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LabelEncoder#

class cuml.preprocessing.LabelEncoder( *, handle_unknown='error', verbose=False, output_type=None, )[source]#

Encode target labels with values between 0 and n_classes - 1.

This transformer should be used to encode target values (y) and not the input X.

Parameters:

handle_unknown{‘error’, ‘ignore’}, default=’error’: Whether to raise an error or ignore if an unknown categorical feature is present during transform (default is to raise). When this parameter is set to ‘ignore’ and an unknown category is encountered during transform or inverse transform, the resulting encoding will be null.
verboseint or boolean, default=False: Sets logging level. It must be one of cuml.common.logger.level_*. See Verbosity Levels for more info.
output_type{None, ‘input’, ‘cupy’, ‘numpy’, ‘cudf’, ‘pandas’}, default=None: Return results and set estimator attributes to the indicated output type. If None, the output type set at the module level (cuml.global_settings.output_type) will be used. See Output Data Type Configuration for more info.

Attributes:

classes_numpy.ndarray of shape (n_classes,): Holds the label for each class.

Methods

`fit`(y)	Fit a LabelEncoder instance to a set of categories.
`fit_transform`(y)	Simultaneously fit and transform an input.
`inverse_transform`(y)	Transform labels back to original encoding.
`transform`(y)	Transform an input into its categorical keys.

Examples

>>> import numpy as np
>>> from cuml.preprocessing import LabelEncoder
>>> y = np.array(["apple", "apple", "banana", "grape"])
>>> le = LabelEncoder()
>>> le.fit_transform(y)
array([0, 0, 1, 2], dtype=int8)
>>> le.classes_
array(['apple', 'banana', 'grape'], dtype='<U6')

as_sklearn()[source]#

Convert this estimator into an equivalent scikit-learn (or scikit-learn extension) estimator.

Returns:

sklearn.base.BaseEstimator: A scikit-learn compatible estimator instance that mirrors the trained state of the current estimator.

fit(y)[source]#

Fit a LabelEncoder instance to a set of categories.

Parameters:

yarray-like (device or host) shape = n_samples: Dense matrix with any dtype. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

selfLabelEncoder: Fitted label encoder.

fit_transform(y)[source]#

Simultaneously fit and transform an input.

This is functionally equivalent to (but faster than) LabelEncoder().fit(y).transform(y).

Parameters:

yarray-like (device or host) shape = n_samples: Dense matrix with any dtype. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

ycuDF, CuPy or NumPy object depending on cuML’s output type configuration, shape = n_samples

Encoded labels.

For more information on how to configure cuML’s output type, refer to: Output Data Type Configuration.

classmethod from_sklearn(model)[source]#

Create a cuml estimator from a scikit-learn estimator.

Parameters:

modelsklearn.base.BaseEstimator: A compatible scikit-learn (or scikit-learn extension) estimator.

Returns:

cls: A new instance of this cuml estimator class that mirrors the state of the input estimator.

Notes

output_type of the estimator is set to “numpy” by default, as these cannot be inferred from training arguments. If something different is required, then please use cuml’s output_type configuration utilities.

get_params(deep=True)[source]#: Returns a dict of all params owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is there in this method, then it doesn’t have to override this method

inverse_transform(y)[source]#

Transform labels back to original encoding.

Parameters:

yarray-like (device or host) shape = n_samples: Dense matrix with any dtype. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

y_originalcuDF, CuPy or NumPy object depending on cuML’s output type configuration, shape = n_samples

Original encoding.

For more information on how to configure cuML’s output type, refer to: Output Data Type Configuration.

set_params(**params)[source]#: Accepts a dict of params and updates the corresponding ones owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is, there in this method, then it doesn’t have to override this method

transform(y)[source]#

Transform an input into its categorical keys.

This is intended for use with small inputs relative to the size of the dataset. For fitting and transforming an entire dataset, prefer fit_transform.

Parameters:

yarray-like (device or host) shape = n_samples: Dense matrix with any dtype. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

ycuDF, CuPy or NumPy object depending on cuML’s output type configuration, shape = n_samples

Encoded labels.

For more information on how to configure cuML’s output type, refer to: Output Data Type Configuration.