diff --git a/.github/workflows/doc_build.yml b/.github/workflows/doc_build.yml index b70957a6a..498cb8bfb 100644 --- a/.github/workflows/doc_build.yml +++ b/.github/workflows/doc_build.yml @@ -10,6 +10,8 @@ on: jobs: doc_build: runs-on: ubuntu-latest + permissions: + contents: write steps: - name: checkout and set up diff --git a/.github/workflows/ligthtwood.yml b/.github/workflows/ligthtwood.yml index 5986e43f7..b9c7b9296 100644 --- a/.github/workflows/ligthtwood.yml +++ b/.github/workflows/ligthtwood.yml @@ -26,9 +26,8 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install --no-cache-dir -e . - pip install -r requirements_image.txt - pip install flake8 + python -m pip install setuptools poetry + poetry install -E dev -E image - name: Install dependencies OSX run: | if [ "$RUNNER_OS" == "macOS" ]; then @@ -39,11 +38,11 @@ jobs: CHECK_FOR_UPDATES: False - name: Lint with flake8 run: | - python -m flake8 . + poetry run python -m flake8 . - name: Test with unittest run: | # Run all the "standard" tests - python -m unittest discover tests + poetry run python -m unittest discover tests deploy: runs-on: ubuntu-latest diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 47e0d9457..000000000 --- a/MANIFEST.in +++ /dev/null @@ -1 +0,0 @@ -include requirements*.txt \ No newline at end of file diff --git a/README.md b/README.md index cb1e5f9eb..566e6132b 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,7 @@ We predominantly use PyTorch based approaches, but can support other models. ## Usage -We invite you to check out our [documentation](https://lightwood.io) for specific guidelines and tutorials! Please stay tuned for updates and changes. +We invite you to check out our [documentation](https://mindsdb.github.io/lightwood/) for specific guidelines and tutorials! Please stay tuned for updates and changes. ### Quick use cases Lightwood works with `pandas.DataFrames`. Once a DataFrame is loaded, defined a "ProblemDefinition" via a dictionary. The only thing a user needs to specify is the name of the column to predict (via the key `target`). diff --git a/docssrc/source/index.rst b/docssrc/source/index.rst index 13aa99336..ada17daa5 100644 --- a/docssrc/source/index.rst +++ b/docssrc/source/index.rst @@ -19,7 +19,7 @@ Lightwood works with a variety of data types such as numbers, dates, categories, Our JSON-AI syntax allows users to change any and all parts of the models Lightwood automatically generates. The syntax outlines the specifics details in each step of the modeling pipeline. Users may override default values (for example, changing the type of a column) or alternatively, entirely replace steps with their own methods (ex: use a random forest model for a predictor). Lightwood creates a "JSON-AI" object from this syntax which can then be used to automatically generate python code to represent your pipeline. -For details as to how Lightwood works, check out the `Lightwood Philosophy `_ . +For details as to how Lightwood works, check out the `Lightwood Philosophy `_ . Quick Guide ======================= @@ -124,7 +124,7 @@ BYOM: Bring your own models Lightwood supports user architectures/approaches so long as you follow the abstractions provided within each step. -Our `tutorials `_ provide specific use cases for how to introduce customization into your pipeline. Check out "custom cleaner", "custom splitter", "custom explainer", and "custom mixer". Stay tuned for further updates. +Our `tutorials `_ provide specific use cases for how to introduce customization into your pipeline. Check out "custom cleaner", "custom splitter", "custom explainer", and "custom mixer". Stay tuned for further updates. Contribute to Lightwood diff --git a/lightwood/__about__.py b/lightwood/__about__.py index 43a661b89..72c02c861 100644 --- a/lightwood/__about__.py +++ b/lightwood/__about__.py @@ -1,6 +1,6 @@ __title__ = 'lightwood' __package_name__ = 'lightwood' -__version__ = '23.12.4.0' +__version__ = '24.3.3.0' __description__ = "Lightwood is a toolkit for automatic machine learning model building" __email__ = "community@mindsdb.com" __author__ = 'MindsDB Inc' diff --git a/lightwood/api/json_ai.py b/lightwood/api/json_ai.py index 0e697cb56..25c65a3b8 100644 --- a/lightwood/api/json_ai.py +++ b/lightwood/api/json_ai.py @@ -91,6 +91,11 @@ def lookup_encoder( "positive_domain" ] = "$statistical_analysis.positive_domain" + if problem_defintion.target_weights is not None: + encoder_dict["args"][ + "target_weights" + ] = problem_defintion.target_weights + # Time-series representations require more advanced flags if tss.is_timeseries: gby = tss.group_by if tss.group_by is not None else [] diff --git a/lightwood/api/predictor.py b/lightwood/api/predictor.py index 1f82f0605..5b464533d 100644 --- a/lightwood/api/predictor.py +++ b/lightwood/api/predictor.py @@ -28,6 +28,7 @@ class PredictorInterface: You can also use the predictor to now estimate new data: - ``predict``: Deploys the chosen best model, and evaluates the given data to provide target estimates. + - ``test``: Similar to predict, but user also passes an accuracy function that will be used to compute a metric with the generated predictions. - ``save``: Saves the Predictor object for further use. The ``PredictorInterface`` is created via J{ai}son's custom code creation. A problem inherits from this class with pre-populated routines to fill out expected results, given the nature of each problem type. @@ -127,12 +128,27 @@ def adjust(self, new_data: pd.DataFrame, old_data: Optional[pd.DataFrame] = None def predict(self, data: pd.DataFrame, args: Dict[str, object] = {}) -> pd.DataFrame: """ - Intakes raw data to provide predicted values for your trained model. + Intakes raw data to provide model predictions. + + :param data: Data (n_samples, n_columns) that the model will use as input to predict the corresponding target value for each sample. + :param args: any parameters used to customize inference behavior. Wrapped as a ``PredictionArguments`` object. + + :returns: A dataframe containing predictions and additional sample-wise information. `n_samples` rows. + """ # noqa + pass + + def test( + self, data: pd.DataFrame, metrics: list, args: Dict[str, object] = {}, strict: bool = False + ) -> pd.DataFrame: + """ + Intakes raw data to compute values for a list of provided metrics using a Lightwood predictor. :param data: Data (n_samples, n_columns) that the model(s) will evaluate on and provide the target prediction. + :param metrics: A list of metrics to evaluate the model's performance on. :param args: parameters needed to update the predictor ``PredictionArguments`` object, which holds any parameters relevant for prediction. + :param strict: If True, the function will raise an error if the model does not support any of the requested metrics. Otherwise it skips them. - :returns: A dataframe of predictions of the same length of input. + :returns: A dataframe with `n_metrics` columns, each cell containing the respective score of each metric. """ # noqa pass diff --git a/lightwood/data/encoded_ds.py b/lightwood/data/encoded_ds.py index d9ba4e498..f11437a7b 100644 --- a/lightwood/data/encoded_ds.py +++ b/lightwood/data/encoded_ds.py @@ -14,7 +14,7 @@ def __init__(self, encoders: Dict[str, BaseEncoder], data_frame: pd.DataFrame, t Note: normal behavior is to cache encoded representations to avoid duplicated computations. If you want an option to disable, this please open an issue. - :param encoders: list of Lightwood encoders used to encode the data per each column. + :param encoders: dictionary of Lightwood encoders used to encode the data per each column. :param data_frame: original dataframe. :param target: name of the target column to predict. """ # noqa diff --git a/lightwood/encoder/__init__.py b/lightwood/encoder/__init__.py index e71623d62..d98fa9d9c 100644 --- a/lightwood/encoder/__init__.py +++ b/lightwood/encoder/__init__.py @@ -8,7 +8,6 @@ from lightwood.encoder.array.ts_num_array import TsArrayNumericEncoder from lightwood.encoder.text.short import ShortTextEncoder from lightwood.encoder.text.vocab import VocabularyEncoder -from lightwood.encoder.text.rnn import RnnEncoder as TextRnnEncoder from lightwood.encoder.categorical.simple_label import SimpleLabelEncoder from lightwood.encoder.categorical.onehot import OneHotEncoder from lightwood.encoder.categorical.binary import BinaryEncoder @@ -22,7 +21,7 @@ __all__ = ['BaseEncoder', 'DatetimeEncoder', 'Img2VecEncoder', 'NumericEncoder', 'TsNumericEncoder', - 'TsArrayNumericEncoder', 'ShortTextEncoder', 'VocabularyEncoder', 'TextRnnEncoder', 'OneHotEncoder', + 'TsArrayNumericEncoder', 'ShortTextEncoder', 'VocabularyEncoder', 'OneHotEncoder', 'CategoricalAutoEncoder', 'TimeSeriesEncoder', 'ArrayEncoder', 'MultiHotEncoder', 'TsCatArrayEncoder', 'NumArrayEncoder', 'CatArrayEncoder', 'SimpleLabelEncoder', 'PretrainedLangEncoder', 'BinaryEncoder', 'DatetimeNormalizerEncoder', 'MFCCEncoder'] diff --git a/lightwood/encoder/numeric/numeric.py b/lightwood/encoder/numeric/numeric.py index a2b261e3b..30e2dc962 100644 --- a/lightwood/encoder/numeric/numeric.py +++ b/lightwood/encoder/numeric/numeric.py @@ -1,5 +1,6 @@ import math -from typing import Union +from typing import Union, Dict +from copy import deepcopy as dc import torch import numpy as np @@ -20,11 +21,15 @@ class NumericEncoder(BaseEncoder): The ``absolute_mean`` is computed