx5sJP5(m$_YLOLalRJ#<{N)~e(V
zZ^<1$MhyOH@r}+yzKke3EX!|qu1`tvw_(!nQorJyZ~9(K@%#R^zrT7GBPOY?sL}qr
zoo}4t?HDB&+&<ME)qYdPjW<}-b5x(Fs^3=8?{26UAo1N3_q86X2mKoETHVUxff}jb
z!nOa57Kyjf{Rko>YvBLYi-`YgvZf>>Lrm6GJwr*>k};z~Ox9G<Atr077)e%1<+k7R
z?h&x~|M(Xfi8p~%FX{AX8anZvAr&j#!vC$C{xhLIP;co`In*BdD1AcfWB;L1AIB|y
zs{ZS70)6z6;-uGusr1tCj;e%#iOPdatowQo^%8dY4xH+XJ)i2wHlF+Av16X9642_F
zSH|Rf9#MGDQ@=aZUt<10U&J*~UbN-i3x%yqC8FZ*7^%E_kpw5YnY<gs|5P&CmKwlb
z15vvbg&vcP_b;RH7G*5o2=jm9Kn+5`youWf9~VeU!uvskA$#OzbSsC>W~To%%n(E^
z^)jkUy&~P@&n0*hEk@qb?Vo@@M=2j<j~Q1cWyTGT4&`XWINER-P5vT`KO09!;SVBl
zNrNMM#l|OMt40KmSt@wUR6300$ggtbQ8Kc;dMDy<-gviobnsyE$1*cPJ7cl`81^5F
zDlg*Wu$6Zshj`>Zy627<kEW$wLv^XwQQg0{%aH7Ua^p$D1gJhW5tXlLZ%D}hCas!;
zmPap&`$wJ(%TiNNU23Yt<Zje(Wk`>{JEqZpQv?H_z6Ly<{tWRANctMnTl8m&Z`jb+
z7-rFb8&xpO#x_+9@4&Fs9ERsg*n#0)s6I6hl^EtrD1c!BS~dpxQw{DFxo4WH_n=$q
zeN>lPC>g^*#<LlGK!1@4h5>!e;6wV0#WxJ-YX(c`FBRV~psyJ$qrV(g7_7jS41#95
z5~`(EG5L`s9ROBC^{F+e1h7^@0RSIM3xj_`+dj8>x{LhxO^W&ys-@PUy3}Wqw9jo$
z+&Mh|8S94#wVr_u3~WU8pWA$neV<A}4L-NoBnr-L^k5!#2|Bmg%#K^wF}==hw&G|Z
zp4;$&wT*M!E_0N#?=P@pfbNhMfbOJi1O5B%U3pS&s&+xO)NWLl+9OFD=$&UhyRPln
z)JW}RU>^hfQT;#<VBe>{L=6UdP!s^wgL&8`2<TVrc!(X-3+Q1SEiFKgaE@Qg90ll6
z>=>ZOqy?bIY1=@b)hjeYoq%eolc+9rN|H9vlU$+aBGhRH&M<Hm)erO>_I>I+YB10X
zq5!BK%)>50Krgc6C3Z|Npx@wVX#x5z=lGq>QGi~?jsbc_S^#>L_Wcj}YfvrqJ*rDx
zm!u8!xF6^Z25vI&1F9eBkJ$IApHPE=-Vy~s^<W-$2?BbX9e-xW^a6SZM@tLPUpU8K
zWsU;$H|!Xoze@{1|DbJF=$m*BjZl9=wbWmzF7>x0?_Z&J8OVT7=DAcxR6o#6*!QW-
zsKG!V5CuT>U><e}0-A*#v$A7)0nLV^r3Gkq&M}9~QGkYF#{kVKEdb3$+Xg!4<d8`h
z@G%ON8>*%9pt@9EN!mca?=q><{7spfseBCNXP^M8A80}B`&1#+V4#IX0Z=`dhh2hz
z7GcMt?3i9ai{WT#0a~1Md{E{nKuci904*sk04+t^20A2T%z|ED=W41-L$y>HRF^6%
zNgL?(X6r83xLK>ID#yS>3_OhL2l@#1eX2ZaFwhF30H_|!!!AKUE3#uaJEj-VN;q0t
zfL7)ltH>M$XjSYOppQxmK&#QVfv)^@{EA;sbZx4tL$y>5RF|qLNgHUrL)F4+ZLZc-
z)ncGF19edSK<i@Pr|O{w1FbI#fa<|K>=FdD0XsHi$MgdF7><?}ppSEojbx4jv@v!J
z&?eFX&<NT#(2<3Y)!#F)%qG<os->Plb*U#MX#=f4Bw=`yx@DTGW(+iEparTQXiMz-
zR4deApshs#P(7H3U4nqNVaK-Ym|j5J;b>_A+MaXlAafL;9kFA8J|!&x?L^xKTE5Mc
zN7_ue6rnmpwUi6hrJk0g4YYfc-=Ew*^=O28hJh{&Jd5fF+7<ggB_Fa32HIT|0M&ze
z*d++)bL{v$JEj-V7jU$+0PVp!z9@4Pppn=yKzm9HK%;2eKo@`b*MYKQs%%oxP%RaM
z>QcQVX#;(6a-D2TPUdf_Vj1YoKp#{;P&f8{Dh@Rms7Dk4)q{E1B?#zC?AVtb(+g-n
z94#$C<2lC!nWF&hj~xTlD=h$3v~8fcO2U7*vP^_(sU%dFN|vMzw6%)3J#^RQ2sMC#
zfeZ{n^#dJ@eV-bF8VvMhQ2<mA=3$p0ps%o_j~&ws=ujLjM4+*r&NxvFejj}py4&xg
z567-cjgZ;nee}HT@>*&n8h9W5$?Wnq2>v&`iGT0^H{1XJJ$}EEJ0{DYGR{svdwlMQ
z^AYj<)5ZCe1icNkS4_{?YWljX5f#sPkJhQaB%;Ik8DmDSemkXE^x&=y#`{v9t(I_W
z?6sW{*$PzO+voYSDYKhBaLLtZ$%cQrKmEsx{i|)p=TpA2kAE=bJm+yR(4O~c`CAQ!
zoQybHYs%fR3qDV&|97hvVU3nYlsmiqRK+S=Qz}<DQfo!__aoZOC|ZBT@k1%!v;XFl
ze>$%J_UYpvOhKEszrX&IjkNivo86=4|Fm-YmXwLz8uu-+V^+keVr!aY9=b2$^05`i
zhCl30>Bif8EJfemn-L`|Jv}<p;`m1YbX-4A|LyZ14+jf6{vLhYh%x%ODd+WZQ~uJ&
zjd(#HHzJIjZ>C&qz2xZliM9XnxT)M;-ah~F{kQ-B`u6(Qi~s)fkMF;I{<i=2zY<w)
z#PNx1Qm)Nxb!SnAV-dR#tgPC2(Uz0}CkH&dqx?rHaf{w4-(&60lv52$?XG$9qkp`A
z{p-`eUi|l;pQpd=KOQ)czboU332Q5!Pgyx|>%`ul{1~yf|A~5s|2Q5|aPXx(?xgb(
zbD!xuZ1C+15&v}m`X3Me_32+P{`=40_TN7L@%)d!AxWA4=(Om3%J|jYzKV(YA*J*e
z*;*f3y)<R&-N9<WmtXzk?e#zY{Er9!`t+|Cf7{Q~fBXE$^Yef1skc5_y#7Q)kz$`z
zd-1(<5i8fXdb?1@i~o3g{f~G5<In$i@UKsQ+kgN0dHQdk|9Jl6H$HG_{@|~-q{QSK
zw&<H7d;jsc{^u9}<K6%G^FJQ^ZU1`l-+z9d{@dq2-UR#pue<r+#v<1a{^N1|&u9MU
z7yskk|M>H_{p-`eUi|l;pQr!!`H$ycf9h4dC?=^<sQ3OUxS)J8UyX+9Qx@vIe+nK$
zbu87OKLwA26!=r{c>1OBb)AR1mBi|e#7FY=7d}(@L-1<||IZZv2>f~oshVoSzoq!c
z--#ilYN|IvNYzr4{w-Bo{@gq{gj7v6C4^KhH8q4(Z8h!RQgvirZ-$VnsiudJs-<Rx
zkgBcT3L#ZT&HT4iU77u?5K=YO+aaWCso5c<YO8lbNYznuLP*tBbN?+>Pj2J8A*5=m
zc_E}~srezKYO4hyr0S^mLP*tB?}w18rxyNOs=ko;AcRy+wJ3yCE%jjtsoH9B2&p=1
zNeHRBYH0|mdTLn+srqX9zoi-ouN5JrYO0kXq-v>EA*5=nk3vY*QL95p)m3XkNYzto
zLrB$EABT`?pg#GxR70WqX$YyBYF!AaTI#b9Qnl6k5K?v2h7eM9)y5D~_0;Dfr0T1b
z5K;})rVvsM)n-ZYLnZ%krcjfg8QT&{tftx;O01UJ7D}wP+8#=*j`|{$SY5Rvlvq8r
zGn80;wJVfZ1GPJpSVOf(Vx|Ab`I_7dHEXE7|2iB`+4hAJtEu*f604;Sgc7T*z6>Q+
zM;#0$R#$x$O01qb6iTeVIvh%@fjSaOtfBf^V*m5r!o7fd>u5;1n(ElU9+oH1$3uzL
zR3}1-)lw%ziPcu8LW$K;r$dR=RcAtp)l+9fiPcx<LWwm{=R=7#R2L-nKkrrC3%FM=
zhLo$RE`^k<rM~&s>G4A5+fZUP)pw!9YN^Yi#A>T6p~UK_tD(f|s%xRd>Z$KTiPcxv
zLy0v|H$sUuR5vB|Kkt3q3%K`x2q{-n{TNcNmij5ATy1qra{u#nnwOQgLy6T?KZg>l
zrS60htF3+sC00lM8cM9L`Yn`LJ@tDivHI$dP+|?#pP|GWs=p-mKUWFv1+0?4L(0`u
zcSFk6QW@|K^M5~XZIv;kTpg83a*y11z7h0cdHs`J`h(?}C0!wuwEjr>1ClTMzw!x*
zdgLsUeCXejdep3vZSude|MtxXeavh!PSgK3j-FBW|1={xQ`0lb@t<a-$I&wi`>!*?
zVDjCFod0!37)Q@2*MFLkocHJ%<^E4I(&OkE<@v8OvKi(5uQLiT%J*MqgqtV#TK@k!
zBaEXN6_AnIq~$RZ^yCrzO&dMAf-+>g|Fa?WJPZAw4a)KKObg4P9sVz7Y7eUCT119z
z`TsoZJ>MwO(=00Ewf?^uPtUN}|ILW^pe!yUw*Aj@4C46Uf1G9D`%ietXp$xV<1B+m
z)GSN>UyXSW%~CRE`~P>Efq4e;EG?rpNXw|^n_-$?85!ZR|JM;Tud*^mk^9Cl2U6wW
z^DAf%VMl)j?P2Ws)FW);V?E`uV?NeXL0b4&Pes~6AM3%#@x!57suHS8RhFdvv7R5g
z?s$Ag&aWa=6$Yv@@F=SPV?EWd?^D%LgFn_&Llk_hM-S#<m!OaJ)MUq6?3i93>#2>S
zh4`@^exsxg=U7+fD4&z6haCg7zO(?e0qy&NHiT-a$537BaY@=h--@ex=2-WG5vmab
zjTvZy>IWKueV=NI8VvLaQ2<mA=3$p0pii=6Gj>cbpv`f#v;b|vIkuEJ3eZ;AF+f{O
z3qae@wt@P822!<!YN>XpF4bO=HqeXxcdieQ4{xeEFwl{Kr%?SsJ7M3aI->>yb%_F?
zdN2>W1Oa`T9iL&x^a9!iM@tLPXF12NGDiU_zkXnVc1J7tTXc9j8Qw%a2i;Q7qq@`!
zk}=<+lkx0t(e<GJq6j|ugxg>#^oR1B5s~zJif_I}M_+%7E{cA%_~u)5^!2ysV(9lm
zl{-BaTihG^SLQHHJ=5M$E!Brfw<I0kqKkv-Qyx?Tcu7KmZ_)Kd%K-pCgxwFirQ%Ut
zDnT*^fQ)AY=uh7(f&oBZ15or6#Ww)xYXC{~lf^dx=xYE2=nq5{0E4i-4*+I57^<a)
zF!{119ROZ|>Qg>c0vIZx0Dxg=IRIb<42N#15vVRTQZfdBjAsLQmHsFZ3;_BXz-aoG
z_yz!d4PXrYvEmy5^fiES^v9zLfY-3S4*+KRI#f$dU~-})9RS{d>Qj?Y31G5>0sy9<
z<p2P#Q=waG8mddZDH#Jm#<KxTr$0jk1Ax8;@D}}<;u`?;HGo<4-xl8hpsxYUrvDDA
z0GNaAeE=}ixlk?jE|c>l=>RYvs!uIIC4l!N6aer(TB!k82;EX2pt{r|$ru1Ko(<qb
z`in&{0O)G~OXx2Z-vFSm0W71xTzmt7z6P*@{z_B<unJozfRCVBYBiH<B<TRK7OGEu
zj7k8XNGJf{Q?wiau&~xax725-F121V27rub1K2=+qX-57eGTAq`YGZY0Q5D0P4qX5
zZvfEO0JhNIiYfrMVS66{%yc_cOMSuQ4oNxy?1bu5yHE*Ww}b)!_MqhefQ7Xex~2A^
zy3~Hj7yvS!4d4L%FGVl_=xYE6>3=1@0YF~^I7I)j_yz!d4d4j<uTcfSQEcx6fSDeH
zYN_K)o{*#iz)7e+bqbXLPD>~N;0#)+0XPfYQs+=z>bztO02$8)aDo0s5exwO8o(v`
z--vGj(ANOIrT?Ay1^|5x;4=Lyr~=?BwoU-opyJD)OkS6y1HcWaK6Mk70Dh2A0KkuE
zr3T<9=$5*L>Qc8QV*to_Hh`b$-x0w8psxY^LjPCs4FLKYz;E<_7vBJ&uL1l)|4&o_
z@E5jD0DnWZ)LkYs;0I5g05U@LsZ6K@kXb?j01u$$0DwnU7U-7Bit19?Bx3-`cs79S
z^mB+{0MOR}!szD|-vFSm0py~eTYLk6z6OwoeqK}okPqAY0AQy1p<1c{lLaN|SOJBg
z`cz?50w^M(0Dz)sIRN01RSdeNile&JgOV`-WIP){3Hl{PFaYRl0Hx@c7T*A%uK|>y
zUsikrfW8J$j{ZZa0^nh6?*o9DJ_6NJ<(aG?Ne6(6P<<*Kl>jP9C;*@`TB!l30^L$o
zQC;d$$ru1Ko(-TH{punZ0Q5D08uV+5ZvfEO0BX^%ExrLjUjwK^zb>i(sE4f+Kz*o|
zYQSVeNjd;L1{J>qhDrd9BoqM97_HO*G=Yxa<wA9-rjjuLWIP+d6ZD@H!2qDI0W_oE
zTzmt7z6Q{OeoOHU0Qwq0EBdWb1wb2YodDWG#RVXf?Iq~|&;hDXbwnkArz8{r&<U;7
z0Ca|quiT;HD|eDH0AxHHz%%r_h+qKF*8rZS-&K4AfW8JGKlSWV-NiQm=xYGa(SIIQ
z0K9;$6F?8BmU@xNNJ%;X^n{9U!l4pCw1ffxV$gB`z*9>v=$48_b*bKxF#u#d8$ciW
zZV?Os`WiqSeUJDC0DTSMCHj5EHvs5s0R8C4qY8ioZ0`ernf8ZjDKC>sk`4fgP<<*1
zl>m|@6aX*)Ee8NRwG4!AsX?eNHCQqRfQ)AY7()ML5exwO8o(>`ec~Gc^fiE?^oNOW
z0MOR}hSMK`DgZ`edmjMI^i`;q8pY&jNjd;nP<?6)Dglg@PyoO<v{C~w9y)%r2o=9s
zBpCxh#<KxTpg&Or1Ax8;@CN-!;u`?;HGs+Vr-*L=(ANN_(w~MZ0N%vb31B)@OU+>N
zElD~6%!KMwvrq}(Z3zVc%tk9U0PjG@uN9%X)Lh9J05YBp;9dIjL@)s8YXI}<FA(1V
zpsxYENB@2C4FLKYz(V>TpbCIR*g64x2-Q-HnOq`C2Y{teeQFsh0W6nL0Kf{gQUkCO
zI)1GP)uld?i~%6y*#K74Un7D6Kwkq`OaEi>4FLKYz$f%S72g1$uK}#1{~4+PSdXm}
zzy_$6+Q{VRl5_w_f$CG6PzhkOgaQDzpp_bct<WvC4b`Q#OU3|@@oWHJ(BC0~0YF~^
z*hznv_yz!d4PZC@J>nYx^fiFJ^!K3(fc@Ay0UUs8sV|v4C`ku^ub}$WAyfi5ETI5^
zBWO7Q;JNi{=$1N)>QcufV*to_Hh|;wPl#Xu(ANM?(my4>0YF~^I8FbI_yz!d4d5*O
zbEpE~Jht}%z)UYdwbVr>FG<n?;2Wqu^(`s^d?%p*fXiq(0N}ax3Uo_dMRlobk}&{e
zJR89G^skFx0MOR}ZqUCez5zgA1NeddkK!8u^fiE==-)yW0JpKd4*+KRGgM36Ve%JA
zIsp6%)u(<#C4k>06aerCTB!l}6FPnn2NgevBN+og#<Kz3rJn)+7~(wu=xYEO;kZ;L
z@eKg_8bD_H4~TC7(ANO6(9eo00J3501dtu7rE)MCCP@c?oKW#QHK+uTTS5T<dC*D?
zKwjvU%7^Mw`6Xij$apq@0`v=tU{(Nq4WJPH!r~hM^fiDY^oxpb0MOR}iqS8QDgYkD
z)(M~lR7;g)vXmqp07^sksWPYpP*y?#0Oin14ZuUtE%h*}OFbeP13<>J0hFg-K?DPU
zz6MZ{ez^Ds0DTRh68*~J8vyh*fGYH>q6&aVv2_Bd2Gvs4nXDm62Y{MTeX15J0o0aI
z06-nIQUg#Ix~1x&x>SA17yvS!4WI%2h9VdM^fiFT=szyL0YF~^XhgrU_yz!d4WJ4A
z2vh;k6k8{NC!kvDNhX^~(gC13RG(^rN&qb-6adf)t<(UthHj}gs4mr3G6sN*X9H+Q
zzr6?s0DTRh1O1NT8vyh*fT!qp65jxzuK{$X??M#-Ph;x@@C;P^Br%iEO40$KD^&a>
z3Mv6~mrww}b7(mL;Kk*6=$3i`)unn!#sHA<YydCPj}*ZGpsxY+q#q@|0YF~^h^8MS
zz5zgA1L#FR7F7WB#`Zn{m}wuVmU1&0CrJkY4^*Fe36%i)N+<xJA6gCocyWn`Zm9%R
zm+CJW13<>J0eI;v5exwO8bBibB=HRZ`Wiqo{Q=?|0Q5D0f%FHV3V^}b-Uk3P9Rd|U
zKg;ARl5_y@LG`Jjs01)fLID87(Mk=#2<Z5Y4pf(VRWb&EjAsKFMSrvi1^|5xz@k4!
zd;@^K1~8WXIPnbt`WnD^`mdo1fY-5g0+;~RQWKecLy`^vlc4(4WK;r}BB20)sc5AJ
zU>bBwy@~2l(<Ng7$apq@8T8*0!2qDI0nDU7OMC->z6S6%{n_Fh0Q5D0cj(VS6##Rw
zbpm)7s-@;JIbV_v01Ke{)O)A|@V<ls02ZQ^8h{U=TWS%iOMNIA13<>J0W7A!L<9qX
zz6P+A{xb0m0Qwrha{4R8Hvs5s04wRQLKOfXVe1628mgt%Fu7Ke4geoR#jnVs62PYt
z3IJG#R%!q~gKnwys4lfZG6sN*X9L(s|8o%x0Qwq03jIyu8vyh*fX(!`h;IPU*8sNC
z--apxwqxrA@C8&$?O<}JBpm>DLG`KKs06S_LID7K(Mk=#KIoR(kLpqfBx3-`cs779
z=^qrq0HCh{d`16|_yz!d4d5{SBjOtX^fiF5=^sTE0LQR(0yqxUQYV-^DM<%_Q&4^C
zG%5j{kx&4@S+r6Ea1Oeq&ZD~21<4oyGM)|KBK=Du7y$G&fN$u3E4~3hUjz7#{$=qE
z0Qwrh75Z0E1;90IodCXvYN_i?-jJjNz)h$=^#dva{3xLSfS=HE0Klv3E$EiIjp|ZA
zOU3|@@oWHh=>H;u0YF~^_?7-|;u`?;HGtpg{~^8sKwks+lm1_*0^o0K?*o9D-i2zZ
z4ERlR{7fmT6F?@Y_`y<C0(d||0RUOhasa@qYgXu%%7*Gv*(GBD$apq@9Q4CPFaYRl
z06FRB65jxzuL0zypGSNHfW8KhmwrA}0gxZt`v7331)y50Ad`h8=~w}Uq54!2R01d}
zp#XqlXr%_AICM)ri0V=$Bx3-`cs78N^h=3g0MOR}O4BbRz5zgA11L+socIO+eGT9t
z`VXTDfJd-(0w@pFQWco2C`ku^aHu|236%gUODF)K3R<ZFs0tnb8V;&URg;VXAmiBp
zs?)C_f&oBZ1E@*AmiPt$eGQ;C{W{_s0Q5D0y7cRz3V{09Isr6*YN>`yJ|;;AfXAWw
zR3lUZXe^-sfF@|A1|R~urJAC;)Dx010AxHHz?1ZwiC_TG*8rN+Zy~+`KwkrBNxzl&
z1^|5xpf&wAr~;rZwoU-;pjxUulN}`K0MHStPd$Z70G%Wh0MHq&)Bw1kTk2_4{7W#B
zF#u#d8$cKO&x&9G(ANOE(wBd9;T{0=HGuB)pA+8zpsxWuPyYo}0nh_mCx92BS}Kys
zo|1F`h=S@<(WnFvBcT9*UTCETAQrl%dZW5jAITU1GM){<O+QWq1Ax8;;GzGL_+|yr
z*8uv`?<c+iKwkrhr=Nf-0QzI=1mJ~gDaB->Bpm>fp!!rYDgg|TPyoO{v{C~w2)d;P
zqq@`($ru1Ko(<q-`mcy!0MOR}eDsHkZvfEO0EW>YF1`UkUjrCHe<Z2^cokbGfKgB_
zHJV9Fk`4f4p!(EUR00?$p#XsKXr%_=HRzUl9o3~KNX7t=@oWGS>AxX@0YF~^m_&cF
z_yz!d4PXlWsp1;|^fiEK^xs4k0MoH`0+<2SQg1OiQ<4q<v!MFa+o%LETS5T<???-S
z&!KI94i{g>ESn+2pAl*<RD1{#)urZ1(*E%E{_82*%g@~qq2@ENfPwc={U5%5ANxMF
z5H<M2*B^+24`1uSJnRzm;p;{0_#r!{*N3kc<7jF5@bwbTajDEvK773lI|k@-v{GA9
zE1+9yC8|rUl8jkVGM>GnKBB)`1hb;(>lL+z{#x<PilVPq)W`Hc5#OvR`g%ouN`D=y
z-07cT>s(Rmp;~GKlN%-JSW%xt#Xl#4N&uTA6j)K4(Mk=#7U-7Rit19^Bx3-`cs79T
z^uG|n0HCh{?4Z9>d;@^K2C$3%Zt)EO`WnC<`g>6Yz&>o90QN(*)Bz^Hl%xZ|L8w0U
z6)FK7l28D^VYE^Ma0I%gzD9MaqmnTIWIP+dG5W_vFaYRl04M056yE@#uK}E*e_DJ4
zfW8KBhW=Sp0dNjmCxG)%Ep>s(i;{EzxCGUwzCk5`ZzU7}@EuyI0k{m^Qddx2>Z)W6
z02$8)aE<==A{YSlHGu2%Z-{RI(ANNN(*Hqx1Ax8;@FV@7PzAs(Y@GmZL$%b;Ox}^C
z1Hdm(ed<?K0{Bfr0RX?Fl^TFQpj+xsRG0coG6sN*X9M_~{#_9a0Qwq02K;_7uK7^!
z0YF~^$V5N0_yz!d4d4O#Sx^N)R&1RBvO%?!{8F(?<&dNUKp0e?%85z<xg-<-kQ=Sk
z0OWygsl2Ezl}|DTfQ)AY$WOn32nGOs4WJ<XLgJehKwkqWOuvZu1^|5xpeX%fr~;rk
zwoU*KLbX&0CQC}v0iYCApDK+?0A(Z;08kdK)Bu!&ZmEY*UFu=U7yvS!4d4;_<wY<6
z=xYEK=vNfq0HCh{gwwAiz5zgA1E@^D3aS98imemCqfjkXjmhegbO5LU)u(Er5<o2p
z1pw4WD>VRhpyS^=Ma92&Dj5Sn#<Ky`r{6#X1Ax8;(2)LP;u`?;HGs$IHxl0fpsxWm
zrr!ir07PKx1ke;J{*ePFpOmBnKr^U5)f|-oT1Y4Wpry1hcq`iWiB<T;r_0@_{w4kq
zQmFWY8!G<bCQ19ms$s`HJ;r6Y5~12L(4K(~sQweHj@ZWq9cu82RVPt!Vx<T3uuIU1
zRcCf|v159jSUruSrRBux8P2hb%u!CPp2dy<+ErQr+Ku-8XR6(yTIxAemwH~3Hqbr(
zGu0Ou=)u5?sD7Z4*!QWPsKG#^L;+Aen1@}0fJU=p3_GS5&|WxNT7brKj=g1$0<;fy
z3{W>(sXavFpj*m=>QXOB#ymu2Jo_QqmwrDH%tMsEeu&1?PY~ZcMCt2?Xn*=%@y$b&
zzJ7=*`iZD=rzc_Se26AP#lL>Z<UmO}9-@Pw;tz?a1TaKGfrseJXr%_=73h}op}N#i
z$ru1Ko(*6a{ox`Q0Q5D05%fojZvfEO0A8g(N_+!=z6LOwzJ)3P#$f9NFczw%#xXfw
zk`4f`LB$^uQ3+szgaQC2qLmteH=tW;5~@p0mW%-)<JkbF(4Q)T0YF~^m`49i@eKg_
z8o+e=GsHIl=xYFP(VvMb0A^w91n@RgOU-8T9Z5O>%z=tOB%%_)yAlcjn1@zs0Omut
z)B;qOdQUP2fQ)AYc%S}45exwO8o&qi7m05G(ANMyq`z2v1Ax8;u!R0nQ~|IITPJ|!
zP%X8B$(53H09XYTe@H|nfYlNT09b=oY5>+kx75d|F7=6I3;-F=2Jk8Ubs`u5^fiFb
z=&u*w0HCh{Y@okUd;@^K2Jkuk6jTAQ30o(C%}_11g~_dwbO6`}6@N%XC4es^6acUT
zt<(VQgl?%_s4lfzG6sN*X9L(nf3FAz0DTQ$AN~E}8vyh*fCKcu6yE@#uK^sS{}rkL
zIE1Yez+tGCI>O}Fl5_w#3Kf4yL?wXZ5()q~fmUh&PC~cTDO8s_Eg1tq#<KyOp?_8c
z1Ax8;aE|_Y@eKg_8o&km7sWRK=xYF%=zoJM0KUc63E(@Zmb%R36-hb(T!o51B%%_)
z_Yw*KxQ<q80B%6H)J;^E`av=VfQ)AY_>ulkA{YSlHGo_6Z;NjL(ANNdrhi9#1Ax8;
z@C*H4Q3b$n*g66H4%JeBF!`q>9RU7<ia#Wx62M&v1ps8gzq2cYXT&xzc<Y~bE`DR`
z&k-sUR7+(>b*Tp=X`fiFEt_qhdLO?Kl7)e+3}i#~pIBwbzE9;q4L-3769p$$dN2>W
z1f5vrWXD|Wm|iDVxpB0#oLJ@I9P`Q?<-{r<b_~$`(gM%|wCyw1{)=`#ooCy(5vm|m
zOBF(Oslt-9f%d+2;IA$HFGQ#!3>0Oc7^)v=aqQ!hp{T(?ONauXdN2>W1OY9{j-}W!
zy?~a+(b5964Ch!@<|shRVaEV{NLm2;Fl`$seknq~N&N^^d_@q|r7B3$2726o>R*w8
za0V)&`hix)zE4#_4F+0O6adwOdDtZg=%egdjUCeqXmuPdEkJ8<jx}YD0<;!(4A9zW
zrS_ax2Rg3KQE_!H8S|Vc<Jr%7_31Yd!93^D*Uxzk=|3jEdCsG+pYtB4-$;D(oJU_j
z=QXC^1Xb?z2y6pSOSsedpK1yfSLaMVDM`n3p8WISxH?BAfEE%8Jm<AUD>VSEpj)an
zs!O$zi~%6y*#O$oZzqBQKwkrBPrrls1^|5xpd<aK#5VxwYXF_-cSaQeE^M6uo`!0v
zXPE3FNe6&uq54!;R08NGp#XsHXr%_=Iq3L}7*za5jARS|8P5jLgZ_&m7y$G&fJpj1
z#Ww)xYXDL7qs2D>=xYEm^n0NSfLLsu0D42kZ^SU^mZSqf98~;93@QP<B%uI+zG$Te
zpdWPnMhq%`BSta?fQ)AY=uh7(f&oBZ15or6#Ww)xYXC{~lf^dx=xYE2=nq5{0E4h~
z0vHSxzY)Xa%aU{ecm*ncBL<ZKhDs;^U>I7d0T>P)zY&9q--wZn0U+bq0A8g(N(2Ld
zz6LOwz9qf^KwkqGLw~IJ1^|5xU>yDNr~=?MY@GmJhl<~bVRE7*9RS{dir<JqC4k8i
z3ILdbR%!sILdS2!pyD@TBx3-`cs79P^k;}*0MOR}-l9KKd;@^K1~7~M+u|Dl^fiFl
z^xr`h0CTW)0+<UGf1YJ>o+KRr=0nAA#Gn$udlCu&cpt6Q04#)#--toQZ^TH(0Fd!)
z03Xs{EP??*UjtY|f2sHe0DTQ$8U5wr8vyh*fEDytq6&aj*g64x1Qov#!{i!CIsmMN
zir<JqC4f&P6aerkTB!k82OYl=gNomXk&FQ#<JkZ<(BCM60YF~^_?&)<_yz!d4PX=f
z&EgvX^fiDj^tYl4fNj`10c?kg--uyyha?>Uc0$E(#Gn$uZV3ed?2#4*-%I=cH>vkQ
z#m8Y$@o`v5+9y_lH>tm5;2;BEq54m(4q@M?4x<L2SRD}sCsuke54!}NSbfcoN7*sG
zPOOgMXlXgII?g$skU7eU)k*9apr@n-pr>h@H>q*bf{#<2foiF<s4jI*k~Yx5o7CqS
zxWK?gR6o#5*vFq+QG<bgD+++>!945|1oS(0yv&a21@sDzmKLB_Imc@<M*;dhb_~$#
z(gM&MwC}%`x(U@%KcKqQkCMFqTIweTZZU8h)erP%?EBOm)L@{$hytK`Fb}%~0sWO7
ze`Ck=0{T0SmKLCYaE^b<90llK*fBu=mKK2CrEOMdS+39wIRt1%RD78e)ekhVLNhb)
z00UW2{Xnx~AK%+V4F;NB6adwOdDtZgXbyG^W5@IYniEG$3(#DgV{VzF0L_CP12ivM
zslABggKnw(s4i7NGUi1@#<O2U3eqnmf_V|4uU|w8(=Q^vc@d$nUqp)1FDAZu5uvYN
zM2gdY5LNE<64>J2xcAo<@k#2EP%Tx8$<mT^yoi*6>QiM=380*W0xu#Dp_LkdhoM{Q
z5mc8dFBt<s#<KxbpkGl01Ax8;5Kh06_yz!d4WKgpD&iXe^fiF0^dChP0M)Q{0;mqv
zQZ<;YDM<%_T2OteHYx$skx&3YU9?gIP!GDL>Z7_;1IZWwGM){fA^pchFaYRl0FTpe
zB)$PaUjt}Nzlrz;0DTQ0f__s}0q_L2P5@6rwNx`En@iFGpaoQ)YKck!tt1ox&>F4O
z0JMQ_skW#t)lM=7fQ)AYXivX`2nGOs4WJ|ar^Gh^=xYF-=yw+10HCh{xadEPDgd6r
z)(M~sR7*X}WLHT#0Ca=uQ{7Ps;5i8e06dRYY5-n<ZmAxqF7={h3;-F=1`tWVrw9fB
zeGMRrezf=o0DTQ0hJG*c4FLKYKrH><r~;r5woU+UsFsRj(j!R+fR~{9R9{pA=qI57
zfOxc01CRjSQvFd~$}1TIK*qBHDEf&a7y$G&fF%0K;u`?;HGl#12a0b1(ANM4(I1Q|
z0ES@e1n@FcOTEIRPm&G*L!tWAFjN8<E};N`5oo0bU?g-)y^88mqa<Sh$apq@(ey15
z3;_BXz!>^t#Ww)xYXIZuj~CwnpsxYEM*nqG0WbkuCxD4iE%gSIlO*W?Fd3>(O+h7q
zsS*kRn1)tr0N#Xdsp+ULHA6B6fQ)AYc#Hl_5exwO8o(_2Z;NjL(ANNF(|<>N1Ax8;
zFo*tJQ~~fVwoU-^pjv7^lM5v20Pr4EpL!pa02WFp0N?{@Vemz??bp}`-KYBu{p+U`
z^&wRJfDfumEs>;srrPz-#qQlLjz*}Z3@l?{IjaA}Y6bRvY9(s$iPb7maAKtg^RP?M
ziPcB!xSAc)>%?jej+T}atF@ft$1+DbvHAo%2I!~K0?>7|ZJ?Wu)NJ!WvF}sVXHfBF
zRaAUgRgyN)>+i-a+>r-AZNHI$&lyNT^#k36eV^Kl8Vq!cC;+Ml^RP=0(5>vajUCeq
z=yn_}EkM8E9CyeZ1?W!f7@)hP1)#fW+dzAK*(zh9<$t88Jy0#R7uBWqNzw)y+qQbe
zE>}K}Q2QA;z`&QNexL`jj}Jqm1_M1L3V`atJnRw#^e{UfVaN0W`ZbQ07NAEt$73=_
z0eT!e2IvWC0q9BE_pi`XP%U*D)uqlz^8OWimVt8&oJaKoy?}k6x`-MK^pYq5st5D1
zOAyd+*zsF-OfR6{;b>_AdYN;)B6Ad=SFvM&UXvDpeoxy5`uE+tWq5IV9jc{npt{se
zN!maI7pFfk@FN31q56T|!ajag2{jn#&!PaR9?ZioK|t@Y<1g%(UO<1v(b59+H_q{Q
znWF&x13L!jpJ=7_s{9vpOZ|=NQg<a|UX^7$`&BtZnD`k{?|D_GuV0lj(a$Wtc~z#b
zUzH!ApGAE0s!U(MDrcpi4OQ;+?AYSoxbNaL2UJUiF_}}6j#uSeP<<*lDgoq?P~cTL
zFIuSq$Oj!)=cu?kmy7`*<JkZT(k~=}IW3{D0TiZRM0^8)z6MZ~elhV40Qwq0arzIU
z3V;&WIsue~imP)bOH0xLpbS)8oud*!ISB;-JcL$i03L>pt8-LbolC|5knwB)73fzK
z!2qDI0ff`9B)$PaUjwL2zl!(<0DTRhD*Z=M1wb`yodBvs#nm~JH6`f)Pzx%q&QS@V
zj)VdL>Y|kzfO^nzb&iUwbIBM0GM){fA^pchFaYRl0FTpeB)$PaUjt}Nzlrz;0DTQ0
zf__s}0q_L2P5@6r#nm~J%_Zpo&;ly1&QS@Vm4pHSTBDU3fHu%^b&iUwbIBM0GM){f
zJ^c<M7y$G&fR6N^65jxzuK{$T-&uSEfW8LcqW?6i0C)ylCx9+cadpmQS4lbmbc2ek
zb5sI&PC@|y&!d$ZfES?S>KqkU=aMl1WIP){B>kQu7y$G&fGGOW;u`?;HGmlUy~H;F
z=xYG6^n0TUfIiqd0l1;!>YPcBBpm==f{LqiR08NHp#Xq*v{D0*03BE7sJJ?pi~%6y
z*#H#%L=g-C`WiqI{bcbC0Qwrh0Qv*PHvs5s0E6fcMil@<uyq1>87i*Mne<800bnRp
zT%Ds5z;Fo#0E|E@H2@=_<LVq0SLc#30AxHHz-aoG2nGOs4PXrYvEmy5^fiES^v8>D
z0MOR}UZejyssNaPtrNgRsJJ?3a*`w+0477l)j28wOqEaoz%*%L@Hc7Oud#t|qfCd2
zpVvioskbC)pI8O{&^METSq!|5>OZlXjeVbb2Q~P_YK|y4vC@Ni*d^%1YA!pz%Z}-F
zVl@v(OUsGXe9m!!%u!CP-ouUo`o6RPbRlgUD84tX|IqgVRQ!|_Dt<~zlJ}pfE@of}
z14~i;K$l_Pr<S7z16?5sfa<|K>=FcYB|ENS$MgdF5ssD?psP8@H8Mv5x)wVI=*Q9m
z&`)UFKm#9|{1mFC)}gx8XOgsm20k>oo`DSvY((_~{T%!FsxoRY&`qKMs2<G2E<r#y
zv*Q+aOfR5YakR7m-Nrd?mpKa1FR){P?vNIM?xcPH3f%?OQoB)IYL6uEU!i*$*vG(r
zR6o!I*!QU~QG<aV6a_%_U><e}0{Rs@9%9Gz0(uxnOAF8=oa5IrM*(^iI|k@6X#wbQ
z+BVR@`%x#LTIwXKOP!LW4K(n6)M*CJFmM*t5A+=N@ten}!9XvF0-$;@54!{by~vK2
z*fG6;euJZ>1?ab&<99Mg0eTrb2Iv)O0q9lQW`*iMMPGx8fBXj3rLIfz{uO$Iftw8c
zfa(YOBldmjC)8k|w?qL@J(!1Gf`Hy;$Di3Ty@1}q(b59+7tZllnWF&x4Lb(t?`WlV
zn)3&AeD53;-#eF#In9yr?9-gT>E9K>oaWHir#TsNO8<<g_nhX?*QYs|=w}w+oaWHi
zr#TPM&w?s<dRA<4Z`^mapA9O$cg|!ENjgq*!l2@N=cojbOG1IuoZM)o1|Sb~eD53;
z-#eF#0U+bq0P@o>Ac6rvUjryezmWLmJdeHxP?&xZ@eKg_8bDF{#ZU!6acrFc9)ybT
zoikZdk`4f+pyGSys02_(LID6}(Mk<KIq3M_IV!$)E*S$r#<KxDLchES1^|5xpaT7h
z;u`?;HGpvXmBcpy=xYF#=~qD&09CPd0(cZEzIV=KbxAq^)PRcboud*!EeQnx)J7{c
