Featuretools Transformer for Scikit-Learn Pipeline use.
Featuretools' DFS as a scikit-learn transformer
pip install featuretools_sklearn_transformer
To use the transformer in a pipeline, initialize an instance of the transformer by passing in
the parameters you would like to use for calculating features. To fit the model and generate features for
the training data, pass in an entityset or list of entities and relationships containing only the relevant
training data as the
X input, along with the training targets as the
y input. To generate a feature matrix from test data, pass in
an entityset containing only the relevant test data as the
The input supplied for
X can take several formats:
- To use a Featuretools EntitySet without cutoff times, simply pass in the EntitySet
- To use a Featuretools EntitySet with a cutoff times DataFrame, pass in a tuple of the form (EntitySet, cutoff_time_df)
- To use a list of Entities and Relationships without cutoff times, pass a tuple of the form (entities, relationships)
- To use a list of Entities and Relationships with a cutoff times DataFrame, pass a tuple of the form ((entities, relationships), cutoff_time_df)
Note that because this transformer requires a Featuretools EntitySet or Entities and relationships as input, it does not currently work
with certain methods such as
sklearn.model_selection.GridSearchCV which expect the
to be an iterable which can be split by the method.
The example below shows how to use the transformer with an EntitySet, both with and without a cutoff time DataFrame.
import featuretools as ft import pandas as pd from featuretools.wrappers import DFSTransformer from sklearn.pipeline import Pipeline from sklearn.ensemble import ExtraTreesClassifier # Get example data train_es = ft.demo.load_mock_customer(return_entityset=True, n_customers=3) test_es = ft.demo.load_mock_customer(return_entityset=True, n_customers=2) y = [True, False, True] # Build pipeline pipeline = Pipeline(steps=[ ('ft', DFSTransformer(target_entity="customers", max_features=2)), ('et', ExtraTreesClassifier(n_estimators=100)) ]) # Fit and predict pipeline.fit(X=train_es, y=y) # fit on customers in training entityset pipeline.predict_proba(test_es) # predict probability of each class on test entityset pipeline.predict(test_es) # predict on test entityset # Same as above, but using cutoff times train_ct = pd.DataFrame() train_ct['customer_id'] = [1, 2, 3] train_ct['time'] = pd.to_datetime(['2014-1-1 04:00', '2014-1-2 17:20', '2014-1-4 09:53']) pipeline.fit(X=(train_es, train_ct), y=y) test_ct = pd.DataFrame() test_ct['customer_id'] = [1, 2] test_ct['time'] = pd.to_datetime(['2014-1-4 13:48', '2014-1-5 15:32']) pipeline.predict_proba((test_es, test_ct)) pipeline.predict((test_es, test_ct))
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