in the ``prepare`` method and is just the mean of the absolute values of all numbers feed to prepare (which are not none) ``none`` stands for any number that is an actual python ``None`` value or any sort of non-numeric value (a string, nan, inf) - """ # noqa + """ # noqa - def __init__(self, data_type: dtype = None, is_target: bool = False, positive_domain: bool = False): + def __init__(self, data_type: dtype = None, + target_weights: Dict[float, float] = None, + is_target: bool = False, + positive_domain: bool = False): """ :param data_type: The data type of the number (integer, float, quantity) + :param target_weights: a dictionary of weights to use on the examples. :param is_target: Indicates whether the encoder refers to a target column or feature column (True==target) :param positive_domain: Forces the encoder to always output positive values """ @@ -34,12 +39,19 @@ def __init__(self, data_type: dtype = None, is_target: bool = False, positive_do self.decode_log = False self.output_size = 4 if not self.is_target else 3 + # Weight-balance info if encoder represents target + self.target_weights = None + self.index_weights = None + if self.is_target and target_weights is not None: + self.target_weights = dc(target_weights) + self.index_weights = torch.tensor(list(self.target_weights.values())) + def prepare(self, priming_data: pd.Series): """ "NumericalEncoder" uses a rule-based form to prepare results on training (priming) data. The averages etc. are taken from this distribution. :param priming_data: an iterable data structure containing numbers numbers which will be used to compute the values used for normalizing the encoded representations - """ # noqa + """ # noqa if self.is_prepared: raise Exception('You can only call "prepare" once for a given encoder.') @@ -57,7 +69,8 @@ def encode(self, data: Union[np.ndarray, pd.Series]): if isinstance(data, pd.Series): data = data.values - inp_data = np.nan_to_num(data.astype(float), nan=0, posinf=np.finfo(np.float32).max, neginf=np.finfo(np.float32).min) # noqa + inp_data = np.nan_to_num(data.astype(float), nan=0, posinf=np.finfo(np.float32).max, + neginf=np.finfo(np.float32).min) # noqa if not self.positive_domain: sign = np.vectorize(self._sign_fn, otypes=[float])(inp_data) else: @@ -97,7 +110,7 @@ def decode(self, encoded_values: torch.Tensor, decode_log: bool = None) -> list: :param decode_log: Whether to decode the ``log`` or ``linear`` part of the representation, since the encoded vector contains both a log and a linear part :returns: The decoded array - """ # noqa + """ # noqa if not self.is_prepared: raise Exception('You need to call "prepare" before calling "encode" or "decode".') @@ -145,3 +158,22 @@ def decode(self, encoded_values: torch.Tensor, decode_log: bool = None) -> list: ret[mask_none] = None return ret.tolist() # TODO: update signature on BaseEncoder and replace all encs to return ndarrays + + def get_weights(self, label_data): + # get a sorted list of intervals to assign weights. Keys are the interval edges. + target_weight_keys = np.array(list(self.target_weights.keys())) + target_weight_values = np.array(list(self.target_weights.values())) + sorted_indices = np.argsort(target_weight_keys) + + # get sorted arrays for vector numpy operations + target_weight_keys = target_weight_keys[sorted_indices] + target_weight_values = target_weight_values[sorted_indices] + + # find the indices of the bins according to the keys. clip to the length of the weight values (search sorted + # returns indices from 0 to N with N = len(target_weight_keys). + assigned_target_weight_indices = np.clip(a=np.searchsorted(target_weight_keys, label_data), + a_min=0, + a_max=len(target_weight_keys) - 1).astype(np.int32) + + return target_weight_values[assigned_target_weight_indices] + diff --git a/lightwood/encoder/text/__init__.py b/lightwood/encoder/text/__init__.py index 3f3220eab..62656d92a 100644 --- a/lightwood/encoder/text/__init__.py +++ b/lightwood/encoder/text/__init__.py @@ -1,8 +1,7 @@ from lightwood.encoder.text.pretrained import PretrainedLangEncoder -from lightwood.encoder.text.rnn import RnnEncoder from lightwood.encoder.text.tfidf import TfidfEncoder from lightwood.encoder.text.short import ShortTextEncoder from lightwood.encoder.text.vocab import VocabularyEncoder -__all__ = ['PretrainedLangEncoder', 'RnnEncoder', 'TfidfEncoder', 'ShortTextEncoder', 'VocabularyEncoder'] +__all__ = ['PretrainedLangEncoder', 'TfidfEncoder', 'ShortTextEncoder', 'VocabularyEncoder'] diff --git a/lightwood/encoder/text/helpers/pretrained_helpers.py b/lightwood/encoder/text/helpers/pretrained_helpers.py index 3af0f033d..b3203b9a2 100644 --- a/lightwood/encoder/text/helpers/pretrained_helpers.py +++ b/lightwood/encoder/text/helpers/pretrained_helpers.py @@ -4,7 +4,6 @@ Basic helper functions for PretrainedLangEncoder """ import torch -from transformers import AdamW class TextEmbed(torch.utils.data.Dataset): @@ -26,48 +25,3 @@ def __getitem__(self, idx): def __len__(self): return len(self.labels) - - -def train_model(model, dataset, device, scheduler=None, log=None, optim=None, n_epochs=4): - """ - Generic training function, given an arbitrary model. - - Given a model, train for n_epochs. - - model - torch.nn model; - dataset - torch.DataLoader; dataset to train - device - torch.device; cuda/cpu - log - lightwood.logger.log; print output - optim - transformers.optimization.AdamW; optimizer - n_epochs - number of epochs to train - - """ - if log is None: - from lightwood.helpers.log import log - log = log.debug - losses = [] - model.train() - if optim is None: - optim = AdamW(model.parameters(), lr=5e-5) - - for epoch in range(n_epochs): - total_loss = 0 - for batch in dataset: - optim.zero_grad() - - inpids = batch['input_ids'].to(device) - attn = batch['attention_mask'].to(device) - labels = batch['labels'].to(device) - outputs = model(inpids, attention_mask=attn, labels=labels) - loss = outputs[0] - - total_loss += loss.item() - - loss.backward() - optim.step() - - if scheduler is not None: - scheduler.step() - - log("Epoch", epoch + 1, "Loss", total_loss) - return model, losses diff --git a/lightwood/encoder/text/helpers/rnn_helpers.py b/lightwood/encoder/text/helpers/rnn_helpers.py deleted file mode 100644 index 2d9d3d140..000000000 --- a/lightwood/encoder/text/helpers/rnn_helpers.py +++ /dev/null @@ -1,817 +0,0 @@ -# flake8: noqa -# -*- coding: utf-8 -*- -""" -Translation with a Sequence to Sequence Network and Attention -************************************************************* -**Author**: `Sean Robertson `_ - -In this project we will be teaching a neural network to translate from -French to English. - -:: - - [KEY: > input, = target, < output] - - > il est en train de peindre un tableau . - = he is painting a picture . - < he is painting a picture . - - > pourquoi ne pas essayer ce vin delicieux ? - = why not try that delicious wine ? - < why not try that delicious wine ? - - > elle n est pas poete mais romanciere . - = she is not a poet but a novelist . - < she not not a poet but a novelist . - - > vous etes trop maigre . - = you re too skinny . - < you re all alone . - -... to varying degrees of success. - -This is made possible by the simple but powerful idea of the `sequence -to sequence network `__, in which two -recurrent neural networks work together to transform one sequence to -another. An encoder network condenses an input sequence into a vector, -and a decoder network unfolds that vector into a new sequence. - -.. figure:: /_static/img/seq-seq-images/seq2seq.png - :alt: - -To improve upon this model we'll use an `attention -mechanism `__, which lets the decoder -learn to focus over a specific range of the input sequence. - -**Recommended Reading:** - -I assume you have at least installed PyTorch, know Python, and -understand Tensors: - -- https://pytorch.org/ For installation instructions -- :doc:`/beginner/deep_learning_60min_blitz` to get started with PyTorch in general -- :doc:`/beginner/pytorch_with_examples` for a wide and deep overview -- :doc:`/beginner/former_torchies_tutorial` if you are former Lua Torch user - - -It would also be useful to know about Sequence to Sequence networks and -how they work: - -- `Learning Phrase Representations using RNN Encoder-Decoder for - Statistical Machine Translation `__ -- `Sequence to Sequence Learning with Neural - Networks `__ -- `Neural Machine Translation by Jointly Learning to Align and - Translate `__ -- `A Neural Conversational Model `__ - -You will also find the previous tutorials on -:doc:`/intermediate/char_rnn_classification_tutorial` -and :doc:`/intermediate/char_rnn_generation_tutorial` -helpful as those concepts are very similar to the Encoder and Decoder -models, respectively. - -And for more, read the papers that introduced these topics: - -- `Learning Phrase Representations using RNN Encoder-Decoder for - Statistical Machine Translation `__ -- `Sequence to Sequence Learning with Neural - Networks `__ -- `Neural Machine Translation by Jointly Learning to Align and - Translate `__ -- `A Neural Conversational Model `__ - - -**Requirements** -""" -from __future__ import unicode_literals, print_function, division -import math -import time -from io import open -import unicodedata -import string -import re -import random -import operator - -import torch -import torch.nn as nn -from torch import