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mercury-dataschema

mercury-dataschema is a submodule of the Mercury library which acts as a utility tool that, given a Pandas DataFrame, its DataSchema class auto-infers feature types and automatically calculates different statistics depending on them.

This type inference isn't solely based on data types but in the information the variables contain. For example: if a feature is encoded as a float but its cardinality is 2, we can be sure it's a binary feature.

This package is used by other Mercury submodules, and you also can use it separately from the rest of the library.

As an idea (there are plenty of them, though), it is particularly useful when preprocessing datasets. Having to specify the typical categorical_cols and coninuous_cols is over!

Mercury project at BBVA

Mercury is a collaborative library that was developed by the Advanced Analytics community at BBVA. Originally, it was created as an InnerSource project but after some time, we decided to release certain parts of the project as Open Source. That's the case with the mercury-dataschema package.

If you're interested in learning more about the Mercury project, we recommend reading this blog post from www.bbvaaifactory.com

User installation

The easiest way to install mercury-dataschema is using pip:

pip install -U mercury-dataschema

Example

from mercury.dataschema.schemagen import DataSchema
from mercury.dataschema.feature import FeatType

dataset = UCIDataset().load()   # Any Dataframe 

schma = (DataSchema()         # Generate a lazy Schema object
    .generate(dataset)        # Manually trigger its construction (it mostly infers data types...)
    .calculate_statistics())  # Manually trigger extra statistic calculations for each feature

Then, we can inspect all the features with

schma.feats
{'ID': Discrete Feature (NAME=None, dtype=DataType.INTEGER),
 'LIMIT_BAL': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'SEX': Binary Feature (NAME=None, dtype=DataType.INTEGER),
 'EDUCATION': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'MARRIAGE': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'AGE': Discrete Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_0': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_2': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_3': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_4': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_5': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_6': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'BILL_AMT1': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT2': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT3': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT4': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT5': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT6': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT1': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT2': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT3': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT4': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT5': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT6': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'default.payment.next.month': Binary Feature (NAME=None, dtype=DataType.INTEGER)}

And we can get extra feature statistics by inspecting the .stats attribute of the Feature objects.

schma.feats['BILL_AMT4'].stats
{'num_nan': 0,
 'percent_nan': 0.0,
 'samples': 30000,
 'percent_unique': 0.7182666666666667,
 'cardinality': 21548,
 'min': -170000.0,
 'max': 891586.0,
 'distribution': [3.3333333333333335e-05,
  0.0,
  3.3333333333333335e-05,
  0.0,
  0.0,
  3.3333333333333335e-05,
  0.0,
  3.3333333333333335e-05,
  3.3333333333333335e-05,
  0.0,
  3.3333333333333335e-05,
  6.666666666666667e-05,
  6.666666666666667e-05,
  0.00016666666666666666,
  ...,
  0.0,
  0.0,
  0.0,
  0.0,
  0.0,
  3.3333333333333335e-05],
 'distribution_bins': [-170000.0,
  -163898.93103448275,
  -157797.8620689655,
  -151696.7931034483,
  ...,
  867181.724137931,
  873282.7931034482,
  879383.8620689653,
  885484.9310344828,
  891586.0]}

schma.feats
{'ID': Discrete Feature (NAME=None, dtype=DataType.INTEGER),
 'LIMIT_BAL': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'SEX': Binary Feature (NAME=None, dtype=DataType.INTEGER),
 'EDUCATION': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'MARRIAGE': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'AGE': Discrete Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_0': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_2': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_3': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_4': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_5': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'PAY_6': Categorical Feature (NAME=None, dtype=DataType.INTEGER),
 'BILL_AMT1': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT2': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT3': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT4': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT5': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'BILL_AMT6': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT1': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT2': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT3': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT4': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT5': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'PAY_AMT6': Discrete Feature (NAME=None, dtype=DataType.FLOAT),
 'default.payment.next.month': Binary Feature (NAME=None, dtype=DataType.INTEGER)}

Note how for different features, the computed statistics vary:

schma.feats['default.payment.next.month'].stats
{'num_nan': 0,
 'percent_nan': 0.0,
 'samples': 30000,
 'percent_unique': 6.666666666666667e-05,
 'cardinality': 2,
 'distribution': [0.7788, 0.2212],
 'distribution_bins': [0, 1],
 'domain': [1, 0]}

Saving and loading schemas

You can serialize and reload DataSchemas so you can reuse them in the future.

PATH = 'schma.json'
# Save the schema
schma.save(PATH)

# Load it back!
recovered = DataSchema.load(PATH)

Help and support

This library is currently maintained by a dedicated team of data scientists and machine learning engineers from BBVA.

Documentation

website: https://bbva.github.io/mercury-dataschema/

Email

mercury.group@bbva.com