OneVsRestClassifier

class cuml.multiclass.OneVsRestClassifier(estimator, *, verbose=False, output_type=None)

Wrapper around Sckit-learn’s class with the same name. The input can be any kind of cuML compatible array, and the output type follows cuML’s output type configuration rules.

Before passing the data to scikit-learn, it is converted to host (numpy) array. Under the hood the data is partitioned for binary classification, and it is transformed back to the device by the cuML estimator. These copies back and forth the device and the host have some overhead. For more details see issue rapidsai/cuml#2876.

For documentation see scikit-learn’s OneVsRestClassifier.

Parameters:

estimatorcuML estimator

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{‘input’, ‘array’, ‘dataframe’, ‘series’, ‘df_obj’, ‘numba’, ‘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.

Examples

>>> from cuml.linear_model import LogisticRegression
>>> from cuml.multiclass import OneVsRestClassifier
>>> from cuml.datasets.classification import make_classification

>>> X, y = make_classification(n_samples=10, n_features=6,
...                            n_informative=4, n_classes=3,
...                            random_state=137)

>>> cls = OneVsRestClassifier(LogisticRegression())
>>> cls.fit(X, y)
OneVsRestClassifier(estimator=LogisticRegression())
>>> cls.predict(X)
array([1, 1, 0, 1, 1, 1, 2, 2, 1, 2])