optim -import torch.nn.functional as F -from lightwood.helpers.torch import LightwoodAutocast -from lightwood.helpers.log import log - -default_device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -###################################################################### -# Loading data files -# ================== -# -# The data for this project is a set of many thousands of English to -# French translation pairs. -# -# `This question on Open Data Stack -# Exchange `__ -# pointed me to the open translation site https://tatoeba.org/ which has -# downloads available at https://tatoeba.org/eng/downloads - and better -# yet, someone did the extra work of splitting language pairs into -# individual text files here: https://www.manythings.org/anki/ -# -# The English to French pairs are too big to include in the repo, so -# download to ``data/eng-fra.txt`` before continuing. The file is a tab -# separated list of translation pairs: -# -# :: -# -# I am cold. J'ai froid. -# -# .. Note:: -# Download the data from -# `here `_ -# and extract it to the current directory. - -###################################################################### -# Similar to the character encoding used in the character-level RNN -# tutorials, we will be representing each word in a language as a one-hot -# vector, or giant vector of zeros except for a single one (at the index -# of the word). Compared to the dozens of characters that might exist in a -# language, there are many many more words, so the encoding vector is much -# larger. We will however cheat a bit and trim the data to only use a few -# thousand words per language. -# -# .. figure:: /_static/img/seq-seq-images/word-encoding.png -# :alt: -# -# - - -###################################################################### -# We'll need a unique index per word to use as the inputs and targets of -# the networks later. To keep track of all this we will use a helper class -# called ``Lang`` which has word → index (``word2index``) and index → word -# (``index2word``) dictionaries, as well as a count of each word -# ``word2count`` to use to later replace rare words. -# - -SOS_token = 0 -EOS_token = 1 -UNK_TOKEN = 2 - - -class Lang: - def __init__(self, name): - self.name = name - self.word2index = {} - self.word2count = {} - self.index2word = {0: "SOS", 1: "EOS", 2: "UNK"} - self.n_words = 3 # Count SOS and EOS - - def addSentence(self, sentence): - for word in sentence.split(' '): - self.addWord(word) - - def addWord(self, word): - if word not in self.word2index: - self.word2index[word] = self.n_words - self.word2count[word] = 0 - self.index2word[self.n_words] = word - self.n_words += 1 - - self.word2count[word] += 1 - - # @NOTE: Very slow, especially for a large language, can be made faster by making index2word a list - def removeWord(self, word): - word_index = self.word2index[word] - del self.word2index[word] - del self.word2count[word] - del self.index2word[word_index] - - self.n_words -= 1 - - for index in [x for x in self.index2word.keys() if x > word_index]: - word = self.index2word[index] - new_index = index - 1 - - self.word2index[word] = new_index - - del self.index2word[index] - self.index2word[new_index] = word - - def getLeastOccurring(self, n=1): - if n == 1: - return min(self.word2count, key=self.word2count.get) - else: - sorted_word2count = sorted(self.word2count.items(), key=operator.itemgetter(1)) - return [x[0] for x in sorted_word2count[:n]] - - -###################################################################### -# The files are all in Unicode, to simplify we will turn Unicode -# characters to ASCII, make everything lowercase, and trim most -# punctuation. -# - -# Turn a Unicode string to plain ASCII, thanks to -# https://stackoverflow.com/a/518232/2809427 -def unicodeToAscii(s): - return ''.join( - c for c in unicodedata.normalize('NFD', s) - if unicodedata.category(c) != 'Mn' - ) - -# Lowercase, trim, and remove non-letter characters - - -def normalizeString(s): - s = unicodeToAscii(s.lower().strip()) - s = re.sub(r"([.!?])", r" \1", s) - s = re.sub(r"[^a-zA-Z.!?]+", r" ", s) - return s - - -###################################################################### -# To read the data file we will split the file into lines, and then split -# lines into pairs. The files are all English → Other Language, so if we -# want to translate from Other Language → English I added the ``reverse`` -# flag to reverse the pairs. -# - -def readLangs(lang1, lang2, reverse=False): - log.debug("Reading lines...") - - # Read the file and split into lines - lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\ - read().strip().split('\n') - - # Split every line into pairs and normalize - pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines] - - # Reverse pairs, make Lang instances - if reverse: - pairs = [list(reversed(p)) for p in pairs] - input_lang = Lang(lang2) - output_lang = Lang(lang1) - else: - input_lang = Lang(lang1) - output_lang = Lang(lang2) - - return input_lang, output_lang, pairs - - -###################################################################### -# Since there are a *lot* of example sentences and we want to train -# something quickly, we'll trim the data set to only relatively short and -# simple sentences. Here the maximum length is 10 words (that includes -# ending punctuation) and we're filtering to sentences that translate to -# the form "I am" or "He is" etc. (accounting for apostrophes replaced -# earlier). -# - -MAX_LENGTH = 100 - - -###################################################################### -# The full process for preparing the data is: -# -# - Read text file and split into lines, split lines into pairs -# - Normalize text, filter by length and content -# - Make word lists from sentences in pairs -# - -def prepareData(lang1, lang2, reverse=False): - input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse) - log.debug("Read %s sentence pairs" % len(pairs)) - #pairs = filterPairs(pairs) - log.debug("Trimmed to %s sentence pairs" % len(pairs)) - log.debug("Counting words...") - for pair in pairs: - input_lang.addSentence(pair[0]) - output_lang.addSentence(pair[1]) - log.debug("Counted words:") - log.debug(input_lang.name, input_lang.n_words) - log.debug(output_lang.name, output_lang.n_words) - return input_lang, output_lang, pairs - - -###################################################################### -# The Seq2Seq Model -# ================= -# -# A Recurrent Neural Network, or RNN, is a network that operates on a -# sequence and uses its own output as input for subsequent steps. -# -# A `Sequence to Sequence network `__, or -# seq2seq network, or `Encoder Decoder -# network `__, is a model -# consisting of two RNNs called the encoder and decoder. The encoder reads -# an input sequence and outputs a single vector, and the decoder reads -# that vector to produce an output sequence. -# -# .. figure:: /_static/img/seq-seq-images/seq2seq.png -# :alt: -# -# Unlike sequence prediction with a single RNN, where every input -# corresponds to an output, the seq2seq model frees us from sequence -# length and order, which makes it ideal for translation between two -# languages. -# -# Consider the sentence "Je ne suis pas le chat noir" → "I am not the -# black cat". Most of the words in the input sentence have a direct -# translation in the output sentence, but are in slightly different -# orders, e.g. "chat noir" and "black cat". Because of the "ne/pas" -# construction there is also one more word in the input sentence. It would -# be difficult to produce a correct translation directly from the sequence -# of input words. -# -# With a seq2seq model the encoder creates a single vector which, in the -# ideal case, encodes the "meaning" of the input sequence into a single -# vector — a single point in some N dimensional space of sentences. -# - - -###################################################################### -# The Encoder -# ----------- -# -# The encoder of a seq2seq network is a RNN that outputs some value for -# every word from the input sentence. For every input word the encoder -# outputs a vector and a hidden state, and uses the hidden state for the -# next input word. -# -# .. figure:: /_static/img/seq-seq-images/encoder-network.png -# :alt: -# -# - -class EncoderRNN(nn.Module): - def __init__(self, - input_size, - hidden_size, - device=''): - super(EncoderRNN, self).__init__() - self.hidden_size = hidden_size - - self.embedding = nn.Embedding(input_size, hidden_size) - self.gru = nn.GRU(hidden_size, hidden_size) - - if(device == ''): - device = default_device - else: - device = torch.device(device) - self.device = device - - def forward(self, input, hidden): - with LightwoodAutocast(): - embedded = self.embedding(input).view(1, 1, -1) - output = embedded - output, hidden = self.gru(output, hidden) - return output, hidden - - def initHidden(self): - return torch.zeros(1, 1, self.hidden_size, device=self.device) - -###################################################################### -# The Decoder -# ----------- -# -# The decoder is another RNN that takes the encoder output vector(s) and -# outputs a sequence of words to create the translation. -# - - -###################################################################### -# Simple Decoder -# ^^^^^^^^^^^^^^ -# -# In the simplest seq2seq decoder we use only last output of the encoder. -# This last output is sometimes called the *context vector* as it encodes -# context from the entire sequence. This context vector is used as the -# initial hidden state of the decoder. -# -# At every step of decoding, the decoder is given an input token and -# hidden state. The initial input token is the start-of-string ```` -# token, and the first hidden state is the context vector (the encoder's -# last hidden state). -# -# .. figure:: /_static/img/seq-seq-images/decoder-network.png -# :alt: -# -# - -class DecoderRNN(nn.Module): - def __init__(self, hidden_size, output_size, device=''): - super(DecoderRNN, self).