z0Ck|_lkKSZWV>Vx02$8)P@jGS5exwO8bCw(kBM&p(ANMSr{73?1Ax8;(3pM`Q~?ly
ztrI{~sQ6?%lTS+00iYRFe6k&t09r^W0H7sWsR3vO9iMDR#V6Y(V*to_Hh{MD+lgQR
z(ANOk)9)a@0YF~^=t%!5@eKg_8bBxdolymV3tK0Er=jAL?M!x&qyxaSQ1QujR08NG
zp#XsHXr%_=Iq3LgJ1RceE*S$r#<KzRp#P!>1^|5xAd-Gh@eKg_8bB2NXz>jI`WiqC
z{a&a7AQoFEfZkB?$#y2)l5_xwgNjeKqY}VN5()t5i&km?`a#Dh+fnh!cF7n3GM){f
zKYgzV1^|5xK+#VW-vFSm0VL5+7T*A%uK^68KM+*_48qn4U@%mCvYpA7CFub03RHZu
z9hCrvN+<we7+R?T7!DnuY)8c>+a+TF$apq@SLu%u!2qDI0gR?^iEjYV*8s-QA1l5A
zKwkqGM}Iu30C){sCxF+X;*;%6PL!krz#CBU$#zr%m@J_HfGN_#;8SVaud$En-=0WO
z)1cyqo>B2b&yuuHtQz=#dtwFyZ!s_v)qi3&3;XyXG1TA_tJ$L9#7Yn5VV9s2t9RIO
z4m+mTiPc;jEiETj?{bdwWR7xTH6J?$=mKd0=zFw-PFns<QSU>w)IwC3`aqI4(69Yp
zKwHGXhYT!6^#fgkeV<y28Vq!qC;+Ml^RP=0(B<s7f*sQf=t>+dEkIXsjvvV!1?Xz*
z7@%vU1)ytb+d%EB{S@^vR7-t=>QbLd(gqs1+F!@OXAG=I^#k32eV^Kh8VvMvQ2<mA
z=3$p0pegLQi5=4m=w=)(EkL($j$37p0(2X84AAY;0?;pL-@ih4K(*9PRF~Q%$@^F6
zZU*)+uou-2bRYKdFA|{!13e%Lfa<|K>=Fd@OLjcSj_C#TD;zB?Ko4<_hh>fe^ayqg
z(66Nhphs!jK<%sj2z3l9{%KlNmpUOy8))Ea|0Dya7&wjU2YLqkK6Mr~80a}s08|g=
zVV5AF=h^WBJEj-Vi#S?ZfL`JpzmYi#&~LG0fPN<}0KH86{uO!!D*hQJRQxkclDvO~
ze$T*l25zAGf!@SEe&!oB80e3p0H_|!!!AKUe`3d5?3i9aZ{uib0s1rNct_?aK!3rG
z0s5=70Q5K72B?1f<aem}Q2<nz`cslN(9`}GrN0>Xn}NHiexMn03DAtF!9X*K0-$;@
z54!{b&CHGuuw!}w&4Qz)1!z{zF`LX$fM&;z0h$A?)Xp2jpj#>@s!QdPj5%+V@$B=)
z-1PH^V9p!q>+{CE^z(^t&Kv3L^Tz!23y5#d8|mxw#)9+<p~{_J7+c&M_q}~m1gfQq
zGFeQLzqiYf?5UP9Lx!=*(eCV(MyTRY@goAL1W-akf%C?aXaxgkI{ZTQO{x@hOO-}-
zsWOr=0AxHHKw0|bL@)s8YXA??e^`72fW8Ls2>tTn8vyh*fC}_0q6&a;Z0`l2XIcrW
zr7AO7MUoBxRiWaamqI0gY7z<nsE$@J0QrMX22}&PrD~$OR4vIE05YBppf>$FA{YSl
zHGsPG>xpjw(ANO!({CWY0YF~^Xh{DtQ~~ffw)X<iGi?ObQjM8xB1s2;2&nkyrBDgr
z2?+%NJc(8?0L`@-bW1fyb*UDTF#u#d8$e6?twb;Y=xYG2>9-Nz0HCh{w58upd;@^K
z2GE{<2UG#j5!-tK=$SqR)l!|9>?}zK02fsJ^HQh;@Qj240J@+R3?LuCHdW6;w^UbD
zm+B@N13<>J0d%MToCpR0eGTAw`Y(uY0MOR}deDDSd;@^K1`tWVC#nF5!uDPOdZy7(
zEfvFLFG)H8#6rbCFNI0~eIyhB;6^JLfaV$p9lr{PieH73i~%6y*#P>|?<ax*Kwkrh
zr=K9c0YF~^=uh7(z5zgA15or6Q3XH}w)X<iGfjqyUxj0Gpd=jt20_KI!l4qt5D5hU
zyeusY{t9jT8TRQe^4~Wp$_Ld_Ls4C7m?Z6&)f0CP&ws}HAwmsjU<3mrQT;EguVUY)
zMxh43td151FROYm54!}ttXk|ih8@%EWpyl$mX?>*ah&6LnWMa{zJ?tG^mVjSTTv6B
z<KMADb*VQbV^)-mXRoM9^e2m8Rup}`qNdQFD!y4!^!19GM*mIm&5EM0SJZU+Gf?GD
ze+ygZikb=4QnQ$RTau0yH5)2E2!Tofb0id4QFEn*!QZ8AuPA(~H7}o8=0U~R4p3cc
zfh6q}b>~^nu4_9sHB#>}@IC_zQT;3G1MK7L2B^U+>O)bmqV!-Mb_rThi`j7rJEqr)
zT8g8kWkoIH9GA-+Wks#Pjsdz7t<+Z3D(IH_2-T%lOUA4y8P8r(Yv`{P!K^6ydPRLq
z{}b`eilVPq)Ti{<iEmaEeZ8VSqrV<i?(_}VI#<+2sFwPi$rMRCR@5e__?iML0c?>_
zU`1_}76#u&+g?%6>gT8kwH+#c6adwwc1Y4*Q78Evg|9~JWMCHqyHWisY7h4DV*seZ
zD{7x8SW$W~54!}dsQv7CfF09oMSY2*rDa7O<Q%_}Im(JUgdGF)Fj}dts3Xwvo9(Fh
z&34I{6(!@@E9w~i<06<9MPIL|6ZB7tZ&nn2y`oOhKP|plQS|kSIz#^~s@&=4uywAe
z^HA}d?Mz;jq+>;0f{GszKqY{0B@|du-$@IDU#4xZsAv4IdRL(0>KqkU=aLM1)x(PV
zo`LHO+(7lOsGHcw)j4YLiuzF$tSCL0hh2hJ)KBbqiyhNzMcu~H(z2p{<{a<H9A!oQ
zf*k|&SF}=FQNKaA)bFS+^@n84ijwi{74;|mzeF%AioRY^f78D!zFAT9^@_@nTgK0b
zde4fYuUAwi`k7JXPJaMf=ZeY#)lylR%qB_4ipmZZKk<i30AUgetf-vQ!r-}Ro9C!E
zaUK<+aznLL9#oggE6Mwxqw+D3pMe6X{uNaa`#x0&HF!l876mIx59VQ)pcPew9gDJK
zdabBpI9gg(RB_JnL7Ahhs1n#QKue;P+KMU#9arb5xH^}NSy3{cy`sv}FDHUoQS|kS
zdWinR;+qvkU$3Y~=$9AYJV(*jE2;whil}m@hhytpQI(+L>YT|cl60)7s!(xtj!FR4
zBotUt)un~OYtXh=RD}!8f1EaWZ8KF9s-<e7x>RjR+AHevjAA3IHqOvY)nTA61NBh-
zE2=*BeX0R!@QP|E3RaXJ%)>50E9x<Je4HKAYehA}(bBS_8gq_KWR9|;BCunCHkB5D
zK0(_CdSw5Z)}IgiD@8pC6<<3*b*bi(w1GaA$>*89uvSymf`OI{v_ka*ZH;}OYJ(aK
zw5=!rst5D1OAyd@?AV?i(+g+^94#$CJ93Us$s7e}C+rxYouvh!F4{KGfjxIdZ&T%(
zs;8migAJ%I)kTsv(EUd?yl`Ptb-c;IKvxF3q56S#$G%TJhZ+p@c~JmV59VQ)AfPX>
zV-I#rFQ6~tXlVf&$vO6vISSAy>=>ZY(gM&J+BQ&6rb1n+`O0rny`Wku7S*MCOVS3K
z{hPv->x?MTRP|xN%|INgAE*cW_$RVagMs!H1wi#+9(D-=+K(OM*)hF<Cg5mk0otE)
z^vWCssKSl`nkX#*O`>fB?RdAbYVGdYR3$^jTPLV4HBgc^&{~-*4=VfFLrv8n1_m=Q
z1l14pW$fcE6Vza!K2ZQv59VQ)AfQ9paTq(M7trB2T3UdP;2cNF90lmB*fBsyNee(n
z)3$*gc>DF1XS!mAT2L)D2Gym;O40^e@I=Cn`QKuNj$>dv1Fxa_fxeD?d=~^Y80bV%
z08|g=VV5AFZ?NMec1$mzlX0}P0G+})PL(+d&}rB)K;M)WfKI1v1MPV=a(J#xMK`G#
zQ1Q>*p}N#eN!mca%e*1$#)ltls%A0pHUqO!{XpNrK7L0LH5lkzQ2<mA=3$p0pzpHd
zJa$Ykp!0FGv;bYeIld=z6rk^8#{gX@Edc$1whgqxpnbk!e`AF%f@-M`QC(`WByFH=
z-(S%wUljmd!oX4nmZAEAF2}x4tw0S1x>6JX)q{E1B?#y$cKnDP(+lWo94#$C*Km$&
zWsU;$W9%59pGXTpKc#I0tyr+@XZ7E`6rt8Z#g9j#;>RN;X#*|y<#&G_ZF4X}ZD3#{
z1D~V%fu>*||H=VsFwo7S0H_|!!!AKUx3J?@c1$mz+i<kB0Nu_xej#%dpgXW*fbNtQ
zfbODg1AV7Rl{QyKRotX@L$%Z%RF~Q-NgJq&JzpoPe37PV9|QkidshNbRk8lv7Zltc
zxsm&lT9%@PzN@2Ul24x|WUi&)28bwE$Suq@B^A?BUv8w7CC}V*3mr)msrM3aUor^y
zte0hK{>%Pz&pE$?jNamM&IK=g@7ewCcV_1Iee;`hKIYDu<trt_Vett<Q^ofvYnu4X
zNocz5iXc=43sH*XAoPe*@TgKSzX&}htT{sHamB|IHXm(-o)llQ2|Z=g1fi#uWR1|q
zHTUf-KD7Bh>ltxX9PJ=J#nBG7oEo7aJ6e1c(YZ<|>p3OEH%f*K@d-jR#rG)dx8gG=
zp;@*of>0GKL@APk(DO>c3rfNKBJ`rL<_Mve6dy0!e6$gIMSRI7^r}r0gnp+aYlN2H
z++}>Ty-oI6uZgSTFnaOnXZ^vJQzLY9<HWz;ezHa<>;IGtKPnlni%$^xllU&qAQYcD
z3B6&vA_!H%LX;vo2>nGVcvC5uUxeNg)*K=9w&LTjHXm(--VtB23H?8tCJ6maN!AFB
zSN``Cv;HowitQ=Hr`VptmQy2iVVk$Uyz-F<vc)`X8H$Tfu}`J=1feCxcQKwNK64UU
z+IB?{s)B_mMRE{YMk!cUDVSe`mJ`+-A+)^Wqnph~8=)1%mux~S+B89^yOOLCT6W6C
z>Xn}oG1Nm`6$@a*r&s`E%c&9i_aT>mT3MocCu<cYLscb1HSq~T|0ceR*(TyMC!vqn
zt_VU^un?t44nnId1#2h;^NY}$!kQz5)>3?|ZS&DasF(PXP3WUGO%Pf~N!AEeCRr2{
zuX*c=t8ZHCiBCUkeOpeAQ1#G>Ys}g}$<R>A&`5lO(8l6>l(mWY%t>fd+Z92m3KpUi
z$wBC2O2KAI!Tch$xv-W?LWhU+7SoJE9<_bc5g*-)&9b%-#Y5@|Ww!Nk@zu}zgw6Zz
z#k()-zD%s~5lYaM=x6I)C?WPBO<w4ge4Tx=zju?{YY*{{p9n4ER`xto`KC{2F);0L
zkDf<jE^?#zz=1Ry&)K*p<1-D<o;00r_%BJ=H_IM-UA<p`E9)HTQF*+twk$|>i}l^7
zKldSbo2(8wwIl)eQ+TO28V6oZ)6+Fwub-Sg_GYJ8?^n*S7UwgT`j*<tQ{NryKlRE1
z78DT~?HRn451Dp#?z<h1v!^C39{G0T_5Ak4iV2oFi7ebFvFftXTe;7eKt90xFb}9v
zCN1^MI{r%k`4>*zI>J&qzxMnykz09IzmzN9Gmf&b=k6>XG-)eu+}1baqo*_UI5tj;
zYrm~tf1&-#ZMxs64@q+wi1V$OwEgl}i&4}nFKpU<Gdwl{(kR}rC;$D&q0g5m9B(sy
z<Ln0OOv8nLkY?jwT`+KGvoUKDAQyM&TcJ<jUR{S>qx=8|QmzN=Bi*Py;Q6#a9@w<r
zXBJt99i*4sbkRCt{~m|$t7j*d|Gx3I6MB4ltX89E&lFG6_2-oIWIZl)Q+2!POJ^0k
z=>OF@9$`6?7?pIIXGXVwH0|g0{CNAm%O(juY{bq}aZ}P*bhX;cG7?X7tN*X%`|f?8
zH=0$oi!Hs^smp8A6Epa&-b<dHyYDnVxnoP|>Ie5T-|{y;o+#2!wE5=|@sIg5ZrjeL
zd(MmhV`;0)dVdUYEL@9gM%#hP|2YF;6}xy2jXSH~4}YH9erl+O18F~J?o!|vq*=JY
zfixRGMCF0mldzjR7VPH&4y0TU*hjj3q}-(dFG#8%bl8DDJdgL)=@lbiymL82zaO_;
z4SlD-Tbi!>4*9V4;87PCyzZvzcGD*hemt;O-xThD?YpjPC#Lh3yBlnuAo@|o-yXj{
zE;^leN=X^w;hw?5+(#~Wabp_a^Zk#V_8-{F6ORonIq<D?p1!o^+AF2M<*mI2`d68n
z#y6h#`Sk6%-!i|l@xLvYo6cL@_@Db>p3I{=&O9_^UOMMzJ8ZvD?hD;N_?c5q?U++1
zjTj&Xhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?H
zhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;
zfa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~
zw;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX
z3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{
z0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}
zMo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?H
zhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;
zfa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~
zw;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=id
za56CMaF3oxVlMJH--=1wFORi^RqWz9H0~^aIK3@AUcKg(ywEH8Ix||MNU!8@8xEvV
zJmFUkJhO0tV^13OTf^m$T0L|fc1^OQ^9@`{!me5E%dKj=09V!-(lzO@-S7(o9$!_j
z;x6zLlhx0&)EA$A2Hh+9$rHEdeBc9pS6??vzxDOoL6_6(ZO`<GTXc+9_llmh((27e
zR`#4>sTI%8Y|Og!MpyvbKc`RZ@$b*^wk7xW*d7|o>kJ<``c~_BcBAg`$-#bstVVLn
zPu%OIvc%`zeqPZtj-Oum_3x{K7qe@h@2nJ^K9og1G4*nT&`mtL+O3#|$A_^A3O{KS
zx6mL3DyhF#p!Ab*Vt^QM0s|D+oIol;5(C6QfiggGtw8A~<HP_l-~<LJt~r5Jf+Plr
zfdXZK;#z^yPsWJ>V!#OuP+W5YsRT(35Ca9u0L8TerJsxw1H^z67@)Z31X2mog&Dwn
z+A1$>+I=%THX)Se)8<QSV*aI3V-ecCTp^WtutxFV7w{mB;sVE>Gzp(;`1-t4=&);&
z9R@r*5`H*5uT}OB{)@)7wJ&hFq%-K6bl9$+*W`arn9yIJ9~&RPAR)b~)dGL+q^>_1
zmeEw7ZyP@9*G>MF`{?=Q^g7yn+S(rKeA+(ReA=ij>U`RL+I-sIN~rT`D{Av;x71hX
z(^l8!(|(}Mr#-CAr;Sfi=hIHs=F|Fms`F`8p3*qQGqhO#O2iGLR_}S97Mi)<;2vD@
zh!|hTAmhopNj&J!yT-4s!=6cY81U>!*mXFr$o^^ga{Q8Yif6PwK)!Vfkxsuu3sGZI
zRfqv%Kw*I58r=xQ05MS57@)XT*jkk8LktiD6xZk;AO?tm!o~o_wZhh-R3Bo17@)XD
z_W&_K3=}p7D6SQ@7Nz<S1H=HuHM$2}n*q$H9ronE|2Xvd@&wv%JAc{}`&t{dhIzS=
zM)9x@IPf5i;sVE>Gzni{7eM=MJI3hxCz{19pVC{O?_c3*&st~J2J7ns>X%vl$N(R2
zeSJW{rl7&^4(O%lm(#J|_8;!*`hdRL`hZoN)%5|1+WLSy+WG(wZGFHFZG8at+s68U
zue9|68>RID-L>@r>V1{HKHzwp=^JM^SV!^9vD+Zex$#`*89*y2&rdgs2fqZavfcpr
zMsa~-Pnv{}dHH%=)9m58S@ZP^-THuX*IvEws&DpsC(qtrwu+h;qQ|o?Zg+;P7-iAp
zS;{vh=DxQwd%csKj`dD6wRq;I#j}OddZ*i3Jag0H*+DIyJ)*65TBEIZI;h37y;?k*
zs;ze#K<k~1K02n9N8B(9CdUV(c<}2UTvOIj-GisBpOST>ertG$TNKY6B($)s-*m9r
zl)XqZK;vsgy7{T{#6Y2BfZ|%AY*wliF;Ju#ptx3~o1ZFA3=~QRD6SRCW~E9I14Wtv
zifcu>`Kj{6K%r!S;##3>R;m;+P^1~4xK^Z_pDIrb6iNmtt`*8=eF!Us^-gpiO8zzu
z=B*mFYWBQT_+>V(z{|1TzzO)adAc;8Hjn4x6qfn46wi!Cl)RNk+{jz$>{4dO1^8t)
z?mc)9jBD97%G);$pW+$Cvz|v{E^=DGnYZUCucZ(-@>(9{?uir1IwD-ptAt-><H~qK
z&k@)1>MzP^_!Q6b7|&?^ChjDXd0_x?BQIs|EhEQ?d(-mr9e$aOE92dxD{(C^zTR6#
z!=reX$9Ps)esA7;PtbQ_pkNuG_XY*aKUpURhyn8%ptxo}r-Voh5Ca9v0L8U}<)5q*
z1H^#&3{YG%pHo6428e-zWq{&Z!SYYmi2-83d<H14na?R95(C6Q!7@N`tzh{l>%;&t
zU_JvB*UabCH4(M-PVDM}fjgUxS(6Y_v5V)>xU+`O!EsGC^3Q8+-YPe0QJzO?6c2ux
zjVt5Hx>3IcUXJw!8a|Vx>3qxeaFRV{)#k|pU(z{ru}DPxyY6s3=f=PKW|={L`uTIk
z+V>yjekoi(-|0^3`jcT9P4)AgVp@;-Z>{Os=iABY+Ie;CRCDFLIyPQC--%z^pqy97
zwe#xO*phpDY!8iPE!Fd#xL2LylY{*Nxpux2Kc}AW#4f4lJMq6I?yMA@K9rAE&v#;*
ztK5odczhV2sMI@+Q9LvHB;~C<;zr&|XP1(nr|guxeS=?S<H~rlo;Po^OKEtv`1NOT
z&Ee-f+k*N{_S=q*IBJ$Xif0bSv&9|yR_GJBm-=V&4eTTx=iM5GNBn^_iU+@d2Wb=+
zIQFDb{Cnb=t$)IVog8ruc8%Hrjy(xKkZ#l-@X-H~b=X0=CLNBW9#^L?`{k!Fy??f3
z!G`%AUYM=-&;GS+dY`9Gbkh50i)P$jx5_VE&o8If-=66ax9Auf+EeYHeXRA*-dU&i
z&-Sb53A5s*)c)BJt$((@X3qXu)2?nmujm=a+!uZQ`>No@{KEvbf7V~?pLKRu`)9ML
ze`fSnGgKb;p&<}aSi8UVb&E#v;FsCBGM=m(^;^S3zvy0hZ(MWuIhXyC{kEgSzFGFD
zf96>KjMM&S@}28kU5gvcs1^A+Hi`$o%*K`RWZkIW8lE249}t(7xMq?a#4CFeewfw1
zTy#3czwG_pB%MLmq{DV;J-X35gUS;F1;qfp4=pI>$u2QK3^<DcifhhdmT-vyVxXWH
zptx30%#&SWfEaKV0~FVs#Vp|x1H?c<F+g#xpqMAS!~ikiECwj9Ig44sB?gFrf?|N;
zT0t>Sc8LLEz*!7XTyqw)X5rHLn`SZT7!KyGI#%lb+-A>9g<odl%6PJVf6W}bqTyj4
zD}TUzTHAb7hv%8fUI;r2Z8)<3k`6!2uxIm|=F_U{16X{LvfhbJR@XamUr%Mdlhz(-
zoZ^{$$L5cYxM9?q^c)#}e&CncxH6uso5Ta+h%@7v5*LO4e-_u^hvD|nc%pdbU_7Jq
zgpEE?j+IB;aIDn*xy_CX@XKsm8Bf;lubE?4G(5zS{D^0N7T4g1%fvG}PuQ{hjdJH?
z0C6MdRmU~6;{yCL8&}4Yb;oO-vxJ6+IFg_7>_Kr2ez;6LD=h0bY5%jF&qaP|THN49
z?a9xvQ9Sr%Hm-~(>qh<7@Yr8S)A^R`A@qBHhni)N_Sc~OHH<zYPA!jqkWnM@^J^3j
zewmFc<H@>FzcoDciwdHDhJKn&+Wb)BnpyP{pukn6q|^Qy>i%bj|9%*4FDg$A6n+M1
zo=4$tW2zxBP}CTpxK`9!oa##q6n+LMt`+_^rWz6hMU4T9YelWaslLQO;b(y2TH$YF
zsv$8@)EJ<+R@7RY>Prk1eg-J675+A+8WICVjRA^lj<z_h4{($*bMj*zzEL!J9;s10
z_+>V(j3?_x{nqdvhV=oq`L6K8@cd3uuZ1=|oEMCGN;-#*dA5%7IXC{*H_Ht2)6WMk
z*1rEJ_e<gWdBS&6*Pjf_XsVwl9MgK#e``(et>>51>uKi+pK7k2Cp=y|Px#UX^*rH3
z?L6VJCDrqUTWaSCd)3K#p76Qn)$@ce<#K(%>xX;vJQ8z}`LsVC*tFhfmY|AVJcq`e
zWsdSe`5j{*ujg3HR&f79%IS{k@C$g5j^fDqfn!g~`5o2qJlm77>nN_AA2{|T>?2(+
z@2C#EFe$DHU6{D8uM2>#Nr&U88qchHyt=;P=I^Win3({7)p+Hr$FG-v-gRXErBQl*
zIlZP9&$6!0nY7aC&6;TO?Cz&(Jln0sv)Luocy?~s$kDf2$8+z-YCQ8yZuyCOom3vI
z#j|x<Jo89V&qtr9osXVeLp>ioNL?SmsDFkQ%U_9pkWs5<_jBNv*|;*EteeCG`bGAn
z!_T?wm+ZG49rn$#cW?h};RE_>)IZbW8THTPJ2roG?eDJP`yKrNNTc4vFW^BM#RZN%
zX%rv*7<&?SjoJZ@Jqi0rH);=f=zra#FQoq2CT6F9#;d%rY4^?W*o08^_om_J%xF6n
z_VQXkh#9S?);~ahz%U;CG8<RMlXa7L;2Q43upV86G*kA&wc-5PevUcfn#i0dyM<-H
zAnKnPeUkE4Ui&+1`0s{x|LXU9-n_{!1;5P3mGNXf+k?D)L%+qIguT3>XO{wwJqi0r
z&n}s_Z@_y%|4f+3F@CntrWf_`vncg_057@eqIJUlJ(l6rKg-*V=U^$^rw$goFT3{K
zU|iU6{|tVajVt5H`h691@PdY?$91h9x(<5|!rqr1IDa8w*Km8FUWik6bUluVd<*l^
zFN_YKIi=YfBlPk8z!USX)f)At{yt##$!e7Xo^PP1tM50%bzM$>RDB=7yLVIGZ}Rum
z_W`Wtzm@j^EJ1xAz%Q%sH~Guz`%TufmhwJ;RaM^yu-aXf_nW-TqOX5n6}*^r-J_l#
z&`&!*;9Ga~{D7(I_?hAU!F>%*7l;93;2~jv=C3^@4MA=Y1H^!73{YG%jY<L{28e-&
zgaL|c4@pCi8^i!HU>XAy*G!|5fQSKN;2~jv;@U&f5ab3iKn$41K;emNTNb3c#rp1J
z&rUA?edBE>_~{iRU%YcUgWh|ZcCS1bDEz{J$5+*>sK4)iaIU|;ig}Qbe_i6wbF6=&
zS<Lb&z1!h@S+j6~18Fus)>A`zT%Eq`m!HDgL5JNxul--A0tZsAhuQXkr%LNCS%)7;
zm*-vlb&b><qosKs-z@p&RP3}&eZGl0UnEnH3uBb|5%$yjy7lii@9Yn(`QV@5yfr@7
zQg4b+?9R5Em}QOE%{dcSSe}?L<8TGOmcRd*)i<-@3QKI4o~=8tnZ~Du?3mhO&vJ|B
z;I0vs@20YtuA2`{?y=nRWi_{7)7oY54NWGubZ`2e<yhJ!ugtn1Bpf*~_GXEW%PpA|
z4&J>{rf<94)|c`T1H?d4VZe3!Yaw@=tPVM~B;m1Ijh;PIJc)0)8v0Iuw=_Kt7nK&w
z?|Q~$_46$C#iyScwHqJ5AR)b~)dFc0ufo%wwa%;!ws`jbvQ^Z)5YuqsAEep%`u;ZB
zJ~vAiY?$BSh1nKdhh3xm00;8EdT92MZqy$9*Y~^7@S(#Fba@_ZZtctcu@S%jqxe9c
zuJ_N>^z68hsr28{?4}ogb9Lk0JI*opR4?DYQ@62z9d4N~efb&hHgL_^mxLaCwPDcF
zHCx%5(7Fd+p0bU7)W2(Loi{$`AFo=l%$DBpqp)&mWj<j^i_i4w9kq>J3Troi)`v&<
zy3UCWZRyt(`-!|%^A=zE3qAa$fz3Bn4%YWK%5A+VA2C1-6g381r@t0n>W#*Mm($pD
z%J^9_^BwYG>%pTg=yA8GwOoGH*DLwS6SwDl;A7Mt_R)hhidVnP>PH6nc<<`sc4x?n
zQI=h1;Q|NJ+4%ojHoebNCpzi=!>&<&fCDMl1J@zVwddOoxT?Hgm)9NT*1kMGCilnW
zbyQ>3b?^G`IJJEC`uj4=uMO=y{s15Mx7}rrZuPdz*sw3A`~PB@+xa`g2WJej9I871
zx#(w3vWnK77dy=wY`Oi$&Ashji{NeJfBpS~fB;JkW&A9Id;PqxVaP{*3F`RSkF0vH
zf1POe%OeS1&HQJTh!3-DnA3OC^REuI#D|WEiksZl@OX4?`J#Np05MQl7<hR5Yg+$|
zcT@XkyqnrT)8lSoX@Q5W9=rOwS^BN7Z}@#o_^4ku`B(1K4$^2n12zQ>es@5xb}8SK
znET#J^KjuGq}lk`{~2=8jN9v0`GvQG4!cJA0S=^G53}t75BtH&I{e6)p1n>yxAm3Z
zSIXmKa(_&$i_fv{T~05j_RrYL5v^FOj7gTa)czT-TW!XUfmsoj6t#cGtIk-_yLZP3
zORCyGV+U5QT(LT2oTaAPKVvaF)2}2fvReMDyx;th|2C)PZ{5bfZaEgZ?SS90xd|zO
zEh_kjS}k$!_7B}~ekF~c<+i~oA2C1-*kRz|>95iFnVlwb@?{!7bME*Yjh{Jo1LtD=
zOmxYz$H(OUm^{8l<7bZXPq~Q!V&GwApy2vzV{dkf^?v0HTRN-QMgOnP@yUZ95A4-9
zg~rdEe5+{Zc^T%pj<#<eq*1rw7w{mB;sVE>G>VV&(ID$<=L<oHU88n@V^5lG4|q5)
zN!H;9(y@=CqxHmmew4%fG3e-TLC5$QbU9u0-<0vS_*%7Q^&6kWUR-|HKXUqJ_Vw)+
zNB%xIiM{;NGabW%lGyI?ryIT&cYvMp|7Ovrsf+o-DRIS`zM8~Je-w9Q;(^cE+C#?{
zS}q@86Dr5f-%~${N6$SH^3tFrHZ~%qQOeED{2#sDD%TvC#Q6B9XGHBwW-ZQVEcGq5
zl}A|4Bt|8j=Kj~d>$-Mgy5Z*x^(6UV2C93h`*uD!HTPF*d#E^&xi9(PoPW5hcr!I=
z7A|lg&Bn($kB~!qD)nH{VfVqc&iz&3K+5$n+aB<6?whQ`52Ry1*xc7h&53nMD2I8l
z&@t}<I>z~-%jt+?;yec>uDv&TfAendX7S+UHUF+Dez%tC+BNfs_mkP$JyYfc9k|YF
z+^F%xv9(z|K4xBsE&bXE@3sD&wzEeo?f7zb(slN7U}*Url~Q^8vcqrI5b3camRyP~
zmdTgz{%4#`uYc~94_du-k)^&n)_>}i11vMT{iA6=ujegyH`qR5(pHLRXecMik=p*l
zM&bKtIFLs1`f7N7CgZ|CNVD;AUM=Lus{b51>>A|<IFNEZ%(e$SoL?#H@B``C*V@r~
zVtpmbVLm@}%pZe}_m$A)bo9p%$IxGEp4D*vEAM1+Zr%83%h#^*Zs*U|{?E!RK0Unm
zz<rypvwdxDob(gvSFiUBTAg-*y?-FE>}#=EyyEpM``r(mV|S}XCwCI*p*@Gif7baL
z8#iP8yf#8V^yrI?udF%6gCZiMJ%hLM<L&z{n<V;Y#or#kJ}#R2XJ{v9$uk?(ebkNO
zM{QAYAdTX!+N|O&G#MBEL7I(^bGISiS*O&4Lx)|X`~U}1u7}z7fQNH<WgUJX9sBb;
zT2HJKM>(uZf{uBx&@t}<x}1*RmFSNlj)`*}l>XYeRmFF`G9{V$45{x^PxQ||ztJ)L
z%7<Bee@1^1`mVEKHT=)LG;BM2zB3PbQuNQxt!$t8i$^M7?He`Hmi}SIP7~iNp2-6$
zT^VH4e>nPf@-wq8vLVy1&V9GTaTYdW=c%|UX{=L9$`B9t3<f_*I){P%+IbL0(f4UM
zkVf$mB|Nimfn!ga<Tum&XBxk{4!cI}0LPw$eWV+;2Rxi7F6*#^beuEds9o%LfpS=1
z2_5tKp=16SbU7X4WB6T({utsI;#$xD9!*W^mBJcL8Q;Jy@;knIW89V0*OOW5<srS6
z_WqIAAMH`^<1Hzy{m=Wa)T=p$`}bT>qs^pbR%t{+xn6fN__T7ZDvzCz%vQ{=P<LF|
zSL~??i$}iQcs-A<R(n}S;%Oe{K61f}8?)D)!e5f+GSF5#*TyL7ZzcBj*d7|oA&uhQ
z(eQ4Yj0^uD&Bh<A^2s2Jmr{7qb=Wn^4{#vmdcZ!?joJg=Y_&b~`ap*r=r}LQQM=f;
z2j#F%96Ht|LB~8;=yE#7`7u6*-<9Z(A&y~uZS~nY-v(^T;&XS_J>@0(YnA5Acx%_B
zZ7irmOtaFW|JAzV*w<%PPvzUTJl3eXNI(4jJGDmtn8Do^d3UKM^pp*Ys#y+RWVa_)
zOt92RWLE!Q%lF;;KHu~GkDc}(pz$-ble1(U?R+ky_!TuANTYZj8lIcUxbP3sZ2Xq0
zzZ`Oiq{FUJet-ih*8}#EZqy#|&J7zm`c~_By?)SP2RhDubJQ;OA456pcL5#iE1_dP
zKXf@A?<+CRkMS}5uGITuS{y@vZFSS{wwx2cJ68<;X?#$`MYd!^ivag6S^RY1d%uM4
zKg(8r5%BhZM0({8UY^qiU1P5w+|_NL(A_roEaCmxDIV^VSasRxt-R5!s$Fd3XNkuK
zmK^vNjh~^NoF#A6RnHkTiod14iUVmBZ-<6=&}3Zr2Wd9GR~>~14!OQ&P91iQ@&g=5
zxgM~Obfflw=iNAGeW1e*bev!5s9o%%h;rDs2Rhb?L&v%#=yE#dUEqBs#`!TmhToOw
zk0FjBu9aBZ;>7jg+u87u!Tv48_}M>d2mE?XjGtYP+a<Q1yw1AJD?e(j7(eri96rLP
zhk8fPygTzE^BEJ!2Y4Umy-r<To1U1#)0fs<d!_WZ^nMc!<s?}{J1^BJe02>6(kLGM
zdc<U0_y=h=zJ|x`N!T^Y4{#vmdcZ!?joJgAr`jHR{h-4RbezNMs9o#_i*ne13_A9^
zfR6Q*(B*W@AH%#0ysyMKKgP%KyAu5|#4)Tl7_u~Ln}3yUY)#wI^|B&Tc#qLpN7n^y
zXD6?$9r#MdQ8uUC^_EjZwlm)szqlJ(YAO$?Q6??*%sPIn_mXGl?mNv}dkyrjGL^>9
z&`!>hiO;L&J{!gVK*NDF3vZ2+aN(!f_~%rA8DvxSTy0&4U9<QBo?H*uN4imazzde@
z2OV~x<2-Rk?P6bRl*2xX(6Mh1bgUDHE~jH2Eas14-UZ%QVw@l2WB6T({utueIPHCC
z&&1A|nKe@RvHi&-8;Snfvc;uaJ$o&KcN_lor*(z?m38j6s3RBIEB)tRICbj?JGo;^
z>FNjfvyJC{K7D)cxAcA!4do<xdZBuLy;1nX8V;mUystF8gC^s`KS;CjFRA`=NcTkw
zzq$^)M)?5_q+Ac!N4imaz*{HP4?65XFQeAm;pZ3o^TS{42MZnhk3q+N7trN&%;(2E
zSj->8ybHXq#5h03$MCxn{V~L`nOa;s_DSic=fv;MZ#o?qTDIj0<~3|qoufkk{X&JN
z31tuPl+Ld`|4igo?pyxG#}mc)nP1uX-xkcJ@iVlOv*fkUcUFo{AL{UZC;Jnhq~buz
z>5l3fHN3qh<HA2kv+@6ysPMocKTOD}!>*%z$oYW-Dc1w`kuH~aR0p0%O3wN~haKo$
z_o(-Sqjqr42+Cn!Yv|ZV5jytmfi9<GT@vQ=V;(H#k73>g-dAFrALC>AU5Wk};#i~>
z*Lrlj+BQLqpKa{;z1ZsLI%Dn2-&rZf&$9ZZT=AZ9l$~hv&m-a=YjNX$?uU6Yjh~^R
zoFpTkn0mQE=qAHFPu6fCjk*h*sZPRm7XE0}Uk2G<(qY#uet;+Q1p7!gY7cnxr20UI
z9q9eEc5~D&&Pzf$?9UG!`@urT{$tSPbgZw$x+Ki!$2?ffAH%#0ysyMKKgP%KyAu5|
z#4)kihcYjB)IYxa?h6suzVvy2O^ew_dDwGz77r5Zn|vC#ZD;GBMR%NeXvjS3pP`+c
zC8Mj|ifMR!nBo4JucwLwY1H5D8r}etap51N+4!5QC_He;&h9yN*fq)za3JM+z&_HA
z+5=v4jhyv?4m;4lRqO5WeT8%1;4jV@fsTExp<^FK=yE#NiDP{w)+J#+Kjy(={ut(6
z;C&^=`7u6*-<9Z(A&%jFXzd>UpAHiJv&L<GGd_AcgL%%2|6^&Z%bcI>u>C^0FC69r
z^&*)&1|}%`uN!~wBIYBCKUHZQGI!SP=Mw$-9C&8o0>_@T?@{hjCaQij$XSvOySYQN
zp9?tlB<v&IK2q*dfEOgy2RiIPpQ^QA?$EPy$>&$f=OxMe^UM3e%IVne0_()Fz7p$_
zFrOduU@?CT^Dgkd665?BAH(lT^v4j#1P@Ao&HAKAv0@R{r^IJqNXz2Iip_}(6#ow@
zA5<ZzWKgM~(m`eVRS?yV2nY-vK6qU4q~YVNtwiQ1YisfOSWva@Zb41Ne{H(EMMehp
zD=V^&3m82%bhy>(Eiw!pH|({R+66a%HNnl=Mx->GW^JqBJgwl^>}3RH26;wVpHVJU
z8yXxj+!`@vOz1ebp<^P$BHXM#BB8t6ENeR<{jBZ9XHb(6<)X(d>)(}&7Td)(>cy~G
z)(*<Wj^Z=2pQmumI(&3Ma9D8I$iZW+!J`AL69<nPKGDiVdX%-3_!RZ=ed9;9m4ljO
Ve{G|GjRY$pHWor`B-jXR`Tq{uFB$*<

