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.

encoders

onehot_encoder