__init__() - self.hidden_size = hidden_size - - self.embedding = nn.Embedding(output_size, hidden_size) - self.gru = nn.GRU(hidden_size, hidden_size) - self.out = nn.Linear(hidden_size, output_size) - self.softmax = nn.LogSoftmax(dim=1) - - if(device == ''): - device = default_device - else: - device = torch.device(device) - self.device = device - - def forward(self, input, hidden): - with LightwoodAutocast(): - output = self.embedding(input).view(1, 1, -1) - output = F.relu(output) - output, hidden = self.gru(output, hidden) - output = self.softmax(self.out(output[0])) - return output, hidden - - def initHidden(self): - return torch.zeros(1, 1, self.hidden_size, device=self.device) - -###################################################################### -# I encourage you to train and observe the results of this model, but to -# save space we'll be going straight for the gold and introducing the -# Attention Mechanism. -# - - -###################################################################### -# Attention Decoder -# ^^^^^^^^^^^^^^^^^ -# -# If only the context vector is passed betweeen the encoder and decoder, -# that single vector carries the burden of encoding the entire sentence. -# -# Attention allows the decoder network to "focus" on a different part of -# the encoder's outputs for every step of the decoder's own outputs. First -# we calculate a set of *attention weights*. These will be multiplied by -# the encoder output vectors to create a weighted combination. The result -# (called ``attn_applied`` in the code) should contain information about -# that specific part of the input sequence, and thus help the decoder -# choose the right output words. -# -# .. figure:: https://i.imgur.com/1152PYf.png -# :alt: -# -# Calculating the attention weights is done with another feed-forward -# layer ``attn``, using the decoder's input and hidden state as inputs. -# Because there are sentences of all sizes in the training data, to -# actually create and train this layer we have to choose a maximum -# sentence length (input length, for encoder outputs) that it can apply -# to. Sentences of the maximum length will use all the attention weights, -# while shorter sentences will only use the first few. -# -# .. figure:: /_static/img/seq-seq-images/attention-decoder-network.png -# :alt: -# -# - -class AttnDecoderRNN(nn.Module): - def __init__(self, - hidden_size, - output_size, - dropout_p=0.1, - max_length=MAX_LENGTH, - device=''): - super(AttnDecoderRNN, self).__init__() - self.hidden_size = hidden_size - self.output_size = output_size - self.dropout_p = dropout_p - self.max_length = max_length - - self.embedding = nn.Embedding(self.output_size, self.hidden_size) - self.attn = nn.Linear(self.hidden_size * 2, self.max_length) - self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size) - self.dropout = nn.Dropout(self.dropout_p) - self.gru = nn.GRU(self.hidden_size, self.hidden_size) - self.out = nn.Linear(self.hidden_size, self.output_size) - - if(device == ''): - device = default_device - else: - device = torch.device(device) - self.device = device - - def forward(self, input, hidden, encoder_outputs): - with LightwoodAutocast(): - embedded = self.embedding(input).view(1, 1, -1) - embedded = self.dropout(embedded) - - attn_weights = F.softmax( - self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1) - attn_applied = torch.bmm(attn_weights.unsqueeze(0), - encoder_outputs.unsqueeze(0)) - - output = torch.cat((embedded[0], attn_applied[0]), 1) - output = self.attn_combine(output).unsqueeze(0) - - output = F.relu(output) - output, hidden = self.gru(output, hidden) - - output = F.log_softmax(self.out(output[0]), dim=1) - return output, hidden, attn_weights - - def initHidden(self): - return torch.zeros(1, 1, self.hidden_size, device=self.device) - - -###################################################################### -# .. note:: There are other forms of attention that work around the length -# limitation by using a relative position approach. Read about "local -# attention" in `Effective Approaches to Attention-based Neural Machine -# Translation `__. -# -# Training -# ======== -# -# Preparing Training Data -# ----------------------- -# -# To train, for each pair we will need an input tensor (indexes of the -# words in the input sentence) and target tensor (indexes of the words in -# the target sentence). While creating these vectors we will append the -# EOS token to both sequences. -# - -def indexesFromSentence(lang, sentence): - - return [lang.word2index[word] if word in lang.word2index else UNK_TOKEN for word in (str(sentence).split(' ') if sentence is not None else [None])] - - -def tensorFromSentence(lang, sentence, device=default_device): - indexes = indexesFromSentence(lang, sentence) - indexes.append(EOS_token) - return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1) - - -###################################################################### -# Training the Model -# ------------------ -# -# To train we run the input sentence through the encoder, and keep track -# of every output and the latest hidden state. Then the decoder is given -# the ```` token as its first input, and the last hidden state of the -# encoder as its first hidden state. -# -# "Teacher forcing" is the concept of using the real target outputs as -# each next input, instead of using the decoder's guess as the next input. -# Using teacher forcing causes it to converge faster but `when the trained -# network is exploited, it may exhibit -# instability `__. -# -# You can observe outputs of teacher-forced networks that read with -# coherent grammar but wander far from the correct translation - -# intuitively it has learned to represent the output grammar and can "pick -# up" the meaning once the teacher tells it the first few words, but it -# has not properly learned how to create the sentence from the translation -# in the first place. -# -# Because of the freedom PyTorch's autograd gives us, we can randomly -# choose to use teacher forcing or not with a simple if statement. Turn -# ``teacher_forcing_ratio`` up to use more of it. -# -teacher_forcing_ratio = 0.5 - - -def train( - input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, - max_length=MAX_LENGTH, device=default_device): - - encoder_hidden = encoder.initHidden() - - encoder_optimizer.zero_grad() - decoder_optimizer.zero_grad() - - input_length = input_tensor.size(0) - target_length = target_tensor.size(0) - - encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device) - - loss = 0 - - with LightwoodAutocast(): - for ei in range(min(input_length, len(encoder_outputs))): - encoder_output, encoder_hidden = encoder( - input_tensor[ei], encoder_hidden) - encoder_outputs[ei] = encoder_output[0, 0] - - decoder_input = torch.tensor([[SOS_token]], device=device) - - decoder_hidden = encoder_hidden - - use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False - - if use_teacher_forcing: - # Teacher forcing: Feed the target as the next input - for di in range(target_length): - if isinstance(decoder, AttnDecoderRNN): - decoder_output, decoder_hidden, decoder_attention = decoder( - decoder_input, decoder_hidden, encoder_outputs) - else: - decoder_output, decoder_hidden = decoder( - decoder_input, decoder_hidden) - loss += criterion(decoder_output, target_tensor[di]) - decoder_input = target_tensor[di] # Teacher forcing - - else: - # Without teacher forcing: use its own predictions as the next input - for di in range(target_length): - if isinstance(decoder, AttnDecoderRNN): - decoder_output, decoder_hidden, decoder_attention = decoder( - decoder_input, decoder_hidden, encoder_outputs) - else: - decoder_output, decoder_hidden = decoder( - decoder_input, decoder_hidden) - topv, topi = decoder_output.topk(1) - decoder_input = topi.squeeze().detach() # detach from history as input - - loss += criterion(decoder_output, target_tensor[di]) - if decoder_input.item() == EOS_token: - break - - loss.backward() - - encoder_optimizer.step() - decoder_optimizer.step() - - return loss.item() / target_length - - -###################################################################### -# This is a helper function to print time elapsed and estimated time -# remaining given the current time and progress %. -# - - -def asMinutes(s): - m = math.floor(s / 60) - s -= m * 60 - return '%dm %ds' % (m, s) - - -def timeSince(since, percent): - now = time.time() - s = now - since - es = s / (percent) - rs = es - s - return '%s (- %s)' % (asMinutes(s), asMinutes(rs)) - - -###################################################################### -# The whole training process looks like this: -# -# - Start a timer -# - Initialize optimizers and criterion -# - Create set