literal 0
HcmV?d00001

diff --git a/setup.py b/setup.py
index 3c2c87521..ad3d04e62 100644
--- a/setup.py
+++ b/setup.py
@@ -45,6 +45,7 @@
 
     # docs
     'm2r>=0.2.0,<0.3',
+    'nbsphinx>=0.5.0,<0.7',
     'Sphinx>=1.7.1,<3',
     'sphinx_rtd_theme>=0.2.4,<0.5',
     'autodocsumm>=0.1.10',
diff --git a/tutorials/02_Single_Table_Modeling.ipynb b/tutorials/02_Single_Table_Modeling.ipynb
index 0f5649a3e..fa536bd3d 100644
--- a/tutorials/02_Single_Table_Modeling.ipynb
+++ b/tutorials/02_Single_Table_Modeling.ipynb
@@ -248,17 +248,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-09 20:41:29,959 - INFO - table - Loading transformer CategoricalTransformer for field country\n",
-      "2020-07-09 20:41:29,961 - INFO - table - Loading transformer CategoricalTransformer for field gender\n",
-      "2020-07-09 20:41:29,962 - INFO - table - Loading transformer NumericalTransformer for field age\n",
-      "2020-07-09 20:41:29,981 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
+      "2020-07-09 21:18:32,974 - INFO - table - Loading transformer CategoricalTransformer for field country\n",
+      "2020-07-09 21:18:32,975 - INFO - table - Loading transformer CategoricalTransformer for field gender\n",
+      "2020-07-09 21:18:32,975 - INFO - table - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-09 21:18:32,991 - INFO - gaussian - Fitting GaussianMultivariate(distribution=\"GaussianUnivariate\")\n"
      ]
     }
    ],
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -345,71 +345,71 @@
        "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>USA</td>\n",
-       "      <td>F</td>\n",
-       "      <td>29</td>\n",
+       "      <td>M</td>\n",
+       "      <td>38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>FR</td>\n",
-       "      <td>F</td>\n",
-       "      <td>44</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
-       "      <td>UK</td>\n",
-       "      <td>M</td>\n",
-       "      <td>38</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>F</td>\n",
+       "      <td>34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
        "      <td>ES</td>\n",
-       "      <td>M</td>\n",
-       "      <td>19</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>47</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
-       "      <td>USA</td>\n",
+       "      <td>ES</td>\n",
        "      <td>F</td>\n",
-       "      <td>55</td>\n",
+       "      <td>29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>5</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>27</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>6</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>F</td>\n",
-       "      <td>27</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>7</td>\n",
-       "      <td>UK</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>M</td>\n",
+       "      <td>38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>8</td>\n",
-       "      <td>FR</td>\n",
+       "      <td>ES</td>\n",
        "      <td>F</td>\n",
-       "      <td>55</td>\n",
+       "      <td>32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>9</td>\n",
-       "      <td>UK</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>43</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>F</td>\n",
+       "      <td>36</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -417,19 +417,19 @@
       ],
       "text/plain": [
        "   user_id country gender  age\n",
-       "0        0     USA      F   29\n",
-       "1        1      FR      F   44\n",
-       "2        2      UK      M   38\n",
-       "3        3      ES      M   19\n",
-       "4        4     USA      F   55\n",
-       "5        5     USA    NaN   27\n",
-       "6        6     USA      F   27\n",
-       "7        7      UK    NaN    7\n",
-       "8        8      FR      F   55\n",
-       "9        9      UK    NaN   43"
+       "0        0     USA      M   38\n",
+       "1        1      UK    NaN   23\n",
+       "2        2     USA      F   34\n",
+       "3        3      ES    NaN   47\n",
+       "4        4      ES      F   29\n",
+       "5        5      UK      F   39\n",
+       "6        6      FR    NaN   40\n",
+       "7        7      ES      M   38\n",
+       "8        8      ES      F   32\n",
+       "9        9      ES      F   36"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -450,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -484,37 +484,37 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
-       "      <td>BG</td>\n",
-       "      <td>M</td>\n",
-       "      <td>30</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>F</td>\n",
+       "      <td>48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>USA</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>61</td>\n",
+       "      <td>38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
        "      <td>USA</td>\n",
        "      <td>M</td>\n",
-       "      <td>35</td>\n",
+       "      <td>29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>ES</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>34</td>\n",
+       "      <td>BG</td>\n",
+       "      <td>M</td>\n",
+       "      <td>22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
        "      <td>USA</td>\n",
-       "      <td>F</td>\n",
-       "      <td>41</td>\n",
+       "      <td>M</td>\n",
+       "      <td>43</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -522,14 +522,14 @@
       ],
       "text/plain": [
        "   user_id country gender  age\n",
-       "0        0      BG      M   30\n",
-       "1        1     USA    NaN   61\n",
-       "2        2     USA      M   35\n",
-       "3        3      ES    NaN   34\n",
-       "4        4     USA      F   41"
+       "0        0      UK      F   48\n",
+       "1        1     USA    NaN   38\n",
+       "2        2     USA      M   29\n",
+       "3        3      BG      M   22\n",
+       "4        4     USA      M   43"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -552,14 +552,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-09 20:48:49,870 - INFO - __init__ - Loading table census\n"
+      "2020-07-09 21:18:33,085 - INFO - __init__ - Loading table census\n"
      ]
     }
    ],
@@ -578,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {
     "scrolled": false
    },
@@ -739,7 +739,7 @@
        "4             0             0              40            Cuba   <=50K  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -766,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -789,54 +789,55 @@
    "metadata": {},
    "source": [
     "Once the instance is created, we can fit it to our data. Bear in mind that this process might take some\n",
-    "time to finish, especially on non-GPU enabled systems."
+    "time to finish, especially on non-GPU enabled systems, so in this case we will be passing only a\n",
+    "subsample of the data to accelerate the process."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2020-07-09 20:51:21,668 - INFO - table - Loading transformer NumericalTransformer for field age\n",
-      "2020-07-09 20:51:21,669 - INFO - table - Loading transformer LabelEncodingTransformer for field workclass\n",
-      "2020-07-09 20:51:21,670 - INFO - table - Loading transformer NumericalTransformer for field fnlwgt\n",
-      "2020-07-09 20:51:21,670 - INFO - table - Loading transformer LabelEncodingTransformer for field education\n",
-      "2020-07-09 20:51:21,671 - INFO - table - Loading transformer NumericalTransformer for field education-num\n",
-      "2020-07-09 20:51:21,672 - INFO - table - Loading transformer LabelEncodingTransformer for field marital-status\n",
-      "2020-07-09 20:51:21,672 - INFO - table - Loading transformer LabelEncodingTransformer for field occupation\n",
-      "2020-07-09 20:51:21,673 - INFO - table - Loading transformer LabelEncodingTransformer for field relationship\n",
-      "2020-07-09 20:51:21,673 - INFO - table - Loading transformer LabelEncodingTransformer for field race\n",
-      "2020-07-09 20:51:21,674 - INFO - table - Loading transformer LabelEncodingTransformer for field sex\n",
-      "2020-07-09 20:51:21,674 - INFO - table - Loading transformer NumericalTransformer for field capital-gain\n",
-      "2020-07-09 20:51:21,675 - INFO - table - Loading transformer NumericalTransformer for field capital-loss\n",
-      "2020-07-09 20:51:21,675 - INFO - table - Loading transformer NumericalTransformer for field hours-per-week\n",
-      "2020-07-09 20:51:21,676 - INFO - table - Loading transformer LabelEncodingTransformer for field native-country\n",
-      "2020-07-09 20:51:21,678 - INFO - table - Loading transformer LabelEncodingTransformer for field income\n"
+      "2020-07-09 21:18:33,488 - INFO - table - Loading transformer NumericalTransformer for field age\n",
+      "2020-07-09 21:18:33,489 - INFO - table - Loading transformer LabelEncodingTransformer for field workclass\n",
+      "2020-07-09 21:18:33,489 - INFO - table - Loading transformer NumericalTransformer for field fnlwgt\n",
+      "2020-07-09 21:18:33,490 - INFO - table - Loading transformer LabelEncodingTransformer for field education\n",
+      "2020-07-09 21:18:33,490 - INFO - table - Loading transformer NumericalTransformer for field education-num\n",
+      "2020-07-09 21:18:33,490 - INFO - table - Loading transformer LabelEncodingTransformer for field marital-status\n",
+      "2020-07-09 21:18:33,491 - INFO - table - Loading transformer LabelEncodingTransformer for field occupation\n",
+      "2020-07-09 21:18:33,491 - INFO - table - Loading transformer LabelEncodingTransformer for field relationship\n",
+      "2020-07-09 21:18:33,491 - INFO - table - Loading transformer LabelEncodingTransformer for field race\n",
+      "2020-07-09 21:18:33,492 - INFO - table - Loading transformer LabelEncodingTransformer for field sex\n",
+      "2020-07-09 21:18:33,492 - INFO - table - Loading transformer NumericalTransformer for field capital-gain\n",
+      "2020-07-09 21:18:33,493 - INFO - table - Loading transformer NumericalTransformer for field capital-loss\n",
+      "2020-07-09 21:18:33,493 - INFO - table - Loading transformer NumericalTransformer for field hours-per-week\n",
+      "2020-07-09 21:18:33,493 - INFO - table - Loading transformer LabelEncodingTransformer for field native-country\n",
+      "2020-07-09 21:18:33,494 - INFO - table - Loading transformer LabelEncodingTransformer for field income\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1, Loss G: 1.9379, Loss D: -0.1297\n",
-      "Epoch 2, Loss G: 1.6529, Loss D: 0.1716\n",
-      "Epoch 3, Loss G: 1.0939, Loss D: 0.0208\n",
-      "Epoch 4, Loss G: 0.9390, Loss D: -0.0918\n",
-      "Epoch 5, Loss G: -0.0550, Loss D: 0.0415\n",
-      "Epoch 6, Loss G: -0.0864, Loss D: -0.0104\n",
-      "Epoch 7, Loss G: -0.3865, Loss D: 0.0074\n",
-      "Epoch 8, Loss G: -1.0150, Loss D: -0.0708\n",
-      "Epoch 9, Loss G: -0.8685, Loss D: -0.1289\n",
-      "Epoch 10, Loss G: -1.1524, Loss D: -0.0487\n"
+      "Epoch 1, Loss G: 1.9512, Loss D: -0.0182\n",
+      "Epoch 2, Loss G: 1.9884, Loss D: -0.0663\n",
+      "Epoch 3, Loss G: 1.9710, Loss D: -0.1339\n",
+      "Epoch 4, Loss G: 1.8960, Loss D: -0.2061\n",
+      "Epoch 5, Loss G: 1.9155, Loss D: -0.3062\n",
+      "Epoch 6, Loss G: 1.9699, Loss D: -0.3906\n",
+      "Epoch 7, Loss G: 1.8614, Loss D: -0.5142\n",
+      "Epoch 8, Loss G: 1.8446, Loss D: -0.6448\n",
+      "Epoch 9, Loss G: 1.7619, Loss D: -0.7488\n",
+      "Epoch 10, Loss G: 1.6732, Loss D: -0.7961\n"
      ]
     }
    ],
    "source": [
-    "model.fit(census)"
+    "model.fit(census.sample(1000))"
    ]
   },
   {
@@ -851,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -868,7 +869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -912,182 +913,182 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>38</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>212843</td>\n",
-       "      <td>Prof-school</td>\n",
-       "      <td>10</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Tech-support</td>\n",
-       "      <td>Unmarried</td>\n",
+       "      <td>50</td>\n",
+       "      <td>Local-gov</td>\n",
+       "      <td>169719</td>\n",
+       "      <td>1st-4th</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Widowed</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Husband</td>\n",
        "      <td>White</td>\n",
        "      <td>Male</td>\n",
-       "      <td>49</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
+       "      <td>114</td>\n",
+       "      <td>8</td>\n",
+       "      <td>38</td>\n",
+       "      <td>Columbia</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>30</td>\n",
-       "      <td>Federal-gov</td>\n",
-       "      <td>321819</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>14</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Craft-repair</td>\n",
-       "      <td>Unmarried</td>\n",
+       "      <td>32</td>\n",
+       "      <td>?</td>\n",
+       "      <td>152479</td>\n",
+       "      <td>1st-4th</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Wife</td>\n",
        "      <td>Black</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>51</td>\n",
-       "      <td>-3</td>\n",
-       "      <td>39</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>-42</td>\n",
+       "      <td>20</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Jamaica</td>\n",
+       "      <td>&gt;50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>25</td>\n",
+       "      <td>22</td>\n",
        "      <td>Private</td>\n",
-       "      <td>169771</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Other-service</td>\n",
-       "      <td>Not-in-family</td>\n",
+       "      <td>69617</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Separated</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Husband</td>\n",
        "      <td>White</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>-37</td>\n",
-       "      <td>-2</td>\n",
-       "      <td>39</td>\n",
-       "      <td>United-States</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>38</td>\n",
+       "      <td>Guatemala</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>47</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>116751</td>\n",
-       "      <td>Some-college</td>\n",
-       "      <td>6</td>\n",
-       "      <td>Divorced</td>\n",
+       "      <td>25</td>\n",
+       "      <td>?</td>\n",
+       "      <td>652858</td>\n",
+       "      <td>10th</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
        "      <td>Handlers-cleaners</td>\n",
        "      <td>Not-in-family</td>\n",
        "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2057</td>\n",
-       "      <td>41</td>\n",
-       "      <td>United-States</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>152</td>\n",
+       "      <td>-27</td>\n",
+       "      <td>39</td>\n",
+       "      <td>Cuba</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>38</td>\n",
+       "      <td>43</td>\n",
        "      <td>Private</td>\n",
-       "      <td>315119</td>\n",
-       "      <td>Assoc-acdm</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Never-married</td>\n",
-       "      <td>Craft-repair</td>\n",
-       "      <td>Husband</td>\n",
-       "      <td>Black</td>\n",
+       "      <td>301956</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Married-civ-spouse</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Wife</td>\n",
+       "      <td>White</td>\n",
        "      <td>Male</td>\n",
-       "      <td>-11</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>49</td>\n",
-       "      <td>United-States</td>\n",
+       "      <td>-133</td>\n",
+       "      <td>-12</td>\n",
+       "      <td>39</td>\n",
+       "      <td>India</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>36</td>\n",
-       "      <td>State-gov</td>\n",
-       "      <td>172646</td>\n",
-       "      <td>Masters</td>\n",
-       "      <td>6</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Machine-op-inspct</td>\n",
-       "      <td>Wife</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>-50</td>\n",
-       "      <td>-2</td>\n",
+       "      <td>66</td>\n",
+       "      <td>Private</td>\n",
+       "      <td>401171</td>\n",
+       "      <td>Prof-school</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Separated</td>\n",
+       "      <td>Protective-serv</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>-124</td>\n",
+       "      <td>-1</td>\n",
        "      <td>40</td>\n",
-       "      <td>Japan</td>\n",
+       "      <td>Cuba</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>40</td>\n",
+       "      <td>52</td>\n",
        "      <td>Private</td>\n",
-       "      <td>163368</td>\n",
-       "      <td>Some-college</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Transport-moving</td>\n",
-       "      <td>Own-child</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>-13</td>\n",
-       "      <td>5</td>\n",
-       "      <td>39</td>\n",
-       "      <td>United-States</td>\n",
+       "      <td>278399</td>\n",
+       "      <td>Bachelors</td>\n",
+       "      <td>12</td>\n",
+       "      <td>Never-married</td>\n",
+       "      <td>Prof-specialty</td>\n",
+       "      <td>Unmarried</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>122567</td>\n",
+       "      <td>-6</td>\n",
+       "      <td>47</td>\n",
+       "      <td>Columbia</td>\n",
        "      <td>&lt;=50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>23</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>369324</td>\n",
-       "      <td>Some-college</td>\n",
-       "      <td>11</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
-       "      <td>Tech-support</td>\n",
-       "      <td>Husband</td>\n",
+       "      <td>36</td>\n",
+       "      <td>Federal-gov</td>\n",
+       "      <td>229817</td>\n",
+       "      <td>HS-grad</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Married-AF-spouse</td>\n",
+       "      <td>Farming-fishing</td>\n",
+       "      <td>Not-in-family</td>\n",
        "      <td>White</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>-60</td>\n",
-       "      <td>5</td>\n",
-       "      <td>40</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>8</td>\n",
+       "      <td>19</td>\n",
+       "      <td>38</td>\n",
+       "      <td>Portugal</td>\n",
+       "      <td>&gt;50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>32</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>192521</td>\n",
-       "      <td>Bachelors</td>\n",
-       "      <td>10</td>\n",
-       "      <td>Married-civ-spouse</td>\n",
+       "      <td>27</td>\n",
+       "      <td>Federal-gov</td>\n",
+       "      <td>306972</td>\n",
+       "      <td>Some-college</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Never-married</td>\n",
        "      <td>Exec-managerial</td>\n",
-       "      <td>Not-in-family</td>\n",
-       "      <td>White</td>\n",
+       "      <td>Husband</td>\n",
+       "      <td>Asian-Pac-Islander</td>\n",
        "      <td>Female</td>\n",
-       "      <td>-15</td>\n",
-       "      <td>2</td>\n",
-       "      <td>43</td>\n",
-       "      <td>United-States</td>\n",
-       "      <td>&lt;=50K</td>\n",
+       "      <td>42144</td>\n",
+       "      <td>3</td>\n",
+       "      <td>39</td>\n",
+       "      <td>Japan</td>\n",
+       "      <td>&gt;50K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>28</td>\n",
-       "      <td>Private</td>\n",
-       "      <td>244118</td>\n",
-       "      <td>HS-grad</td>\n",
-       "      <td>9</td>\n",
-       "      <td>Separated</td>\n",
-       "      <td>Prof-specialty</td>\n",
-       "      <td>Not-in-family</td>\n",
+       "      <td>Local-gov</td>\n",
+       "      <td>416161</td>\n",
+       "      <td>1st-4th</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Divorced</td>\n",
+       "      <td>Adm-clerical</td>\n",
+       "      <td>Unmarried</td>\n",
        "      <td>White</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>-21</td>\n",
-       "      <td>0</td>\n",
-       "      <td>39</td>\n",
-       "      <td>United-States</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>-349</td>\n",
+       "      <td>1090</td>\n",
+       "      <td>61</td>\n",
+       "      <td>Guatemala</td>\n",
        "      <td>&gt;50K</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1096,43 +1097,55 @@
       ],
       "text/plain": [
        "   age     workclass  fnlwgt      education  education-num  \\\n",
-       "0   38       Private  212843    Prof-school             10   \n",
-       "1   30   Federal-gov  321819        HS-grad             14   \n",
-       "2   25       Private  169771        HS-grad             13   \n",
-       "3   47       Private  116751   Some-college              6   \n",
-       "4   38       Private  315119     Assoc-acdm             13   \n",
-       "5   36     State-gov  172646        Masters              6   \n",
-       "6   40       Private  163368   Some-college              4   \n",
-       "7   23       Private  369324   Some-college             11   \n",
-       "8   32       Private  192521      Bachelors             10   \n",
-       "9   28       Private  244118        HS-grad              9   \n",
+       "0   50     Local-gov  169719        1st-4th              9   \n",
+       "1   32             ?  152479        1st-4th              9   \n",
+       "2   22       Private   69617      Bachelors              0   \n",
+       "3   25             ?  652858           10th             16   \n",
+       "4   43       Private  301956   Some-college              8   \n",
+       "5   66       Private  401171    Prof-school             13   \n",
+       "6   52       Private  278399      Bachelors             12   \n",
+       "7   36   Federal-gov  229817        HS-grad              8   \n",
+       "8   27   Federal-gov  306972   Some-college              8   \n",
+       "9   28     Local-gov  416161        1st-4th              8   \n",
        "\n",
-       "        marital-status          occupation    relationship    race      sex  \\\n",
-       "0        Never-married        Tech-support       Unmarried   White     Male   \n",
-       "1   Married-civ-spouse        Craft-repair       Unmarried   Black   Female   \n",
-       "2        Never-married       Other-service   Not-in-family   White   Female   \n",
-       "3             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
-       "4        Never-married        Craft-repair         Husband   Black     Male   \n",
-       "5   Married-civ-spouse   Machine-op-inspct            Wife   White     Male   \n",
-       "6   Married-civ-spouse    Transport-moving       Own-child   White   Female   \n",
-       "7   Married-civ-spouse        Tech-support         Husband   White   Female   \n",
-       "8   Married-civ-spouse     Exec-managerial   Not-in-family   White   Female   \n",
-       "9            Separated      Prof-specialty   Not-in-family   White     Male   \n",
+       "        marital-status          occupation    relationship  \\\n",
+       "0              Widowed                   ?         Husband   \n",
+       "1        Never-married        Adm-clerical            Wife   \n",
+       "2            Separated                   ?         Husband   \n",
+       "3   Married-civ-spouse   Handlers-cleaners   Not-in-family   \n",
+       "4   Married-civ-spouse                   ?            Wife   \n",
+       "5            Separated     Protective-serv       Unmarried   \n",
+       "6        Never-married      Prof-specialty       Unmarried   \n",
+       "7    Married-AF-spouse     Farming-fishing   Not-in-family   \n",
+       "8        Never-married     Exec-managerial         Husband   \n",
+       "9             Divorced        Adm-clerical       Unmarried   \n",
        "\n",
-       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
-       "0            49            -1              40   United-States   <=50K  \n",
-       "1            51            -3              39   United-States   <=50K  \n",
-       "2           -37            -2              39   United-States   <=50K  \n",
-       "3             0          2057              41   United-States   <=50K  \n",
-       "4           -11            -1              49   United-States   <=50K  \n",
-       "5           -50            -2              40           Japan   <=50K  \n",
-       "6           -13             5              39   United-States   <=50K  \n",
-       "7           -60             5              40   United-States   <=50K  \n",
-       "8           -15             2              43   United-States   <=50K  \n",
-       "9           -21             0              39   United-States    >50K  "
+       "                  race      sex  capital-gain  capital-loss  hours-per-week  \\\n",
+       "0                White     Male           114             8              38   \n",
+       "1                Black     Male           -42            20              21   \n",
+       "2                White     Male             6            11              38   \n",
+       "3                White   Female           152           -27              39   \n",
+       "4                White     Male          -133           -12              39   \n",
+       "5                Black   Female          -124            -1              40   \n",
+       "6                Other     Male        122567            -6              47   \n",
+       "7                White     Male             8            19              38   \n",
+       "8   Asian-Pac-Islander   Female         42144             3              39   \n",
+       "9                White   Female          -349          1090              61   \n",
+       "\n",
+       "  native-country  income  \n",
+       "0       Columbia   <=50K  \n",
+       "1        Jamaica    >50K  \n",
+       "2      Guatemala   <=50K  \n",
+       "3           Cuba   <=50K  \n",
+       "4          India   <=50K  \n",
+       "5           Cuba   <=50K  \n",
+       "6       Columbia   <=50K  \n",
+       "7       Portugal    >50K  \n",
+       "8          Japan    >50K  \n",
+       "9      Guatemala    >50K  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1156,16 +1169,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-28.22245129315498"
+       "-144.971907591418"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }

From bf2b3940af23034988eb7dc9a3d58f0d2ebf9186 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 21:29:58 +0200
Subject: [PATCH 29/33] Add pandoc dependency

---
 .github/workflows/docs.yml  | 1 +
 .github/workflows/tests.yml | 4 ++--
 .travis.yml                 | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 12d63d205..02e92fd67 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -18,6 +18,7 @@ jobs:
 
     - name: Build
       run: |
+        sudo apt install pandoc
         python -m pip install --upgrade pip
         pip install -e .[dev]
         make docs
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 0f882e819..7357cc190 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -24,12 +24,12 @@ jobs:
     - if: matrix.os == 'ubuntu-latest'
       name: Install graphviz - Ubuntu
       run: |
-        sudo apt-get install graphviz
+        sudo apt-get install graphviz pandoc
 
     - if: matrix.os == 'macos-latest'
       name: Install graphviz - MacOS
       run: |
-        brew install graphviz
+        brew install graphviz pandoc
 
     - name: Install dependencies
       run: |
diff --git a/.travis.yml b/.travis.yml
index 8204e8dfc..8345682c4 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -9,7 +9,7 @@ python:
 # Command to install dependencies
 install:
   - sudo apt-get update
-  - sudo apt-get install graphviz
+  - sudo apt-get install graphviz pandoc
   - pip install -U tox-travis codecov
 
 after_success: codecov

From 815111bd1d7c15b1976d2ac2f608db052d852e33 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 21:39:57 +0200
Subject: [PATCH 30/33] Update the tutorials

---
 docs/index.rst                                |   2 +-
 docs/metadata.rst                             |   4 +-
 docs/tutorials/04_Working_with_Metadata.ipynb | 463 +++++++++---------
 docs/tutorials/demo_metadata.json             |  24 +-
 docs/tutorials/sdv.pkl                        | Bin 255975 -> 0 bytes
 tutorials/04_Working_with_Metadata.ipynb      | 463 +++++++++---------
 6 files changed, 465 insertions(+), 491 deletions(-)
 delete mode 100644 docs/tutorials/sdv.pkl

diff --git a/docs/index.rst b/docs/index.rst
index c0f4a2cc6..b79a5add0 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -12,12 +12,12 @@
 
    tutorials/02_Single_Table_Modeling
    tutorials/03_Relational_Data_Modeling
+   metadata
 
 .. toctree::
    :caption: Resources
    :hidden:
 
-   metadata
    API Reference <api/sdv>
    contributing
    history
diff --git a/docs/metadata.rst b/docs/metadata.rst
index c9f4d8607..d7f7dd933 100644
--- a/docs/metadata.rst
+++ b/docs/metadata.rst
@@ -1,5 +1,5 @@
-Metadata
-========
+Working with Metadata
+=====================
 
 In order to use **SDV** you will need a ``Metadata`` object alongside your data.
 
