Source code for evalml.pipelines.components.transformers.preprocessing.featuretools

from featuretools import EntitySet, calculate_feature_matrix, dfs

from evalml.pipelines.components.transformers.transformer import Transformer
from evalml.utils.gen_utils import (
    _convert_to_woodwork_structure,
    _convert_woodwork_types_wrapper
)


[docs]class DFSTransformer(Transformer): """Featuretools DFS component that generates features for ww.DataTables and pd.DataFrames""" name = "DFS Transformer" hyperparameter_ranges = {}
[docs] def __init__(self, index='index', random_state=0, **kwargs): """Allows for featuretools to be used in EvalML Arguments: index (string): The name of the column that contains the indices. If no column with this name exists, then featuretools.EntitySet() creates a column with this name to serve as the index column. Defaults to 'index' random_state (int, np.random.RandomState): seed for the random number generator """ parameters = {"index": index} if not isinstance(index, str): raise TypeError(f"Index provided must be string, got {type(index)}") self.index = index self.features = None parameters.update(kwargs) super().__init__(parameters=parameters, random_state=random_state)
def _make_entity_set(self, X): """helper method that creates and returns the entity set given the datatable X """ ft_es = EntitySet() if self.index not in X.columns: es = ft_es.entity_from_dataframe(entity_id="X", dataframe=X, index=self.index, make_index=True) else: es = ft_es.entity_from_dataframe(entity_id="X", dataframe=X, index=self.index) return es
[docs] def fit(self, X, y=None): """Fits the DFSTransformer Transformer component Arguments: X (ww.DataTable, pd.DataFrame, np.array): The input data to transform, of shape [n_samples, n_features] y (ww.DataColumn, pd.Series, np.ndarray, optional): The target training data of length [n_samples] """ X = _convert_to_woodwork_structure(X) X = _convert_woodwork_types_wrapper(X.to_dataframe()) X.columns = X.columns.astype(str) es = self._make_entity_set(X) self.features = dfs(entityset=es, target_entity='X', features_only=True) return self
[docs] def transform(self, X, y=None): """Computes the feature matrix for the input X using featuretools' dfs algorithm. Arguments: X (ww.DataTable, pd.DataFrame or np.ndarray): The input training data to transform. Has shape [n_samples, n_features] """ X = _convert_to_woodwork_structure(X) X = _convert_woodwork_types_wrapper(X.to_dataframe()) X.columns = X.columns.astype(str) es = self._make_entity_set(X) feature_matrix = calculate_feature_matrix(features=self.features, entityset=es) return feature_matrix