label_encoder#

A transformer that encodes target labels using values between 0 and num_classes - 1.

Module Contents#

Classes Summary#

LabelEncoder

A transformer that encodes target labels using values between 0 and num_classes - 1.

Contents#

class evalml.pipelines.components.transformers.encoders.label_encoder.LabelEncoder(positive_label=None, random_seed=0, **kwargs)[source]#

A transformer that encodes target labels using values between 0 and num_classes - 1.

Parameters
  • positive_label (int, str) – The label for the class that should be treated as positive (1) for binary classification problems. Ignored for multiclass problems. Defaults to None.

  • random_seed (int) – Seed for the random number generator. Defaults to 0. Ignored.

Attributes

hyperparameter_ranges

{}

modifies_features

False

modifies_target

True

name

Label Encoder

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 label encoder.

fit_transform

Fit and transform data using the label encoder.

inverse_transform

Decodes the target data.

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

Transform the target using the fitted label encoder.

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)[source]#

Fits the label encoder.

Parameters
  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Ignored.

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

Returns

self

Raises

ValueError – If input y is None.

fit_transform(self, X, y)[source]#

Fit and transform data using the label encoder.

Parameters
  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features].

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

Returns

The original features and an encoded version of the target.

Return type

pd.DataFrame, pd.Series

inverse_transform(self, y)[source]#

Decodes the target data.

Parameters

y (pd.Series) – Target data.

Returns

The decoded version of the target.

Return type

pd.Series

Raises

ValueError – If input y is None.

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]#

Transform the target using the fitted label encoder.

Parameters
  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Ignored.

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

Returns

The original features and an encoded version of the target.

Return type

pd.DataFrame, pd.Series

Raises

ValueError – If input y is None.