diff --git a/docs/tutorials/04_Working_with_Metadata.ipynb b/docs/tutorials/04_Working_with_Metadata.ipynb
index 91b001036..5c79f8ebf 100644
--- a/docs/tutorials/04_Working_with_Metadata.ipynb
+++ b/docs/tutorials/04_Working_with_Metadata.ipynb
@@ -1,21 +1,72 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Working with Metadata\n",
+    "\n",
+    "In order to work with complex dataset structures you will need to pass additional information\n",
+    "about you data to SDV.\n",
+    "\n",
+    "This is done by using a Metadata.\n",
+    "\n",
+    "Let's have a quick look at how to do it.\n",
+    "\n",
+    "## Load demo data\n",
+    "\n",
+    "We will load the demo dataset included in SDV for the rest of the session."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sdv import load_demo"
+    "from sdv import load_demo\n",
+    "\n",
+    "metadata, tables = load_demo(metadata=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A part from the tables dict, this is returning a Metadata object that contains all the information\n",
+    "about our dataset."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Metadata\n",
+       "  root_path: /home/xals/Projects/MIT/SDV/tutorials\n",
+       "  tables: ['users', 'sessions', 'transactions']\n",
+       "  relationships:\n",
+       "    sessions.user_id -> users.user_id\n",
+       "    transactions.session_id -> sessions.session_id"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "metadata, tables = load_demo(metadata=True)"
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This Metadata can also be represented by using a dict object:"
    ]
   },
   {
@@ -58,60 +109,22 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'users':    user_id country gender  age\n",
-       " 0        0     USA      M   34\n",
-       " 1        1      UK      F   23\n",
-       " 2        2      ES   None   44\n",
-       " 3        3      UK      M   22\n",
-       " 4        4     USA      F   54\n",
-       " 5        5      DE      M   57\n",
-       " 6        6      BG      F   45\n",
-       " 7        7      ES   None   41\n",
-       " 8        8      FR      F   23\n",
-       " 9        9      UK   None   30,\n",
-       " 'sessions':    session_id  user_id  device       os\n",
-       " 0           0        0  mobile  android\n",
-       " 1           1        1  tablet      ios\n",
-       " 2           2        1  tablet  android\n",
-       " 3           3        2  mobile  android\n",
-       " 4           4        4  mobile      ios\n",
-       " 5           5        5  mobile  android\n",
-       " 6           6        6  mobile      ios\n",
-       " 7           7        6  tablet      ios\n",
-       " 8           8        6  mobile      ios\n",
-       " 9           9        8  tablet      ios,\n",
-       " 'transactions':    transaction_id  session_id           timestamp  amount  approved\n",
-       " 0               0           0 2019-01-01 12:34:32   100.0      True\n",
-       " 1               1           0 2019-01-01 12:42:21    55.3      True\n",
-       " 2               2           1 2019-01-07 17:23:11    79.5      True\n",
-       " 3               3           3 2019-01-10 11:08:57   112.1     False\n",
-       " 4               4           5 2019-01-10 21:54:08   110.0     False\n",
-       " 5               5           5 2019-01-11 11:21:20    76.3      True\n",
-       " 6               6           7 2019-01-22 14:44:10    89.5      True\n",
-       " 7               7           8 2019-01-23 10:14:09   132.1     False\n",
-       " 8               8           9 2019-01-27 16:09:17    68.0      True\n",
-       " 9               9           9 2019-01-29 12:10:48    99.9      True}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "tables"
+    "## Creating a Metadata object from scratch\n",
+    "\n",
+    "In this section we will have a look at how to create a Metadata object from scratch.\n",
+    "\n",
+    "The simplest way to do it is by populating it passing the tables of your dataset together\n",
+    "with some additional information.\n",
+    "\n",
+    "Let's start by creating an empty metadata object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,141 +134,92 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "new_meta.add_table('users', data=tables['users'], primary_key='user_id')"
+    "Now we can start by adding the parent table from our dataset, `users`,\n",
+    "indicating that the primary key is the field called `user_id`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "new_meta.add_table('sessions', data=tables['sessions'], primary_key='session_id',\n",
-    "                   parent='users', foreign_key='user_id')"
+    "users_data = tables['users']\n",
+    "new_meta.add_table('users', data=users_data, primary_key='user_id')"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 8,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "transactions_fields = {\n",
-    "    'timestamp': {\n",
-    "        'type': 'datetime',\n",
-    "        'format': '%Y-%m-%d'\n",
-    "    }\n",
-    "}\n",
-    "new_meta.add_table('transactions', tables['transactions'], fields_metadata=transactions_fields,\n",
-    "                   primary_key='transaction_id', parent='sessions')"
+    "Next, let's add the sessions table, indicating that:\n",
+    "- The primary key is the field `session_id`\n",
+    "- The `users` table is parent to this table\n",
+    "- The relationship between the `users` and `sessions` table is created by the field called `user_id`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'tables': {'users': {'fields': {'gender': {'type': 'categorical'},\n",
-       "    'user_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'country': {'type': 'categorical'},\n",
-       "    'age': {'type': 'numerical', 'subtype': 'integer'}},\n",
-       "   'primary_key': 'user_id'},\n",
-       "  'sessions': {'fields': {'session_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'user_id': {'type': 'id',\n",
-       "     'subtype': 'integer',\n",
-       "     'ref': {'table': 'users', 'field': 'user_id'}},\n",
-       "    'os': {'type': 'categorical'},\n",
-       "    'device': {'type': 'categorical'}},\n",
-       "   'primary_key': 'session_id'},\n",
-       "  'transactions': {'fields': {'timestamp': {'type': 'datetime',\n",
-       "     'format': '%Y-%m-%d'},\n",
-       "    'session_id': {'type': 'id',\n",
-       "     'subtype': 'integer',\n",
-       "     'ref': {'table': 'sessions', 'field': 'session_id'}},\n",
-       "    'transaction_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'approved': {'type': 'boolean'},\n",
-       "    'amount': {'type': 'numerical', 'subtype': 'float'}},\n",
-       "   'primary_key': 'transaction_id'}}}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_meta.to_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "new_meta.to_dict() == metadata.to_dict()"
+    "sessions_data = tables['sessions']\n",
+    "new_meta.add_table(\n",
+    "    'sessions',\n",
+    "    data=sessions_data,\n",
+    "    primary_key='session_id',\n",
+    "    parent='users',\n",
+    "    foreign_key='user_id'\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 11,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "new_meta.to_json('demo_metadata.json')"
+    "Finally, let's add the transactions table.\n",
+    "\n",
+    "In this case, we will pass some additional information to indicate that\n",
+    "the `timestamp` field should be actually parsed and interpreted as a\n",
+    "datetime field."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "loaded = Metadata('demo_metadata.json')"
+    "transactions_fields = {\n",
+    "    'timestamp': {\n",
+    "        'type': 'datetime',\n",
+    "        'format': '%Y-%m-%d'\n",
+    "    }\n",
+    "}\n",
+    "transactions_data = tables['transactions']\n",
+    "new_meta.add_table(\n",
+    "    'transactions',\n",
+    "    transactions_data,\n",
+    "    fields_metadata=transactions_fields,\n",
+    "    primary_key='transaction_id',\n",
+    "    parent='sessions'\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 13,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "loaded.to_dict() == new_meta.to_dict()"
+    "Let's see what our Metadata looks like right now:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -278,10 +242,10 @@
        "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
        "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
        "</g>\n",
@@ -291,10 +255,10 @@
        "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
        "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
        "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
@@ -303,7 +267,7 @@
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>users&#45;&gt;sessions</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-377.2088C101.5,-363.9446 101.5,-349.6377 101.5,-335.7493\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-335.7312 106,-325.7312 101.5,-330.7312 101.5,-325.7312 101.5,-325.7312 101.5,-325.7312 101.5,-330.7312 97,-325.7313 101.5001,-335.7312 101.5001,-335.7312\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"105,-325.7312 101.5001,-335.7312 98,-325.7313 105,-325.7312\"/>\n",
        "<text text-anchor=\"middle\" x=\"197\" y=\"-347.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;sessions.user_id &#45;&gt; users.user_id</text>\n",
        "</g>\n",
        "<!-- transactions -->\n",
@@ -312,11 +276,11 @@
        "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 191,-.5 191,-.5 197,-.5 203,-6.5 203,-12.5 203,-12.5 203,-132.5 203,-132.5 203,-138.5 197,-144.5 191,-144.5 191,-144.5 12,-144.5 12,-144.5 6,-144.5 0,-138.5 0,-132.5 0,-132.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
        "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
        "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
@@ -325,114 +289,137 @@
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>sessions&#45;&gt;transactions</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-196.3927C101.5,-183.0141 101.5,-168.7483 101.5,-154.8345\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-154.7831 106,-144.7831 101.5,-149.7831 101.5,-144.7831 101.5,-144.7831 101.5,-144.7831 101.5,-149.7831 97,-144.7831 101.5001,-154.7831 101.5001,-154.7831\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"105,-144.7831 101.5001,-154.7831 98,-144.7831 105,-144.7831\"/>\n",
        "<text text-anchor=\"middle\" x=\"232\" y=\"-166.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;transactions.session_id &#45;&gt; sessions.session_id</text>\n",
        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7fc36bc596d8>"
+       "<graphviz.dot.Digraph at 0x7f1b2b6cf630>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "metadata.visualize()"
+    "new_meta.visualize()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'tables': {'users': {'fields': {'gender': {'type': 'categorical'},\n",
+       "    'age': {'type': 'numerical', 'subtype': 'integer'},\n",
+       "    'user_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'country': {'type': 'categorical'}},\n",
+       "   'primary_key': 'user_id'},\n",
+       "  'sessions': {'fields': {'os': {'type': 'categorical'},\n",
+       "    'device': {'type': 'categorical'},\n",
+       "    'user_id': {'type': 'id',\n",
+       "     'subtype': 'integer',\n",
+       "     'ref': {'table': 'users', 'field': 'user_id'}},\n",
+       "    'session_id': {'type': 'id', 'subtype': 'integer'}},\n",
+       "   'primary_key': 'session_id'},\n",
+       "  'transactions': {'fields': {'timestamp': {'type': 'datetime',\n",
+       "     'format': '%Y-%m-%d'},\n",
+       "    'amount': {'type': 'numerical', 'subtype': 'float'},\n",
+       "    'session_id': {'type': 'id',\n",
+       "     'subtype': 'integer',\n",
+       "     'ref': {'table': 'sessions', 'field': 'session_id'}},\n",
+       "    'approved': {'type': 'boolean'},\n",
+       "    'transaction_id': {'type': 'id', 'subtype': 'integer'}},\n",
+       "   'primary_key': 'transaction_id'}}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_meta.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pretty similar to the original metadata, right?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
-       " -->\n",
-       "<!-- Title: Metadata Pages: 1 -->\n",
-       "<svg width=\"371pt\" height=\"500pt\"\n",
-       " viewBox=\"0.00 0.00 370.50 500.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 496)\">\n",
-       "<title>Metadata</title>\n",
-       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-496 366.5,-496 366.5,4 -4,4\"/>\n",
-       "<!-- users -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>users</title>\n",
-       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
-       "</g>\n",
-       "<!-- sessions -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>sessions</title>\n",
-       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
-       "</g>\n",
-       "<!-- users&#45;&gt;sessions -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>users&#45;&gt;sessions</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-377.2088C101.5,-363.9446 101.5,-349.6377 101.5,-335.7493\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-335.7312 106,-325.7312 101.5,-330.7312 101.5,-325.7312 101.5,-325.7312 101.5,-325.7312 101.5,-330.7312 97,-325.7313 101.5001,-335.7312 101.5001,-335.7312\"/>\n",
-       "<text text-anchor=\"middle\" x=\"197\" y=\"-347.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;sessions.user_id &#45;&gt; users.user_id</text>\n",
-       "</g>\n",
-       "<!-- transactions -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>transactions</title>\n",
-       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 191,-.5 191,-.5 197,-.5 203,-6.5 203,-12.5 203,-12.5 203,-132.5 203,-132.5 203,-138.5 197,-144.5 191,-144.5 191,-144.5 12,-144.5 12,-144.5 6,-144.5 0,-138.5 0,-132.5 0,-132.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
-       "</g>\n",
-       "<!-- sessions&#45;&gt;transactions -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>sessions&#45;&gt;transactions</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-196.3927C101.5,-183.0141 101.5,-168.7483 101.5,-154.8345\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-154.7831 106,-144.7831 101.5,-149.7831 101.5,-144.7831 101.5,-144.7831 101.5,-144.7831 101.5,-149.7831 97,-144.7831 101.5001,-154.7831 101.5001,-154.7831\"/>\n",
-       "<text text-anchor=\"middle\" x=\"232\" y=\"-166.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;transactions.session_id &#45;&gt; sessions.session_id</text>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>\n"
-      ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7fc36bc59198>"
+       "True"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "new_meta.visualize()"
+    "new_meta.to_dict() == metadata.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving the Metadata as a JSON file\n",
+    "\n",
+    "The Metadata object can also be saved as a JSON file, which later on we can load:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_meta.to_json('demo_metadata.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded = Metadata('demo_metadata.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded.to_dict() == new_meta.to_dict()"
    ]
   }
  ],
diff --git a/docs/tutorials/demo_metadata.json b/docs/tutorials/demo_metadata.json
index 2be7ecc2e..f59aa96da 100644
--- a/docs/tutorials/demo_metadata.json
+++ b/docs/tutorials/demo_metadata.json
@@ -2,6 +2,9 @@
     "tables": {
         "users": {
             "fields": {
+                "gender": {
+                    "type": "categorical"
+                },
                 "age": {
                     "type": "numerical",
                     "subtype": "integer"
@@ -10,9 +13,6 @@
                     "type": "id",
                     "subtype": "integer"
                 },
-                "gender": {
-                    "type": "categorical"
-                },
                 "country": {
                     "type": "categorical"
                 }
@@ -24,6 +24,9 @@
                 "os": {
                     "type": "categorical"
                 },
+                "device": {
+                    "type": "categorical"
+                },
                 "user_id": {
                     "type": "id",
                     "subtype": "integer",
@@ -35,9 +38,6 @@
                 "session_id": {
                     "type": "id",
                     "subtype": "integer"
-                },
-                "device": {
-                    "type": "categorical"
                 }
             },
             "primary_key": "session_id"
@@ -48,9 +48,9 @@
                     "type": "datetime",
                     "format": "%Y-%m-%d"
                 },
-                "transaction_id": {
-                    "type": "id",
-                    "subtype": "integer"
+                "amount": {
+                    "type": "numerical",
+                    "subtype": "float"
                 },
                 "session_id": {
                     "type": "id",
@@ -63,9 +63,9 @@
                 "approved": {
                     "type": "boolean"
                 },
-                "amount": {
-                    "type": "numerical",
-                    "subtype": "float"
+                "transaction_id": {
+                    "type": "id",
+                    "subtype": "integer"
                 }
             },
             "primary_key": "transaction_id"
diff --git a/docs/tutorials/sdv.pkl b/docs/tutorials/sdv.pkl
deleted file mode 100644
index d3c88b5a4eca4045c109b46b6393d46f5be7a46d..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 255975
zcmd@7cYIV;+x?GIq=nu=x(b9My($otUWHKvR18T7Oh!nUorI=C6Anc|njnZEponw?
zR0IrFI*5Suj!F?wLG1Ef*V<=hGn{xk`S`r@`{U>H+}AmKt+n^r^PW93=S*gX;hAF+
zdkv_D|Ajr>{Mj&XhDsy68GYVN;oY)i$dI95La*4qULE0nVzror{>go#62n?WB_}4v
zMa4H`JIwn)w_N@XkuMF5QhgG=S$y8CRC~oGCaJjS<fOQScyG2Cdx&bu@o@v9R9sY2
zY_&f2Xn|q71iUbB_QY=4W$f6bs9sS?QQjP!vcDPD!PdjPVVHbQpEp-{w;af;goLEX
z{!vM8Z|-gx(XQsrLp{>p)0@}l&DSj}tfZ*uzOjkk{6231eZ!LzV^yNJpwC+<yjxBL
z`m4BpQEG7HOR<B!g}Y^gB8kYjUfv=cpm$ts-(HE{qCRi2aCZ@(w|IEB2V~sA{bRik
zcFTlaL`$$^VsbR)l5EArC&l)ORo+s`Y{n!c$0w=5-qJpA8TW%cbPV|PNl<YyQGLB-
z6M3{gvGKjor<~9Gko!S5#LPHARG(Py!#?jL;pitfKR&r%tnO9bUD91DIXRgW6Jv3=
zCB!FsEBL$>!`+3ug&}K;a(lwvxDl1Y-QhlOWenvm!On7<s)X}6Dz>+`s?YnV2E`lh
zt>!Mm^+Jx`>h6Ncfu5Xjuh;=`F|pnnK5tDuVIh)`=&j}R)~0~T$?{23QSpgUF|tk)
zy>)!vx-!8$!G=t-o=mcSxVxUu+u)u_V$Kc2-I&~C;qGegaCdcgg?lC<P?F;M#U>_2
z_3Q6_+~;j1sAOefRmF<qZLIIi1l2Dp$=ifp`R<RF@Aqi=Ufzggno<4aF7h_@d7qFY
zgDr1)@4gA>@}y=Q)xW<=7!cdb+sx-}j$_DriB3r98ygkxZIOt3BC%URNRe(V4;9H9
z-5bZ0CD+pDZ58e=7^8Y6Rdd*3t);^=0!in?ysfe1+xWa~!wo<GQ0;u)_Tlc*9O~X<
zRJ&(Ig*7v)D`?yf7`LO(`;>ktMa3t?5AGK?B-Y!hqrSPxLxv3Yb{@x79@96jzt=U6
zH?tpZdt7`U@6$f-GhBwUz}0}LzKPy0KJT;Pd@OWn=j|HaE<>w`L$&!|tB9@soeyX7
zza(!rF19XDKjG~jeoycmxZ%H65yzRE)hc3Me*Tx_eU4+dc-s3sI-Fw+xo>;(zg7_!
z{f7v2c!3>Sy1YHmA&|SGXV5C*vVVxl{v*7|4$WJ5Bhev{`+hI~YZY;gN2t{*BG92H
zJ3QIS8-)&m+|9fEuT{hi|J(u{qLY)O-R0auKgQ?n74GiT(cO8R+cnM&ps{Ws>>bW0
zvzFdIBpAT3%g<w#f3|K0J9y)8>_9AVGtc|;0pgxk!+zB_`Cc49_$laHSG!e2pu<Zt
z*@vC$EAY${*3thQ6tuSb;@0)^dE@o-Njx4D30z10<nhs;PkQmmefvhnB=k-07w`4z
zCD)73nkptbIj(O~Tzp~}R#uo-KOshHGs&IUEg$B~-uEndZ*ppr?AsUf>)SVIY6CE}
zfj;k`a6T>ej>9alc9UbhgJW>3`VYp-pNg&4FBvn4QYvb2SR$U5qg0r8i1x!ckudMe
zyyJT}@V+u`>=>`l<sBN{E;H|sM&4l^J9ca<D}be4h9vLs=w{iu6c#?&)H}lRj&yln
zb>n8sO9);p2YW}2<D*V)SE6^cdx%?)A6?UH$@pVj-m(7iU-6D}j6XiQS;h?B*DUYr
zF7E_BQAFMgc%pkkay0(0TW%aL`03~kpLY_TL);y)C}Z3YyZa@(M~!uN@_8rg=XJSH
z61`J8Vj)y;%X86GpLbfg8&5rNhP!KU9Z&apX9Ta~w`3j94Ce^_5~Ab!#(HOQF$W%^
zhy0J#bzG%0le}*;&DWPC?`$m4KsK;I^#c;m2!ThyJF-A)aTarY-nqfEcvogIPp`o^
zycW&>w<#>(6I@h$FO`6op!Y(Y!~2*+vKt&13XtiT&Ky6(58MP-hu{|ZydM(W;&Aul
z&gJTVi3)3Ozg7h;+a(~s)aP9m?oM#`$I5XhyJaOW_jy-@y9Z;v3~iSEuaosQZ5_O}
znRlh-UFGtAB(YKB+@swS-1Y)+H+9QHbG6UACNR)Fk2k)=g?ZPyliX|V1AOfBeiAsq
z`5}dtx60qOsrOUMyUykPER_Q^bKWrfbusKo{rVVmv({ra8+_i4m<=B*=9Yc#^QPdy
z0X6uBkoP8VbT7qiTJ3J;-r#4xnaq><@Q#de$Mx+MiI*(z7Mzs!?HjAlJi>a#_Kw0s
zyjNTdo?*6{XPm@Xw6~4+Zg+XVz>BndTMzFJcZI0Y-kmP*E<6Cx-tDe~_8ymaue*-4
z_l@@McX<ya^H~jNqbfE&iAVg>z15AO4!XQwh3l?|+y&9~u*-Wycm0|}A9Z<;=|deK
z?LFc0p2T|6ue5S*j<dj1`fOOY!aCSn?`g0*<MW>NKg<wL^q%v1&xd352Vh?Cc`t^$
zv$?aoGY8Jd`NZ~*=j0vaY0q}Uyq7S>H$LyT`UDy0B)t;)MJC4LtmnIqGAz%YA|vHW
zATrW>xspb1UmHNV;`3gWTq19cey&gSUhCogUhcT2?q;!MW-byEz1MqqZ-l$E<G?q4
z-XHXHbPNt0Cy$Oq?~gsaKe=)6%az<1{1(R*<Wc<+y|;bdpY>zCZ$gas&V3JwUo7vh
zF7I!Al9gxlSnuyT@w&C-((2OLn|c4Rynnj9f9Z3;NPS)OcT8eT99|P}LYb6UEmE#>
z;v?hYd#fm1?j*+~C9BvlH6XILN*EFwALhMVX@tsv3lX2nh^j9TgpSIDW+gt^zcjE{
zqRPz71CnvyasTH2-HpS_O93yVvOo_$ZcIWvUJsKKk`wh562szgG8u`BgfNv=h904^
zameg4q`h|f$uqag0i|6=zAoWK42@QI?Wa>}(JH5C!~B{|Vo*{-UzJPb##u+bZOyCl
zHt*=@s9t=bRJqy1K9S(G<D+n9n+LLPVQwm~wB*dYZ$f+@`uU)A<a2zYI|{EUDnA9u
z#16tsd9Q>RRe+LALYA2-D9vR1by=^!YLWfpVqWSS8x|R<1-WZtBV&>hRG2D+Ja2u6
zXKFTVSi8O|3}Lh?g6dL5QGKcyYB*m8qvMi<V62KlUsW8sN8);g$e`*$_9(#~a{Yx%
zLsb&3e>hQyRI#|D^uh>Jr2@gQ_+I)<LY0Qr4ihSaZL^Hgsw}o+R5?_adI&YVUB(Xm
zGmTacW8XdjNUozEK?~;qsyuTQn9J;ToMWhpaH7XOxd~T6oeJ;$s=&mBdq4lBb>p$q
za;L;>pS`wnt!m|Sw6d}d{q*QJFHE0ZWySmxT@OEW>iohRS<1ff*!KD@-fZ6dZ1~g@
zn~rvFczDYL8~V&FmwDEsV~&p=ez(;xyK?QG-6C6^&R>+<7}ICOnlGoe+&it3JG{?_
zeJyqPdhW%$TRz;m!{1-DS=H&eHxjFtZ@=7AK2P5IAG#;3oAS%B`u*>W?-lv`A8T?H
zXn*L`)nWseEZ=$j^70+$>UJ6Z{p#LxqhE|2o3&@kM~z#2RO^X9tM{He=%a?)7FQpf
zYtUaAXWsp3|M0~<IwTkCU-Y|meOgrhu*A+D^D0h%zW(b8?p?kT?#e5A|Izx!t8?<b
zyW+HGSm)@vHJ&Nz-np!LpE^T_gw4Jk{@MAQ*79Y!7Y?~JX7#(Biu}FWYW!o9A7{2n
z>XW}f<(j`%&HBsehR;mjF!1ulk!|-i{5YX-$3m+!AH30^a^{N1?pCke+vEE3iIMF}
zwtRJ+>*SK3@(<oS^ir|B4VL`)Wq4A_S{cvnthl!Pb8lwseX8x=+m|L3JvRNF4UZRm
zAv(jfzT0}8?bNa1%xIiNY#uNw&%o>Pd*ACf;qIxZ$+y=oc&Jh5{pCt8-FRYIt6Ya3
zTfeIC+FO~j6{x#3eBQ<iU0?25sQRtGa~HdIR{Y`5I^A+yIM(IdJ0E><_3CHV#_gXp
z&2n`4l#X?l^~nBOx0i}P`=G1D>Y8C&&o?}_p-=3-(vN>Oqjdet8A{BnfBX57LmsZ)
z|Gntpo10WSyZJ!E<PuGP$^G|`gExv7f9}d(PhESXc;(hbzc1tM@z<i!l{1};IX}RY
zvHJ4-tD>?V`g7OJCvx2_IV<n%w@zos5?krWmOp>V<?T^z{;%DpsTx^3OnJ9yZujmI
zd0vb-^4M?LW}Z50wR!f6ch0q0Pkgv({KVgmo_yiRtq$R@*M2|h<NZYsR$duiGp1OT
z^?l9`et$##YOh|HIPaxxLw>B#s?7752X8)Bds(X%b4zrdIJaNls@3CvFFEwV@ZDLy
z`t`-z0~)MpJt_9$kt11ZcdNB*Vulro)u;b9de@nZ9ZDA%+<jtL;l2ewnQ{I00QVoi
z{nc~w#Hkb7WPGX5zTSn-ttzm7!Qn|w@-C|O@T#zef9I+@zWB_g*HRkIdm*gy`bK*u
z4c<|&`wVxtrFCKl_?mVexv%8YcXmFquzRkjR?S`JI$2{}wOZL%W>~hWRGFq$kK-e{
zmdL#K&b9hCnjB27Q^jg@V%wBAdiv(Q@Jdw5%^SY>i0chzcdz--(Xi9+sA=)*s$5yQ
z;Aa1QH$UG~v&YkA+&jKXs8PILch{9NC%zc<d2)2h4>`W?+Vhi=*Us<gG5^DTo!cFs
zU+C0=GA%a0P;TAgjEx%(KE39vMr}9L|6pSIlTm{<<$A2%_c?aoP8j&=&ANBLf4u#Q
z*LoctKl8wrrX}Bcc~_r5KigGks_T#S0}D6FeCGK0V}qvL$a^d-W2-S^`n<J%{_G>A
zGDWofY~9K&v%0ozRm{q<W%8S=6RQ<z9$CEYqH1FX#|$3cxn<&<0ek-HQ()YT=Q2n9
z(Cm$b{YPHEJ9F2;wmYABqvG{$ZLVi(zVy_xV+YT~d#A^RzqWLC=Ep9+GxJpUIV-!k
ze*gB1S&xmnwBb4L;@`jgV$}Ye%@(vhaO&BE%eFLsWZ2S2le)#_`mJ-%-(D?rbMo|W
zF3-xhF7j-<mS?xVJt)U}zgCUh_*LDF)i$>;_C&*#)wi8#lY9NL1)G0cI`2sT1A8(T
z`g`L$U;mvlp-$l`qc&tnywUH8H9Jc@5&c<*>0K(H+1z=~%?VRad(T9?R4kWoQKrm!
zyZu^xO4NbMuic%tcE+LCdR{+t{r7C&d$!MAabomicNR2AD5AO--xJqn<haN8Jzn+v
zqo+1qo&QSF*9%Q;QS{=fv$>u<7d>l1wkp$Fj2)c&aMN@3j#T(5xt290a>A{tUliI9
z`Qe2;RkP-4(&OS&t3K~AZORR+#zPINPF;KTPOGmzS<r3gk(awfjy!YYdiQ+YDs1V}
zpybsL%DAe$((KsWs_mNg>{MZEvm;kJ-JN`8^=D_kt?%pBI3efqPV=^RUi#GL_tx|s
z_|^XR^Y+}lVE03p*8KVFtR-clD_$78uh=UW*LGNR?AtRFi@w%yRoH<u1@m7m<??Nd
zJ6&UVy}U)|x#DO3@%QUxN2Ywf#k2l_u30BEZ&ba?)GIr_eL2$C;{9vQi#Et|VXC)Y
zfucE!_dL0D#*Cg7pS<8LdeOVM&h18DoE|>>os|dcovGL-$JXY{$92tG?smubj*k1=
zz5J(o?N5K){zjSpy$5f8`Ndi9<geYh(x)x%-q`WP`Vv(NM-2J-#;zPMl)W_P@aT$h
zSJ&=}89A}ktroGhD}I-K$`_0BKmXWAUynOp=|GkV$5yo;+2Qu=S-VDb-m`Gg>hGSc
zHob$d-=sb7=Iweb>D_D>cXVCf`})KAx@3yjern0Ohj;cp7q|V93nee_Z%}B#XG6;M
zoLHjKr4iq)&+HjjaaWn)3!nV<#a4Z*<<C8*)a9x(U%Q*|&BFN0Up~5```AlUyH}hx
zxNy<cmp-mpb>~|Lv%M8xf6~x*H!LllWn`Xff1GH2aBj=dW8NtEZo3S#jxJdqon`v&
zw_on^^k1#FZQMO=)F&Ipj%j`Ql?T5sKHyTv1zl!6(6e}(B9E--bhvfH{X3%S{JM8p
z>E)Aa?ke46_twY(6O)T)xb@Aj<5jMYA76R*z9z-yx6jpa$H)`gkN@!c-~-+p?`Mx0
zb*%cx+uNI5eKW_JbvZNFS^Y`ED?3|$+Pd4RJ98hK-lEgTYsTd`k#ug<{?|L?e0;(a
zn=a3~`gD!W^NweFrt+?D`@HsJ*7XZ>*BaODT*2Qzz5UwqZC{+bb70R?z6WYtDlnpD
zwlU{+p1B^g`24Uk8_M3ESvqHq?`m$_UFT+=0_)#dR`yCnt4YnvZb{s+rSy!?e>{=5
ze#UZRy8ZC;l9yDz3*#0IcqGS)AseUUc{C}1jpeQL#$6n7a7}~HCw=thH^YAznLK=9
zv8+`(9m;&H(NBZt9LZ2JtlX?-^#@u-3cBCe8-4YSS_i+GH)6}gA*I$=ex~@8eS0bm
z-~ZXA=SLnGm*=->%|9FEnU%A{%hmU7nYFuT=AIYE{giD|V(Y<$lj_fX{FgT(Di<wv
zwPu?w{dfP+vD3Pkm*2kiaW8kTt=C#iSY7l&+=ymx<vaaDfqVyV96E4g_Kp2}UBB%a
ze0Z$8{;)=q<DaTBBRb2)gips5ozi0cq%(h1yf&j;f!Q5$)|fMW+S?DRR@dGgH1Sfx
zpcjVz{Pl}PoBSRfe!fY8mDl#3X!}ycoSu`mXM4G`^}x&Vxtkq0yx?M&lWPz3YtVaq
zyGQy=FZNTD55DX)p;+lGb8ED^GwH)~eYO?*x!QM!zblddcDqqORd{Muoo}Px9C7t@
zw~KK<emG-An<Hfl-g)Nder>A_DX>3t*#SS7%29t#$4A;Hm!Cc`<Mo}_elA<LcKq=M
z<p#f)aZlF<xl`JwtmwV@z_PJfZY}<F<;Qs@J}|z={Jhg^>~(D}b**ZK7YA)h7<WB!
zQ-S2BeGaF*TwqQAJMZ8A{^ndO)2!HK?TS`^wnV;cy)RyR^YVa=Z*F>TS{W6NbGal{
zNzUl_E@}USL{%BB4v`s?`KGHoDlw9OQc+bPx6`MsTBr)46;4<AUvjji9+hD{K_~y7
zpwoU_Z_3|DddDRt;jFtF;$!i)3a-Lbb?p084b*V=k3CdP>;$gAR4r-U_Nm&m?JF+-
zdjzTuR7=%Gb*Xxiv@f{c?ewPi`lY%}Rec5;FwhV++5NX$uAm;nzE3@l8t&nRlSf}K
zH4?>r$D;@Hu!~17mOS<~P-Av%!jAg7=YQR3PtYZfC+MoiBNsCsxrXuZvc<z|6%Q{@
zJo>7{Bi9p_ia`Arww`-|Hbvg2o<JqgC$VFI;ypY9ZBE+;s^3BWHbS+4YN?i}F4ans
zHqgU0#!f8y%;^Z#nt?V9v_<s;ZHIlIYL6NWw1X%Bst5D1OAyeG?D!NrrWepoI9iB6
zWBG<dOakAqh>wX?ozd4L7hWF!m6l5y`ZDWj?7Gx5sJP5(m$_YLOLalRJ#<{N)~e(V
zZ^<1$MhyOH@r}+yzKke3EX!|qu1`tvw_(!nQorJyZ~9(K@%#R^zrT7GBPOY?sL}qr
zoo}4t?HDB&+&<ME)qYdPjW<}-b5x(Fs^3=8?{26UAo1N3_q86X2mKoETHVUxff}jb
z!nOa57Kyjf{Rko>YvBLYi-`YgvZf>>Lrm6GJwr*>k};z~Ox9G<Atr077)e%1<+k7R
z?h&x~|M(Xfi8p~%FX{AX8anZvAr&j#!vC$C{xhLIP;co`In*BdD1AcfWB;L1AIB|y
zs{ZS70)6z6;-uGusr1tCj;e%#iOPdatowQo^%8dY4xH+XJ)i2wHlF+Av16X9642_F
zSH|Rf9#MGDQ@=aZUt<10U&J*~UbN-i3x%yqC8FZ*7^%E_kpw5YnY<gs|5P&CmKwlb
z15vvbg&vcP_b;RH7G*5o2=jm9Kn+5`youWf9~VeU!uvskA$#OzbSsC>W~To%%n(E^
z^)jkUy&~P@&n0*hEk@qb?Vo@@M=2j<j~Q1cWyTGT4&`XWINER-P5vT`KO09!;SVBl
zNrNMM#l|OMt40KmSt@wUR6300$ggtbQ8Kc;dMDy<-gviobnsyE$1*cPJ7cl`81^5F
zDlg*Wu$6Zshj`>Zy627<kEW$wLv^XwQQg0{%aH7Ua^p$D1gJhW5tXlLZ%D}hCas!;
zmPap&`$wJ(%TiNNU23Yt<Zje(Wk`>{JEqZpQv?H_z6Ly<{tWRANctMnTl8m&Z`jb+
z7-rFb8&xpO#x_+9@4&Fs9ERsg*n#0)s6I6hl^EtrD1c!BS~dpxQw{DFxo4WH_n=$q
zeN>lPC>g^*#<LlGK!1@4h5>!e;6wV0#WxJ-YX(c`FBRV~psyJ$qrV(g7_7jS41#95
z5~`(EG5L`s9ROBC^{F+e1h7^@0RSIM3xj_`+dj8>x{LhxO^W&ys-@PUy3}Wqw9jo$
z+&Mh|8S94#wVr_u3~WU8pWA$neV<A}4L-NoBnr-L^k5!#2|Bmg%#K^wF}==hw&G|Z
zp4;$&wT*M!E_0N#?=P@pfbNhMfbOJi1O5B%U3pS&s&+xO)NWLl+9OFD=$&UhyRPln
z)JW}RU>^hfQT;#<VBe>{L=6UdP!s^wgL&8`2<TVrc!(X-3+Q1SEiFKgaE@Qg90ll6
z>=>ZOqy?bIY1=@b)hjeYoq%eolc+9rN|H9vlU$+aBGhRH&M<Hm)erO>_I>I+YB10X
zq5!BK%)>50Krgc6C3Z|Npx@wVX#x5z=lGq>QGi~?jsbc_S^#>L_Wcj}YfvrqJ*rDx
zm!u8!xF6^Z25vI&1F9eBkJ$IApHPE=-Vy~s^<W-$2?BbX9e-xW^a6SZM@tLPUpU8K
zWsU;$H|!Xoze@{1|DbJF=$m*BjZl9=wbWmzF7>x0?_Z&J8OVT7=DAcxR6o#6*!QW-
zsKG!V5CuT>U><e}0-A*#v$A7)0nLV^r3Gkq&M}9~QGkYF#{kVKEdb3$+Xg!4<d8`h
z@G%ON8>*%9pt@9EN!mca?=q><{7spfseBCNXP^M8A80}B`&1#+V4#IX0Z=`dhh2hz
z7GcMt?3i9ai{WT#0a~1Md{E{nKuci904*sk04+t^20A2T%z|ED=W41-L$y>HRF^6%
zNgL?(X6r83xLK>ID#yS>3_OhL2l@#1eX2ZaFwhF30H_|!!!AKUE3#uaJEj-VN;q0t
zfL7)ltH>M$XjSYOppQxmK&#QVfv)^@{EA;sbZx4tL$y>5RF|qLNgHUrL)F4+ZLZc-
z)ncGF19edSK<i@Pr|O{w1FbI#fa<|K>=FdD0XsHi$MgdF7><?}ppSEojbx4jv@v!J
z&?eFX&<NT#(2<3Y)!#F)%qG<os->Plb*U#MX#=f4Bw=`yx@DTGW(+iEparTQXiMz-
zR4deApshs#P(7H3U4nqNVaK-Ym|j5J;b>_A+MaXlAafL;9kFA8J|!&x?L^xKTE5Mc
zN7_ue6rnmpwUi6hrJk0g4YYfc-=Ew*^=O28hJh{&Jd5fF+7<ggB_Fa32HIT|0M&ze
z*d++)bL{v$JEj-V7jU$+0PVp!z9@4Pppn=yKzm9HK%;2eKo@`b*MYKQs%%oxP%RaM
z>QcQVX#;(6a-D2TPUdf_Vj1YoKp#{;P&f8{Dh@Rms7Dk4)q{E1B?#zC?AVtb(+g-n
z94#$C<2lC!nWF&hj~xTlD=h$3v~8fcO2U7*vP^_(sU%dFN|vMzw6%)3J#^RQ2sMC#
zfeZ{n^#dJ@eV-bF8VvMhQ2<mA=3$p0ps%o_j~&ws=ujLjM4+*r&NxvFejj}py4&xg
z567-cjgZ;nee}HT@>*&n8h9W5$?Wnq2>v&`iGT0^H{1XJJ$}EEJ0{DYGR{svdwlMQ
z^AYj<)5ZCe1icNkS4_{?YWljX5f#sPkJhQaB%;Ik8DmDSemkXE^x&=y#`{v9t(I_W
z?6sW{*$PzO+voYSDYKhBaLLtZ$%cQrKmEsx{i|)p=TpA2kAE=bJm+yR(4O~c`CAQ!
zoQybHYs%fR3qDV&|97hvVU3nYlsmiqRK+S=Qz}<DQfo!__aoZOC|ZBT@k1%!v;XFl
ze>$%J_UYpvOhKEszrX&IjkNivo86=4|Fm-YmXwLz8uu-+V^+keVr!aY9=b2$^05`i
zhCl30>Bif8EJfemn-L`|Jv}<p;`m1YbX-4A|LyZ14+jf6{vLhYh%x%ODd+WZQ~uJ&
zjd(#HHzJIjZ>C&qz2xZliM9XnxT)M;-ah~F{kQ-B`u6(Qi~s)fkMF;I{<i=2zY<w)
z#PNx1Qm)Nxb!SnAV-dR#tgPC2(Uz0}CkH&dqx?rHaf{w4-(&60lv52$?XG$9qkp`A
z{p-`eUi|l;pQpd=KOQ)czboU332Q5!Pgyx|>%`ul{1~yf|A~5s|2Q5|aPXx(?xgb(
zbD!xuZ1C+15&v}m`X3Me_32+P{`=40_TN7L@%)d!AxWA4=(Om3%J|jYzKV(YA*J*e
z*;*f3y)<R&-N9<WmtXzk?e#zY{Er9!`t+|Cf7{Q~fBXE$^Yef1skc5_y#7Q)kz$`z
zd-1(<5i8fXdb?1@i~o3g{f~G5<In$i@UKsQ+kgN0dHQdk|9Jl6H$HG_{@|~-q{QSK
zw&<H7d;jsc{^u9}<K6%G^FJQ^ZU1`l-+z9d{@dq2-UR#pue<r+#v<1a{^N1|&u9MU
z7yskk|M>H_{p-`eUi|l;pQr!!`H$ycf9h4dC?=^<sQ3OUxS)J8UyX+9Qx@vIe+nK$
zbu87OKLwA26!=r{c>1OBb)AR1mBi|e#7FY=7d}(@L-1<||IZZv2>f~oshVoSzoq!c
z--#ilYN|IvNYzr4{w-Bo{@gq{gj7v6C4^KhH8q4(Z8h!RQgvirZ-$VnsiudJs-<Rx
zkgBcT3L#ZT&HT4iU77u?5K=YO+aaWCso5c<YO8lbNYznuLP*tBbN?+>Pj2J8A*5=m
zc_E}~srezKYO4hyr0S^mLP*tB?}w18rxyNOs=ko;AcRy+wJ3yCE%jjtsoH9B2&p=1
zNeHRBYH0|mdTLn+srqX9zoi-ouN5JrYO0kXq-v>EA*5=nk3vY*QL95p)m3XkNYzto
zLrB$EABT`?pg#GxR70WqX$YyBYF!AaTI#b9Qnl6k5K?v2h7eM9)y5D~_0;Dfr0T1b
z5K;})rVvsM)n-ZYLnZ%krcjfg8QT&{tftx;O01UJ7D}wP+8#=*j`|{$SY5Rvlvq8r
zGn80;wJVfZ1GPJpSVOf(Vx|Ab`I_7dHEXE7|2iB`+4hAJtEu*f604;Sgc7T*z6>Q+
zM;#0$R#$x$O01qb6iTeVIvh%@fjSaOtfBf^V*m5r!o7fd>u5;1n(ElU9+oH1$3uzL
zR3}1-)lw%ziPcu8LW$K;r$dR=RcAtp)l+9fiPcx<LWwm{=R=7#R2L-nKkrrC3%FM=
