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)

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.

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.
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.

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.

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)

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)

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