of training pairs -# - Start empty losses array for plotting -# -# Then we call ``train`` many times and occasionally print the progress (% -# of examples, time so far, estimated time) and average loss. -# - -def trainIters(encoder, - decoder, - input_lang, - output_lang, - input_rows, - output_rows, - n_iters, - print_every=1000, - plot_every=100, - learning_rate=0.01, - loss_breakpoint=0.0001, - max_length=MAX_LENGTH, - device=default_device): - start = time.time() - plot_losses = [] - print_loss_total = 0 # Reset every print_every - plot_loss_total = 0 # Reset every plot_every - - encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate) - decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate) - - random_index = random.randint(0, len(input_rows)) - - training_pairs = [ - [ - tensorFromSentence(input_lang, input_rows[random_index], device=device), - tensorFromSentence(output_lang, output_rows[random_index], device=device) - ] for i in range(n_iters) ] - criterion = nn.NLLLoss() - - for iter in range(1, n_iters + 1): - training_pair = training_pairs[iter - 1] - input_tensor = training_pair[0] - target_tensor = training_pair[1] - - loss = train(input_tensor, target_tensor, encoder, - decoder, encoder_optimizer, decoder_optimizer, criterion, max_length, device=device) - print_loss_total += loss - plot_loss_total += loss - - print_loss_avg = print_loss_total / print_every - - if print_loss_avg < loss_breakpoint: - - log.debug('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters), - iter, iter / n_iters * 100, print_loss_avg)) - - break - - if iter % print_every == 0: - print_loss_avg = print_loss_total / print_every - print_loss_total = 0 - log.debug('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters), - iter, iter / n_iters * 100, print_loss_avg)) - - if iter % plot_every == 0: - plot_loss_avg = plot_loss_total / plot_every - plot_losses.append(plot_loss_avg) - plot_loss_total = 0 - - # showPlot(plot_losses) - - -###################################################################### -# Evaluation -# ========== -# -# Evaluation is mostly the same as training, but there are no targets so -# we simply feed the decoder's predictions back to itself for each step. -# Every time it predicts a word we add it to the output string, and if it -# predicts the EOS token we stop there. We also store the decoder's -# attention outputs for display later. -# - -def evaluate(encoder, decoder, input_lang, output_lang, sentence, max_length=MAX_LENGTH, device=default_device): - with torch.no_grad(): - input_tensor = tensorFromSentence(input_lang, sentence, device=device) - input_length = input_tensor.size()[0] - encoder_hidden = encoder.initHidden() - - encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device) - - for ei in range(input_length): - encoder_output, encoder_hidden = encoder(input_tensor[ei], - encoder_hidden) - encoder_outputs[ei] += encoder_output[0, 0] - - decoder_input = torch.tensor([[SOS_token]], device=device) # SOS - - decoder_hidden = encoder_hidden - - decoded_words = [] - decoder_attentions = torch.zeros(max_length, max_length) - - for di in range(max_length): - decoder_output, decoder_hidden, decoder_attention = decoder( - decoder_input, decoder_hidden, encoder_outputs) - decoder_attentions[di] = decoder_attention.data - topv, topi = decoder_output.data.topk(1) - if topi.item() == EOS_token: - decoded_words.append('') - break - else: - decoded_words.append(output_lang.index2word[topi.item()]) - - decoder_input = topi.squeeze().detach() - - return decoded_words, decoder_attentions[:di + 1] - - -###################################################################### -# We can evaluate random sentences from the training set and print out the -# input, target, and output to make some subjective quality judgements: -# - -# this function is never used? -def evaluateRandomly(encoder, pairs, decoder, n=10, max_length=MAX_LENGTH, device=default_device): - for i in range(n): - pair = random.choice(pairs) - log.debug('>', pair[0]) - log.debug('=', pair[1]) - output_words, attentions = evaluate(encoder, decoder, pair[0], max_length=MAX_LENGTH, device=device) - output_sentence = ' '.join(output_words) - log.debug('<', output_sentence) - log.debug('') - - -###################################################################### -# For a better viewing experience we will do the extra work of adding axes -# and labels: -# - -def showAttention(input_sentence, output_words, attentions): - # Set up figure with colorbar - fig = plt.figure() - ax = fig.add_subplot(111) - cax = ax.matshow(attentions.numpy(), cmap='bone') - fig.colorbar(cax) - - # Set up axes - ax.set_xticklabels([''] + input_sentence.split(' ') + - [''], rotation=90) - ax.set_yticklabels([''] + output_words) - - # Show label at every tick - ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) - ax.yaxis.set_major_locator(ticker.MultipleLocator(1)) - - plt.show() diff --git a/lightwood/encoder/text/rnn.py b/lightwood/encoder/text/rnn.py deleted file mode 100644 index 275e58c3e..000000000 --- a/lightwood/encoder/text/rnn.py +++ /dev/null @@ -1,123 +0,0 @@ -# flake8: noqa -from lightwood.encoder.text.helpers.rnn_helpers import * -from lightwood.encoder.base import BaseEncoder -from lightwood.helpers.log import log -import math - - -class RnnEncoder(BaseEncoder): - - def __init__(self, - encoded_vector_size=256, - train_iters=75000, - stop_on_error=0.0001, - learning_rate=0.01, - is_target=False, - device=''): - super().__init__(is_target) - self._stop_on_error = stop_on_error - self._learning_rate = learning_rate - self._encoded_vector_size = encoded_vector_size - self._train_iters = train_iters - self._input_lang = None - self._output_lang = None - self._encoder = None - self._decoder = None - if(device == ''): - device = default_device - else: - device = torch.device(device) - self.device = device - - def prepare(self, priming_data): - if self.is_prepared: - raise Exception('You can only call "prepare" once for a given encoder.') - - no_null_sentences = [x if x is not None else '' for x in priming_data] - estimated_time = 1 / 937 * self._train_iters * len(no_null_sentences) - log_every = math.ceil(self._train_iters / 100) - log.info('We will train an encoder for this text, on a CPU it will take about {min} minutes'.format( - min=estimated_time)) - - self._input_lang = Lang('input') - self._output_lang = self._input_lang - - for row in no_null_sentences: - if row is not None: - self._input_lang.addSentence(row) - - max_length = max(map(len, no_null_sentences)) - - hidden_size = self._encoded_vector_size - self._encoder = EncoderRNN(self._input_lang.n_words, hidden_size).to(self.device) - self._decoder = DecoderRNN(hidden_size, self._output_lang.n_words).to(self.device) - - trainIters(self._encoder, - self._decoder, - self._input_lang, - self._output_lang, - no_null_sentences, - no_null_sentences, - self._train_iters, - print_every=int(log_every), - learning_rate=self._learning_rate, - loss_breakpoint=self._stop_on_error, - max_length=max_length, - device=self.device) - - self.is_prepared = True - - def encode(self, column_data): - if not self.is_prepared: - raise Exception('You need to call "prepare" before calling "encode" or "decode".') - - no_null_sentences = [x if x is not None else '' for x in column_data] - ret = [] - with torch.no_grad(): - for row in no_null_sentences: - - encoder_hidden = self._encoder.initHidden() - input_tensor = tensorFromSentence(self._input_lang, row, device=self.device) - input_length = input_tensor.size(0) - - #encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=self.device) - - loss = 0 - - for ei in range(input_length): - encoder_output, encoder_hidden = self._encoder( - input_tensor[ei], encoder_hidden) - #encoder_outputs[ei] = encoder_output[0, 0] - - # use the last hidden state as the encoded vector - ret.append(encoder_hidden.tolist()[0][0]) - - return torch.Tensor(ret) - - def decode(self, encoded_values_tensor, max_length=100): - - ret = [] - with torch.no_grad(): - for decoder_hiddens in encoded_values_tensor: - decoder_hidden = torch.FloatTensor([[decoder_hiddens.tolist()]]).to(self.device) - - decoder_input = torch.tensor([[SOS_token]], device=self.device) # SOS - - decoded_words = [] - - for di in range(max_length): - decoder_output, decoder_hidden = self._decoder( - decoder_input, decoder_hidden) - - topv, topi = decoder_output.data.topk(1) - if topi.item() == EOS_token: - decoded_words.append('') - break - else: - decoded_words.append(self._output_lang.index2word[topi.item()]) - - decoder_input = topi.squeeze().detach() - - ret.append(' '.join(decoded_words)) - - return ret diff --git a/lightwood/helpers/codegen.py b/lightwood/helpers/codegen.py index 6e2070fec..faa0dd3ef 100644 --- a/lightwood/helpers/codegen.py +++ b/lightwood/helpers/codegen.py @@ -505,6 +505,50 @@ def _timed_call(encoded_ds): predict_body = align(predict_body, 2) + # ----------------- # + # Test Body + # ----------------- # + test_body = """ +preds = self.predict(data, args) +preds = preds.rename(columns={'prediction': self.target}) +filtered = [] + +# filter metrics if not supported +for metric in metrics: + # metric should be one of: an actual function, registered in the model class, or supported by the evaluator + if not (callable(metric) or metric in self.accuracy_functions or metric in mdb_eval_accuracy_metrics): + if