zhLo$RE`^k<rM~&s>G4A5+fZUP)pw!9YN^Yi#A>T6p~UK_tD(f|s%xRd>Z$KTiPcxv
zLy0v|H$sUuR5vB|Kkt3q3%K`x2q{-n{TNcNmij5ATy1qra{u#nnwOQgLy6T?KZg>l
zrS60htF3+sC00lM8cM9L`Yn`LJ@tDivHI$dP+|?#pP|GWs=p-mKUWFv1+0?4L(0`u
zcSFk6QW@|K^M5~XZIv;kTpg83a*y11z7h0cdHs`J`h(?}C0!wuwEjr>1ClTMzw!x*
zdgLsUeCXejdep3vZSude|MtxXeavh!PSgK3j-FBW|1={xQ`0lb@t<a-$I&wi`>!*?
zVDjCFod0!37)Q@2*MFLkocHJ%<^E4I(&OkE<@v8OvKi(5uQLiT%J*MqgqtV#TK@k!
zBaEXN6_AnIq~$RZ^yCrzO&dMAf-+>g|Fa?WJPZAw4a)KKObg4P9sVz7Y7eUCT119z
z`TsoZJ>MwO(=00Ewf?^uPtUN}|ILW^pe!yUw*Aj@4C46Uf1G9D`%ietXp$xV<1B+m
z)GSN>UyXSW%~CRE`~P>Efq4e;EG?rpNXw|^n_-$?85!ZR|JM;Tud*^mk^9Cl2U6wW
z^DAf%VMl)j?P2Ws)FW);V?E`uV?NeXL0b4&Pes~6AM3%#@x!57suHS8RhFdvv7R5g
z?s$Ag&aWa=6$Yv@@F=SPV?EWd?^D%LgFn_&Llk_hM-S#<m!OaJ)MUq6?3i93>#2>S
zh4`@^exsxg=U7+fD4&z6haCg7zO(?e0qy&NHiT-a$537BaY@=h--@ex=2-WG5vmab
zjTvZy>IWKueV=NI8VvLaQ2<mA=3$p0pii=6Gj>cbpv`f#v;b|vIkuEJ3eZ;AF+f{O
z3qae@wt@P822!<!YN>XpF4bO=HqeXxcdieQ4{xeEFwl{Kr%?SsJ7M3aI->>yb%_F?
zdN2>W1Oa`T9iL&x^a9!iM@tLPXF12NGDiU_zkXnVc1J7tTXc9j8Qw%a2i;Q7qq@`!
zk}=<+lkx0t(e<GJq6j|ugxg>#^oR1B5s~zJif_I}M_+%7E{cA%_~u)5^!2ysV(9lm
zl{-BaTihG^SLQHHJ=5M$E!Brfw<I0kqKkv-Qyx?Tcu7KmZ_)Kd%K-pCgxwFirQ%Ut
zDnT*^fQ)AY=uh7(f&oBZ15or6#Ww)xYXC{~lf^dx=xYE2=nq5{0E4i-4*+I57^<a)
zF!{119ROZ|>Qg>c0vIZx0Dxg=IRIb<42N#15vVRTQZfdBjAsLQmHsFZ3;_BXz-aoG
z_yz!d4PXrYvEmy5^fiES^v9zLfY-3S4*+KRI#f$dU~-})9RS{d>Qj?Y31G5>0sy9<
z<p2P#Q=waG8mddZDH#Jm#<KxTr$0jk1Ax8;@D}}<;u`?;HGo<4-xl8hpsxYUrvDDA
z0GNaAeE=}ixlk?jE|c>l=>RYvs!uIIC4l!N6aer(TB!k82;EX2pt{r|$ru1Ko(<qb
z`in&{0O)G~OXx2Z-vFSm0W71xTzmt7z6P*@{z_B<unJozfRCVBYBiH<B<TRK7OGEu
zj7k8XNGJf{Q?wiau&~xax725-F121V27rub1K2=+qX-57eGTAq`YGZY0Q5D0P4qX5
zZvfEO0JhNIiYfrMVS66{%yc_cOMSuQ4oNxy?1bu5yHE*Ww}b)!_MqhefQ7Xex~2A^
zy3~Hj7yvS!4d4L%FGVl_=xYE6>3=1@0YF~^I7I)j_yz!d4d4j<uTcfSQEcx6fSDeH
zYN_K)o{*#iz)7e+bqbXLPD>~N;0#)+0XPfYQs+=z>bztO02$8)aDo0s5exwO8o(v`
z--vGj(ANOIrT?Ay1^|5x;4=Lyr~=?BwoU-opyJD)OkS6y1HcWaK6Mk70Dh2A0KkuE
zr3T<9=$5*L>Qc8QV*to_Hh`b$-x0w8psxY^LjPCs4FLKYz;E<_7vBJ&uL1l)|4&o_
z@E5jD0DnWZ)LkYs;0I5g05U@LsZ6K@kXb?j01u$$0DwnU7U-7Bit19?Bx3-`cs79S
z^mB+{0MOR}!szD|-vFSm0py~eTYLk6z6OwoeqK}okPqAY0AQy1p<1c{lLaN|SOJBg
z`cz?50w^M(0Dz)sIRN01RSdeNile&JgOV`-WIP){3Hl{PFaYRl0Hx@c7T*A%uK|>y
zUsikrfW8J$j{ZZa0^nh6?*o9DJ_6NJ<(aG?Ne6(6P<<*Kl>jP9C;*@`TB!l30^L$o
zQC;d$$ru1Ko(-TH{punZ0Q5D08uV+5ZvfEO0BX^%ExrLjUjwK^zb>i(sE4f+Kz*o|
zYQSVeNjd;L1{J>qhDrd9BoqM97_HO*G=Yxa<wA9-rjjuLWIP+d6ZD@H!2qDI0W_oE
zTzmt7z6Q{OeoOHU0Qwq0EBdWb1wb2YodDWG#RVXf?Iq~|&;hDXbwnkArz8{r&<U;7
z0Ca|quiT;HD|eDH0AxHHz%%r_h+qKF*8rZS-&K4AfW8JGKlSWV-NiQm=xYGa(SIIQ
z0K9;$6F?8BmU@xNNJ%;X^n{9U!l4pCw1ffxV$gB`z*9>v=$48_b*bKxF#u#d8$ciW
zZV?Os`WiqSeUJDC0DTSMCHj5EHvs5s0R8C4qY8ioZ0`ernf8ZjDKC>sk`4fgP<<*1
zl>m|@6aX*)Ee8NRwG4!AsX?eNHCQqRfQ)AY7()ML5exwO8o(>`ec~Gc^fiE?^oNOW
z0MOR}hSMK`DgZ`edmjMI^i`;q8pY&jNjd;nP<?6)Dglg@PyoO<v{C~w9y)%r2o=9s
zBpCxh#<KxTpg&Or1Ax8;@CN-!;u`?;HGs+Vr-*L=(ANN_(w~MZ0N%vb31B)@OU+>N
zElD~6%!KMwvrq}(Z3zVc%tk9U0PjG@uN9%X)Lh9J05YBp;9dIjL@)s8YXI}<FA(1V
zpsxYENB@2C4FLKYz(V>TpbCIR*g64x2-Q-HnOq`C2Y{teeQFsh0W6nL0Kf{gQUkCO
zI)1GP)uld?i~%6y*#K74Un7D6Kwkq`OaEi>4FLKYz$f%S72g1$uK}#1{~4+PSdXm}
zzy_$6+Q{VRl5_w_f$CG6PzhkOgaQDzpp_bct<WvC4b`Q#OU3|@@oWHJ(BC0~0YF~^
z*hznv_yz!d4PZC@J>nYx^fiFJ^!K3(fc@Ay0UUs8sV|v4C`ku^ub}$WAyfi5ETI5^
zBWO7Q;JNi{=$1N)>QcufV*to_Hh|;wPl#Xu(ANM?(my4>0YF~^I8FbI_yz!d4d5*O
zbEpE~Jht}%z)UYdwbVr>FG<n?;2Wqu^(`s^d?%p*fXiq(0N}ax3Uo_dMRlobk}&{e
zJR89G^skFx0MOR}ZqUCez5zgA1NeddkK!8u^fiE==-)yW0JpKd4*+KRGgM36Ve%JA
zIsp6%)u(<#C4k>06aerCTB!l}6FPnn2NgevBN+og#<Kz3rJn)+7~(wu=xYEO;kZ;L
z@eKg_8bD_H4~TC7(ANO6(9eo00J3501dtu7rE)MCCP@c?oKW#QHK+uTTS5T<dC*D?
zKwjvU%7^Mw`6Xij$apq@0`v=tU{(Nq4WJPH!r~hM^fiDY^oxpb0MOR}iqS8QDgYkD
z)(M~lR7;g)vXmqp07^sksWPYpP*y?#0Oin14ZuUtE%h*}OFbeP13<>J0hFg-K?DPU
zz6MZ{ez^Ds0DTRh68*~J8vyh*fGYH>q6&aVv2_Bd2Gvs4nXDm62Y{MTeX15J0o0aI
z06-nIQUg#Ix~1x&x>SA17yvS!4WI%2h9VdM^fiFT=szyL0YF~^XhgrU_yz!d4WJ4A
z2vh;k6k8{NC!kvDNhX^~(gC13RG(^rN&qb-6adf)t<(UthHj}gs4mr3G6sN*X9H+Q
zzr6?s0DTRh1O1NT8vyh*fT!qp65jxzuK{$X??M#-Ph;x@@C;P^Br%iEO40$KD^&a>
z3Mv6~mrww}b7(mL;Kk*6=$3i`)unn!#sHA<YydCPj}*ZGpsxY+q#q@|0YF~^h^8MS
zz5zgA1L#FR7F7WB#`Zn{m}wuVmU1&0CrJkY4^*Fe36%i)N+<xJA6gCocyWn`Zm9%R
zm+CJW13<>J0eI;v5exwO8bBibB=HRZ`Wiqo{Q=?|0Q5D0f%FHV3V^}b-Uk3P9Rd|U
zKg;ARl5_y@LG`Jjs01)fLID87(Mk=#2<Z5Y4pf(VRWb&EjAsKFMSrvi1^|5xz@k4!
zd;@^K1~8WXIPnbt`WnD^`mdo1fY-5g0+;~RQWKecLy`^vlc4(4WK;r}BB20)sc5AJ
zU>bBwy@~2l(<Ng7$apq@8T8*0!2qDI0nDU7OMC->z6S6%{n_Fh0Q5D0cj(VS6##Rw
zbpm)7s-@;JIbV_v01Ke{)O)A|@V<ls02ZQ^8h{U=TWS%iOMNIA13<>J0W7A!L<9qX
zz6P+A{xb0m0Qwrha{4R8Hvs5s04wRQLKOfXVe1628mgt%Fu7Ke4geoR#jnVs62PYt
z3IJG#R%!q~gKnwys4lfZG6sN*X9L(s|8o%x0Qwq03jIyu8vyh*fX(!`h;IPU*8sNC
z--apxwqxrA@C8&$?O<}JBpm>DLG`KKs06S_LID7K(Mk=#KIoR(kLpqfBx3-`cs779
z=^qrq0HCh{d`16|_yz!d4d5{SBjOtX^fiF5=^sTE0LQR(0yqxUQYV-^DM<%_Q&4^C
zG%5j{kx&4@S+r6Ea1Oeq&ZD~21<4oyGM)|KBK=Du7y$G&fN$u3E4~3hUjz7#{$=qE
z0Qwrh75Z0E1;90IodCXvYN_i?-jJjNz)h$=^#dva{3xLSfS=HE0Klv3E$EiIjp|ZA
zOU3|@@oWHh=>H;u0YF~^_?7-|;u`?;HGtpg{~^8sKwks+lm1_*0^o0K?*o9D-i2zZ
z4ERlR{7fmT6F?@Y_`y<C0(d||0RUOhasa@qYgXu%%7*Gv*(GBD$apq@9Q4CPFaYRl
z06FRB65jxzuL0zypGSNHfW8KhmwrA}0gxZt`v7331)y50Ad`h8=~w}Uq54!2R01d}
zp#XqlXr%_AICM)ri0V=$Bx3-`cs78N^h=3g0MOR}O4BbRz5zgA11L+socIO+eGT9t
z`VXTDfJd-(0w@pFQWco2C`ku^aHu|236%gUODF)K3R<ZFs0tnb8V;&URg;VXAmiBp
zs?)C_f&oBZ1E@*AmiPt$eGQ;C{W{_s0Q5D0y7cRz3V{09Isr6*YN>`yJ|;;AfXAWw
zR3lUZXe^-sfF@|A1|R~urJAC;)Dx010AxHHz?1ZwiC_TG*8rN+Zy~+`KwkrBNxzl&
z1^|5xpf&wAr~;rZwoU-;pjxUulN}`K0MHStPd$Z70G%Wh0MHq&)Bw1kTk2_4{7W#B
zF#u#d8$cKO&x&9G(ANOE(wBd9;T{0=HGuB)pA+8zpsxWuPyYo}0nh_mCx92BS}Kys
zo|1F`h=S@<(WnFvBcT9*UTCETAQrl%dZW5jAITU1GM){<O+QWq1Ax8;;GzGL_+|yr
z*8uv`?<c+iKwkrhr=Nf-0QzI=1mJ~gDaB->Bpm>fp!!rYDgg|TPyoO{v{C~w2)d;P
zqq@`($ru1Ko(<q-`mcy!0MOR}eDsHkZvfEO0EW>YF1`UkUjrCHe<Z2^cokbGfKgB_
zHJV9Fk`4f4p!(EUR00?$p#XsKXr%_=HRzUl9o3~KNX7t=@oWGS>AxX@0YF~^m_&cF
z_yz!d4PXlWsp1;|^fiEK^xs4k0MoH`0+<2SQg1OiQ<4q<v!MFa+o%LETS5T<???-S
z&!KI94i{g>ESn+2pAl*<RD1{#)urZ1(*E%E{_82*%g@~qq2@ENfPwc={U5%5ANxMF
z5H<M2*B^+24`1uSJnRzm;p;{0_#r!{*N3kc<7jF5@bwbTajDEvK773lI|k@-v{GA9
zE1+9yC8|rUl8jkVGM>GnKBB)`1hb;(>lL+z{#x<PilVPq)W`Hc5#OvR`g%ouN`D=y
z-07cT>s(Rmp;~GKlN%-JSW%xt#Xl#4N&uTA6j)K4(Mk=#7U-7Rit19^Bx3-`cs79T
z^uG|n0HCh{?4Z9>d;@^K2C$3%Zt)EO`WnC<`g>6Yz&>o90QN(*)Bz^Hl%xZ|L8w0U
z6)FK7l28D^VYE^Ma0I%gzD9MaqmnTIWIP+dG5W_vFaYRl04M056yE@#uK}E*e_DJ4
zfW8KBhW=Sp0dNjmCxG)%Ep>s(i;{EzxCGUwzCk5`ZzU7}@EuyI0k{m^Qddx2>Z)W6
z02$8)aE<==A{YSlHGu2%Z-{RI(ANNN(*Hqx1Ax8;@FV@7PzAs(Y@GmZL$%b;Ox}^C
z1Hdm(ed<?K0{Bfr0RX?Fl^TFQpj+xsRG0coG6sN*X9M_~{#_9a0Qwq02K;_7uK7^!
z0YF~^$V5N0_yz!d4d4O#Sx^N)R&1RBvO%?!{8F(?<&dNUKp0e?%85z<xg-<-kQ=Sk
z0OWygsl2Ezl}|DTfQ)AY$WOn32nGOs4WJ<XLgJehKwkqWOuvZu1^|5xpeX%fr~;rk
zwoU*KLbX&0CQC}v0iYCApDK+?0A(Z;08kdK)Bu!&ZmEY*UFu=U7yvS!4d4;_<wY<6
z=xYEK=vNfq0HCh{gwwAiz5zgA1E@^D3aS98imemCqfjkXjmhegbO5LU)u(Er5<o2p
z1pw4WD>VRhpyS^=Ma92&Dj5Sn#<Ky`r{6#X1Ax8;(2)LP;u`?;HGs$IHxl0fpsxWm
zrr!ir07PKx1ke;J{*ePFpOmBnKr^U5)f|-oT1Y4Wpry1hcq`iWiB<T;r_0@_{w4kq
zQmFWY8!G<bCQ19ms$s`HJ;r6Y5~12L(4K(~sQweHj@ZWq9cu82RVPt!Vx<T3uuIU1
zRcCf|v159jSUruSrRBux8P2hb%u!CPp2dy<+ErQr+Ku-8XR6(yTIxAemwH~3Hqbr(
zGu0Ou=)u5?sD7Z4*!QWPsKG#^L;+Aen1@}0fJU=p3_GS5&|WxNT7brKj=g1$0<;fy
z3{W>(sXavFpj*m=>QXOB#ymu2Jo_QqmwrDH%tMsEeu&1?PY~ZcMCt2?Xn*=%@y$b&
zzJ7=*`iZD=rzc_Se26AP#lL>Z<UmO}9-@Pw;tz?a1TaKGfrseJXr%_=73h}op}N#i
z$ru1Ko(*6a{ox`Q0Q5D05%fojZvfEO0A8g(N_+!=z6LOwzJ)3P#$f9NFczw%#xXfw
zk`4f`LB$^uQ3+szgaQC2qLmteH=tW;5~@p0mW%-)<JkbF(4Q)T0YF~^m`49i@eKg_
z8o+e=GsHIl=xYFP(VvMb0A^w91n@RgOU-8T9Z5O>%z=tOB%%_)yAlcjn1@zs0Omut
z)B;qOdQUP2fQ)AYc%S}45exwO8o&qi7m05G(ANMyq`z2v1Ax8;u!R0nQ~|IITPJ|!
zP%X8B$(53H09XYTe@H|nfYlNT09b=oY5>+kx75d|F7=6I3;-F=2Jk8Ubs`u5^fiFb
z=&u*w0HCh{Y@okUd;@^K2Jkuk6jTAQ30o(C%}_11g~_dwbO6`}6@N%XC4es^6acUT
zt<(VQgl?%_s4lfzG6sN*X9L(nf3FAz0DTQ$AN~E}8vyh*fCKcu6yE@#uK^sS{}rkL
zIE1Yez+tGCI>O}Fl5_w#3Kf4yL?wXZ5()q~fmUh&PC~cTDO8s_Eg1tq#<KyOp?_8c
z1Ax8;aE|_Y@eKg_8o&km7sWRK=xYF%=zoJM0KUc63E(@Zmb%R36-hb(T!o51B%%_)
z_Yw*KxQ<q80B%6H)J;^E`av=VfQ)AY_>ulkA{YSlHGo_6Z;NjL(ANNdrhi9#1Ax8;
z@C*H4Q3b$n*g66H4%JeBF!`q>9RU7<ia#Wx62M&v1ps8gzq2cYXT&xzc<Y~bE`DR`
z&k-sUR7+(>b*Tp=X`fiFEt_qhdLO?Kl7)e+3}i#~pIBwbzE9;q4L-3769p$$dN2>W
z1f5vrWXD|Wm|iDVxpB0#oLJ@I9P`Q?<-{r<b_~$`(gM%|wCyw1{)=`#ooCy(5vm|m
zOBF(Oslt-9f%d+2;IA$HFGQ#!3>0Oc7^)v=aqQ!hp{T(?ONauXdN2>W1OY9{j-}W!
zy?~a+(b5964Ch!@<|shRVaEV{NLm2;Fl`$seknq~N&N^^d_@q|r7B3$2726o>R*w8
za0V)&`hix)zE4#_4F+0O6adwOdDtZg=%egdjUCeqXmuPdEkJ8<jx}YD0<;!(4A9zW
zrS_ax2Rg3KQE_!H8S|Vc<Jr%7_31Yd!93^D*Uxzk=|3jEdCsG+pYtB4-$;D(oJU_j
z=QXC^1Xb?z2y6pSOSsedpK1yfSLaMVDM`n3p8WISxH?BAfEE%8Jm<AUD>VSEpj)an
zs!O$zi~%6y*#O$oZzqBQKwkrBPrrls1^|5xpd<aK#5VxwYXF_-cSaQeE^M6uo`!0v
zXPE3FNe6&uq54!;R08NGp#XsHXr%_=Iq3L}7*za5jARS|8P5jLgZ_&m7y$G&fJpj1
z#Ww)xYXDL7qs2D>=xYEm^n0NSfLLsu0D42kZ^SU^mZSqf98~;93@QP<B%uI+zG$Te
zpdWPnMhq%`BSta?fQ)AY=uh7(f&oBZ15or6#Ww)xYXC{~lf^dx=xYE2=nq5{0E4h~
z0vHSxzY)Xa%aU{ecm*ncBL<ZKhDs;^U>I7d0T>P)zY&9q--wZn0U+bq0A8g(N(2Ld
zz6LOwz9qf^KwkqGLw~IJ1^|5xU>yDNr~=?MY@GmJhl<~bVRE7*9RS{dir<JqC4k8i
z3ILdbR%!sILdS2!pyD@TBx3-`cs79P^k;}*0MOR}-l9KKd;@^K1~7~M+u|Dl^fiFl
z^xr`h0CTW)0+<UGf1YJ>o+KRr=0nAA#Gn$udlCu&cpt6Q04#)#--toQZ^TH(0Fd!)
z03Xs{EP??*UjtY|f2sHe0DTQ$8U5wr8vyh*fEDytq6&aj*g64x1Qov#!{i!CIsmMN
zir<JqC4f&P6aerkTB!k82OYl=gNomXk&FQ#<JkZ<(BCM60YF~^_?&)<_yz!d4PX=f
z&EgvX^fiDj^tYl4fNj`10c?kg--uyyha?>Uc0$E(#Gn$uZV3ed?2#4*-%I=cH>vkQ
z#m8Y$@o`v5+9y_lH>tm5;2;BEq54m(4q@M?4x<L2SRD}sCsuke54!}NSbfcoN7*sG
zPOOgMXlXgII?g$skU7eU)k*9apr@n-pr>h@H>q*bf{#<2foiF<s4jI*k~Yx5o7CqS
zxWK?gR6o#5*vFq+QG<bgD+++>!945|1oS(0yv&a21@sDzmKLB_Imc@<M*;dhb_~$#
z(gM&MwC}%`x(U@%KcKqQkCMFqTIweTZZU8h)erP%?EBOm)L@{$hytK`Fb}%~0sWO7
ze`Ck=0{T0SmKLCYaE^b<90llK*fBu=mKK2CrEOMdS+39wIRt1%RD78e)ekhVLNhb)
z00UW2{Xnx~AK%+V4F;NB6adwOdDtZgXbyG^W5@IYniEG$3(#DgV{VzF0L_CP12ivM
zslABggKnw(s4i7NGUi1@#<O2U3eqnmf_V|4uU|w8(=Q^vc@d$nUqp)1FDAZu5uvYN
zM2gdY5LNE<64>J2xcAo<@k#2EP%Tx8$<mT^yoi*6>QiM=380*W0xu#Dp_LkdhoM{Q
z5mc8dFBt<s#<KxbpkGl01Ax8;5Kh06_yz!d4WKgpD&iXe^fiF0^dChP0M)Q{0;mqv
zQZ<;YDM<%_T2OteHYx$skx&3YU9?gIP!GDL>Z7_;1IZWwGM){fA^pchFaYRl0FTpe
zB)$PaUjt}Nzlrz;0DTQ0f__s}0q_L2P5@6rwNx`En@iFGpaoQ)YKck!tt1ox&>F4O
z0JMQ_skW#t)lM=7fQ)AYXivX`2nGOs4WJ|ar^Gh^=xYF-=yw+10HCh{xadEPDgd6r
z)(M~sR7*X}WLHT#0Ca=uQ{7Ps;5i8e06dRYY5-n<ZmAxqF7={h3;-F=1`tWVrw9fB
zeGMRrezf=o0DTQ0hJG*c4FLKYKrH><r~;r5woU+UsFsRj(j!R+fR~{9R9{pA=qI57
zfOxc01CRjSQvFd~$}1TIK*qBHDEf&a7y$G&fF%0K;u`?;HGl#12a0b1(ANM4(I1Q|
z0ES@e1n@FcOTEIRPm&G*L!tWAFjN8<E};N`5oo0bU?g-)y^88mqa<Sh$apq@(ey15
z3;_BXz!>^t#Ww)xYXIZuj~CwnpsxYEM*nqG0WbkuCxD4iE%gSIlO*W?Fd3>(O+h7q
zsS*kRn1)tr0N#Xdsp+ULHA6B6fQ)AYc#Hl_5exwO8o(_2Z;NjL(ANNF(|<>N1Ax8;
zFo*tJQ~~fVwoU-^pjv7^lM5v20Pr4EpL!pa02WFp0N?{@Vemz??bp}`-KYBu{p+U`
z^&wRJfDfumEs>;srrPz-#qQlLjz*}Z3@l?{IjaA}Y6bRvY9(s$iPb7maAKtg^RP?M
ziPcB!xSAc)>%?jej+T}atF@ft$1+DbvHAo%2I!~K0?>7|ZJ?Wu)NJ!WvF}sVXHfBF
zRaAUgRgyN)>+i-a+>r-AZNHI$&lyNT^#k36eV^Kl8Vq!cC;+Ml^RP=0(5>vajUCeq
z=yn_}EkM8E9CyeZ1?W!f7@)hP1)#fW+dzAK*(zh9<$t88Jy0#R7uBWqNzw)y+qQbe
zE>}K}Q2QA;z`&QNexL`jj}Jqm1_M1L3V`atJnRw#^e{UfVaN0W`ZbQ07NAEt$73=_
z0eT!e2IvWC0q9BE_pi`XP%U*D)uqlz^8OWimVt8&oJaKoy?}k6x`-MK^pYq5st5D1
zOAyd+*zsF-OfR6{;b>_AdYN;)B6Ad=SFvM&UXvDpeoxy5`uE+tWq5IV9jc{npt{se
zN!maI7pFfk@FN31q56T|!ajag2{jn#&!PaR9?ZioK|t@Y<1g%(UO<1v(b59+H_q{Q
znWF&x13L!jpJ=7_s{9vpOZ|=NQg<a|UX^7$`&BtZnD`k{?|D_GuV0lj(a$Wtc~z#b
zUzH!ApGAE0s!U(MDrcpi4OQ;+?AYSoxbNaL2UJUiF_}}6j#uSeP<<*lDgoq?P~cTL
zFIuSq$Oj!)=cu?kmy7`*<JkZT(k~=}IW3{D0TiZRM0^8)z6MZ~elhV40Qwq0arzIU
z3V;&WIsue~imP)bOH0xLpbS)8oud*!ISB;-JcL$i03L>pt8-LbolC|5knwB)73fzK
z!2qDI0ff`9B)$PaUjwL2zl!(<0DTRhD*Z=M1wb`yodBvs#nm~JH6`f)Pzx%q&QS@V
zj)VdL>Y|kzfO^nzb&iUwbIBM0GM){fA^pchFaYRl0FTpeB)$PaUjt}Nzlrz;0DTQ0
zf__s}0q_L2P5@6r#nm~J%_Zpo&;ly1&QS@Vm4pHSTBDU3fHu%^b&iUwbIBM0GM){f
zJ^c<M7y$G&fR6N^65jxzuK{$T-&uSEfW8LcqW?6i0C)ylCx9+cadpmQS4lbmbc2ek
zb5sI&PC@|y&!d$ZfES?S>KqkU=aMl1WIP){B>kQu7y$G&fGGOW;u`?;HGmlUy~H;F
z=xYG6^n0TUfIiqd0l1;!>YPcBBpm==f{LqiR08NHp#Xq*v{D0*03BE7sJJ?pi~%6y
z*#H#%L=g-C`WiqI{bcbC0Qwrh0Qv*PHvs5s0E6fcMil@<uyq1>87i*Mne<800bnRp
zT%Ds5z;Fo#0E|E@H2@=_<LVq0SLc#30AxHHz-aoG2nGOs4PXrYvEmy5^fiES^v8>D
z0MOR}UZejyssNaPtrNgRsJJ?3a*`w+0477l)j28wOqEaoz%*%L@Hc7Oud#t|qfCd2
zpVvioskbC)pI8O{&^METSq!|5>OZlXjeVbb2Q~P_YK|y4vC@Ni*d^%1YA!pz%Z}-F
zVl@v(OUsGXe9m!!%u!CP-ouUo`o6RPbRlgUD84tX|IqgVRQ!|_Dt<~zlJ}pfE@of}
z14~i;K$l_Pr<S7z16?5sfa<|K>=FcYB|ENS$MgdF5ssD?psP8@H8Mv5x)wVI=*Q9m
z&`)UFKm#9|{1mFC)}gx8XOgsm20k>oo`DSvY((_~{T%!FsxoRY&`qKMs2<G2E<r#y
zv*Q+aOfR5YakR7m-Nrd?mpKa1FR){P?vNIM?xcPH3f%?OQoB)IYL6uEU!i*$*vG(r
zR6o!I*!QU~QG<aV6a_%_U><e}0{Rs@9%9Gz0(uxnOAF8=oa5IrM*(^iI|k@6X#wbQ
z+BVR@`%x#LTIwXKOP!LW4K(n6)M*CJFmM*t5A+=N@ten}!9XvF0-$;@54!{by~vK2
z*fG6;euJZ>1?ab&<99Mg0eTrb2Iv)O0q9lQW`*iMMPGx8fBXj3rLIfz{uO$Iftw8c
zfa(YOBldmjC)8k|w?qL@J(!1Gf`Hy;$Di3Ty@1}q(b59+7tZllnWF&x4Lb(t?`WlV
zn)3&AeD53;-#eF#In9yr?9-gT>E9K>oaWHir#TsNO8<<g_nhX?*QYs|=w}w+oaWHi
zr#TPM&w?s<dRA<4Z`^mapA9O$cg|!ENjgq*!l2@N=cojbOG1IuoZM)o1|Sb~eD53;
z-#eF#0U+bq0P@o>Ac6rvUjryezmWLmJdeHxP?&xZ@eKg_8bDF{#ZU!6acrFc9)ybT
zoikZdk`4f+pyGSys02_(LID6}(Mk<KIq3M_IV!$)E*S$r#<KxDLchES1^|5xpaT7h
z;u`?;HGpvXmBcpy=xYF#=~qD&09CPd0(cZEzIV=KbxAq^)PRcboud*!EeQnx)J7{c
z0Ck|_lkKSZWV>Vx02$8)P@jGS5exwO8bCw(kBM&p(ANMSr{73?1Ax8;(3pM`Q~?ly
ztrI{~sQ6?%lTS+00iYRFe6k&t09r^W0H7sWsR3vO9iMDR#V6Y(V*to_Hh{MD+lgQR
z(ANOk)9)a@0YF~^=t%!5@eKg_8bBxdolymV3tK0Er=jAL?M!x&qyxaSQ1QujR08NG
zp#XsHXr%_=Iq3LgJ1RceE*S$r#<KzRp#P!>1^|5xAd-Gh@eKg_8bB2NXz>jI`WiqC
z{a&a7AQoFEfZkB?$#y2)l5_xwgNjeKqY}VN5()t5i&km?`a#Dh+fnh!cF7n3GM){f
zKYgzV1^|5xK+#VW-vFSm0VL5+7T*A%uK^68KM+*_48qn4U@%mCvYpA7CFub03RHZu
z9hCrvN+<we7+R?T7!DnuY)8c>+a+TF$apq@SLu%u!2qDI0gR?^iEjYV*8s-QA1l5A
zKwkqGM}Iu30C){sCxF+X;*;%6PL!krz#CBU$#zr%m@J_HfGN_#;8SVaud$En-=0WO
z)1cyqo>B2b&yuuHtQz=#dtwFyZ!s_v)qi3&3;XyXG1TA_tJ$L9#7Yn5VV9s2t9RIO
z4m+mTiPc;jEiETj?{bdwWR7xTH6J?$=mKd0=zFw-PFns<QSU>w)IwC3`aqI4(69Yp
zKwHGXhYT!6^#fgkeV<y28Vq!qC;+Ml^RP=0(B<s7f*sQf=t>+dEkIXsjvvV!1?Xz*
z7@%vU1)ytb+d%EB{S@^vR7-t=>QbLd(gqs1+F!@OXAG=I^#k32eV^Kh8VvMvQ2<mA
z=3$p0pegLQi5=4m=w=)(EkL($j$37p0(2X84AAY;0?;pL-@ih4K(*9PRF~Q%$@^F6
zZU*)+uou-2bRYKdFA|{!13e%Lfa<|K>=Fd@OLjcSj_C#TD;zB?Ko4<_hh>fe^ayqg
z(66Nhphs!jK<%sj2z3l9{%KlNmpUOy8))Ea|0Dya7&wjU2YLqkK6Mr~80a}s08|g=
zVV5AF=h^WBJEj-Vi#S?ZfL`JpzmYi#&~LG0fPN<}0KH86{uO!!D*hQJRQxkclDvO~
ze$T*l25zAGf!@SEe&!oB80e3p0H_|!!!AKUe`3d5?3i9aZ{uib0s1rNct_?aK!3rG
z0s5=70Q5K72B?1f<aem}Q2<nz`cslN(9`}GrN0>Xn}NHiexMn03DAtF!9X*K0-$;@
z54!{b&CHGuuw!}w&4Qz)1!z{zF`LX$fM&;z0h$A?)Xp2jpj#>@s!QdPj5%+V@$B=)
z-1PH^V9p!q>+{CE^z(^t&Kv3L^Tz!23y5#d8|mxw#)9+<p~{_J7+c&M_q}~m1gfQq
zGFeQLzqiYf?5UP9Lx!=*(eCV(MyTRY@goAL1W-akf%C?aXaxgkI{ZTQO{x@hOO-}-
zsWOr=0AxHHKw0|bL@)s8YXA??e^`72fW8Ls2>tTn8vyh*fC}_0q6&a;Z0`l2XIcrW
zr7AO7MUoBxRiWaamqI0gY7z<nsE$@J0QrMX22}&PrD~$OR4vIE05YBppf>$FA{YSl
zHGsPG>xpjw(ANO!({CWY0YF~^Xh{DtQ~~ffw)X<iGi?ObQjM8xB1s2;2&nkyrBDgr
z2?+%NJc(8?0L`@-bW1fyb*UDTF#u#d8$e6?twb;Y=xYG2>9-Nz0HCh{w58upd;@^K
z2GE{<2UG#j5!-tK=$SqR)l!|9>?}zK02fsJ^HQh;@Qj240J@+R3?LuCHdW6;w^UbD
zm+B@N13<>J0d%MToCpR0eGTAw`Y(uY0MOR}deDDSd;@^K1`tWVC#nF5!uDPOdZy7(
zEfvFLFG)H8#6rbCFNI0~eIyhB;6^JLfaV$p9lr{PieH73i~%6y*#P>|?<ax*Kwkrh
zr=K9c0YF~^=uh7(z5zgA15or6Q3XH}w)X<iGfjqyUxj0Gpd=jt20_KI!l4qt5D5hU
zyeusY{t9jT8TRQe^4~Wp$_Ld_Ls4C7m?Z6&)f0CP&ws}HAwmsjU<3mrQT;EguVUY)
zMxh43td151FROYm54!}ttXk|ih8@%EWpyl$mX?>*ah&6LnWMa{zJ?tG^mVjSTTv6B
z<KMADb*VQbV^)-mXRoM9^e2m8Rup}`qNdQFD!y4!^!19GM*mIm&5EM0SJZU+Gf?GD
ze+ygZikb=4QnQ$RTau0yH5)2E2!Tofb0id4QFEn*!QZ8AuPA(~H7}o8=0U~R4p3cc
zfh6q}b>~^nu4_9sHB#>}@IC_zQT;3G1MK7L2B^U+>O)bmqV!-Mb_rThi`j7rJEqr)
zT8g8kWkoIH9GA-+Wks#Pjsdz7t<+Z3D(IH_2-T%lOUA4y8P8r(Yv`{P!K^6ydPRLq
z{}b`eilVPq)Ti{<iEmaEeZ8VSqrV<i?(_}VI#<+2sFwPi$rMRCR@5e__?iML0c?>_
zU`1_}76#u&+g?%6>gT8kwH+#c6adwwc1Y4*Q78Evg|9~JWMCHqyHWisY7h4DV*seZ
zD{7x8SW$W~54!}dsQv7CfF09oMSY2*rDa7O<Q%_}Im(JUgdGF)Fj}dts3Xwvo9(Fh
z&34I{6(!@@E9w~i<06<9MPIL|6ZB7tZ&nn2y`oOhKP|plQS|kSIz#^~s@&=4uywAe
z^HA}d?Mz;jq+>;0f{GszKqY{0B@|du-$@IDU#4xZsAv4IdRL(0>KqkU=aLM1)x(PV
zo`LHO+(7lOsGHcw)j4YLiuzF$tSCL0hh2hJ)KBbqiyhNzMcu~H(z2p{<{a<H9A!oQ
zf*k|&SF}=FQNKaA)bFS+^@n84ijwi{74;|mzeF%AioRY^f78D!zFAT9^@_@nTgK0b
zde4fYuUAwi`k7JXPJaMf=ZeY#)lylR%qB_4ipmZZKk<i30AUgetf-vQ!r-}Ro9C!E
zaUK<+aznLL9#oggE6Mwxqw+D3pMe6X{uNaa`#x0&HF!l876mIx59VQ)pcPew9gDJK
zdabBpI9gg(RB_JnL7Ahhs1n#QKue;P+KMU#9arb5xH^}NSy3{cy`sv}FDHUoQS|kS
zdWinR;+qvkU$3Y~=$9AYJV(*jE2;whil}m@hhytpQI(+L>YT|cl60)7s!(xtj!FR4
zBotUt)un~OYtXh=RD}!8f1EaWZ8KF9s-<e7x>RjR+AHevjAA3IHqOvY)nTA61NBh-
zE2=*BeX0R!@QP|E3RaXJ%)>50E9x<Je4HKAYehA}(bBS_8gq_KWR9|;BCunCHkB5D
zK0(_CdSw5Z)}IgiD@8pC6<<3*b*bi(w1GaA$>*89uvSymf`OI{v_ka*ZH;}OYJ(aK
zw5=!rst5D1OAyd@?AV?i(+g+^94#$CJ93Us$s7e}C+rxYouvh!F4{KGfjxIdZ&T%(
zs;8migAJ%I)kTsv(EUd?yl`Ptb-c;IKvxF3q56S#$G%TJhZ+p@c~JmV59VQ)AfPX>
zV-I#rFQ6~tXlVf&$vO6vISSAy>=>ZY(gM&J+BQ&6rb1n+`O0rny`Wku7S*MCOVS3K
z{hPv->x?MTRP|xN%|INgAE*cW_$RVagMs!H1wi#+9(D-=+K(OM*)hF<Cg5mk0otE)
z^vWCssKSl`nkX#*O`>fB?RdAbYVGdYR3$^jTPLV4HBgc^&{~-*4=VfFLrv8n1_m=Q
z1l14pW$fcE6Vza!K2ZQv59VQ)AfQ9paTq(M7trB2T3UdP;2cNF90lmB*fBsyNee(n
z)3$*gc>DF1XS!mAT2L)D2Gym;O40^e@I=Cn`QKuNj$>dv1Fxa_fxeD?d=~^Y80bV%
z08|g=VV5AFZ?NMec1$mzlX0}P0G+})PL(+d&}rB)K;M)WfKI1v1MPV=a(J#xMK`G#
zQ1Q>*p}N#eN!mca%e*1$#)ltls%A0pHUqO!{XpNrK7L0LH5lkzQ2<mA=3$p0pzpHd
zJa$Ykp!0FGv;bYeIld=z6rk^8#{gX@Edc$1whgqxpnbk!e`AF%f@-M`QC(`WByFH=
z-(S%wUljmd!oX4nmZAEAF2}x4tw0S1x>6JX)q{E1B?#y$cKnDP(+lWo94#$C*Km$&
zWsU;$W9%59pGXTpKc#I0tyr+@XZ7E`6rt8Z#g9j#;>RN;X#*|y<#&G_ZF4X}ZD3#{
z1D~V%fu>*||H=VsFwo7S0H_|!!!AKUx3J?@c1$mz+i<kB0Nu_xej#%dpgXW*fbNtQ
zfbODg1AV7Rl{QyKRotX@L$%Z%RF~Q-NgJq&JzpoPe37PV9|QkidshNbRk8lv7Zltc
zxsm&lT9%@PzN@2Ul24x|WUi&)28bwE$Suq@B^A?BUv8w7CC}V*3mr)msrM3aUor^y
zte0hK{>%Pz&pE$?jNamM&IK=g@7ewCcV_1Iee;`hKIYDu<trt_Vett<Q^ofvYnu4X
zNocz5iXc=43sH*XAoPe*@TgKSzX&}htT{sHamB|IHXm(-o)llQ2|Z=g1fi#uWR1|q
zHTUf-KD7Bh>ltxX9PJ=J#nBG7oEo7aJ6e1c(YZ<|>p3OEH%f*K@d-jR#rG)dx8gG=
zp;@*of>0GKL@APk(DO>c3rfNKBJ`rL<_Mve6dy0!e6$gIMSRI7^r}r0gnp+aYlN2H
z++}>Ty-oI6uZgSTFnaOnXZ^vJQzLY9<HWz;ezHa<>;IGtKPnlni%$^xllU&qAQYcD
z3B6&vA_!H%LX;vo2>nGVcvC5uUxeNg)*K=9w&LTjHXm(--VtB23H?8tCJ6maN!AFB
zSN``Cv;HowitQ=Hr`VptmQy2iVVk$Uyz-F<vc)`X8H$Tfu}`J=1feCxcQKwNK64UU
z+IB?{s)B_mMRE{YMk!cUDVSe`mJ`+-A+)^Wqnph~8=)1%mux~S+B89^yOOLCT6W6C
z>Xn}oG1Nm`6$@a*r&s`E%c&9i_aT>mT3MocCu<cYLscb1HSq~T|0ceR*(TyMC!vqn
zt_VU^un?t44nnId1#2h;^NY}$!kQz5)>3?|ZS&DasF(PXP3WUGO%Pf~N!AEeCRr2{
zuX*c=t8ZHCiBCUkeOpeAQ1#G>Ys}g}$<R>A&`5lO(8l6>l(mWY%t>fd+Z92m3KpUi
z$wBC2O2KAI!Tch$xv-W?LWhU+7SoJE9<_bc5g*-)&9b%-#Y5@|Ww!Nk@zu}zgw6Zz
z#k()-zD%s~5lYaM=x6I)C?WPBO<w4ge4Tx=zju?{YY*{{p9n4ER`xto`KC{2F);0L
zkDf<jE^?#zz=1Ry&)K*p<1-D<o;00r_%BJ=H_IM-UA<p`E9)HTQF*+twk$|>i}l^7
zKldSbo2(8wwIl)eQ+TO28V6oZ)6+Fwub-Sg_GYJ8?^n*S7UwgT`j*<tQ{NryKlRE1
z78DT~?HRn451Dp#?z<h1v!^C39{G0T_5Ak4iV2oFi7ebFvFftXTe;7eKt90xFb}9v
zCN1^MI{r%k`4>*zI>J&qzxMnykz09IzmzN9Gmf&b=k6>XG-)eu+}1baqo*_UI5tj;
zYrm~tf1&-#ZMxs64@q+wi1V$OwEgl}i&4}nFKpU<Gdwl{(kR}rC;$D&q0g5m9B(sy
z<Ln0OOv8nLkY?jwT`+KGvoUKDAQyM&TcJ<jUR{S>qx=8|QmzN=Bi*Py;Q6#a9@w<r
zXBJt99i*4sbkRCt{~m|$t7j*d|Gx3I6MB4ltX89E&lFG6_2-oIWIZl)Q+2!POJ^0k
z=>OF@9$`6?7?pIIXGXVwH0|g0{CNAm%O(juY{bq}aZ}P*bhX;cG7?X7tN*X%`|f?8
zH=0$oi!Hs^smp8A6Epa&-b<dHyYDnVxnoP|>Ie5T-|{y;o+#2!wE5=|@sIg5ZrjeL
zd(MmhV`;0)dVdUYEL@9gM%#hP|2YF;6}xy2jXSH~4}YH9erl+O18F~J?o!|vq*=JY
zfixRGMCF0mldzjR7VPH&4y0TU*hjj3q}-(dFG#8%bl8DDJdgL)=@lbiymL82zaO_;
z4SlD-Tbi!>4*9V4;87PCyzZvzcGD*hemt;O-xThD?YpjPC#Lh3yBlnuAo@|o-yXj{
zE;^leN=X^w;hw?5+(#~Wabp_a^Zk#V_8-{F6ORonIq<D?p1!o^+AF2M<*mI2`d68n
z#y6h#`Sk6%-!i|l@xLvYo6cL@_@Db>p3I{=&O9_^UOMMzJ8ZvD?hD;N_?c5q?U++1
zjTj&Xhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?H
zhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;
zfa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~
zw;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX
z3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{
z0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}
zMo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?H
zhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;
zfa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~
zw;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=gX3=jh{0~FU}Mo1?Hhyhn;fa02~w;=id
za56CMaF3oxVlMJH--=1wFORi^RqWz9H0~^aIK3@AUcKg(ywEH8Ix||MNU!8@8xEvV
zJmFUkJhO0tV^13OTf^m$T0L|fc1^OQ^9@`{!me5E%dKj=09V!-(lzO@-S7(o9$!_j
z;x6zLlhx0&)EA$A2Hh+9$rHEdeBc9pS6??vzxDOoL6_6(ZO`<GTXc+9_llmh((27e
zR`#4>sTI%8Y|Og!MpyvbKc`RZ@$b*^wk7xW*d7|o>kJ<``c~_BcBAg`$-#bstVVLn
zPu%OIvc%`zeqPZtj-Oum_3x{K7qe@h@2nJ^K9og1G4*nT&`mtL+O3#|$A_^A3O{KS
zx6mL3DyhF#p!Ab*Vt^QM0s|D+oIol;5(C6QfiggGtw8A~<HP_l-~<LJt~r5Jf+Plr
zfdXZK;#z^yPsWJ>V!#OuP+W5YsRT(35Ca9u0L8TerJsxw1H^z67@)Z31X2mog&Dwn
z+A1$>+I=%THX)Se)8<QSV*aI3V-ecCTp^WtutxFV7w{mB;sVE>Gzp(;`1-t4=&);&
z9R@r*5`H*5uT}OB{)@)7wJ&hFq%-K6bl9$+*W`arn9yIJ9~&RPAR)b~)dGL+q^>_1
zmeEw7ZyP@9*G>MF`{?=Q^g7yn+S(rKeA+(ReA=ij>U`RL+I-sIN~rT`D{Av;x71hX
z(^l8!(|(}Mr#-CAr;Sfi=hIHs=F|Fms`F`8p3*qQGqhO#O2iGLR_}S97Mi)<;2vD@
zh!|hTAmhopNj&J!yT-4s!=6cY81U>!*mXFr$o^^ga{Q8Yif6PwK)!Vfkxsuu3sGZI
zRfqv%Kw*I58r=xQ05MS57@)XT*jkk8LktiD6xZk;AO?tm!o~o_wZhh-R3Bo17@)XD
z_W&_K3=}p7D6SQ@7Nz<S1H=HuHM$2}n*q$H9ronE|2Xvd@&wv%JAc{}`&t{dhIzS=
zM)9x@IPf5i;sVE>Gzni{7eM=MJI3hxCz{19pVC{O?_c3*&st~J2J7ns>X%vl$N(R2
zeSJW{rl7&^4(O%lm(#J|_8;!*`hdRL`hZoN)%5|1+WLSy+WG(wZGFHFZG8at+s68U
zue9|68>RID-L>@r>V1{HKHzwp=^JM^SV!^9vD+Zex$#`*89*y2&rdgs2fqZavfcpr
zMsa~-Pnv{}dHH%=)9m58S@ZP^-THuX*IvEws&DpsC(qtrwu+h;qQ|o?Zg+;P7-iAp
zS;{vh=DxQwd%csKj`dD6wRq;I#j}OddZ*i3Jag0H*+DIyJ)*65TBEIZI;h37y;?k*
zs;ze#K<k~1K02n9N8B(9CdUV(c<}2UTvOIj-GisBpOST>ertG$TNKY6B($)s-*m9r
zl)XqZK;vsgy7{T{#6Y2BfZ|%AY*wliF;Ju#ptx3~o1ZFA3=~QRD6SRCW~E9I14Wtv
zifcu>`Kj{6K%r!S;##3>R;m;+P^1~4xK^Z_pDIrb6iNmtt`*8=eF!Us^-gpiO8zzu
z=B*mFYWBQT_+>V(z{|1TzzO)adAc;8Hjn4x6qfn46wi!Cl)RNk+{jz$>{4dO1^8t)
z?mc)9jBD97%G);$pW+$Cvz|v{E^=DGnYZUCucZ(-@>(9{?uir1IwD-ptAt-><H~qK
z&k@)1>MzP^_!Q6b7|&?^ChjDXd0_x?BQIs|EhEQ?d(-mr9e$aOE92dxD{(C^zTR6#
z!=reX$9Ps)esA7;PtbQ_pkNuG_XY*aKUpURhyn8%ptxo}r-Voh5Ca9v0L8U}<)5q*
z1H^#&3{YG%pHo6428e-zWq{&Z!SYYmi2-83d<H14na?R95(C6Q!7@N`tzh{l>%;&t
zU_JvB*UabCH4(M-PVDM}fjgUxS(6Y_v5V)>xU+`O!EsGC^3Q8+-YPe0QJzO?6c2ux
zjVt5Hx>3IcUXJw!8a|Vx>3qxeaFRV{)#k|pU(z{ru}DPxyY6s3=f=PKW|={L`uTIk
z+V>yjekoi(-|0^3`jcT9P4)AgVp@;-Z>{Os=iABY+Ie;CRCDFLIyPQC--%z^pqy97
zwe#xO*phpDY!8iPE!Fd#xL2LylY{*Nxpux2Kc}AW#4f4lJMq6I?yMA@K9rAE&v#;*
ztK5odczhV2sMI@+Q9LvHB;~C<;zr&|XP1(nr|guxeS=?S<H~rlo;Po^OKEtv`1NOT
z&Ee-f+k*N{_S=q*IBJ$Xif0bSv&9|yR_GJBm-=V&4eTTx=iM5GNBn^_iU+@d2Wb=+
zIQFDb{Cnb=t$)IVog8ruc8%Hrjy(xKkZ#l-@X-H~b=X0=CLNBW9#^L?`{k!Fy??f3
z!G`%AUYM=-&;GS+dY`9Gbkh50i)P$jx5_VE&o8If-=66ax9Auf+EeYHeXRA*-dU&i
z&-Sb53A5s*)c)BJt$((@X3qXu)2?nmujm=a+!uZQ`>No@{KEvbf7V~?pLKRu`)9ML
ze`fSnGgKb;p&<}aSi8UVb&E#v;FsCBGM=m(^;^S3zvy0hZ(MWuIhXyC{kEgSzFGFD
zf96>KjMM&S@}28kU5gvcs1^A+Hi`$o%*K`RWZkIW8lE249}t(7xMq?a#4CFeewfw1
zTy#3czwG_pB%MLmq{DV;J-X35gUS;F1;qfp4=pI>$u2QK3^<DcifhhdmT-vyVxXWH
zptx30%#&SWfEaKV0~FVs#Vp|x1H?c<F+g#xpqMAS!~ikiECwj9Ig44sB?gFrf?|N;
zT0t>Sc8LLEz*!7XTyqw)X5rHLn`SZT7!KyGI#%lb+-A>9g<odl%6PJVf6W}bqTyj4
zD}TUzTHAb7hv%8fUI;r2Z8)<3k`6!2uxIm|=F_U{16X{LvfhbJR@XamUr%Mdlhz(-
zoZ^{$$L5cYxM9?q^c)#}e&CncxH6uso5Ta+h%@7v5*LO4e-_u^hvD|nc%pdbU_7Jq
zgpEE?j+IB;aIDn*xy_CX@XKsm8Bf;lubE?4G(5zS{D^0N7T4g1%fvG}PuQ{hjdJH?
z0C6MdRmU~6;{yCL8&}4Yb;oO-vxJ6+IFg_7>_Kr2ez;6LD=h0bY5%jF&qaP|THN49
z?a9xvQ9Sr%Hm-~(>qh<7@Yr8S)A^R`A@qBHhni)N_Sc~OHH<zYPA!jqkWnM@^J^3j
zewmFc<H@>FzcoDciwdHDhJKn&+Wb)BnpyP{pukn6q|^Qy>i%bj|9%*4FDg$A6n+M1
zo=4$tW2zxBP}CTpxK`9!oa##q6n+LMt`+_^rWz6hMU4T9YelWaslLQO;b(y2TH$YF
zsv$8@)EJ<+R@7RY>Prk1eg-J675+A+8WICVjRA^lj<z_h4{($*bMj*zzEL!J9;s10
z_+>V(j3?_x{nqdvhV=oq`L6K8@cd3uuZ1=|oEMCGN;-#*dA5%7IXC{*H_Ht2)6WMk
z*1rEJ_e<gWdBS&6*Pjf_XsVwl9MgK#e``(et>>51>uKi+pK7k2Cp=y|Px#UX^*rH3
z?L6VJCDrqUTWaSCd)3K#p76Qn)$@ce<#K(%>xX;vJQ8z}`LsVC*tFhfmY|AVJcq`e
zWsdSe`5j{*ujg3HR&f79%IS{k@C$g5j^fDqfn!g~`5o2qJlm77>nN_AA2{|T>?2(+
z@2C#EFe$DHU6{D8uM2>#Nr&U88qchHyt=;P=I^Win3({7)p+Hr$FG-v-gRXErBQl*
zIlZP9&$6!0nY7aC&6;TO?Cz&(Jln0sv)Luocy?~s$kDf2$8+z-YCQ8yZuyCOom3vI
z#j|x<Jo89V&qtr9osXVeLp>ioNL?SmsDFkQ%U_9pkWs5<_jBNv*|;*EteeCG`bGAn
z!_T?wm+ZG49rn$#cW?h};RE_>)IZbW8THTPJ2roG?eDJP`yKrNNTc4vFW^BM#RZN%
zX%rv*7<&?SjoJZ@Jqi0rH);=f=zra#FQoq2CT6F9#;d%rY4^?W*o08^_om_J%xF6n
z_VQXkh#9S?);~ahz%U;CG8<RMlXa7L;2Q43upV86G*kA&wc-5PevUcfn#i0dyM<-H
zAnKnPeUkE4Ui&+1`0s{x|LXU9-n_{!1;5P3mGNXf+k?D)L%+qIguT3>XO{wwJqi0r
z&n}s_Z@_y%|4f+3F@CntrWf_`vncg_057@eqIJUlJ(l6rKg-*V=U^$^rw$goFT3{K
zU|iU6{|tVajVt5H`h691@PdY?$91h9x(<5|!rqr1IDa8w*Km8FUWik6bUluVd<*l^
zFN_YKIi=YfBlPk8z!USX)f)At{yt##$!e7Xo^PP1tM50%bzM$>RDB=7yLVIGZ}Rum
z_W`Wtzm@j^EJ1xAz%Q%sH~Guz`%TufmhwJ;RaM^yu-aXf_nW-TqOX5n6}*^r-J_l#
z&`&!*;9Ga~{D7(I_?hAU!F>%*7l;93;2~jv=C3^@4MA=Y1H^!73{YG%jY<L{28e-&
zgaL|c4@pCi8^i!HU>XAy*G!|5fQSKN;2~jv;@U&f5ab3iKn$41K;emNTNb3c#rp1J
z&rUA?edBE>_~{iRU%YcUgWh|ZcCS1bDEz{J$5+*>sK4)iaIU|;ig}Qbe_i6wbF6=&
zS<Lb&z1!h@S+j6~18Fus)>A`zT%Eq`m!HDgL5JNxul--A0tZsAhuQXkr%LNCS%)7;
zm*-vlb&b><qosKs-z@p&RP3}&eZGl0UnEnH3uBb|5%$yjy7lii@9Yn(`QV@5yfr@7
zQg4b+?9R5Em}QOE%{dcSSe}?L<8TGOmcRd*)i<-@3QKI4o~=8tnZ~Du?3mhO&vJ|B
z;I0vs@20YtuA2`{?y=nRWi_{7)7oY54NWGubZ`2e<yhJ!ugtn1Bpf*~_GXEW%PpA|
z4&J>{rf<94)|c`T1H?d4VZe3!Yaw@=tPVM~B;m1Ijh;PIJc)0)8v0Iuw=_Kt7nK&w
z?|Q~$_46$C#iyScwHqJ5AR)b~)dFc0ufo%wwa%;!ws`jbvQ^Z)5YuqsAEep%`u;ZB
zJ~vAiY?$BSh1nKdhh3xm00;8EdT92MZqy$9*Y~^7@S(#Fba@_ZZtctcu@S%jqxe9c
zuJ_N>^z68hsr28{?4}ogb9Lk0JI*opR4?DYQ@62z9d4N~efb&hHgL_^mxLaCwPDcF
zHCx%5(7Fd+p0bU7)W2(Loi{$`AFo=l%$DBpqp)&mWj<j^i_i4w9kq>J3Troi)`v&<
zy3UCWZRyt(`-!|%^A=zE3qAa$fz3Bn4%YWK%5A+VA2C1-6g381r@t0n>W#*Mm($pD
z%J^9_^BwYG>%pTg=yA8GwOoGH*DLwS6SwDl;A7Mt_R)hhidVnP>PH6nc<<`sc4x?n
zQI=h1;Q|NJ+4%ojHoebNCpzi=!>&<&fCDMl1J@zVwddOoxT?Hgm)9NT*1kMGCilnW
zbyQ>3b?^G`IJJEC`uj4=uMO=y{s15Mx7}rrZuPdz*sw3A`~PB@+xa`g2WJej9I871
zx#(w3vWnK77dy=wY`Oi$&Ashji{NeJfBpS~fB;JkW&A9Id;PqxVaP{*3F`RSkF0vH
zf1POe%OeS1&HQJTh!3-DnA3OC^REuI#D|WEiksZl@OX4?`J#Np05MQl7<hR5Yg+$|
zcT@XkyqnrT)8lSoX@Q5W9=rOwS^BN7Z}@#o_^4ku`B(1K4$^2n12zQ>es@5xb}8SK
znET#J^KjuGq}lk`{~2=8jN9v0`GvQG4!cJA0S=^G53}t75BtH&I{e6)p1n>yxAm3Z
zSIXmKa(_&$i_fv{T~05j_RrYL5v^FOj7gTa)czT-TW!XUfmsoj6t#cGtIk-_yLZP3
zORCyGV+U5QT(LT2oTaAPKVvaF)2}2fvReMDyx;th|2C)PZ{5bfZaEgZ?SS90xd|zO
zEh_kjS}k$!_7B}~ekF~c<+i~oA2C1-*kRz|>95iFnVlwb@?{!7bME*Yjh{Jo1LtD=
zOmxYz$H(OUm^{8l<7bZXPq~Q!V&GwApy2vzV{dkf^?v0HTRN-QMgOnP@yUZ95A4-9
zg~rdEe5+{Zc^T%pj<#<eq*1rw7w{mB;sVE>G>VV&(ID$<=L<oHU88n@V^5lG4|q5)
zN!H;9(y@=CqxHmmew4%fG3e-TLC5$QbU9u0-<0vS_*%7Q^&6kWUR-|HKXUqJ_Vw)+
zNB%xIiM{;NGabW%lGyI?ryIT&cYvMp|7Ovrsf+o-DRIS`zM8~Je-w9Q;(^cE+C#?{
zS}q@86Dr5f-%~${N6$SH^3tFrHZ~%qQOeED{2#sDD%TvC#Q6B9XGHBwW-ZQVEcGq5
zl}A|4Bt|8j=Kj~d>$-Mgy5Z*x^(6UV2C93h`*uD!HTPF*d#E^&xi9(PoPW5hcr!I=
z7A|lg&Bn($kB~!qD)nH{VfVqc&iz&3K+5$n+aB<6?whQ`52Ry1*xc7h&53nMD2I8l
z&@t}<I>z~-%jt+?;yec>uDv&TfAendX7S+UHUF+Dez%tC+BNfs_mkP$JyYfc9k|YF
z+^F%xv9(z|K4xBsE&bXE@3sD&wzEeo?f7zb(slN7U}*Url~Q^8vcqrI5b3camRyP~
zmdTgz{%4#`uYc~94_du-k)^&n)_>}i11vMT{iA6=ujegyH`qR5(pHLRXecMik=p*l
zM&bKtIFLs1`f7N7CgZ|CNVD;AUM=Lus{b51>>A|<IFNEZ%(e$SoL?#H@B``C*V@r~
zVtpmbVLm@}%pZe}_m$A)bo9p%$IxGEp4D*vEAM1+Zr%83%h#^*Zs*U|{?E!RK0Unm
zz<rypvwdxDob(gvSFiUBTAg-*y?-FE>}#=EyyEpM``r(mV|S}XCwCI*p*@Gif7baL
z8#iP8yf#8V^yrI?udF%6gCZiMJ%hLM<L&z{n<V;Y#or#kJ}#R2XJ{v9$uk?(ebkNO
zM{QAYAdTX!+N|O&G#MBEL7I(^bGISiS*O&4Lx)|X`~U}1u7}z7fQNH<WgUJX9sBb;
zT2HJKM>(uZf{uBx&@t}<x}1*RmFSNlj)`*}l>XYeRmFF`G9{V$45{x^PxQ||ztJ)L
z%7<Bee@1^1`mVEKHT=)LG;BM2zB3PbQuNQxt!$t8i$^M7?He`Hmi}SIP7~iNp2-6$
zT^VH4e>nPf@-wq8vLVy1&V9GTaTYdW=c%|UX{=L9$`B9t3<f_*I){P%+IbL0(f4UM
zkVf$mB|Nimfn!ga<Tum&XBxk{4!cI}0LPw$eWV+;2Rxi7F6*#^beuEds9o%LfpS=1
z2_5tKp=16SbU7X4WB6T({utsI;#$xD9!*W^mBJcL8Q;Jy@;knIW89V0*OOW5<srS6
z_WqIAAMH`^<1Hzy{m=Wa)T=p$`}bT>qs^pbR%t{+xn6fN__T7ZDvzCz%vQ{=P<LF|
zSL~??i$}iQcs-A<R(n}S;%Oe{K61f}8?)D)!e5f+GSF5#*TyL7ZzcBj*d7|oA&uhQ
z(eQ4Yj0^uD&Bh<A^2s2Jmr{7qb=Wn^4{#vmdcZ!?joJg=Y_&b~`ap*r=r}LQQM=f;
z2j#F%96Ht|LB~8;=yE#7`7u6*-<9Z(A&y~uZS~nY-v(^T;&XS_J>@0(YnA5Acx%_B
zZ7irmOtaFW|JAzV*w<%PPvzUTJl3eXNI(4jJGDmtn8Do^d3UKM^pp*Ys#y+RWVa_)
zOt92RWLE!Q%lF;;KHu~GkDc}(pz$-ble1(U?R+ky_!TuANTYZj8lIcUxbP3sZ2Xq0
zzZ`Oiq{FUJet-ih*8}#EZqy#|&J7zm`c~_By?)SP2RhDubJQ;OA456pcL5#iE1_dP
zKXf@A?<+CRkMS}5uGITuS{y@vZFSS{wwx2cJ68<;X?#$`MYd!^ivag6S^RY1d%uM4
zKg(8r5%BhZM0({8UY^qiU1P5w+|_NL(A_roEaCmxDIV^VSasRxt-R5!s$Fd3XNkuK
zmK^vNjh~^NoF#A6RnHkTiod14iUVmBZ-<6=&}3Zr2Wd9GR~>~14!OQ&P91iQ@&g=5
zxgM~Obfflw=iNAGeW1e*bev!5s9o%%h;rDs2Rhb?L&v%#=yE#dUEqBs#`!TmhToOw
zk0FjBu9aBZ;>7jg+u87u!Tv48_}M>d2mE?XjGtYP+a<Q1yw1AJD?e(j7(eri96rLP
zhk8fPygTzE^BEJ!2Y4Umy-r<To1U1#)0fs<d!_WZ^nMc!<s?}{J1^BJe02>6(kLGM
zdc<U0_y=h=zJ|x`N!T^Y4{#vmdcZ!?joJgAr`jHR{h-4RbezNMs9o#_i*ne13_A9^
zfR6Q*(B*W@AH%#0ysyMKKgP%KyAu5|#4)Tl7_u~Ln}3yUY)#wI^|B&Tc#qLpN7n^y
zXD6?$9r#MdQ8uUC^_EjZwlm)szqlJ(YAO$?Q6??*%sPIn_mXGl?mNv}dkyrjGL^>9
z&`!>hiO;L&J{!gVK*NDF3vZ2+aN(!f_~%rA8DvxSTy0&4U9<QBo?H*uN4imazzde@
z2OV~x<2-Rk?P6bRl*2xX(6Mh1bgUDHE~jH2Eas14-UZ%QVw@l2WB6T({utueIPHCC
z&&1A|nKe@RvHi&-8;Snfvc;uaJ$o&KcN_lor*(z?m38j6s3RBIEB)tRICbj?JGo;^
z>FNjfvyJC{K7D)cxAcA!4do<xdZBuLy;1nX8V;mUystF8gC^s`KS;CjFRA`=NcTkw
zzq$^)M)?5_q+Ac!N4imaz*{HP4?65XFQeAm;pZ3o^TS{42MZnhk3q+N7trN&%;(2E
zSj->8ybHXq#5h03$MCxn{V~L`nOa;s_DSic=fv;MZ#o?qTDIj0<~3|qoufkk{X&JN
z31tuPl+Ld`|4igo?pyxG#}mc)nP1uX-xkcJ@iVlOv*fkUcUFo{AL{UZC;Jnhq~buz
z>5l3fHN3qh<HA2kv+@6ysPMocKTOD}!>*%z$oYW-Dc1w`kuH~aR0p0%O3wN~haKo$
z_o(-Sqjqr42+Cn!Yv|ZV5jytmfi9<GT@vQ=V;(H#k73>g-dAFrALC>AU5Wk};#i~>
z*Lrlj+BQLqpKa{;z1ZsLI%Dn2-&rZf&$9ZZT=AZ9l$~hv&m-a=YjNX$?uU6Yjh~^R
zoFpTkn0mQE=qAHFPu6fCjk*h*sZPRm7XE0}Uk2G<(qY#uet;+Q1p7!gY7cnxr20UI
z9q9eEc5~D&&Pzf$?9UG!`@urT{$tSPbgZw$x+Ki!$2?ffAH%#0ysyMKKgP%KyAu5|
z#4)kihcYjB)IYxa?h6suzVvy2O^ew_dDwGz77r5Zn|vC#ZD;GBMR%NeXvjS3pP`+c
zC8Mj|ifMR!nBo4JucwLwY1H5D8r}etap51N+4!5QC_He;&h9yN*fq)za3JM+z&_HA
z+5=v4jhyv?4m;4lRqO5WeT8%1;4jV@fsTExp<^FK=yE#NiDP{w)+J#+Kjy(={ut(6
z;C&^=`7u6*-<9Z(A&%jFXzd>UpAHiJv&L<GGd_AcgL%%2|6^&Z%bcI>u>C^0FC69r
z^&*)&1|}%`uN!~wBIYBCKUHZQGI!SP=Mw$-9C&8o0>_@T?@{hjCaQij$XSvOySYQN
zp9?tlB<v&IK2q*dfEOgy2RiIPpQ^QA?$EPy$>&$f=OxMe^UM3e%IVne0_()Fz7p$_
zFrOduU@?CT^Dgkd665?BAH(lT^v4j#1P@Ao&HAKAv0@R{r^IJqNXz2Iip_}(6#ow@
zA5<ZzWKgM~(m`eVRS?yV2nY-vK6qU4q~YVNtwiQ1YisfOSWva@Zb41Ne{H(EMMehp
zD=V^&3m82%bhy>(Eiw!pH|({R+66a%HNnl=Mx->GW^JqBJgwl^>}3RH26;wVpHVJU
z8yXxj+!`@vOz1ebp<^P$BHXM#BB8t6ENeR<{jBZ9XHb(6<)X(d>)(}&7Td)(>cy~G
z)(*<Wj^Z=2pQmumI(&3Ma9D8I$iZW+!J`AL69<nPKGDiVdX%-3_!RZ=ed9;9m4ljO
Ve{G|GjRY$pHWor`B-jXR`Tq{uFB$*<

