featuretools#

Featuretools DFS component that generates features for the input features.

Module Contents#

Classes Summary#

DFSTransformer

Featuretools DFS component that generates features for the input features.

Contents#

class evalml.pipelines.components.transformers.preprocessing.featuretools.DFSTransformer(index='index', features=None, random_seed=0, **kwargs)[source]#

Featuretools DFS component that generates features for the input features.

Parameters

  • 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. Defaults to 0.

  • features (list) – List of features to run DFS on. Defaults to None. Features will only be computed if the columns used by the feature exist in the input and if the feature itself is not in input. If features is an empty list, no transformation will occur to inputted data.

Attributes

hyperparameter_ranges

{}

modifies_features

True

modifies_target

False

name

DFS Transformer

training_only

False

Methods

clone

Constructs a new component with the same parameters and random state.

contains_pre_existing_features

Determines whether or not features from a DFS Transformer match pipeline input features.

default_parameters

Returns the default parameters for this component.

describe

Describe a component and its parameters.

fit

Fits the DFSTransformer Transformer component.

fit_transform

Fits on X and transforms X.

load

Loads component at file path.

needs_fitting

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

parameters

Returns the parameters which were used to initialize the component.

save

Saves component at file path.

transform

Computes the feature matrix for the input X using featuretools' dfs algorithm.

update_parameters

Updates the parameter dictionary of the component.
clone(self)#

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

static contains_pre_existing_features(dfs_features: Optional[List[featuretools.feature_base.FeatureBase]], input_feature_names: List[str], target: Optional[str] = None)[source]#

Determines whether or not features from a DFS Transformer match pipeline input features.

Parameters

  • dfs_features (Optional[List[FeatureBase]]) – List of features output from a DFS Transformer.

  • input_feature_names (List[str]) – List of input features into the DFS Transformer.

  • target (Optional[str]) – The target whose values we are trying to predict. This is used to know which column to ignore if the target column is present in the list of features in the DFS Transformer’s parameters.

default_parameters(cls)#

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

Default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)#

Describe a component and its parameters.

Parameters

  • print_name (bool, optional) – whether to print name of component

  • return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

fit(self, X, y=None)[source]#

Fits the DFSTransformer Transformer component.

Parameters

  • X (pd.DataFrame, np.array) – The input data to transform, of shape [n_samples, n_features].

  • y (pd.Series) – The target training data of length [n_samples].

Returns

self

fit_transform(self, X, y=None)#

Fits on X and transforms X.

Parameters

  • X (pd.DataFrame) – Data to fit and transform.

  • y (pd.Series) – Target data.

Returns

Transformed X.

Return type

pd.DataFrame

Raises

MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.

static load(file_path)#

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

needs_fitting(self)#

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

Returns

True.

property parameters(self)#

Returns the parameters which were used to initialize the component.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#

Saves component at file path.

Parameters

  • file_path (str) – Location to save file.

  • pickle_protocol (int) – The pickle data stream format.

transform(self, X, y=None)[source]#

Computes the feature matrix for the input X using featuretools’ dfs algorithm.

Parameters

  • X (pd.DataFrame or np.ndarray) – The input training data to transform. Has shape [n_samples, n_features]

  • y (pd.Series, optional) – Ignored.

Returns

Feature matrix

Return type

pd.DataFrame

update_parameters(self, update_dict, reset_fit=True)#

Updates the parameter dictionary of the component.

Parameters

  • update_dict (dict) – A dict of parameters to update.

  • reset_fit (bool, optional) – If True, will set _is_fitted to False.