strict: + raise Exception(f'Invalid metric: {metric}') + else: + log.warning(f'Invalid metric: {metric}. Skipping...') + else: + filtered.append(metric) + +metrics = filtered +try: + labels = self.model_analysis.histograms[self.target]['x'] +except: + if strict: + raise Exception('Label histogram not found') + else: + label_map = None # some accuracy functions will crash without this, be mindful +scores = evaluate_accuracies( + data, + preds[self.target], + self.target, + metrics, + ts_analysis=self.ts_analysis, + labels=labels + ) + +# TODO: remove once mdb_eval returns an actual list +scores = {k: [v] for k, v in scores.items() if not isinstance(v, list)} + +return pd.DataFrame.from_records(scores) # TODO: add logic to disaggregate per-mixer +""" + + test_body = align(test_body, 2) + predictor_code = f""" {IMPORTS} {IMPORT_EXTERNAL_DIRS} @@ -597,6 +641,11 @@ def adjust(self, train_data: Union[EncodedDs, ConcatedEncodedDs, pd.DataFrame], @timed_predictor def predict(self, data: pd.DataFrame, args: Dict = {{}}) -> pd.DataFrame: {predict_body} + + def test( + self, data: pd.DataFrame, metrics: list, args: Dict[str, object] = {{}}, strict: bool = False + ) -> pd.DataFrame: +{test_body} """ try: diff --git a/lightwood/helpers/constants.py b/lightwood/helpers/constants.py index 30d61b5ea..dab2af838 100644 --- a/lightwood/helpers/constants.py +++ b/lightwood/helpers/constants.py @@ -45,6 +45,9 @@ from dataprep_ml.splitters import splitter from dataprep_ml.imputers import * +from mindsdb_evaluator import evaluate_accuracies +from mindsdb_evaluator.accuracy import __all__ as mdb_eval_accuracy_metrics + import pandas as pd from typing import Dict, List, Union, Optional import os diff --git a/lightwood/mixer/base.py b/lightwood/mixer/base.py index a91bb6946..0f6556ff3 100644 --- a/lightwood/mixer/base.py +++ b/lightwood/mixer/base.py @@ -30,7 +30,7 @@ class BaseMixer: def __init__(self, stop_after: float): """ - :param stop_after: Time budget to train this mixer. + :param stop_after: Time budget (in seconds) to train this mixer. """ self.stop_after = stop_after self.supports_proba = False diff --git a/lightwood/mixer/lightgbm.py b/lightwood/mixer/lightgbm.py index 205cae60b..9e347c555 100644 --- a/lightwood/mixer/lightgbm.py +++ b/lightwood/mixer/lightgbm.py @@ -16,7 +16,6 @@ from lightwood.api.types import PredictionArguments from lightwood.data.encoded_ds import EncodedDs - optuna.logging.set_verbosity(optuna.logging.CRITICAL) @@ -95,7 +94,8 @@ def __init__( if not gpu_works: self.device = torch.device('cpu') self.device_str = 'cpu' - log.warning('LightGBM running on CPU, this somewhat slower than the GPU version, consider using a GPU instead') # noqa + log.warning( + 'LightGBM running on CPU, this somewhat slower than the GPU version, consider using a GPU instead') # noqa else: self.device = torch.device('cuda') self.device_str = 'gpu' @@ -137,10 +137,17 @@ def _to_dataset(self, data: Dict[str, Dict], output_dtype: str): if weight_map is not None: data[subset_name]['weights'] = [weight_map[x] for x in label_data] label_data = self.ordinal_encoder.transform(np.array(label_data).reshape(-1, 1)).flatten() - elif output_dtype == dtype.integer: - label_data = label_data.clip(-pow(2, 63), pow(2, 63)).astype(int) - elif output_dtype in self.float_dtypes: - label_data = label_data.astype(float) + elif output_dtype in self.num_dtypes: + if weight_map is not None: + target_encoder = data[subset_name]['ds'].encoders[self.target] + + # get the weights from the numeric target encoder + data[subset_name]['weights'] = target_encoder.get_weights(label_data) + + if output_dtype in self.float_dtypes: + label_data = label_data.astype(float) + elif output_dtype == dtype.integer: + label_data = label_data.clip(-pow(2, 63), pow(2, 63)).astype(int) data[subset_name]['label_data'] = label_data @@ -206,12 +213,15 @@ def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: Only happens sometimes and I can find no pattern as to when, happens for multiple input and target types. Why does the following crash happen and what does it mean? No idea, closest relationships I can find is /w optuna modifying parameters after the dataset is create: https://github.com/microsoft/LightGBM/issues/4019 | But why this would apply here makes no sense. Could have to do with the `train` process of lightgbm itself setting a "set only once" property on a dataset when it starts. Dunno, if you find out replace this comment with the real reason. - ''' # noqa + ''' # noqa kwargs = {} if 'verbose_eval' in inspect.getfullargspec(lightgbm.train).args: kwargs['verbose_eval'] = False - self.model = lightgbm.train(self.params, lightgbm.Dataset(data['train']['data'], label=data['train'] - ['label_data'], weight=data['train']['weights']), **kwargs) + self.model = lightgbm.train(self.params, + lightgbm.Dataset(data['train']['data'], + label=data['train']['label_data'], + weight=data['train']['weights']), + **kwargs) end = time.time() seconds_for_one_iteration = max(0.1, end - start) @@ -232,7 +242,7 @@ def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: # Train the models log.info( - f'Training GBM ({model_generator}) with {self.num_iterations} iterations given {self.stop_after} seconds constraint') # noqa + f'Training GBM ({model_generator}) with {self.num_iterations} iterations given {self.stop_after} seconds constraint') # noqa if self.num_iterations < 1: self.num_iterations = 1 self.params['num_iterations'] = int(self.num_iterations) diff --git a/lightwood/mixer/sktime.py b/lightwood/mixer/sktime.py index 7ebabcfd9..fc2577afe 100644 --- a/lightwood/mixer/sktime.py +++ b/lightwood/mixer/sktime.py @@ -69,6 +69,9 @@ def __init__( :param use_stl: Whether to use de-trenders and de-seasonalizers fitted in the timeseries analysis phase. """ # noqa super().__init__(stop_after) + + assert ts_analysis['tss'].horizon > 1, log.error("Horizon must be greater than 1 when using the SkTime Mixer!") + self.stable = False self.prepared = False self.supports_proba = False diff --git a/lightwood/mixer/unit.py b/lightwood/mixer/unit.py index 18789b15c..955cdbc49 100644 --- a/lightwood/mixer/unit.py +++ b/lightwood/mixer/unit.py @@ -1,10 +1,3 @@ -""" -2021.07.16 - -For encoders that already fine-tune on the targets (namely text) -the unity mixer just arg-maxes the output of the encoder. -""" - from typing import List, Optional import torch @@ -19,19 +12,35 @@ class Unit(BaseMixer): def __init__(self, stop_after: float, target_encoder: BaseEncoder): + """ + The "Unit" mixer serves as a simple wrapper around a target encoder, essentially borrowing + the encoder's functionality for predictions. In other words, it simply arg-maxes the output of the encoder + + Used with encoders that already fine-tune on the targets (namely, pre-trained text ML models). + + Attributes: + :param target_encoder: An instance of a Lightwood BaseEncoder. This encoder is used to decode predictions. + :param stop_after (float): Time budget (in seconds) to train this mixer. + """ # noqa super().__init__(stop_after) self.target_encoder = target_encoder self.supports_proba = False self.stable = True def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: - log.info("Unit Mixer just borrows from encoder") + log.info("Unit mixer does not require training, it passes through predictions from its encoders.") def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args: Optional[dict] = None) -> None: pass def __call__(self, ds: EncodedDs, args: PredictionArguments = PredictionArguments()) -> pd.DataFrame: + """ + Makes predictions using the provided EncodedDs dataset. + Mixer decodes predictions using the target encoder and returns them in a pandas DataFrame. + + :returns ydf (pd.DataFrame): a data frame containing the decoded predictions. + """ if args.predict_proba: # @TODO: depending on the target encoder, this might be enabled log.warning('This model does not output probability estimates') diff --git a/lightwood/mixer/xgboost.py b/lightwood/mixer/xgboost.py index c6b8c9cf9..f7a2c7bde 100644 --- a/lightwood/mixer/xgboost.py +++ b/lightwood/mixer/xgboost.py @@ -119,9 +119,10 @@ def _to_dataset(self, ds: EncodedDs, output_dtype: str, mode='train'): data = data.cpu().numpy() if mode in ('train', 'dev'): + weights = [] label_data = ds.get_column_original_data(self.target) if output_dtype in self.cls_dtypes: - if mode == 'train': # TODO weight maps? + if mode == 'train': self.ordinal_encoder = OrdinalEncoder() self.label_set = list(set(label_data)) self.ordinal_encoder.fit(np.array(list(self.label_set)).reshape(-1, 1)) @@ -131,14 +132,26 @@ def _to_dataset(self, ds: EncodedDs, output_dtype: str, mode='train'): if x in self.label_set: filtered_label_data.append(x) + weight_map = getattr(self.target_encoder, 'target_weights', None) + if weight_map is not None: + weights = [weight_map[x] for x in label_data] + label_data = self.ordinal_encoder.transform(np.array(filtered_label_data).reshape(-1, 1)).flatten() - elif output_dtype == dtype.integer: - label_data = label_data.clip(-pow(2, 63), pow(2, 63)).astype(int) - elif output_dtype in self.float_dtypes: - label_data = label_data.astype(float) + elif output_dtype in self.num_dtypes: + weight_map = getattr(self.target_encoder, 'target_weights', None) + if weight_map is not None: + target_encoder = ds.encoders[self.target] + + # get the weights from the numeric