diff --git a/tutorials/04_Working_with_Metadata.ipynb b/tutorials/04_Working_with_Metadata.ipynb
index 91b001036..5c79f8ebf 100644
--- a/tutorials/04_Working_with_Metadata.ipynb
+++ b/tutorials/04_Working_with_Metadata.ipynb
@@ -1,21 +1,72 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Working with Metadata\n",
+    "\n",
+    "In order to work with complex dataset structures you will need to pass additional information\n",
+    "about you data to SDV.\n",
+    "\n",
+    "This is done by using a Metadata.\n",
+    "\n",
+    "Let's have a quick look at how to do it.\n",
+    "\n",
+    "## Load demo data\n",
+    "\n",
+    "We will load the demo dataset included in SDV for the rest of the session."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sdv import load_demo"
+    "from sdv import load_demo\n",
+    "\n",
+    "metadata, tables = load_demo(metadata=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A part from the tables dict, this is returning a Metadata object that contains all the information\n",
+    "about our dataset."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Metadata\n",
+       "  root_path: /home/xals/Projects/MIT/SDV/tutorials\n",
+       "  tables: ['users', 'sessions', 'transactions']\n",
+       "  relationships:\n",
+       "    sessions.user_id -> users.user_id\n",
+       "    transactions.session_id -> sessions.session_id"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "metadata, tables = load_demo(metadata=True)"
+    "metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This Metadata can also be represented by using a dict object:"
    ]
   },
   {
@@ -58,60 +109,22 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'users':    user_id country gender  age\n",
-       " 0        0     USA      M   34\n",
-       " 1        1      UK      F   23\n",
-       " 2        2      ES   None   44\n",
-       " 3        3      UK      M   22\n",
-       " 4        4     USA      F   54\n",
-       " 5        5      DE      M   57\n",
-       " 6        6      BG      F   45\n",
-       " 7        7      ES   None   41\n",
-       " 8        8      FR      F   23\n",
-       " 9        9      UK   None   30,\n",
-       " 'sessions':    session_id  user_id  device       os\n",
-       " 0           0        0  mobile  android\n",
-       " 1           1        1  tablet      ios\n",
-       " 2           2        1  tablet  android\n",
-       " 3           3        2  mobile  android\n",
-       " 4           4        4  mobile      ios\n",
-       " 5           5        5  mobile  android\n",
-       " 6           6        6  mobile      ios\n",
-       " 7           7        6  tablet      ios\n",
-       " 8           8        6  mobile      ios\n",
-       " 9           9        8  tablet      ios,\n",
-       " 'transactions':    transaction_id  session_id           timestamp  amount  approved\n",
-       " 0               0           0 2019-01-01 12:34:32   100.0      True\n",
-       " 1               1           0 2019-01-01 12:42:21    55.3      True\n",
-       " 2               2           1 2019-01-07 17:23:11    79.5      True\n",
-       " 3               3           3 2019-01-10 11:08:57   112.1     False\n",
-       " 4               4           5 2019-01-10 21:54:08   110.0     False\n",
-       " 5               5           5 2019-01-11 11:21:20    76.3      True\n",
-       " 6               6           7 2019-01-22 14:44:10    89.5      True\n",
-       " 7               7           8 2019-01-23 10:14:09   132.1     False\n",
-       " 8               8           9 2019-01-27 16:09:17    68.0      True\n",
-       " 9               9           9 2019-01-29 12:10:48    99.9      True}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "tables"
+    "## Creating a Metadata object from scratch\n",
+    "\n",
+    "In this section we will have a look at how to create a Metadata object from scratch.\n",
+    "\n",
+    "The simplest way to do it is by populating it passing the tables of your dataset together\n",
+    "with some additional information.\n",
+    "\n",
+    "Let's start by creating an empty metadata object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,141 +134,92 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "new_meta.add_table('users', data=tables['users'], primary_key='user_id')"
+    "Now we can start by adding the parent table from our dataset, `users`,\n",
+    "indicating that the primary key is the field called `user_id`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "new_meta.add_table('sessions', data=tables['sessions'], primary_key='session_id',\n",
-    "                   parent='users', foreign_key='user_id')"
+    "users_data = tables['users']\n",
+    "new_meta.add_table('users', data=users_data, primary_key='user_id')"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 8,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "transactions_fields = {\n",
-    "    'timestamp': {\n",
-    "        'type': 'datetime',\n",
-    "        'format': '%Y-%m-%d'\n",
-    "    }\n",
-    "}\n",
-    "new_meta.add_table('transactions', tables['transactions'], fields_metadata=transactions_fields,\n",
-    "                   primary_key='transaction_id', parent='sessions')"
+    "Next, let's add the sessions table, indicating that:\n",
+    "- The primary key is the field `session_id`\n",
+    "- The `users` table is parent to this table\n",
+    "- The relationship between the `users` and `sessions` table is created by the field called `user_id`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'tables': {'users': {'fields': {'gender': {'type': 'categorical'},\n",
-       "    'user_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'country': {'type': 'categorical'},\n",
-       "    'age': {'type': 'numerical', 'subtype': 'integer'}},\n",
-       "   'primary_key': 'user_id'},\n",
-       "  'sessions': {'fields': {'session_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'user_id': {'type': 'id',\n",
-       "     'subtype': 'integer',\n",
-       "     'ref': {'table': 'users', 'field': 'user_id'}},\n",
-       "    'os': {'type': 'categorical'},\n",
-       "    'device': {'type': 'categorical'}},\n",
-       "   'primary_key': 'session_id'},\n",
-       "  'transactions': {'fields': {'timestamp': {'type': 'datetime',\n",
-       "     'format': '%Y-%m-%d'},\n",
-       "    'session_id': {'type': 'id',\n",
-       "     'subtype': 'integer',\n",
-       "     'ref': {'table': 'sessions', 'field': 'session_id'}},\n",
-       "    'transaction_id': {'type': 'id', 'subtype': 'integer'},\n",
-       "    'approved': {'type': 'boolean'},\n",
-       "    'amount': {'type': 'numerical', 'subtype': 'float'}},\n",
-       "   'primary_key': 'transaction_id'}}}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_meta.to_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "new_meta.to_dict() == metadata.to_dict()"
+    "sessions_data = tables['sessions']\n",
+    "new_meta.add_table(\n",
+    "    'sessions',\n",
+    "    data=sessions_data,\n",
+    "    primary_key='session_id',\n",
+    "    parent='users',\n",
+    "    foreign_key='user_id'\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 11,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "new_meta.to_json('demo_metadata.json')"
+    "Finally, let's add the transactions table.\n",
+    "\n",
+    "In this case, we will pass some additional information to indicate that\n",
+    "the `timestamp` field should be actually parsed and interpreted as a\n",
+    "datetime field."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "loaded = Metadata('demo_metadata.json')"
+    "transactions_fields = {\n",
+    "    'timestamp': {\n",
+    "        'type': 'datetime',\n",
+    "        'format': '%Y-%m-%d'\n",
+    "    }\n",
+    "}\n",
+    "transactions_data = tables['transactions']\n",
+    "new_meta.add_table(\n",
+    "    'transactions',\n",
+    "    transactions_data,\n",
+    "    fields_metadata=transactions_fields,\n",
+    "    primary_key='transaction_id',\n",
+    "    parent='sessions'\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 13,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "loaded.to_dict() == new_meta.to_dict()"
+    "Let's see what our Metadata looks like right now:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -278,10 +242,10 @@
        "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
        "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
        "</g>\n",
@@ -291,10 +255,10 @@
        "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
        "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
        "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
@@ -303,7 +267,7 @@
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>users&#45;&gt;sessions</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-377.2088C101.5,-363.9446 101.5,-349.6377 101.5,-335.7493\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-335.7312 106,-325.7312 101.5,-330.7312 101.5,-325.7312 101.5,-325.7312 101.5,-325.7312 101.5,-330.7312 97,-325.7313 101.5001,-335.7312 101.5001,-335.7312\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"105,-325.7312 101.5001,-335.7312 98,-325.7313 105,-325.7312\"/>\n",
        "<text text-anchor=\"middle\" x=\"197\" y=\"-347.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;sessions.user_id &#45;&gt; users.user_id</text>\n",
        "</g>\n",
        "<!-- transactions -->\n",
@@ -312,11 +276,11 @@
        "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 191,-.5 191,-.5 197,-.5 203,-6.5 203,-12.5 203,-12.5 203,-132.5 203,-132.5 203,-138.5 197,-144.5 191,-144.5 191,-144.5 12,-144.5 12,-144.5 6,-144.5 0,-138.5 0,-132.5 0,-132.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
        "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
        "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
        "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
        "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
@@ -325,114 +289,137 @@
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>sessions&#45;&gt;transactions</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-196.3927C101.5,-183.0141 101.5,-168.7483 101.5,-154.8345\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-154.7831 106,-144.7831 101.5,-149.7831 101.5,-144.7831 101.5,-144.7831 101.5,-144.7831 101.5,-149.7831 97,-144.7831 101.5001,-154.7831 101.5001,-154.7831\"/>\n",
+       "<polygon fill=\"none\" stroke=\"#000000\" points=\"105,-144.7831 101.5001,-154.7831 98,-144.7831 105,-144.7831\"/>\n",
        "<text text-anchor=\"middle\" x=\"232\" y=\"-166.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;transactions.session_id &#45;&gt; sessions.session_id</text>\n",
        "</g>\n",
        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7fc36bc596d8>"
+       "<graphviz.dot.Digraph at 0x7f1b2b6cf630>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "metadata.visualize()"
+    "new_meta.visualize()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'tables': {'users': {'fields': {'gender': {'type': 'categorical'},\n",
+       "    'age': {'type': 'numerical', 'subtype': 'integer'},\n",
+       "    'user_id': {'type': 'id', 'subtype': 'integer'},\n",
+       "    'country': {'type': 'categorical'}},\n",
+       "   'primary_key': 'user_id'},\n",
+       "  'sessions': {'fields': {'os': {'type': 'categorical'},\n",
+       "    'device': {'type': 'categorical'},\n",
+       "    'user_id': {'type': 'id',\n",
+       "     'subtype': 'integer',\n",
+       "     'ref': {'table': 'users', 'field': 'user_id'}},\n",
+       "    'session_id': {'type': 'id', 'subtype': 'integer'}},\n",
+       "   'primary_key': 'session_id'},\n",
+       "  'transactions': {'fields': {'timestamp': {'type': 'datetime',\n",
+       "     'format': '%Y-%m-%d'},\n",
+       "    'amount': {'type': 'numerical', 'subtype': 'float'},\n",
+       "    'session_id': {'type': 'id',\n",
+       "     'subtype': 'integer',\n",
+       "     'ref': {'table': 'sessions', 'field': 'session_id'}},\n",
+       "    'approved': {'type': 'boolean'},\n",
+       "    'transaction_id': {'type': 'id', 'subtype': 'integer'}},\n",
+       "   'primary_key': 'transaction_id'}}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_meta.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pretty similar to the original metadata, right?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
-       " -->\n",
-       "<!-- Title: Metadata Pages: 1 -->\n",
-       "<svg width=\"371pt\" height=\"500pt\"\n",
-       " viewBox=\"0.00 0.00 370.50 500.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 496)\">\n",
-       "<title>Metadata</title>\n",
-       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-496 366.5,-496 366.5,4 -4,4\"/>\n",
-       "<!-- users -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>users</title>\n",
-       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M38,-377.5C38,-377.5 165,-377.5 165,-377.5 171,-377.5 177,-383.5 177,-389.5 177,-389.5 177,-479.5 177,-479.5 177,-485.5 171,-491.5 165,-491.5 165,-491.5 38,-491.5 38,-491.5 32,-491.5 26,-485.5 26,-479.5 26,-479.5 26,-389.5 26,-389.5 26,-383.5 32,-377.5 38,-377.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-476.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">users</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-468.5 177,-468.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-453.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">gender : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-438.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-423.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">country : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-408.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">age : numerical &#45; integer</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"26,-400.5 177,-400.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"34\" y=\"-385.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: user_id</text>\n",
-       "</g>\n",
-       "<!-- sessions -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>sessions</title>\n",
-       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M28.5,-196.5C28.5,-196.5 174.5,-196.5 174.5,-196.5 180.5,-196.5 186.5,-202.5 186.5,-208.5 186.5,-208.5 186.5,-313.5 186.5,-313.5 186.5,-319.5 180.5,-325.5 174.5,-325.5 174.5,-325.5 28.5,-325.5 28.5,-325.5 22.5,-325.5 16.5,-319.5 16.5,-313.5 16.5,-313.5 16.5,-208.5 16.5,-208.5 16.5,-202.5 22.5,-196.5 28.5,-196.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-310.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">sessions</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-302.5 186.5,-302.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-287.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-272.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">user_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-257.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">os : categorical</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-242.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">device : categorical</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"16.5,-234.5 186.5,-234.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-219.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: session_id</text>\n",
-       "<text text-anchor=\"start\" x=\"24.5\" y=\"-204.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (users): user_id</text>\n",
-       "</g>\n",
-       "<!-- users&#45;&gt;sessions -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>users&#45;&gt;sessions</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-377.2088C101.5,-363.9446 101.5,-349.6377 101.5,-335.7493\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-335.7312 106,-325.7312 101.5,-330.7312 101.5,-325.7312 101.5,-325.7312 101.5,-325.7312 101.5,-330.7312 97,-325.7313 101.5001,-335.7312 101.5001,-335.7312\"/>\n",
-       "<text text-anchor=\"middle\" x=\"197\" y=\"-347.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;sessions.user_id &#45;&gt; users.user_id</text>\n",
-       "</g>\n",
-       "<!-- transactions -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>transactions</title>\n",
-       "<path fill=\"#ffec8b\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 191,-.5 191,-.5 197,-.5 203,-6.5 203,-12.5 203,-12.5 203,-132.5 203,-132.5 203,-138.5 197,-144.5 191,-144.5 191,-144.5 12,-144.5 12,-144.5 6,-144.5 0,-138.5 0,-132.5 0,-132.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-129.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transactions</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-121.5 203,-121.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-106.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">timestamp : datetime</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-91.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">session_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-76.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">transaction_id : id &#45; integer</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-61.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">approved : boolean</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-46.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">amount : numerical &#45; float</text>\n",
-       "<polyline fill=\"none\" stroke=\"#000000\" points=\"0,-38.5 203,-38.5 \"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-23.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Primary key: transaction_id</text>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-8.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Foreign key (sessions): session_id</text>\n",
-       "</g>\n",
-       "<!-- sessions&#45;&gt;transactions -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>sessions&#45;&gt;transactions</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M101.5,-196.3927C101.5,-183.0141 101.5,-168.7483 101.5,-154.8345\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"101.5001,-154.7831 106,-144.7831 101.5,-149.7831 101.5,-144.7831 101.5,-144.7831 101.5,-144.7831 101.5,-149.7831 97,-144.7831 101.5001,-154.7831 101.5001,-154.7831\"/>\n",
-       "<text text-anchor=\"middle\" x=\"232\" y=\"-166.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\"> &#160;&#160;transactions.session_id &#45;&gt; sessions.session_id</text>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>\n"
-      ],
       "text/plain": [
-       "<graphviz.dot.Digraph at 0x7fc36bc59198>"
+       "True"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "new_meta.visualize()"
+    "new_meta.to_dict() == metadata.to_dict()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving the Metadata as a JSON file\n",
+    "\n",
+    "The Metadata object can also be saved as a JSON file, which later on we can load:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_meta.to_json('demo_metadata.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loaded = Metadata('demo_metadata.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded.to_dict() == new_meta.to_dict()"
    ]
   }
  ],

