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.