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 import (
    _convert_woodwork_types_wrapper,
    _retain_custom_types_and_initalize_woodwork,
    infer_feature_types
)


[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_seed=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_seed (int): 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_seed=random_seed)
def _make_entity_set(self, X): """Helper method that creates and returns the entity set given the input data""" 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] Returns: self """ X = infer_feature_types(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] y (ww.DataColumn, pd.Series, optional): Ignored. Returns: ww.DataTable: Feature matrix """ X_ww = infer_feature_types(X) X_t = _convert_woodwork_types_wrapper(X_ww.to_dataframe()) X_t.columns = X_t.columns.astype(str) es = self._make_entity_set(X_t) feature_matrix = calculate_feature_matrix(features=self.features, entityset=es) return _retain_custom_types_and_initalize_woodwork(X_ww, feature_matrix)