From 5503b16a96eadff82d78d523db1420aeee801437 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 21:42:07 +0200
Subject: [PATCH 31/33] Remove unneeded files

---
 sdv/metadata.py.composite   | 1054 ---------------------------------
 sdv/metadata.py.non_primary | 1109 -----------------------------------
 2 files changed, 2163 deletions(-)
 delete mode 100644 sdv/metadata.py.composite
 delete mode 100644 sdv/metadata.py.non_primary

diff --git a/sdv/metadata.py.composite b/sdv/metadata.py.composite
deleted file mode 100644
index 10dda41e3..000000000
--- a/sdv/metadata.py.composite
+++ /dev/null
@@ -1,1054 +0,0 @@
-import copy
-import json
-import logging
-import os
-from collections import defaultdict
-
-import graphviz
-import numpy as np
-import pandas as pd
-from rdt import HyperTransformer, transformers
-
-LOGGER = logging.getLogger(__name__)
-
-
-def _read_csv_dtypes(table_meta):
-    """Get the dtypes specification that needs to be passed to read_csv."""
-    dtypes = dict()
-    for name, field in table_meta['fields'].items():
-        field_type = field['type']
-        if field_type == 'categorical':
-            dtypes[name] = str
-        elif field_type == 'id' and field.get('subtype', 'integer') == 'string':
-            dtypes[name] = str
-
-    return dtypes
-
-
-def _parse_dtypes(data, table_meta):
-    """Convert the data columns to the right dtype after loading the CSV."""
-    for name, field in table_meta['fields'].items():
-        field_type = field['type']
-        if field_type == 'datetime':
-            datetime_format = field.get('format')
-            data[name] = pd.to_datetime(data[name], format=datetime_format, exact=False)
-        elif field_type == 'numerical' and field.get('subtype') == 'integer':
-            data[name] = data[name].dropna().astype(int)
-        elif field_type == 'id' and field.get('subtype', 'integer') == 'integer':
-            data[name] = data[name].dropna().astype(int)
-
-    return data
-
-
-def _load_csv(root_path, table_meta):
-    """Load a CSV with the right dtypes and then parse the columns."""
-    relative_path = os.path.join(root_path, table_meta['path'])
-    dtypes = _read_csv_dtypes(table_meta)
-
-    data = pd.read_csv(relative_path, dtype=dtypes)
-    data = _parse_dtypes(data, table_meta)
-
-    return data
-
-
-class MetadataError(Exception):
-    pass
-
-
-class Metadata:
-    """Dataset Metadata.
-
-    The Metadata class provides a unified layer of abstraction over the dataset
-    metadata, which includes both the necessary details to load the data from
-    the hdd and to know how to parse and transform it to numerical data.
-
-    Args:
-        metadata (str or dict):
-            Path to a ``json`` file that contains the metadata or a ``dict`` representation
-            of ``metadata`` following the same structure.
-
-        root_path (str):
-            The path where the ``metadata.json`` is located. Defaults to ``None``.
-    """
-
-    _child_map = None
-    _hyper_transformers = None
-    _metadata = None
-    _parent_map = None
-
-    root_path = None
-
-    _FIELD_TEMPLATES = {
-        'i': {
-            'type': 'numerical',
-            'subtype': 'integer',
-        },
-        'f': {
-            'type': 'numerical',
-            'subtype': 'float',
-        },
-        'O': {
-            'type': 'categorical',
-        },
-        'b': {
-            'type': 'boolean',
-        },
-        'M': {
-            'type': 'datetime',
-        }
-    }
-    _DTYPES = {
-        ('categorical', None): 'object',
-        ('boolean', None): 'bool',
-        ('numerical', None): 'float',
-        ('numerical', 'float'): 'float',
-        ('numerical', 'integer'): 'int',
-        ('datetime', None): 'datetime64',
-        ('id', None): 'int',
-        ('id', 'integer'): 'int',
-        ('id', 'string'): 'str'
-    }
-
-    def _analyze_relationships(self):
-        """Extract information about child-parent relationships.
-
-        Creates the following attributes:
-            * ``_child_map``: set of child tables that each table has.
-            * ``_parent_map``: set ot parents that each table has.
-        """
-        self._child_map = defaultdict(set)
-        self._parent_map = defaultdict(set)
-
-        for table, table_meta in self._metadata['tables'].items():
-            if table_meta.get('use', True):
-                for field_meta in table_meta['fields'].values():
-                    ref = field_meta.get('ref')
-                    if ref:
-                        parent = ref['table']
-                        self._child_map[parent].add(table)
-                        self._parent_map[table].add(parent)
-
-    @staticmethod
-    def _transform_metadata(metadata):
-        """Ensure metadata has the internal SDV format.
-
-        Convert list of tables and list of fields to dicts.
-        Ensure primary keys are defined as lists of fields.
-
-        Args:
-            metadata (dict):
-                Original metadata to format.
-
-        Returns:
-            dict:
-                Formated metadata dict.
-        """
-        new_metadata = copy.deepcopy(metadata)
-        tables = new_metadata['tables']
-        if isinstance(tables, dict):
-            new_metadata['tables'] = {
-                table: meta
-                for table, meta in tables.items()
-                if meta.pop('use', True)
-            }
-            return new_metadata
-
-        new_tables = dict()
-        for table in tables:
-            if table.pop('use', True):
-                new_tables[table.pop('name')] = table
-
-                fields = table['fields']
-                new_fields = dict()
-                for field in fields:
-                    new_fields[field.pop('name')] = field
-
-                table['fields'] = new_fields
-
-                primary_key = table.get('primary_key')
-                if isinstance(primary_key, str):
-                    table['primary_key'] = [primary_key]
-
-        new_metadata['tables'] = new_tables
-
-        return new_metadata
-
-    def __init__(self, metadata=None, root_path=None):
-        if isinstance(metadata, str):
-            self.root_path = root_path or os.path.dirname(metadata)
-            with open(metadata) as metadata_file:
-                metadata = json.load(metadata_file)
-        else:
-            self.root_path = root_path or '.'
-
-        if metadata is not None:
-            self._metadata = self._transform_metadata(metadata)
-        else:
-            self._metadata = {'tables': {}}
-
-        self._hyper_transformers = dict()
-        self._analyze_relationships()
-
-    def get_children(self, table_name):
-        """Get tables for which the given table is parent.
-
-        Args:
-            table_name (str):
-                Name of the table from which to get the children.
-
-        Returns:
-            set:
-                Set of children for the given table.
-        """
-        return self._child_map[table_name]
-
-    def get_parents(self, table_name):
-        """Get tables for with the given table is child.
-
-        Args:
-            table_name (str):
-                Name of the table from which to get the parents.
-
-        Returns:
-            set:
-                Set of parents for the given table.
-        """
-        return self._parent_map[table_name]
-
-    def get_table_meta(self, table_name):
-        """Get the metadata dict for a table.
-
-        Args:
-            table_name (str):
-                Name of table to get data for.
-
-        Returns:
-            dict:
-                table metadata
-
-        Raises:
-            ValueError:
-                If table does not exist in this metadata.
-        """
-        table = self._metadata['tables'].get(table_name)
-        if table is None:
-            raise ValueError('Table "{}" does not exist'.format(table_name))
-
-        return copy.deepcopy(table)
-
-    def get_tables(self):
-        """Get the list of table names.
-
-        Returns:
-            list:
-                table names.
-        """
-        return list(self._metadata['tables'].keys())
-
-    def get_fields(self, table_name):
-        """Get table fields metadata.
-
-        Args:
-            table_name (str):
-                Name of the table to get the fields from.
-
-        Returns:
-            dict:
-                Mapping of field names and their metadata dicts.
-
-        Raises:
-            ValueError:
-                If table does not exist in this metadata.
-        """
-        return self.get_table_meta(table_name)['fields']
-
-    def get_primary_key(self, table_name):
-        """Get the primary key name of the indicated table.
-
-        Args:
-            table_name (str):
-                Name of table for which to get the primary key field.
-
-        Returns:
-            list or None:
-                Primary key field names. ``None`` if the table has no primary key.
-
-        Raises:
-            ValueError:
-                If table does not exist in this metadata.
-        """
-        return self.get_table_meta(table_name).get('primary_key')
-
-    def get_foreign_key(self, parent, child):
-        """Get table foreign key field name.
-
-        Args:
-            parent (str):
-                Name of the parent table.
-            child (str):
-                Name of the child table.
-
-        Returns:
-            str or None:
-                Foreign key field name.
-
-        Raises:
-            ValueError:
-                If the relationship does not exist.
-        """
-        primary = self.get_primary_key(parent)
-
-        for name, field in self.get_fields(child).items():
-            ref = field.get('ref')
-            if ref and ref['field'] == primary:
-                return name
-
-        raise ValueError('{} is not parent of {}'.format(parent, child))
-
-    def load_table(self, table_name):
-        """Load table data.
-
-        Args:
-            table_name (str):
-                Name of the table to load.
-
-        Returns:
-            pandas.DataFrame:
-                DataFrame with the contents of the table.
-
-        Raises:
-            ValueError:
-                If table does not exist in this metadata.
-        """
-        LOGGER.info('Loading table %s', table_name)
-        table_meta = self.get_table_meta(table_name)
-        return _load_csv(self.root_path, table_meta)
-
-    def load_tables(self, tables=None):
-        """Get a dictionary with data from multiple tables.
-
-        If a ``tables`` list is given, only load the indicated tables.
-        Otherwise, load all the tables from this metadata.
-
-        Args:
-            tables (list):
-                List of table names. Defaults to ``None``.
-
-        Returns:
-            dict(str, pandasd.DataFrame):
-                mapping of table names and their data loaded as ``pandas.DataFrame`` instances.
-        """
-        return {
-            table_name: self.load_table(table_name)
-            for table_name in tables or self.get_tables()
-        }
-
-    def get_dtypes(self, table_name, ids=False):
-        """Get a ``dict`` with the ``dtypes`` for each field of a given table.
-
-        Args:
-            table_name (str):
-                Table name for which to retrive the ``dtypes``.
-            ids (bool):
-                Whether or not include the id fields. Defaults to ``False``.
-
-        Returns:
-            dict:
-                Dictionary that contains the field names and data types from a table.
-
-        Raises:
-            ValueError:
-                If a field has an invalid type or subtype or if the table does not
-                exist in this metadata.
-        """
-        dtypes = dict()
-        table_meta = self.get_table_meta(table_name)
-        for name, field in table_meta['fields'].items():
-            field_type = field['type']
-            field_subtype = field.get('subtype')
-            dtype = self._DTYPES.get((field_type, field_subtype))
-            if not dtype:
-                raise MetadataError(
-                    'Invalid type and subtype combination for field {}: ({}, {})'.format(
-                        name, field_type, field_subtype)
-                )
-
-            if ids and field_type == 'id':
-                if (name not in table_meta.get('primary_key', [])) and not field.get('ref'):
-                    raise MetadataError(
-                        'id field `{}` is neither a primary or a foreign key'.format(name))
-
-            if ids or (field_type != 'id'):
-                dtypes[name] = dtype
-
-        return dtypes
-
-    def _get_pii_fields(self, table_name):
-        """Get the ``pii_category`` for each field that contains PII.
-
-        Args:
-            table_name (str):
-                Table name for which to get the pii fields.
-
-        Returns:
-            dict:
-                pii field names and categories.
-        """
-        pii_fields = dict()
-        for name, field in self.get_table_meta(table_name)['fields'].items():
-            if field['type'] == 'categorical' and field.get('pii', False):
-                pii_fields[name] = field['pii_category']
-
-        return pii_fields
-
-    @staticmethod
-    def _get_transformers(dtypes, pii_fields):
-        """Create the transformer instances needed to process the given dtypes.
-
-        Temporary drop-in replacement of ``HyperTransformer._analyze`` method,
-        before RDT catches up.
-
-        Args:
-            dtypes (dict):
-                mapping of field names and dtypes.
-            pii_fields (dict):
-                mapping of pii field names and categories.
-
-        Returns:
-            dict:
-                mapping of field names and transformer instances.
-        """
-        transformers_dict = dict()
-        for name, dtype in dtypes.items():
-            dtype = np.dtype(dtype)
-            if dtype.kind == 'i':
-                transformer = transformers.NumericalTransformer(dtype=int)
-            elif dtype.kind == 'f':
-                transformer = transformers.NumericalTransformer(dtype=float)
-            elif dtype.kind == 'O':
-                anonymize = pii_fields.get(name)
-                transformer = transformers.CategoricalTransformer(anonymize=anonymize)
-            elif dtype.kind == 'b':
-                transformer = transformers.BooleanTransformer()
-            elif dtype.kind == 'M':
-                transformer = transformers.DatetimeTransformer()
-            else:
-                raise ValueError('Unsupported dtype: {}'.format(dtype))
-
-            LOGGER.info('Loading transformer %s for field %s',
-                        transformer.__class__.__name__, name)
-            transformers_dict[name] = transformer
-
-        return transformers_dict
-
-    def _load_hyper_transformer(self, table_name):
-        """Create and return a new ``rdt.HyperTransformer`` instance for a table.
-
-        First get the ``dtypes`` and ``pii fields`` from a given table, then use
-        those to build a transformer dictionary to be used by the ``HyperTransformer``.
-
-        Args:
-            table_name (str):
-                Table name for which to load the HyperTransformer.
-
-        Returns:
-            rdt.HyperTransformer:
-                Instance of ``rdt.HyperTransformer`` for the given table.
-        """
-        dtypes = self.get_dtypes(table_name)
-        pii_fields = self._get_pii_fields(table_name)
-        transformers_dict = self._get_transformers(dtypes, pii_fields)
-        return HyperTransformer(transformers=transformers_dict)
-
-    def transform(self, table_name, data):
-        """Transform data for a given table.
-
-        If the ``HyperTransformer`` for a table is ``None`` it is created.
-
-        Args:
-            table_name (str):
-                Name of the table that is being transformer.
-            data (pandas.DataFrame):
-                Table data.
-
-        Returns:
-            pandas.DataFrame:
-                Transformed data.
-        """
-        hyper_transformer = self._hyper_transformers.get(table_name)
-        if hyper_transformer is None:
-            hyper_transformer = self._load_hyper_transformer(table_name)
-            fields = list(hyper_transformer.transformers.keys())
-            hyper_transformer.fit(data[fields])
-            self._hyper_transformers[table_name] = hyper_transformer
-
-        hyper_transformer = self._hyper_transformers.get(table_name)
-        fields = list(hyper_transformer.transformers.keys())
-        return hyper_transformer.transform(data[fields])
-
-    def reverse_transform(self, table_name, data):
-        """Reverse the transformed data for a given table.
-
-        Args:
-            table_name (str):
-                Name of the table to reverse transform.
-            data (pandas.DataFrame):
-                Data to be reversed.
-
-        Returns:
-            pandas.DataFrame
-        """
-        hyper_transformer = self._hyper_transformers[table_name]
-        reversed_data = hyper_transformer.reverse_transform(data)
-
-        for name, dtype in self.get_dtypes(table_name, ids=True).items():
-            reversed_data[name] = reversed_data[name].dropna().astype(dtype)
-
-        return reversed_data
-
-    # ################### #
-    # Metadata Validation #
-    # ################### #
-
-    def _validate_table(self, table_name, table_meta, table_data=None):
-        """Validate table metadata.
-
-        Validate the type and subtype combination for each field in ``table_meta``.
-        If a field has type ``id``, validate that it either is the ``primary_key`` or
-        has a ``ref`` entry.
-
-        If the table has ``primary_key``, make sure that the corresponding field exists
-        and its type is ``id``.
-
-        If ``table_data`` is provided, also check that the list of columns corresponds
-        to the ones indicated in the metadata and that all the dtypes are valid.
-
-        Args:
-            table_name (str):
-                Name of the table to validate.
-            table_meta (dict):
-                Metadata of the table to validate.
-            table_data (pandas.DataFrame):
-                If provided, make sure that the data matches the one described
-                on the metadata.
-
-        Raises:
-            MetadataError:
-                If there is any error in the metadata or the data does not
-                match the metadata description.
-        """
-        dtypes = self.get_dtypes(table_name, ids=True)
-
-        # Primary key field exists and its type is 'id'
-        primary_key_fields = table_meta.get('primary_key', [])
-        for primary_key in primary_key_fields:
-            pk_field = table_meta['fields'].get(primary_key)
-
-            if not pk_field:
-                raise MetadataError('Primary key is not an existing field.')
-
-            if pk_field['type'] != 'id':
-                raise MetadataError('Primary key is not of type `id`.')
-
-        if table_data is not None:
-            for column in table_data:
-                try:
-                    dtype = dtypes.pop(column)
-                    table_data[column].dropna().astype(dtype)
-                except KeyError:
-                    message = 'Unexpected column in table `{}`: `{}`'.format(table_name, column)
-                    raise MetadataError(message) from None
-                except ValueError as ve:
-                    message = 'Invalid values found in column `{}` of table `{}`: `{}`'.format(
-                        column, table_name, ve)
-                    raise MetadataError(message) from None
-
-            # assert all dtypes are in data
-            if dtypes:
-                raise MetadataError(
-                    'Missing columns on table {}: {}.'.format(table_name, list(dtypes.keys()))
-                )
-
-    def _validate_circular_relationships(self, parent, children=None):
-        """Validate that there is no circular relatioship in the metadata."""
-        if children is None:
-            children = self.get_children(parent)
-
-        if parent in children:
-            raise MetadataError('Circular relationship found for table "{}"'.format(parent))
-
-        for child in children:
-            self._validate_circular_relationships(parent, self.get_children(child))
-
-    def validate(self, tables=None):
-        """Validate this metadata.
-
-        For each table from in metadata ``tables`` entry:
-            * Validate the table metadata is correct.
-
-        * If ``tables`` are provided or they have been loaded, check
-          that all the metadata tables exists in the ``tables`` dictionary.
-        * Validate the type/subtype combination for each field and
-          if a field of type ``id`` exists it must be the ``primary_key``
-          or must have a ``ref`` entry.
-        * If ``primary_key`` entry exists, check that it's an existing
-          field and its type is ``id``.
-        * If ``tables`` are provided or they have been loaded, check
-          all the data types for the table correspond to each column and
-          all the data types exists on the table.
-        * Validate that there is no circular relatioship in the metadata.
-        * Check that all the tables have at most one parent.
-
-        Args:
-            tables (bool, dict):
-                If a dict of table is passed, validate that the columns and
-                dtypes match the metadata. If ``True`` is passed, load the
-                tables from the Metadata instead. If ``None``, omit the data
-                validation. Defaults to ``None``.
-        """
-        tables_meta = self._metadata.get('tables')
-        if not tables_meta:
-            raise MetadataError('"tables" entry not found in Metadata.')
-
-        if tables and not isinstance(tables, dict):
-            tables = self.load_tables()
-
-        for table_name, table_meta in tables_meta.items():
-            if tables:
-                table = tables.get(table_name)
-                if table is None:
-                    raise MetadataError('Table `{}` not found in tables'.format(table_name))
-
-            else:
-                table = None
-
-            self._validate_table(table_name, table_meta, table)
-            self._validate_circular_relationships(table_name)
-
-    def _check_field(self, table, field, exists=False):
-        """Validate the existance of the table and existance (or not) of field."""
-        table_fields = self.get_fields(table)
-        if exists and (field not in table_fields):
-            raise ValueError('Field "{}" does not exist in table "{}"'.format(field, table))
-
-        if not exists and (field in table_fields):
-            raise ValueError('Field "{}" already exists in table "{}"'.format(field, table))
-
-    # ################# #
-    # Metadata Creation #
-    # ################# #
-
-    def add_field(self, table, field, field_type, field_subtype=None, properties=None):
-        """Add a new field to the indicated table.
-
-        Args:
-            table (str):
-                Table name to add the new field, it must exist.
-            field (str):
-                Field name to be added, it must not exist.
-            field_type (str):
-                Data type of field to be added. Required.
-            field_subtype (str):
-                Data subtype of field to be added. Optional.
-                Defaults to ``None``.
-            properties (dict):
-                Extra properties of field like: ref, format, min, max, etc. Optional.
-                Defaults to ``None``.
-
-        Raises:
-            ValueError:
-                If the table does not exist or it already contains the field.
-        """
-        self._check_field(table, field, exists=False)
-
-        field_details = {
-            'type': field_type
-        }
-
-        if field_subtype:
-            field_details['subtype'] = field_subtype
-
-        if properties:
-            field_details.update(properties)
-
-        self._metadata['tables'][table]['fields'][field] = field_details
-
-    @staticmethod
-    def _get_key_subtype(field_meta):
-        """Get the appropriate key subtype."""
-        field_type = field_meta['type']
-        if field_type == 'categorical':
-            field_subtype = 'string'
-        elif field_type in ('numerical', 'id'):
-            field_subtype = field_meta['subtype']
-            if field_subtype not in ('integer', 'string'):
-                raise ValueError(
-                    'Invalid field "subtype" for key field: "{}"'.format(field_subtype)
-                )
-        else:
-            raise ValueError(
-                'Invalid field "type" for key field: "{}"'.format(field_type)
-            )
-
-        return field_subtype
-
-    def set_primary_key(self, table, field):
-        """Set the primary key field of the indicated table.
-
-        The field must exist and either be an integer or categorical field.
-
-        Args:
-            table (str):
-                Name of the table where the primary key will be set.
-            field (str):
-                Field to be used as the new primary key.
-
-        Raises:
-            ValueError:
-                If the table or the field do not exist or if the field has an
-                invalid type or subtype.
-        """
-        self._check_field(table, field, exists=True)
-
-        field_meta = self.get_fields(table).get(field)
-        field_subtype = self._get_key_subtype(field_meta)
-
-        table_meta = self._metadata['tables'][table]
-        table_meta['fields'][field] = {
-            'type': 'id',
-            'subtype': field_subtype
-        }
-        table_meta['primary_key'] = field
-
-    def add_relationship(self, parent, child, foreign_key=None):
-        """Add a new relationship between the parent and child tables.
-
-        The relationship is created by adding a reference (``ref``) on the ``foreign_key``
-        field of the ``child`` table pointing at the ``parent`` primary key.
-
-        Args:
-            parent (str):
-                Name of the parent table.
-            child (str):
-                Name of the child table.
-            foreign_key (str):
-                Field in the child table through which the relationship is created.
-                If ``None``, use the parent primary key name.
-
-        Raises:
-            ValueError:
-                If any of the following happens:
-                    * The parent table does not exist.
-                    * The child table does not exist.
-                    * The parent table does not have a primary key.
-                    * The foreign_key field already exists in the child table.
-                    * The child table already has a parent.
-                    * The new relationship closes a relationship circle.
-        """
-        # Validate table and field names
-        primary_key = self.get_primary_key(parent)
-        if not primary_key:
-            raise ValueError('Parent table "{}" does not have a primary key'.format(parent))
-
-        if foreign_key is None:
-            foreign_key = primary_key
-
-        # Validate relationships
-        if self.get_parents(child):
-            raise ValueError('Table "{}" already has a parent'.format(child))
-
-        grandchildren = self.get_children(child)
-        if grandchildren:
-            self._validate_circular_relationships(parent, grandchildren)
-
-        # Copy primary key details over to the foreign key
-        foreign_key_details = copy.deepcopy(self.get_fields(parent)[primary_key])
-        foreign_key_details['ref'] = {
-            'table': parent,
-            'field': primary_key
-        }
-
-        # Make sure that key subtypes are the same
-        foreign_meta = self.get_fields(child).get(foreign_key)
-        if foreign_meta:
-            foreign_subtype = self._get_key_subtype(foreign_meta)
-            if foreign_subtype != foreign_key_details['subtype']:
-                raise ValueError('Primary and Foreign key subtypes mismatch')
-
-        self._metadata['tables'][child]['fields'][foreign_key] = foreign_key_details
-
-        # Re-analyze the relationships
-        self._analyze_relationships()
-
-    def _get_field_details(self, data, fields):
-        """Get or build all the fields metadata.
-
-        Analyze a ``pandas.DataFrame`` to build a ``dict`` with the name of the column, and
-        their data type and subtype. If ``columns`` are provided, only those columns will be
-        analyzed.
-
-        Args:
-            data (pandas.DataFrame):
-                Table to be analyzed.
-            fields (set):
-                Set of field names or field specifications.
-
-        Returns:
-            dict:
-                Dict of valid fields.
-
-        Raises:
-            TypeError:
-                If a field specification is not a str or a dict.
-            ValueError:
-                If a column from the data analyzed is an unsupported data type or
-        """
-        fields_metadata = dict()
-        for field in fields:
-            dtype = data[field].dtype
-            field_template = self._FIELD_TEMPLATES.get(dtype.kind)
-            if not field_template:
-                raise ValueError('Unsupported dtype {} in column {}'.format(dtype, field))
-
-            field_details = copy.deepcopy(field_template)
-            fields_metadata[field] = field_details
-
-        return fields_metadata
-
-    def add_table(self, name, data=None, fields=None, fields_metadata=None,
-                  primary_key=None, parent=None, foreign_key=None):
-        """Add a new table to this metadata.
-
-        ``fields`` list can be a mixture of field names, which will be build automatically
-        from the data, or dictionaries specifying the field details. If a field needs to be
-        analyzed, data has to be also passed.
-
-        If ``parent`` is given, a relationship will be established between this table
-        and the specified parent.
-
-        Args:
-            name (str):
-                Name of the new table.
-            data (str or pandas.DataFrame):
-                Table to be analyzed or path to the csv file.
-                If it's a relative path, use ``root_path`` to find the file.
-                Only used if fields is not ``None``.
-                Defaults to ``None``.
-            fields (list):
-                List of field names to build. If ``None`` is given, all the fields
-                found in the data will be used.
-                Defaults to ``None``.
-            fields_metadata (dict):
-                Metadata to be used when creating fields. This will overwrite the
-                metadata built from the fields found in data.
-                Defaults to ``None``.
-            primary_key (str):
-                Field name to add as primary key, it must not exists. Defaults to ``None``.
-            parent (str):
-                Table name to refere a foreign key field. Defaults to ``None``.
-            foreign_key (str):
-                Foreing key field name to ``parent`` table primary key. Defaults to ``None``.
-
-        Raises:
-            ValueError:
-                If the table ``name`` already exists or ``data`` is not passed and
-                fields need to be built from it.
-        """
-        if name in self.get_tables():
-            raise ValueError('Table "{}" already exists.'.format(name))
-
-        path = None
-        if data is not None:
-            if isinstance(data, str):
-                path = data
-                if not os.path.isabs(data):
-                    data = os.path.join(self.root_path, data)
-
-                data = pd.read_csv(data)
-
-            fields = set(fields or data.columns)
-            if fields_metadata:
-                fields = fields - set(fields_metadata.keys())
-            else:
-                fields_metadata = dict()
-
-            fields_metadata.update(self._get_field_details(data, fields))
-
-        elif fields_metadata is None:
-            fields_metadata = dict()
-
-        table_metadata = {'fields': fields_metadata}
-        if path:
-            table_metadata['path'] = path
-
-        self._metadata['tables'][name] = table_metadata
-
-        try:
-            if primary_key:
-                self.set_primary_key(name, primary_key)
-
-            if parent:
-                self.add_relationship(parent, name, foreign_key)
-
-        except ValueError:
-            # Cleanup
-            del self._metadata['tables'][name]
-            raise
-
-    # ###################### #
-    # Metadata Serialization #
-    # ###################### #
-
-    def to_dict(self):
-        """Get a dict representation of this metadata.
-
-        Returns:
-            dict:
-                dict representation of this metadata.
-        """
-        return copy.deepcopy(self._metadata)
-
-    def to_json(self, path):
-        """Dump this metadata into a JSON file.
-
-        Args:
-            path (str):
-                Path of the JSON file where this metadata will be stored.
-        """
-        with open(path, 'w') as out_file:
-            json.dump(self._metadata, out_file, indent=4)
-
-    @staticmethod
-    def _get_graphviz_extension(path):
-        if path:
-            path_splitted = path.split('.')
-            if len(path_splitted) == 1:
-                raise ValueError('Path without graphviz extansion.')
-
-            graphviz_extension = path_splitted[-1]
-
-            if graphviz_extension not in graphviz.backend.FORMATS:
-                raise ValueError(
-                    '"{}" not a valid graphviz extension format.'.format(graphviz_extension)
-                )
-
-            return '.'.join(path_splitted[:-1]), graphviz_extension
-
-        return None, None
-
-    def _visualize_add_nodes(self, plot):
-        """Add nodes into a `graphviz.Digraph`.
-
-        Each node represent a metadata table.
-
-        Args:
-            plot (graphviz.Digraph)
-        """
-        for table in self.get_tables():
-            # Append table fields
-            fields = []
-
-            for name, value in self.get_fields(table).items():
-                if value.get('subtype') is not None:
-                    fields.append('{} : {} - {}'.format(name, value['type'], value['subtype']))
-
-                else:
-                    fields.append('{} : {}'.format(name, value['type']))
-
-            fields = r'\l'.join(fields)
-
-            # Append table extra information
-            extras = []
-
-            primary_key = self.get_primary_key(table)
-            if primary_key is not None:
-                extras.append('Primary key: {}'.format(primary_key))
-
-            parents = self.get_parents(table)
-            for parent in parents:
-                foreign_key = self.get_foreign_key(parent, table)
-                extras.append('Foreign key ({}): {}'.format(parent, foreign_key))
-
-            path = self.get_table_meta(table).get('path')
-            if path is not None:
-                extras.append('Data path: {}'.format(path))
-
-            extras = r'\l'.join(extras)
-
-            # Add table node
-            title = r'{%s|%s\l|%s\l}' % (table, fields, extras)
-            plot.node(table, label=title)
-
-    def _visualize_add_edges(self, plot):
-        """Add edges into a `graphviz.Digraph`.
-
-        Each edge represents a relationship between two metadata tables.
-
-        Args:
-            plot (graphviz.Digraph)
-        """
-        for table in self.get_tables():
-            for parent in list(self.get_parents(table)):
-                plot.edge(
-                    parent,
-                    table,
-                    label='   {}.{} -> {}.{}'.format(
-                        table, self.get_foreign_key(parent, table),
-                        parent, self.get_primary_key(parent)
-                    ),
-                    arrowhead='crow'
-                )
-
-    def visualize(self, path=None):
-        """Plot metadata usign graphviz.
-
-        Try to generate a plot using graphviz.
-        If a ``path`` is provided save the output into a file.
-
-        Args:
-            path (str):
-                Output file path to save the plot, it requires a graphviz
-                supported extension. If ``None`` do not save the plot.
-                Defaults to ``None``.
-        """
-        filename, graphviz_extension = self._get_graphviz_extension(path)
-        plot = graphviz.Digraph(
-            'Metadata',
-            format=graphviz_extension,
-            node_attr={
-                "shape": "Mrecord",
-                "fillcolor": "lightgoldenrod1",
-                "style": "filled"
-            },
-        )
-
-        self._visualize_add_nodes(plot)
-        self._visualize_add_edges(plot)
-
-        if filename:
-            plot.render(filename=filename, cleanup=True, format=graphviz_extension)
-        else:
-            return plot
-
-    def __str__(self):
-        tables = self.get_tables()
-        relationships = [
-            '    {}.{} -> {}.{}'.format(
-                table, self.get_foreign_key(parent, table),
-                parent, self.get_primary_key(parent)
-            )
-            for table in tables
-            for parent in list(self.get_parents(table))
-        ]
-
-        return (
-            "Metadata\n"
-            "  root_path: {}\n"
-            "  tables: {}\n"
-            "  relationships:\n"
-            "{}"
-        ).format(
-            os.path.abspath(self.root_path),
-            tables,
-            '\n'.join(relationships)
-        )
diff --git a/sdv/metadata.py.non_primary b/sdv/metadata.py.non_primary
deleted file mode 100644
index 2df337bd2..000000000
--- a/sdv/metadata.py.non_primary
+++ /dev/null
@@ -1,1109 +0,0 @@
-import copy
-import json
-import logging
-import os
-from collections import defaultdict
-
-import graphviz
-import numpy as np
-import pandas as pd
-from rdt import HyperTransformer, transformers
-
-LOGGER = logging.getLogger(__name__)
-
-
-def _read_csv_dtypes(table_meta):
-    """Get the dtypes specification that needs to be passed to read_csv."""
-    dtypes = dict()
-    for name, field in table_meta['fields'].items():
-        field_type = field['type']
-        if field_type == 'categorical':
-            dtypes[name] = str
-        elif field_type == 'id' and field.get('subtype', 'integer') == 'string':
-            dtypes[name] = str
-
-    return dtypes
-
-
-def _parse_dtypes(data, table_meta):
-    """Convert the data columns to the right dtype after loading the CSV."""
-    for name, field in table_meta['fields'].items():
-        field_type = field['type']
-        if field_type == 'datetime':
-            datetime_format = field.get('format')
-            data[name] = pd.to_datetime(data[name], format=datetime_format, exact=False)
-        elif field_type == 'numerical' and field.get('subtype') == 'integer':
-            data[name] = data[name].dropna().astype(int)
-        elif field_type == 'id' and field.get('subtype', 'integer') == 'integer':
-            data[name] = data[name].dropna().astype(int)
-
-    return data
-
-
-def _load_csv(root_path, table_meta):
-    """Load a CSV with the right dtypes and then parse the columns."""
-    relative_path = os.path.join(root_path, table_meta['path'])
-    dtypes = _read_csv_dtypes(table_meta)
-
-    data = pd.read_csv(relative_path, dtype=dtypes)
-    data = _parse_dtypes(data, table_meta)
-
-    return data
-
-
-class MetadataError(Exception):
-    pass
-
-
-class Metadata:
-    """Dataset Metadata.
-
-    The Metadata class provides a unified layer of abstraction over the dataset
-    metadata, which includes both the necessary details to load the data from
-    the hdd and to know how to parse and transform it to numerical data.
-
-    Args:
-        metadata (str or dict):
-            Path to a ``json`` file that contains the metadata or a ``dict`` representation
-            of ``metadata`` following the same structure.
-
-        root_path (str):
-            The path where the ``metadata.json`` is located. Defaults to ``None``.
-    """
-
-    _child_map = None
-    _hyper_transformers = None
-    _metadata = None
-    _parent_map = None
-
-    root_path = None
-
-    _FIELD_TEMPLATES = {
-        'i': {
-            'type': 'numerical',
-            'subtype': 'integer',
-        },
-        'f': {
-            'type': 'numerical',
-            'subtype': 'float',
-        },
-        'O': {
-            'type': 'categorical',
-        },
-        'b': {
-            'type': 'boolean',
-        },
-        'M': {
-            'type': 'datetime',
-        }
-    }
-    _DTYPES = {
-        ('categorical', None): 'object',
-        ('boolean', None): 'bool',
-        ('numerical', None): 'float',
-        ('numerical', 'float'): 'float',
-        ('numerical', 'integer'): 'int',
-        ('datetime', None): 'datetime64',
-        ('id', None): 'int',
-        ('id', 'integer'): 'int',
-        ('id', 'string'): 'str'
-    }
-
-    def _analyze_relationships(self):
-        """Extract information about child-parent relationships.
-
-        Creates the following attributes:
-            * ``_child_map``: set of child tables that each table has.
-            * ``_parent_map``: set ot parents that each table has.
-        """
-        self._child_map = defaultdict(set)
-        self._parent_map = defaultdict(set)
-
-        for table, table_meta in self._metadata['tables'].items():
-            if table_meta.get('use', True):
-                for field_meta in table_meta['fields'].values():
-                    ref = field_meta.get('ref')
-                    if ref:
-                        parent = ref['table']
-                        self._child_map[parent].add(table)
-                        self._parent_map[table].add(parent)
-
-    @staticmethod
-    def _dict_metadata(metadata):
-        """Get a metadata ``dict`` with SDV format.
-
-        For each table create a dict of fields from a previous list of fields.
-
-        Args:
-            metadata (dict):
-                Original metadata to format.
-
-        Returns:
-            dict:
-                Formated metadata dict.
-        """
-        new_metadata = copy.deepcopy(metadata)
-        tables = new_metadata['tables']
-        if isinstance(tables, dict):
-            new_metadata['tables'] = {
-                table: meta
-                for table, meta in tables.items()
-                if meta.pop('use', True)
-            }
-            return new_metadata
-
-        new_tables = dict()
-        for table in tables:
-            if table.pop('use', True):
-                new_tables[table.pop('name')] = table
-
-                fields = table['fields']
-                new_fields = dict()
-                for field in fields:
-                    new_fields[field.pop('name')] = field
-
-                table['fields'] = new_fields
-
-        new_metadata['tables'] = new_tables
-
-        return new_metadata
-
-    def __init__(self, metadata=None, root_path=None):
-        if isinstance(metadata, str):
-            self.root_path = root_path or os.path.dirname(metadata)
-            with open(metadata) as metadata_file:
-                metadata = json.load(metadata_file)
-        else:
-            self.root_path = root_path or '.'
-
-        if metadata is not None:
-            self._metadata = self._dict_metadata(metadata)
-        else:
-            self._metadata = {'tables': {}}
-
-        self._hyper_transformers = dict()
-        self._analyze_relationships()
-
-    def get_children(self, table_name):
-        """Get tables for which the given table is parent.
-
-        Args:
-            table_name (str):
-                Name of the table from which to get the children.
-
-        Returns:
-            set:
-                Set of children for the given table.
-        """
-        return self._child_map[table_name]
-
-    def get_parents(self, table_name):
-        """Get tables for with the given table is child.
-
-        Args:
-            table_name (str):
-                Name of the table from which to get the parents.
-
-        Returns:
-            set:
-                Set of parents for the given table.
-        """
-        return self._parent_map[table_name]
-
-    def get_table_meta(self, table_name):
-        """Get the metadata dict for a table.
-
-        Args:
-            table_name (str):
-                Name of table to get data for.
-
-        Returns:
-            dict:
-                table metadata
-
-        Raises:
-            ValueError:
-                If table does not exist in this metadata.
-        """
-        table = self._metadata['tables'].get(table_name)
-        if table is None:
-            raise ValueError('Table "{}" does not exist'.format(table_name))
-
-        return copy.deepcopy(table)
-
-    def get_tables(self):
-        """Get the list of table names.
-
-        Returns:
-            list:
-                table names.
-        """
-        return list(self._metadata['tables'].keys())
-
-    def get_field_meta(self, table_name, field_name):
-        """Get the metadata dict for a table.
-
-        Args:
-            table_name (str):
-                Name of the table to which the field belongs.
-            field_name (str):
-                Name of the field to get data for.
-
-        Returns:
-            dict:
-                field metadata
-
-        Raises:
-            ValueError:
-                If the table or the field do not exist in this metadata.
-        """
-        field_meta = self.get_fields(table_name).get(field_name)
-        if field_meta is None:
-            raise ValueError(
-                'Table "{}" does not contain a field name "{}"'.format(table_name, field_name))
-
-        return copy.deepcopy(field_meta)
-
-    def get_fields(self, table_name):
-        """Get table fields metadata.
-
-        Args:
-            table_name (str):
-                Name of the table to get the fields from.
-
-        Returns:
-            dict:
-                Mapping of field names and their metadata dicts.
-
-        Raises:
-            ValueError:
-                If table does not exist in this metadata.
-        """
-        return self.get_table_meta(table_name)['fields']
-
-    def get_primary_key(self, table_name):
-        """Get the primary key name of the indicated table.
-
-        Args:
-            table_name (str):
-                Name of table for which to get the primary key field.
-
-        Returns:
-            str or None:
-                Primary key field name. ``None`` if the table has no primary key.
-
-        Raises:
-            ValueError:
-                If table does not exist in this metadata.
-        """
-        return self.get_table_meta(table_name).get('primary_key')
-
-    def get_foreign_key(self, parent, child):
-        """Get table foreign key field name.
-
-        Args:
-            parent (str):
-                Name of the parent table.
-            child (str):
-                Name of the child table.
-
-        Returns:
-            str or None:
-                Foreign key field name.
-
-        Raises:
-            ValueError:
-                If the relationship does not exist.
-        """
-        for name, field in self.get_fields(child).items():
-            ref = field.get('ref')
-            if ref and ref['table'] == parent:
-                return name
-
-        raise ValueError('{} is not parent of {}'.format(parent, child))
-
-    def load_table(self, table_name):
-        """Load table data.
-
-        Args:
-            table_name (str):
-                Name of the table to load.
-
-        Returns:
-            pandas.DataFrame:
-                DataFrame with the contents of the table.
-
-        Raises:
-            ValueError:
-                If table does not exist in this metadata.
-        """
-        LOGGER.info('Loading table %s', table_name)
-        table_meta = self.get_table_meta(table_name)
-        return _load_csv(self.root_path, table_meta)
-
-    def load_tables(self, tables=None):
-        """Get a dictionary with data from multiple tables.
-
-        If a ``tables`` list is given, only load the indicated tables.
-        Otherwise, load all the tables from this metadata.
-
-        Args:
-            tables (list):
-                List of table names. Defaults to ``None``.
-
-        Returns:
-            dict(str, pandasd.DataFrame):
-                mapping of table names and their data loaded as ``pandas.DataFrame`` instances.
-        """
-        return {
-            table_name: self.load_table(table_name)
-            for table_name in tables or self.get_tables()
-        }
-
-    def get_dtypes(self, table_name, ids=False):
-        """Get a ``dict`` with the ``dtypes`` for each field of a given table.
-
-        Args:
-            table_name (str):
-                Table name for which to retrive the ``dtypes``.
-            ids (bool):
-                Whether or not include the id fields. Defaults to ``False``.
-
-        Returns:
-            dict:
-                Dictionary that contains the field names and data types from a table.
-
-        Raises:
-            ValueError:
-                If a field has an invalid type or subtype or if the table does not
-                exist in this metadata.
-        """
-        dtypes = dict()
-        table_meta = self.get_table_meta(table_name)
-        for name, field in table_meta['fields'].items():
-            field_type = field['type']
-            field_subtype = field.get('subtype')
-            dtype = self._DTYPES.get((field_type, field_subtype))
-            if not dtype:
-                raise MetadataError(
-                    'Invalid type and subtype combination for field {}: ({}, {})'.format(
-                        name, field_type, field_subtype)
-                )
-
-            if ids and field_type == 'id':
-                if (name != table_meta.get('primary_key')) and not field.get('ref'):
-                    for child_table in self.get_children(table_name):
-                        if name == self.get_foreign_key(table_name, child_table):
-                            break
-
-                    else:
-                        raise MetadataError(
-                            'id field `{}` is neither a primary or a foreign key'.format(name))
-
-            if ids or (field_type != 'id'):
-                dtypes[name] = dtype
-
-        return dtypes
-
-    def _get_pii_fields(self, table_name):
-        """Get the ``pii_category`` for each field that contains PII.
-
-        Args:
-            table_name (str):
-                Table name for which to get the pii fields.
-
-        Returns:
-            dict:
-                pii field names and categories.
-        """
-        pii_fields = dict()
-        for name, field in self.get_table_meta(table_name)['fields'].items():
-            if field['type'] == 'categorical' and field.get('pii', False):
-                pii_fields[name] = field['pii_category']
-
-        return pii_fields
-
-    @staticmethod
-    def _get_transformers(dtypes, pii_fields):
-        """Create the transformer instances needed to process the given dtypes.
-
-        Temporary drop-in replacement of ``HyperTransformer._analyze`` method,
-        before RDT catches up.
-
-        Args:
-            dtypes (dict):
-                mapping of field names and dtypes.
-            pii_fields (dict):
-                mapping of pii field names and categories.
-
-        Returns:
-            dict:
-                mapping of field names and transformer instances.
-        """
-        transformers_dict = dict()
-        for name, dtype in dtypes.items():
-            dtype = np.dtype(dtype)
-            if dtype.kind == 'i':
-                transformer = transformers.NumericalTransformer(dtype=int)
-            elif dtype.kind == 'f':
-                transformer = transformers.NumericalTransformer(dtype=float)
-            elif dtype.kind == 'O':
-                anonymize = pii_fields.get(name)
-                transformer = transformers.CategoricalTransformer(anonymize=anonymize)
-            elif dtype.kind == 'b':
-                transformer = transformers.BooleanTransformer()
-            elif dtype.kind == 'M':
-                transformer = transformers.DatetimeTransformer()
-            else:
-                raise ValueError('Unsupported dtype: {}'.format(dtype))
-
-            LOGGER.info('Loading transformer %s for field %s',
-                        transformer.__class__.__name__, name)
-            transformers_dict[name] = transformer
-
-        return transformers_dict
-
-    def _load_hyper_transformer(self, table_name):
-        """Create and return a new ``rdt.HyperTransformer`` instance for a table.
-
-        First get the ``dtypes`` and ``pii fields`` from a given table, then use
-        those to build a transformer dictionary to be used by the ``HyperTransformer``.
-
-        Args:
-            table_name (str):
-                Table name for which to load the HyperTransformer.
-
-        Returns:
-            rdt.HyperTransformer:
-                Instance of ``rdt.HyperTransformer`` for the given table.
-        """
-        dtypes = self.get_dtypes(table_name)
-        pii_fields = self._get_pii_fields(table_name)
-        transformers_dict = self._get_transformers(dtypes, pii_fields)
-        return HyperTransformer(transformers=transformers_dict)
-
-    def transform(self, table_name, data):
-        """Transform data for a given table.
-
-        If the ``HyperTransformer`` for a table is ``None`` it is created.
-
-        Args:
-            table_name (str):
-                Name of the table that is being transformer.
-            data (pandas.DataFrame):
-                Table data.
-
-        Returns:
-            pandas.DataFrame:
-                Transformed data.
-        """
-        hyper_transformer = self._hyper_transformers.get(table_name)
-        if hyper_transformer is None:
-            hyper_transformer = self._load_hyper_transformer(table_name)
-            fields = list(hyper_transformer.transformers.keys())
-            hyper_transformer.fit(data[fields])
-            self._hyper_transformers[table_name] = hyper_transformer
-
-        hyper_transformer = self._hyper_transformers.get(table_name)
-        fields = list(hyper_transformer.transformers.keys())
-        return hyper_transformer.transform(data[fields])
-
-    def reverse_transform(self, table_name, data):
-        """Reverse the transformed data for a given table.
-
-        Args:
-            table_name (str):
-                Name of the table to reverse transform.
-            data (pandas.DataFrame):
-                Data to be reversed.
-
-        Returns:
-            pandas.DataFrame
-        """
-        hyper_transformer = self._hyper_transformers[table_name]
-        reversed_data = hyper_transformer.reverse_transform(data)
-
-        for name, dtype in self.get_dtypes(table_name, ids=True).items():
-            reversed_data[name] = reversed_data[name].dropna().astype(dtype)
-
-        return reversed_data
-
-    # ################### #
-    # Metadata Validation #
-    # ################### #
-
-    def _validate_table(self, table_name, table_meta, table_data=None):
-        """Validate table metadata.
-
-        Validate the type and subtype combination for each field in ``table_meta``.
-        If a field has type ``id``, validate that it either is the ``primary_key`` or
-        has a ``ref`` entry.
-
-        If the table has ``primary_key``, make sure that the corresponding field exists
-        and its type is ``id``.
-
-        If ``table_data`` is provided, also check that the list of columns corresponds
-        to the ones indicated in the metadata and that all the dtypes are valid.
-
-        Args:
-            table_name (str):
-                Name of the table to validate.
-            table_meta (dict):
-                Metadata of the table to validate.
-            table_data (pandas.DataFrame):
-                If provided, make sure that the data matches the one described
-                on the metadata.
-
-        Raises:
-            MetadataError:
-                If there is any error in the metadata or the data does not
-                match the metadata description.
-        """
-        dtypes = self.get_dtypes(table_name, ids=True)
-
-        # Primary key field exists and its type is 'id'
-        primary_key = table_meta.get('primary_key')
-        if primary_key:
-            pk_field = table_meta['fields'].get(primary_key)
-
-            if not pk_field:
-                raise MetadataError('Primary key is not an existing field.')
-
-            if pk_field['type'] != 'id':
-                raise MetadataError('Primary key is not of type `id`.')
-
-        if table_data is not None:
-            for column in table_data:
-                try:
-                    dtype = dtypes.pop(column)
-                    table_data[column].dropna().astype(dtype)
-                except KeyError:
-                    message = 'Unexpected column in table `{}`: `{}`'.format(table_name, column)
-                    raise MetadataError(message) from None
-                except ValueError as ve:
-                    message = 'Invalid values found in column `{}` of table `{}`: `{}`'.format(
-                        column, table_name, ve)
-                    raise MetadataError(message) from None
-
-            # assert all dtypes are in data
-            if dtypes:
-                raise MetadataError(
-                    'Missing columns on table {}: {}.'.format(table_name, list(dtypes.keys()))
-                )
-
-    def _validate_circular_relationships(self, parent, children=None):
-        """Validate that there is no circular relatioship in the metadata."""
-        if children is None:
-            children = self.get_children(parent)
-
-        if parent in children:
-            raise MetadataError('Circular relationship found for table "{}"'.format(parent))
-
-        for child in children:
-            self._validate_circular_relationships(parent, self.get_children(child))
-
-    def validate(self, tables=None):
-        """Validate this metadata.
-
-        For each table from in metadata ``tables`` entry:
-            * Validate the table metadata is correct.
-
-        * If ``tables`` are provided or they have been loaded, check
-          that all the metadata tables exists in the ``tables`` dictionary.
-        * Validate the type/subtype combination for each field and
-          if a field of type ``id`` exists it must be the ``primary_key``
-          or must have a ``ref`` entry.
-        * If ``primary_key`` entry exists, check that it's an existing
-          field and its type is ``id``.
-        * If ``tables`` are provided or they have been loaded, check
-          all the data types for the table correspond to each column and
-          all the data types exists on the table.
-        * Validate that there is no circular relatioship in the metadata.
-        * Check that all the tables have at most one parent.
-
-        Args:
-            tables (bool, dict):
-                If a dict of table is passed, validate that the columns and
-                dtypes match the metadata. If ``True`` is passed, load the
-                tables from the Metadata instead. If ``None``, omit the data
-                validation. Defaults to ``None``.
-        """
-        tables_meta = self._metadata.get('tables')
-        if not tables_meta:
-            raise MetadataError('"tables" entry not found in Metadata.')
-
-        if tables and not isinstance(tables, dict):
-            tables = self.load_tables()
-
-        for table_name, table_meta in tables_meta.items():
-            if tables:
-                table = tables.get(table_name)
-                if table is None:
-                    raise MetadataError('Table `{}` not found in tables'.format(table_name))
-
-            else:
-                table = None
-
-            self._validate_table(table_name, table_meta, table)
-            self._validate_circular_relationships(table_name)
-
-    def _check_field(self, table, field, exists=False):
-        """Validate the existance of the table and existance (or not) of field."""
-        table_fields = self.get_fields(table)
-        if exists and (field not in table_fields):
-            raise ValueError('Field "{}" does not exist in table "{}"'.format(field, table))
-
-        if not exists and (field in table_fields):
-            raise ValueError('Field "{}" already exists in table "{}"'.format(field, table))
-
-    # ################# #
-    # Metadata Creation #
-    # ################# #
-
-    def add_field(self, table, field, field_type, field_subtype=None, properties=None):
-        """Add a new field to the indicated table.
-
-        Args:
-            table (str):
-                Table name to add the new field, it must exist.
-            field (str):
-                Field name to be added, it must not exist.
-            field_type (str):
-                Data type of field to be added. Required.
-            field_subtype (str):
-                Data subtype of field to be added. Optional.
-                Defaults to ``None``.
-            properties (dict):
-                Extra properties of field like: ref, format, min, max, etc. Optional.
-                Defaults to ``None``.
-
-        Raises:
-            ValueError:
-                If the table does not exist or it already contains the field.
-        """
-        self._check_field(table, field, exists=False)
-
-        field_details = {
-            'type': field_type
-        }
-
-        if field_subtype:
-            field_details['subtype'] = field_subtype
-
-        if properties:
-            field_details.update(properties)
-
-        self._metadata['tables'][table]['fields'][field] = field_details
-
-    @staticmethod
-    def _get_key_subtype(field_meta):
-        """Get the appropriate key subtype."""
-        field_type = field_meta['type']
-
-        if field_type == 'categorical':
-            field_subtype = 'string'
-
-        elif field_type in ('numerical', 'id'):
-            field_subtype = field_meta['subtype']
-            if field_subtype not in ('integer', 'string'):
-                raise ValueError(
-                    'Invalid field "subtype" for key field: "{}"'.format(field_subtype)
-                )
-
-        else:
-            raise ValueError(
-                'Invalid field "type" for key field: "{}"'.format(field_type)
-            )
-
-        return field_subtype
-
-    def set_primary_key(self, table, field):
-        """Set the primary key field of the indicated table.
-
-        The field must exist and either be an integer or categorical field.
-
-        Args:
-            table (str):
-                Name of the table where the primary key will be set.
-            field (str):
-                Field to be used as the new primary key.
-
-        Raises:
-            ValueError:
-                If the table or the field do not exist or if the field has an
-                invalid type or subtype.
-        """
-        self._check_field(table, field, exists=True)
-
-        field_meta = self.get_fields(table).get(field)
-        field_subtype = self._get_key_subtype(field_meta)
-
-        table_meta = self._metadata['tables'][table]
-        table_meta['fields'][field] = {
-            'type': 'id',
-            'subtype': field_subtype
-        }
-        table_meta['primary_key'] = field
-
-    def add_relationship(self, parent, child, parent_key=None, child_key=None):
-        """Add a new relationship between the parent and child tables.
-
-        The relationship is created by adding a reference (``ref``) on the ``child_key``
-        field of the ``child`` table pointing at the ``parent_key`` field from the
-        ``parent`` table.
-
-        Args:
-            parent (str):
-                Name of the parent table.
-            child (str):
-                Name of the child table.
-            parent_key (str):
-                Field in the parent table through which the relationship is created.
-                If ``None``, use the parent primary key name.
-            child_key (str):
-                Field in the child table through which the relationship is created.
-                If ``None``, use the name of the parent key.
-
-        Raises:
-            ValueError:
-                If any of the following happens:
-                    * The parent or child tables do not exist.
-                    * The parent_key or child_key fields do not exist.
-                    * The child_key already is a foreign key.
-                    * The new relationship closes a relationship circle.
-        """
-        # Validate tables exists
-        self.get_table_meta(parent)
-        self.get_table_meta(child)
-
-        # Validate fields exists
-        if parent_key is None:
-            parent_key = self.get_primary_key(parent)
-            if not parent_key:
-                msg = 'If parent table does not have a primary key, a `parent_key` must be given'
-                raise ValueError(msg.format(parent))
-
-        if child_key is None:
-            child_key = parent_key
-
-        parent_key_meta = copy.deepcopy(self.get_field_meta(parent, parent_key))
-        child_key_meta = copy.deepcopy(self.get_field_meta(child, child_key))
-
-        # Validate relationships
-        child_ref = child_key_meta.get('ref')
-        if child_ref:
-            raise ValueError(
-                'Field "{}.{}" already defines a relationship'.format(child, child_key))
-
-        grandchildren = self.get_children(child)
-        if grandchildren:
-            self._validate_circular_relationships(parent, grandchildren)
-
-        # Make sure that the parent key is an id
-        if parent_key_meta['type'] != 'id':
-            parent_key_meta['subtype'] = self._get_key_subtype(parent_key_meta)
-            parent_key_meta['type'] = 'id'
-
-        # Update the child key meta
-        child_key_meta['subtype'] = self._get_key_subtype(parent_key_meta)
-        child_key_meta['type'] = 'id'
-        child_key_meta['ref'] = {
-            'table': parent,
-            'field': parent_key
-        }
-
-        # Make sure that key subtypes are the same
-        if child_key_meta['subtype'] != parent_key_meta['subtype']:
-            raise ValueError('Parent and Child key subtypes mismatch')
-
-        # Make a backup
-        metadata_backup = copy.deepcopy(self._metadata)
-
-        self._metadata['tables'][parent]['fields'][parent_key] = parent_key_meta
-        self._metadata['tables'][child]['fields'][child_key] = child_key_meta
-
-        # Re-analyze the relationships
-        self._analyze_relationships()
-
-        try:
-            self.validate()
-        except MetadataError:
-            self._metadata = metadata_backup
-            raise
-
-    def _get_field_details(self, data, fields):
-        """Get or build all the fields metadata.
-
-        Analyze a ``pandas.DataFrame`` to build a ``dict`` with the name of the column, and
-        their data type and subtype. If ``columns`` are provided, only those columns will be
-        analyzed.
-
-        Args:
-            data (pandas.DataFrame):
-                Table to be analyzed.
-            fields (set):
-                Set of field names or field specifications.
-
-        Returns:
-            dict:
-                Dict of valid fields.
-
-        Raises:
-            TypeError:
-                If a field specification is not a str or a dict.
-            ValueError:
-                If a column from the data analyzed is an unsupported data type or
-        """
-        fields_metadata = dict()
-        for field in fields:
-            dtype = data[field].dtype
-            field_template = self._FIELD_TEMPLATES.get(dtype.kind)
-            if not field_template:
-                raise ValueError('Unsupported dtype {} in column {}'.format(dtype, field))
-
-            field_details = copy.deepcopy(field_template)
-            fields_metadata[field] = field_details
-
-        return fields_metadata
-
-    def add_table(self, name, data=None, fields=None, fields_metadata=None,
-                  primary_key=None, parent=None, parent_key=None, foreign_key=None):
-        """Add a new table to this metadata.
-
-        ``fields`` list can be a mixture of field names, which will be build automatically
-        from the data, or dictionaries specifying the field details. If a field needs to be
-        analyzed, data has to be also passed.
-
-        If ``parent`` is given, a relationship will be established between this table
-        and the specified parent.
-
-        Args:
-            name (str):
-                Name of the new table.
-            data (str or pandas.DataFrame):
-                Table to be analyzed or path to the csv file.
-                If it's a relative path, use ``root_path`` to find the file.
-                Only used if fields is not ``None``.
-                Defaults to ``None``.
-            fields (list):
-                List of field names to build. If ``None`` is given, all the fields
-                found in the data will be used.
-                Defaults to ``None``.
-            fields_metadata (dict):
-                Metadata to be used when creating fields. This will overwrite the
-                metadata built from the fields found in data.
-                Defaults to ``None``.
-            primary_key (str):
-                Field name to add as primary key, it must not exists. Defaults to ``None``.
-            parent (str):
-                Table name to refere a foreign key field. Defaults to ``None``.
-            parent_key (str):
-                Name of the field from the ``parent`` table that is pointed by the given
-                ``foreign_key``. Defaults to the ``parent`` primary key.
-            foreign_key (str):
-                Name of the field from the added table that forms a relationship with
-                the ``parent`` table. Defaults to the same name as ``parent_key``.
-
-        Raises:
-            ValueError:
-                If the table ``name`` already exists or ``data`` is not passed and
-                fields need to be built from it.
-        """
-        if name in self.get_tables():
-            raise ValueError('Table "{}" already exists.'.format(name))
-
-        path = None
-        if data is not None:
-            if isinstance(data, str):
-                path = data
-                if not os.path.isabs(data):
-                    data = os.path.join(self.root_path, data)
-
-                data = pd.read_csv(data)
-
-            fields = set(fields or data.columns)
-            if fields_metadata:
-                fields = fields - set(fields_metadata.keys())
-            else:
-                fields_metadata = dict()
-
-            fields_metadata.update(self._get_field_details(data, fields))
-
-        elif fields_metadata is None:
-            fields_metadata = dict()
-
-        table_metadata = {'fields': fields_metadata}
-        if path:
-            table_metadata['path'] = path
-
-        self._metadata['tables'][name] = table_metadata
-
-        try:
-            if primary_key:
-                self.set_primary_key(name, primary_key)
-
-            if parent:
-                self.add_relationship(parent, name, parent_key, foreign_key)
-
-        except ValueError:
-            # Cleanup
-            del self._metadata['tables'][name]
-            raise
-
-    # ###################### #
-    # Metadata Serialization #
-    # ###################### #
-
-    def to_dict(self):
-        """Get a dict representation of this metadata.
-
-        Returns:
-            dict:
-                dict representation of this metadata.
-        """
-        return copy.deepcopy(self._metadata)
-
-    def to_json(self, path):
-        """Dump this metadata into a JSON file.
-
-        Args:
-            path (str):
-                Path of the JSON file where this metadata will be stored.
-        """
-        with open(path, 'w') as out_file:
-            json.dump(self._metadata, out_file, indent=4)
-
-    @staticmethod
-    def _get_graphviz_extension(path):
-        if path:
-            path_splitted = path.split('.')
-            if len(path_splitted) == 1:
-                raise ValueError('Path without graphviz extansion.')
-
-            graphviz_extension = path_splitted[-1]
-
-            if graphviz_extension not in graphviz.backend.FORMATS:
-                raise ValueError(
-                    '"{}" not a valid graphviz extension format.'.format(graphviz_extension)
-                )
-
-            return '.'.join(path_splitted[:-1]), graphviz_extension
-
-        return None, None
-
-    def _visualize_add_nodes(self, plot):
-        """Add nodes into a `graphviz.Digraph`.
-
-        Each node represent a metadata table.
-
-        Args:
-            plot (graphviz.Digraph)
-        """
-        for table in self.get_tables():
-            # Append table fields
-            fields = []
-
-            for name, value in self.get_fields(table).items():
-                if value.get('subtype') is not None:
-                    fields.append('{} : {} - {}'.format(name, value['type'], value['subtype']))
-
-                else:
-                    fields.append('{} : {}'.format(name, value['type']))
-
-            fields = r'\l'.join(fields)
-
-            # Append table extra information
-            extras = []
-
-            primary_key = self.get_primary_key(table)
-            if primary_key is not None:
-                extras.append('Primary key: {}'.format(primary_key))
-
-            parents = self.get_parents(table)
-            for parent in parents:
-                foreign_key = self.get_foreign_key(parent, table)
-                extras.append('Foreign key ({}): {}'.format(parent, foreign_key))
-
-            path = self.get_table_meta(table).get('path')
-            if path is not None:
-                extras.append('Data path: {}'.format(path))
-
-            extras = r'\l'.join(extras)
-
-            # Add table node
-            title = r'{%s|%s\l|%s\l}' % (table, fields, extras)
-            plot.node(table, label=title)
-
-    def _visualize_add_edges(self, plot):
-        """Add edges into a `graphviz.Digraph`.
-
-        Each edge represents a relationship between two metadata tables.
-
-        Args:
-            plot (graphviz.Digraph)
-        """
-        for table in self.get_tables():
-            for parent in list(self.get_parents(table)):
-                plot.edge(
-                    parent,
-                    table,
-                    label='   {}.{} -> {}.{}'.format(
-                        table, self.get_foreign_key(parent, table),
-                        parent, self.get_primary_key(parent)
-                    ),
-                    arrowhead='crow'
-                )
-
-    def visualize(self, path=None):
-        """Plot metadata usign graphviz.
-
-        Try to generate a plot using graphviz.
-        If a ``path`` is provided save the output into a file.
-
-        Args:
-            path (str):
-                Output file path to save the plot, it requires a graphviz
-                supported extension. If ``None`` do not save the plot.
-                Defaults to ``None``.
-        """
-        filename, graphviz_extension = self._get_graphviz_extension(path)
-        plot = graphviz.Digraph(
-            'Metadata',
-            format=graphviz_extension,
-            node_attr={
-                "shape": "Mrecord",
-                "fillcolor": "lightgoldenrod1",
-                "style": "filled"
-            },
-        )
-
-        self._visualize_add_nodes(plot)
-        self._visualize_add_edges(plot)
-
-        if filename:
-            plot.render(filename=filename, cleanup=True, format=graphviz_extension)
-        else:
-            return plot
-
-    def __str__(self):
-        tables = self.get_tables()
-        relationships = [
-            '    {}.{} -> {}.{}'.format(
-                table, self.get_foreign_key(parent, table),
-                parent, self.get_primary_key(parent)
-            )
-            for table in tables
-            for parent in list(self.get_parents(table))
-        ]
-
-        return (
-            "Metadata\n"
-            "  root_path: {}\n"
-            "  tables: {}\n"
-            "  relationships:\n"
-            "{}"
-        ).format(
-            os.path.abspath(self.root_path),
-            tables,
-            '\n'.join(relationships)
-        )