target encoder + weights = target_encoder.get_weights(label_data) + + if output_dtype in self.float_dtypes: + label_data = label_data.astype(float) + elif output_dtype == dtype.integer: + label_data = label_data.clip(-pow(2, 63), pow(2, 63)).astype(int) - return data, label_data + return data, label_data, weights else: return data @@ -175,8 +188,8 @@ def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: } # Prepare the data - train_dataset, train_labels = self._to_dataset(train_data, output_dtype, mode='train') - dev_dataset, dev_labels = self._to_dataset(dev_data, output_dtype, mode='dev') + train_dataset, train_labels, train_weights = self._to_dataset(train_data, output_dtype, mode='train') + dev_dataset, dev_labels, dev_weights = self._to_dataset(dev_data, output_dtype, mode='dev') if output_dtype not in self.num_dtypes: self.all_classes = self.ordinal_encoder.categories_[0] @@ -191,7 +204,13 @@ def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: with xgb.config_context(verbosity=0): self.model = model_class(**self.params) - self.model.fit(train_dataset, train_labels, eval_set=[(dev_dataset, dev_labels)]) + if train_weights is not None and dev_weights is not None: + self.model.fit(train_dataset, train_labels, sample_weight=train_weights, + eval_set=[(dev_dataset, dev_labels)], + sample_weight_eval_set=[dev_weights]) + else: + self.model.fit(train_dataset, train_labels, + eval_set=[(dev_dataset, dev_labels)]) end = time.time() seconds_for_one_iteration = max(0.1, end - start) @@ -224,7 +243,13 @@ def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: with xgb.config_context(verbosity=0): self.model = model_class(**self.params) - self.model.fit(train_dataset, train_labels, eval_set=[(dev_dataset, dev_labels)]) + if train_weights is not None and dev_weights is not None: + self.model.fit(train_dataset, train_labels, sample_weight=train_weights, + eval_set=[(dev_dataset, dev_labels)], + sample_weight_eval_set=[dev_weights]) + else: + self.model.fit(train_dataset, train_labels, + eval_set=[(dev_dataset, dev_labels)]) if self.fit_on_dev: self.partial_fit(dev_data, train_data) diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 000000000..6073e41f1 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,6005 @@ +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. + +[[package]] +name = "adagio" +version = "0.2.4" +description = "The Dag IO Framework 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"setuptools", - "wheel", -] \ No newline at end of file +[tool.poetry] +name = "lightwood" +version = "24.3.3.0" +description = "Lightwood is Legos for Machine Learning." +authors = ["MindsDB Inc."] +license = "GPL-3.0-only" +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<3.12" +type_infer = ">=0.0.15" +dataprep_ml = ">=0.0.18" +mindsdb-evaluator = ">=0.0.12" +numpy = ">1.23.0" +nltk = ">=3.8, <3.9" +pandas = ">=2.0.0, <2.1.0" +torch = ">=2.0.0" +requests = ">=2.0.0" +transformers = ">=4.34.0" +optuna = ">=3.1.0,<4.0.0" +scipy = ">=1.5.4" +psutil = ">=5.7.0" +scikit-learn = ">=1.0.0" +dataclasses_json = ">=0.5.4" +dill = "==0.3.6" +sktime = ">=0.24.0,<0.25.0" +statsforecast = "~=1.6.0" +torch_optimizer = "==0.1.0" +black = "==23.3.0" +typing_extensions = ">= 4.8.0" +colorlog = "==6.5.0" +xgboost = ">=1.6.0, <=1.8.0" +tab-transformer-pytorch = ">= 0.2.1" + +# dependencies for optional packages +autopep8 = {version = ">=1.5.7", optional = true} +flake8 = {version = ">=6.0.0", optional = true} +librosa = {version = "==0.8.1", optional = true} +lightgbm = {version = ">=3.3.0,<=3.3.3", optional = true} +pystan = {version = "==2.19.1.1", optional = true} +prophet = {version = "==1.1", optional = true} +neuralforecast = {version = ">=1.6.4,<1.7.0", optional = true} +mxnet = {version = ">=1.6.0,<2.0.0", optional = true} +gluonts = {version = ">=0.13.2,<0.14.0", optional = true} +torchvision = {version = ">=0.15.0", optional = true} +pillow = {version = ">8.3.1", optional = true} +qiskit = {version = "==0.31.0", optional = true} +shap = {version = ">=0.40.0", optional = true} +pyod = {version = "==1.0.4", optional = true} +suod = {version = ">=0.1.3", optional = true} + +[tool.poetry.extras] +dev = [ + "autopep8", + "flake8", +] +audio = [ + "librosa", +] +extra = [ + "lightgbm", +] +extra_ts = [ + "pystan", + "prophet", + "neuralforecast", + "mxnet", + "gluonts", +] +image = [ + "torchvision", + "pillow", +] +quantum = [ + "qiskit", +] +xai = [ + "shap", + "pyod", + "suod", +] diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 9352c20b9..000000000 --- a/requirements.txt +++ /dev/null @@ -1,34 +0,0 @@ -type_infer >=0.0.15 -dataprep_ml >=0.0.18 -mindsdb-evaluator >=0.0.11 -numpy -nltk >=3.8, <3.9 -python-dateutil >=2.8.2 -pandas >=2.0.0, <2.1.0 -schema >=0.6.8 -torch >=2.0.0, <2.1 -requests >=2.0.0 -transformers -optuna >=3.1.0,<4.0.0 -scipy >=1.5.4 -psutil >=5.7.0 -setuptools >=21.2.1 -wheel >=0.32.2 -scikit-learn >=1.0.0 -dataclasses_json >=0.5.4 -dill ==0.3.6 -sktime >=0.24.0,<0.25.0 -statsforecast ~=1.6.0 -torch_optimizer ==0.1.0 -black ==23.3.0 -typing_extensions -colorlog ==6.5.0 -statsmodels >=0.12.0 -langid==1.1.6 -pydateinfer==0.3.0 -protobuf<3.21.0 -xgboost>=1.6.0, <=1.8.0 -tab-transformer-pytorch >= 0.2.1 -typing-inspect -six -regex diff --git a/requirements_audio.txt b/requirements_audio.txt deleted file mode 100644 index af5199f30..000000000 --- a/requirements_audio.txt +++ /dev/null @@ -1 +0,0 @@ -librosa==0.8.1 \ No newline at end of file diff --git a/requirements_dev.txt b/requirements_dev.txt deleted file mode 100644 index 3df33ad25..000000000 --- a/requirements_dev.txt +++ /dev/null @@ -1 +0,0 @@ -autopep8 >=1.5.7 diff --git a/requirements_extra.txt b/requirements_extra.txt deleted file mode 100644 index 9a5b8e5aa..000000000 --- a/requirements_extra.txt +++ /dev/null @@ -1 +0,0 @@ -lightgbm >=3.3.0,<=3.3.3 diff --git a/requirements_extra_ts.txt b/requirements_extra_ts.txt deleted file mode 100644 index ea70c95c1..000000000 --- a/requirements_extra_ts.txt +++ /dev/null @@ -1,5 +0,0 @@ -pystan==2.19.1.1 -prophet==1.1 -neuralforecast >=1.6.4, <1.7.0 -mxnet >=1.6.0, <2.0.0 -gluonts >= 0.13.2, <0.14.0 diff --git a/requirements_image.txt b/requirements_image.txt deleted file mode 100644 index a66506e04..000000000 --- a/requirements_image.txt +++ /dev/null @@ -1,2 +0,0 @@ -torchvision -pillow >8.3.1 diff --git a/requirements_quantum.txt b/requirements_quantum.txt deleted file mode 100644 index acb72a81a..000000000 --- a/requirements_quantum.txt +++ /dev/null @@ -1 +0,0 @@ -qiskit==0.31.0 diff --git a/requirements_xai.txt b/requirements_xai.txt deleted file mode 100644 index 852fe1d87..000000000 --- a/requirements_xai.txt +++ /dev/null @@ -1,3 +0,0 @@ -shap >= 0.40.0 -pyod==1.0.4 -suod diff --git a/setup.py b/setup.py deleted file mode 100644 index c4a75b0b3..000000000 --- a/setup.py +++ /dev/null @@ -1,66 +0,0 @@ -import sys -import setuptools -import os - - -def remove_requirements(requirements, name, replace=''): - new_requirements = [] - for requirement in requirements: - if requirement.split(' ')[0] != name: - new_requirements.append(requirement) - elif replace is not None: - new_requirements.append(replace) - return new_requirements - - -sys_platform = sys.platform - -about = {} -with open("lightwood/__about__.py") as fp: - exec(fp.read(), about) - -with open("README.md", "r") as fh: - long_description = fh.read() - -with open('requirements.txt') as req_file: - requirements = [req.strip() for req in req_file.read().splitlines()] - -extra_requirements = {} -for fn in os.listdir('.'): - if fn.startswith('requirements_') and fn.endswith('.txt'): - extra_name = fn.replace('requirements_', '').replace('.txt', '') - with open(fn) as fp: - extra = [req.strip() for req in fp.read().splitlines()] - extra_requirements[extra_name] = extra -full_requirements = [] -for v in extra_requirements.values(): - full_requirements += v -extra_requirements['all_extras'] = list(set(full_requirements)) - -# Windows specific requirements -if sys_platform in ['win32', 'cygwin', 'windows']: - # These have to be installed manually or via the installers in windows - requirements = remove_requirements(requirements, 'torch') - -setuptools.setup( - name=about['__title__'], - version=about['__version__'], - url=about['__github__'], - download_url=about['__pypi__'], - license=about['__license__'], - author=about['__author__'], - author_email=about['__email__'], - description=about['__description__'], - long_description=long_description, - long_description_content_type="text/markdown", - packages=setuptools.find_packages(exclude=["tests", "tests.