label_encoder¶
A transformer that encodes target labels using values between 0 and num_classes - 1.
Module Contents¶
Classes Summary¶
A transformer that encodes target labels using values between 0 and num_classes - 1. |
Contents¶
-
class
evalml.pipelines.components.transformers.encoders.label_encoder.LabelEncoder(random_seed=0, **kwargs)[source]¶ A transformer that encodes target labels using values between 0 and num_classes - 1.
- Parameters
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
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the label encoder.
Fit and transform data using the label encoder.
Decodes the target data.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
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.