OneVsOneClassifier#

class cuml.multiclass.OneVsOneClassifier(estimator, *, verbose=False, output_type=None)[source]#

One-vs-one multiclass classifier using device-resident multiclass orchestration. The input can be any kind of cuML compatible array, and the output type follows cuML’s output type configuration rules.

Each pairwise binary problem is constructed on the device and the resulting votes and confidence values are combined with CuPy, avoiding unnecessary device-to-host transfers.

For documentation see scikit-learn’s OneVsOneClassifier.

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{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.

Methods

`decision_function`(X)	Combine pairwise votes with normalized confidence.
`fit`(X, y[, sample_weight])	Fit one binary estimator per class pair on device.
`predict`(X)	Predict labels from pairwise votes and confidence.

Examples

>>> from cuml.linear_model import LogisticRegression
>>> from cuml.multiclass import OneVsOneClassifier
>>> 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 = OneVsOneClassifier(LogisticRegression())
>>> cls.fit(X, y)
OneVsOneClassifier(estimator=LogisticRegression())
>>> cls.predict(X)
array([1, 1, 0, 1, 1, 1, 2, 2, 1, 2])

decision_function(X)[source]#

Combine pairwise votes with normalized confidence.

Only available if estimator implements decision_function.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features): Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

resultscuDF, CuPy or NumPy object depending on cuML’s output type configuration, shape = (n_samples, 1)

Decision function values

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

fit( X, y, sample_weight=None, ) → OneVsOneClassifier[source]#

Fit one binary estimator per class pair on device.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features): Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.
yarray-like (device or host) shape = (n_samples, 1): Dense matrix with any dtype. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.
sample_weightarray-like (device or host) shape = (n_samples,), default=None: The weights for each observation in X. If None, all observations are assigned equal weight. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

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

predict(X)[source]#

Predict labels from pairwise votes and confidence.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features): Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

predscuDF, CuPy or NumPy object depending on cuML’s output type configuration, shape = (n_samples, 1)

Predicted values

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

score(X, y, sample_weight=None, **kwargs)[source]#

Scoring function for classifier estimators based on mean accuracy.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features): Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.
yarray-like (device or host) shape = (n_samples, 1): Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.
sample_weightarray-like (device or host) shape = (n_samples,), default=None: The weights for each observation in X. If None, all observations are assigned equal weight. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.

Returns:

scorefloat: Accuracy of self.predict(X) wrt. y (fraction where y == pred_y)

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