*"]), - package_data={'project': ['requirements.txt']}, - install_requires=requirements, - extras_require=extra_requirements, - classifiers=[ - "Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", - ], - python_requires=">=3.8,<3.12" -) diff --git a/tests/integration/basic/test_categorical.py b/tests/integration/basic/test_categorical.py index 9a5e95d40..2321efad6 100644 --- a/tests/integration/basic/test_categorical.py +++ b/tests/integration/basic/test_categorical.py @@ -69,3 +69,11 @@ def test_2_binary_no_analysis(self): self.assertTrue(balanced_accuracy_score(test['target'], predictions['prediction']) > 0.5) self.assertTrue('confidence' not in predictions.columns) + metrics = ['balanced_accuracy_score', 'accuracy_score', 'precision_score'] + results = predictor.test(test, metrics) + + for metric in metrics: + assert metric in results.columns + assert 0.5 < results[metric].iloc[0] < 1.0 + + print(results) diff --git a/tests/unit_tests/encoder/numeric/test_numeric.py b/tests/unit_tests/encoder/numeric/test_numeric.py index b81ef7a06..0a65eb046 100644 --- a/tests/unit_tests/encoder/numeric/test_numeric.py +++ b/tests/unit_tests/encoder/numeric/test_numeric.py @@ -50,7 +50,7 @@ def test_encode_and_decode(self): def test_positive_domain(self): data = pd.Series([-1, -2, -100, 5, 10, 15]) for encoder in [NumericEncoder(), TsNumericEncoder()]: - encoder.is_target = True # only affects target values + encoder.is_target = True # only affects target values encoder.positive_domain = True encoder.prepare(data) decoded_vals = encoder.decode(encoder.encode(data)) @@ -110,3 +110,27 @@ def test_nan_encoding(self): assert is_none(dec) else: assert not is_none(x) or x != 0.0 + + def test_weights(self): + num_bins = 10 + data = np.random.normal(loc=0.0, scale=1.0, size=1000) + hist, bin_edges = np.histogram(data, bins=num_bins, density=False) + + # constrict bins so that final histograms align, throw out minimum bin as the np.searchsorted is left justified + # and this leads always to a singleton bin that contains the lowest value. + bin_edges = bin_edges[1:] + + # construct target weight mapping. This mapping will round each entry to the lower bin edge. + target_weights = {bin_edge: bin_edge for bin_edge in bin_edges} + self.assertTrue(type(target_weights) is dict) + + # apply weight mapping + encoder = NumericEncoder(is_target=True, target_weights=target_weights) + generated_weights = encoder.get_weights(label_data=data) + + self.assertTrue(type(generated_weights) is np.ndarray) + + # distributions should match + gen_hist, _ = np.histogram(generated_weights, bins=num_bins, density=False) + + self.assertTrue(np.all(np.equal(hist, gen_hist))) diff --git a/tests/unit_tests/encoder/text/test_rnn.py b/tests/unit_tests/encoder/text/test_rnn.py deleted file mode 100644 index 1c979eec5..000000000 --- a/tests/unit_tests/encoder/text/test_rnn.py +++ /dev/null @@ -1,36 +0,0 @@ -import unittest -from lightwood.encoder.text import RnnEncoder -import pandas as pd - - -class TestRnnEncoder(unittest.TestCase): - @unittest.skip("Currently not using this encoder.") - def test_encode_and_decode(self): - sentences = ["Everyone really likes the newest benefits", - "The Government Executive articles housed on the website are not able to be searched", - "Most of Mrinal Sen 's work can be found in European collections . ", - "Would you rise up and defeaat all evil lords in the town ? ", - None - - ] - - encoder = RnnEncoder(encoded_vector_size=10, train_iters=7500) - encoder.prepare(pd.Series(sentences), pd.Series(sentences)) - encoder.encode(sentences) - - # test de decoder - - ret = encoder.encode(["Everyone really likes the newest benefits"]) - print('encoded vector:') - print(ret) - print('decoded vector') - ret2 = encoder.decode(ret) - print(ret2) - - @unittest.skip("Currently not using this encoder.") - def test_encoder_on_cpu(self): - pass - - @unittest.skip("Currently not using this encoder.") - def test_encoder_on_cuda(self): - pass diff --git a/tests/unit_tests/mixer/test_lgbm.py b/tests/unit_tests/mixer/test_lgbm.py new file mode 100644 index 000000000..f95866119 --- /dev/null +++ b/tests/unit_tests/mixer/test_lgbm.py @@ -0,0 +1,87 @@ +import unittest +import numpy as np +import pandas as pd +from lightwood.api.types import ProblemDefinition +from lightwood.api.high_level import json_ai_from_problem, code_from_json_ai, predictor_from_code +import importlib + +np.random.seed(42) + + +@unittest.skipIf(importlib.util.find_spec('lightgbm') is None, "LightGBM is not available, skipping LightGBM tests.") +class TestBasic(unittest.TestCase): + + def get_submodels(self): + submodels = [ + { + 'module': 'LightGBM', + 'args': { + 'stop_after': '$problem_definition.seconds_per_mixer', + 'fit_on_dev': True, + 'target': '$target', + 'dtype_dict': '$dtype_dict', + 'target_encoder': '$encoders[self.target]', + 'use_optuna': True + } + }, + ] + return submodels + + def test_0_regression(self): + """ + This test mocks a dataset intended to demonstrate the efficacy of weighting. The operation does not successfully + test if the weighting procedure works as intended, but does test the code for bugs. + """ + + # generate data that mocks an observational skew by adding a linear selection to data + data_size = 100000 + loc = 100.0 + scale = 10.0 + eps = .1 + target_data = np.random.normal(loc=loc, scale=scale, size=data_size) + epsilon = np.random.normal(loc=0.0, scale=loc * eps, size=len(target_data)) + feature_data = target_data + epsilon + df = pd.DataFrame({'feature': feature_data, 'target': target_data}) + + hist, bin_edges = np.histogram(target_data, bins=10, density=False) + fracs = np.linspace(1, 100, len(hist)) + fracs = fracs / fracs.sum() + target_size = 10000 + skewed_arr_list = [] + for i in range(len(hist)): + frac = fracs[i] + low_edge = bin_edges[i] + high_edge = bin_edges[i + 1] + + bin_array = target_data[target_data <= high_edge] + bin_array = bin_array[bin_array >= low_edge] + + # select only a fraction fo the elements in this bin + bin_array = bin_array[:int(target_size * frac)] + + skewed_arr_list.append(bin_array) + + skewed_arr = np.concatenate(skewed_arr_list) + epsilon = np.random.normal(loc=0.0, scale=loc * eps, size=len(skewed_arr)) + skewed_feat = skewed_arr + epsilon + skew_df = pd.DataFrame({'feature': skewed_feat, 'target': skewed_arr}) + + # generate data set weights to remove bias. + hist, bin_edges = np.histogram(skew_df['target'].to_numpy(), bins=10, density=False) + hist = 1 - hist / hist.sum() + target_weights = {bin_edge: bin_frac for bin_edge, bin_frac in zip(bin_edges, hist)} + + pdef = ProblemDefinition.from_dict({'target': 'target', 'target_weights': target_weights, 'time_aim': 80}) + jai = json_ai_from_problem(skew_df, pdef) + + jai.model['args']['submodels'] = self.get_submodels() + code = code_from_json_ai(jai) + predictor = predictor_from_code(code) + + predictor.learn(skew_df) + output_df = predictor.predict(df) + + output_mean = output_df['prediction'].mean() + + self.assertTrue(np.all(np.isclose(output_mean, loc, atol=0., rtol=.03)), + msg=f"the output mean {output_mean} is not close to {loc}") diff --git a/tests/unit_tests/mixer/test_xgboost.py b/tests/unit_tests/mixer/test_xgboost.py new file mode 100644 index 000000000..7daac0fca --- /dev/null +++ b/tests/unit_tests/mixer/test_xgboost.py @@ -0,0 +1,85 @@ +import unittest +import numpy as np +import pandas as pd +from lightwood.api.types import ProblemDefinition +from lightwood.api.high_level import json_ai_from_problem, code_from_json_ai, predictor_from_code + +np.random.seed(42) + + +class TestBasic(unittest.TestCase): + + def get_submodels(self): + submodels = [ + { + 'module': 'XGBoostMixer', + 'args': { + 'stop_after': '$problem_definition.seconds_per_mixer', + 'fit_on_dev': True, + 'target': '$target', + 'dtype_dict': '$dtype_dict', + 'target_encoder': '$encoders[self.target]', + 'use_optuna': True + } + }, + ] + return submodels + + def test_0_regression(self): + """ + This test mocks a dataset intended to demonstrate the efficacy of weighting. The operation does not successfully + test if the weighting procedure works as intended, but does test the code for bugs. + """ + + # generate data that mocks an observational skew by adding a linear selection to data + data_size = 100000 + loc = 100.0 + scale = 10.0 + eps = .1 + target_data = np.random.normal(loc=loc, scale=scale, size=data_size) + epsilon = np.random.normal(loc=0.0, scale=loc * eps, size=len(target_data)) + feature_data = target_data + epsilon + df = pd.DataFrame({'feature': feature_data, 'target': target_data}) + + hist, bin_edges = np.histogram(target_data, bins=10, density=False) + fracs = np.linspace(1, 100, len(hist)) + fracs = fracs / fracs.sum() + target_size = 10000 + skewed_arr_list = [] + for i in range(len(hist)): + frac = fracs[i] + low_edge = bin_edges[i] + high_edge = bin_edges[i + 1] + + bin_array = target_data[target_data <= high_edge] + bin_array = bin_array[bin_array >= low_edge] + + # select only a fraction fo the elements in this bin + bin_array = bin_array[:int(target_size * frac)] + + skewed_arr_list.append(bin_array) + + skewed_arr = np.concatenate(skewed_arr_list) + epsilon = np.random.normal(loc=0.0, scale=loc * eps, size=len(skewed_arr)) + skewed_feat = skewed_arr + epsilon + skew_df = pd.DataFrame({'feature': skewed_feat, 'target': skewed_arr}) + + # generate data set weights to remove bias. + hist, bin_edges = np.histogram(skew_df['target'].to_numpy(), bins=10, density=False) + hist = 1 - hist / hist.sum() + target_weights = {bin_edge: bin_frac for bin_edge, bin_frac in zip(bin_edges, hist)} + + pdef = ProblemDefinition.from_dict({'target': 'target', 'target_weights': target_weights, 'time_aim': 80}) + jai = json_ai_from_problem(skew_df, pdef) + + jai.model['args']['submodels'] = self.get_submodels() + code = code_from_json_ai(jai) + predictor = predictor_from_code(code) + + predictor.learn(skew_df) + output_df = predictor.predict(df) + + output_mean = output_df['prediction'].mean() + + self.assertTrue(np.all(np.isclose(output_mean, loc, atol=0., rtol=.03)), + msg=f"the output mean {output_mean} is not close to {loc}")