From 96395e89ea9789210f4aaf90e58fcbd0d1b7ec65 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 22:21:22 +0200
Subject: [PATCH 32/33] Recover parameters properly

---
 sdv/metadata/table.py                     | 11 ++++
 sdv/tabular/copulas.py                    | 61 +++++++++++++++++------
 tests/integration/tabular/test_copulas.py | 24 ++++++---
 3 files changed, 74 insertions(+), 22 deletions(-)

diff --git a/sdv/metadata/table.py b/sdv/metadata/table.py
index 893549272..598a4a725 100644
--- a/sdv/metadata/table.py
+++ b/sdv/metadata/table.py
@@ -386,3 +386,14 @@ def to_json(self, path):
         """
         with open(path, 'w') as out_file:
             json.dump(self._metadata, out_file, indent=4)
+
+    @classmethod
+    def from_json(cls, path):
+        """Load a Table from a JSON
+
+        Args:
+            path (str):
+                Path of the JSON file to load
+        """
+        with open(path, 'r') as in_file:
+            return cls(json.load(in_file))
diff --git a/sdv/tabular/copulas.py b/sdv/tabular/copulas.py
index dc82dd862..7ae00120f 100644
--- a/sdv/tabular/copulas.py
+++ b/sdv/tabular/copulas.py
@@ -20,8 +20,9 @@ class GaussianCopula(BaseTabularModel):
             ``categorical_fuzzy``.
     """
 
-    DISTRIBUTION = copulas.univariate.GaussianUnivariate
+    DEFAULT_DISTRIBUTION = copulas.univariate.Univariate
     _distribution = None
+    _categorical_transformer = None
     _model = None
 
     HYPERPARAMETERS = {
@@ -51,6 +52,7 @@ class GaussianCopula(BaseTabularModel):
             ]
         }
     }
+    DEFAULT_TRANSFORMER = 'one_hot_encoding'
     CATEGORICAL_TRANSFORMERS = {
         'categorical': rdt.transformers.CategoricalTransformer(fuzzy=False),
         'categorical_fuzzy': rdt.transformers.CategoricalTransformer(fuzzy=True),
@@ -61,10 +63,24 @@ class GaussianCopula(BaseTabularModel):
         'O': rdt.transformers.OneHotEncodingTransformer
     }
 
-    def __init__(self, distribution=None, categorical_transformer='categorical',
-                 *args, **kwargs):
+    def __init__(self, distribution=None, categorical_transformer=None, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self._distribution = distribution or self.DISTRIBUTION
+
+        if self._metadata is not None and 'model_kwargs' in self._metadata._metadata:
+            model_kwargs = self._metadata._metadata['model_kwargs']
+            if distribution is None:
+                distribution = model_kwargs['distribution']
+
+            if categorical_transformer is None:
+                categorical_transformer = model_kwargs['categorical_transformer']
+
+            self._size = model_kwargs['_size']
+
+        self._distribution = distribution or self.DEFAULT_DISTRIBUTION
+
+        categorical_transformer = categorical_transformer or self.DEFAULT_TRANSFORMER
+        self._categorical_transformer = categorical_transformer
+
         self.TRANSFORMER_TEMPLATES['O'] = self.CATEGORICAL_TRANSFORMERS[categorical_transformer]
 
     def _update_metadata(self):
@@ -72,10 +88,15 @@ def _update_metadata(self):
         univariates = parameters['univariates']
         columns = parameters['columns']
 
-        fields = self._metadata.get_fields()
-        for field_name, univariate in zip(columns, univariates):
-            field_meta = fields[field_name]
-            field_meta['distribution'] = univariate['type']
+        distributions = {}
+        for column, univariate in zip(columns, univariates):
+            distributions[column] = univariate['type']
+
+        self._metadata._metadata['model_kwargs'] = {
+            'distribution': distributions,
+            'categorical_transformer': self._categorical_transformer,
+            '_size': self._size
+        }
 
     def _fit(self, data):
         """Fit the model to the table.
@@ -86,6 +107,7 @@ def _fit(self, data):
         """
         self._model = copulas.multivariate.GaussianMultivariate(distribution=self._distribution)
         self._model.fit(data)
+        self._update_metadata()
 
     def _sample(self, size):
         """Sample ``size`` rows from the model.
@@ -100,16 +122,24 @@ def _sample(self, size):
         """
         return self._model.sample(size)
 
-    def get_parameters(self):
+    def get_parameters(self, flatten=False):
         """Get copula model parameters.
 
         Compute model ``covariance`` and ``distribution.std``
         before it returns the flatten dict.
 
+        Args:
+            flatten (bool):
+                Whether to flatten the parameters or not before
+                returning them.
+
         Returns:
             dict:
-                Copula flatten parameters.
+                Copula parameters.
         """
+        if not flatten:
+            return self._model.to_dict()
+
         values = list()
         triangle = np.tril(self._model.covariance)
 
@@ -195,7 +225,7 @@ def _unflatten_gaussian_copula(self, model_parameters):
 
         return model_parameters
 
-    def set_parameters(self, parameters):
+    def set_parameters(self, parameters, unflatten=False):
         """Set copula model parameters.
 
         Add additional keys after unflatte the parameters
@@ -204,10 +234,13 @@ def set_parameters(self, parameters):
         Args:
             dict:
                 Copula flatten parameters.
+            unflatten (bool):
+                Whether the parameters need to be unflattened or not.
         """
-        parameters = unflatten_dict(parameters)
-        parameters.setdefault('distribution', self.distribution)
+        if unflatten:
+            parameters = unflatten_dict(parameters)
+            parameters.setdefault('distribution', self._distribution)
 
-        parameters = self._unflatten_gaussian_copula(parameters)
+            parameters = self._unflatten_gaussian_copula(parameters)
 
         self._model = copulas.multivariate.GaussianMultivariate.from_dict(parameters)
diff --git a/tests/integration/tabular/test_copulas.py b/tests/integration/tabular/test_copulas.py
index a40fbe7bb..0a34a0e15 100644
--- a/tests/integration/tabular/test_copulas.py
+++ b/tests/integration/tabular/test_copulas.py
@@ -27,7 +27,14 @@ def test_gaussian_copula():
     )
     gc.fit(users)
 
-    sampled = gc.sample()
+    parameters = gc.get_parameters()
+    new_gc = GaussianCopula(
+        table_metadata=gc.get_metadata(),
+        categorical_transformer='one_hot_encoding',
+    )
+    new_gc.set_parameters(parameters)
+
+    sampled = new_gc.sample()
 
     # test shape is right
     assert sampled.shape == users.shape
@@ -38,11 +45,12 @@ def test_gaussian_copula():
     # country codes have been replaced with new ones
     assert set(sampled.country.unique()) != set(users.country.unique())
 
-    assert gc.get_metadata().to_dict() == {
-        'fields': {
-            'user_id': {'type': 'id', 'subtype': 'integer'},
-            'country': {'type': 'categorical'},
-            'gender': {'type': 'categorical'},
-            'age': {'type': 'numerical', 'subtype': 'integer'}
-        }
+    metadata = gc.get_metadata().to_dict()
+    assert metadata['fields'] == {
+        'user_id': {'type': 'id', 'subtype': 'integer'},
+        'country': {'type': 'categorical'},
+        'gender': {'type': 'categorical'},
+        'age': {'type': 'numerical', 'subtype': 'integer'}
     }
+
+    assert 'model_kwargs' in metadata

From d9acc57e5e0f48750a22934c1e611bb48c8df9a4 Mon Sep 17 00:00:00 2001
From: Carles Sala <carles@pythiac.com>
Date: Thu, 9 Jul 2020 22:42:09 +0200
Subject: [PATCH 33/33] Add release notes for v0.3.5

---
 HISTORY.md                        | 18 ++++++++
 docs/tutorials/demo_metadata.json | 74 -------------------------------
 2 files changed, 18 insertions(+), 74 deletions(-)
 delete mode 100644 docs/tutorials/demo_metadata.json

diff --git a/HISTORY.md b/HISTORY.md
index 00b164949..3b9e68395 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -1,5 +1,23 @@
 # History
 
+## 0.3.5 - 2020-07-09
+
+This release introduces a new subpackage `sdv.tabular` with models designed specifically
+for single table modeling, while still providing all the usual conveniences from SDV, such
+as:
+
+* Seamless multi-type support
+* Missing data handling
+* PII anonymization
+
+Currently implemented models are:
+
+* GaussianCopula: Multivariate distributions modeled using copula functions. This is stronger
+  version, with more marginal distributions and options, than the one used to model multi-table
+  datasets.
+* CTGAN: GAN-based data synthesizer that can generate synthetic tabular data with high fidelity.
+
+
 ## 0.3.4 - 2020-07-04
 
 ## New Features
diff --git a/docs/tutorials/demo_metadata.json b/docs/tutorials/demo_metadata.json
deleted file mode 100644
index f59aa96da..000000000
--- a/docs/tutorials/demo_metadata.json
+++ /dev/null
@@ -1,74 +0,0 @@
-{
-    "tables": {
-        "users": {
-            "fields": {
-                "gender": {
-                    "type": "categorical"
-                },
-                "age": {
-                    "type": "numerical",
-                    "subtype": "integer"
-                },
-                "user_id": {
-                    "type": "id",
-                    "subtype": "integer"
-                },
-                "country": {
-                    "type": "categorical"
-                }
-            },
-            "primary_key": "user_id"
-        },
-        "sessions": {
-            "fields": {
-                "os": {
-                    "type": "categorical"
-                },
-                "device": {
-                    "type": "categorical"
-                },
-                "user_id": {
-                    "type": "id",
-                    "subtype": "integer",
-                    "ref": {
-                        "table": "users",
-                        "field": "user_id"
-                    }
-                },
-                "session_id": {
-                    "type": "id",
-                    "subtype": "integer"
-                }
-            },
-            "primary_key": "session_id"
-        },
-        "transactions": {
-            "fields": {
-                "timestamp": {
-                    "type": "datetime",
-                    "format": "%Y-%m-%d"
-                },
-                "amount": {
-                    "type": "numerical",
-                    "subtype": "float"
-                },
-                "session_id": {
-                    "type": "id",
-                    "subtype": "integer",
-                    "ref": {
-                        "table": "sessions",
-                        "field": "session_id"
-                    }
-                },
-                "approved": {
-                    "type": "boolean"
-                },
-                "transaction_id": {
-                    "type": "id",
-                    "subtype": "integer"
-                }
-            },
-            "primary_key": "transaction_id"
-        }
-    }
-}
\ No newline at end of file