ballet.eng.external.featuretools module

class ballet.eng.external.featuretools.DFSTransformer(target_entity=None, agg_primitives=None, trans_primitives=None, allowed_paths=None, max_depth=2, ignore_entities=None, ignore_variables=None, seed_features=None, drop_contains=None, drop_exact=None, where_primitives=None, max_features=- 1, verbose=False)[source]

Bases: sklearn.base.TransformerMixin

Transformer using Scikit-Learn interface for Pipeline uses.

fit(X, y=None)[source]

Wrapper for DFS

Calculates a list of features given a dictionary of entities and a list of relationships. Alternatively, an EntitySet can be passed instead of the entities and relationships.

Parameters

  • X – (ft.Entityset or tuple): Entityset to calculate features on. If a tuple is passed it can take one of these forms: (entityset, cutoff_time_dataframe), (entities, relationships), or ((entities, relationships), cutoff_time_dataframe)

  • y – (iterable): Training targets

See also

synthesis.dfs()

get_params(deep=True)[source]
transform(X)[source]

Wrapper for calculate_feature_matrix

Calculates a feature matrix for a the given input data and calculation times.

Parameters

X – (ft.Entityset or tuple): Entityset to calculate features on. If a tuple is passed it can take one of these forms: (entityset, cutoff_time_dataframe), (entities, relationships), or ((entities, relationships), cutoff_time_dataframe)

See also

computational_backends.calculate_feature_matrix()