insurance_charges_model/prediction/transformers.py
def fit(self, X, y=None):
        """Fit the transformer to a dataset."""
        entityset = ft.EntitySet(id="Transactions")
        if "index" not in X.columns:
            entityset = entityset.entity_from_dataframe(entity_id=self.target_entity,
                                                        dataframe=X,
                                                        make_index=True,
                                                        index="index")
        else:
            entityset = entityset.entity_from_dataframe(entity_id=self.target_entity,
                                                        dataframe=X)

        feature_matrix, feature_defs = ft.dfs(entityset=entityset,
                                              target_entity=self.target_entity,
                                              trans_primitives=self.trans_primitives,
                                              ignore_variables=self.ignore_variables)

        self.feature_defs = feature_defs
        return self
Fit the transformer to a dataset.
Fit the transformer to a dataset.
Fit the transformer to a dataset .
def fit(self, X, y=None):
        entityset = ft.EntitySet(id="Transactions")
        if "index" not in X.columns:
            entityset = entityset.entity_from_dataframe(entity_id=self.target_entity,
                                                        dataframe=X,
                                                        make_index=True,
                                                        index="index")
        else:
            entityset = entityset.entity_from_dataframe(entity_id=self.target_entity,
                                                        dataframe=X)
        feature_matrix, feature_defs = ft.dfs(entityset=entityset,
                                              target_entity=self.target_entity,
                                              trans_primitives=self.trans_primitives,
                                              ignore_variables=self.ignore_variables)
        self.feature_defs = feature_defs
        return self
def fit ( self , X , y = None ) : entityset = ft . EntitySet ( id = "Transactions" ) if "index" not in X . columns : entityset = entityset . entity_from_dataframe ( entity_id = self . target_entity , dataframe = X , make_index = True , index = "index" ) else : entityset = entityset . entity_from_dataframe ( entity_id = self . target_entity , dataframe = X ) feature_matrix , feature_defs = ft . dfs ( entityset = entityset , target_entity = self . target_entity , trans_primitives = self . trans_primitives , ignore_variables = self . ignore_variables ) self . feature_defs = feature_defs return self
Fit the transformer to a dataset.
Fit the transformer to a dataset .
"""Fit the transformer to a dataset."""
self
X
y