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.