Components: transform#

autogluon.eda.analysis.transform#

ApplyFeatureGenerator

This wrapper provides transformed features to all children shadowing outer datasets with the updated one after application of FeatureGenerator.

ApplyFeatureGenerator#

class autogluon.eda.analysis.transform.ApplyFeatureGenerator(parent: Optional[AbstractAnalysis] = None, children: Optional[List[AbstractAnalysis]] = None, state: Optional[AnalysisState] = None, category_to_numbers: bool = False, feature_generator: Optional[AbstractFeatureGenerator] = None, **kwargs)[source]#

This wrapper provides transformed features to all children shadowing outer datasets with the updated one after application of FeatureGenerator.

Parameters

  • category_to_numbers (bool, default = False) – if True’, then transform `category variables into their codes. This is useful when wrapped analyses expect numeric values

  • feature_generator (Optional[AbstractFeatureGenerator], default = None) – feature generator to use for the transformation. If None is provided then AutoMLPipelineFeatureGenerator is applied.

  • parent (Optional[AbstractAnalysis], default = None) – parent Analysis

  • children (Optional[List[AbstractAnalysis]], default None) – wrapped analyses; these will receive sampled args during fit call

  • kwargs –

:param See also autogluon.features.AbstractFeatureGenerator():

Examples

>>> from autogluon.eda.analysis.base import BaseAnalysis, Namespace
>>> import pandas as pd
>>> import numpy as np
>>> df_train = pd.DataFrame(...)
>>> df_test = pd.DataFrame(...)
>>>
>>> analysis = BaseAnalysis(train_data=df_train, test_data=df_test, label='D', children=[
>>>     Namespace(namespace='feature_generator_numbers', children=[
>>>         ApplyFeatureGenerator(category_to_numbers=True, children=[
>>>             # SomeAnalysis()  # This analysis will be called with transformed `train_data` and `test_data`
>>>         ])
>>>     ]),
>>>     # SomeAnalysis()  # This analysis will be called with the original features
>>> ])