Supported Algorithms

Note

To verify that oneDAL is being used for these algorithms, you can enable verbose mode. See verbose mode documentation for details.

Applying Extension for Scikit-learn* impacts the following scikit-learn estimators:

on CPU

Classification

Algorithm

Parameters

Data formats

sklearn.svm.SVC

All parameters are supported

No limitations

sklearn.svm.NuSVC

All parameters are supported

No limitations

sklearn.ensemble.RandomForestClassifier

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'gini'

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesClassifier

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'gini'

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsClassifier

  • For algorithm == 'kd_tree':

    all parameters except metric != 'euclidean' or 'minkowski' with p != 2

  • For algorithm == 'brute':

    all parameters except metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Multi-output and sparse data are not supported

sklearn.linear_model.LogisticRegression

All parameters are supported except:

  • solver not in ['lbfgs', 'newton-cg']

  • penalty in ['l1', 'elasticnet']

  • sample_weight != None

  • class_weight != None

Sparse data is not supported.

Regression

Algorithm

Parameters

Data formats

sklearn.svm.SVR

All parameters are supported

No limitations

sklearn.svm.NuSVR

All parameters are supported

No limitations

sklearn.ensemble.RandomForestRegressor

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'mse'

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesRegressor

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'mse'

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsRegressor

All parameters are supported except:

  • metric != 'euclidean' or 'minkowski' with p != 2

Multi-output and sparse data are not supported

sklearn.linear_model.LinearRegression

All parameters are supported except:

  • sample_weight != None

  • positive = True

Only dense data is supported.

sklearn.linear_model.Ridge

All parameters are supported except:

  • solver != 'auto'

  • sample_weight != None

  • positive = True

  • alpha must be a scalar

Only dense data is supported.

sklearn.linear_model.ElasticNet

All parameters are supported except:

  • sample_weight != None

Multi-output and sparse data are not supported, #observations should be >= #features.

sklearn.linear_model.Lasso

All parameters are supported except:

  • sample_weight != None

Multi-output and sparse data are not supported, #observations should be >= #features.

Clustering

Algorithm

Parameters

Data formats

sklearn.cluster.KMeans

All parameters are supported except:

  • algorithm != 'lloyd' (‘elkan’ falls back to ‘lloyd’)

  • n_clusters = 1

  • sample_weight must be None, constant, or equal weights

  • init = 'k-means++' falls back to CPU

No limitations

sklearn.cluster.DBSCAN

All parameters are supported except:

  • metric != 'euclidean' or 'minkowski' with p != 2

  • algorithm not in ['brute', 'auto']

Only dense data is supported

Dimensionality Reduction

Algorithm

Parameters

Data formats

sklearn.decomposition.PCA

All parameters are supported except:

  • svd_solver not in ['full', 'covariance_eigh', 'onedal_svd']

  • For scikit-learn < 1.5: 'full' solver is automatically mapped to 'covariance_eigh'

Sparse data is not supported

sklearn.manifold.TSNE

All parameters are supported except:

  • metric != ‘euclidean’ or 'minkowski' with p != 2

  • n_components can only be 2

Refer to TSNE acceleration details to learn more.

Sparse data is not supported

Nearest Neighbors

Algorithm

Parameters

Data formats

sklearn.neighbors.NearestNeighbors

  • For algorithm == ‘kd_tree’:

    all parameters except metric != 'euclidean' or 'minkowski' with p != 2

  • For algorithm == ‘brute’:

    all parameters except metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Sparse data is not supported

Other Tasks

Algorithm

Parameters

Data formats

sklearn.covariance.EmpiricalCovariance

All parameters are supported

Only dense data is supported

sklearnex.basic_statistics.BasicStatistics

All parameters are supported

Supported data formats:

  • Dense data

  • CSR sparse matrices

  • Sample weights not supported for CSR data format

sklearn.model_selection.train_test_split

All parameters are supported

Supported data formats:

  • Only dense data is supported

  • Only integer and 32/64-bits floating point types are supported

  • Data with more than 3 dimensions is not supported

  • Only np.ndarray inputs are supported.

sklearn.utils.assert_all_finite

All parameters are supported

Only dense data is supported

sklearn.metrics.pairwise_distance

All parameters are supported except:

  • metric not in ['cosine', 'correlation']

Supported data formats:

  • Only dense data is supported

  • Y must be None

  • Input dtype must be np.float64

sklearn.metrics.roc_auc_score

All parameters are supported except:

  • average != None

  • sample_weight != None

  • max_fpr != None

  • multi_class != None

No limitations

on GPU

See also

oneAPI and GPU support in Extension for Scikit-learn*

Classification

Algorithm

Parameters

Data formats

sklearn.svm.SVC

All parameters are supported except:

  • kernel = 'sigmoid_poly'

  • class_weight != None

Only binary dense data is supported

sklearn.ensemble.RandomForestClassifier

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'gini'

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesClassifier

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'gini'

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsClassifier

All parameters are supported except:

  • algorithm != 'brute'

  • weights = 'callable'

  • metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported

sklearn.linear_model.LogisticRegression

All parameters are supported except:

  • solver != 'newton-cg'

  • class_weight != None

  • sample_weight != None

  • penalty != 'l2'

  • dual = True

  • intercept_scaling != 1

  • multi_class = 'multinomial'

  • warm_start = True

  • l1_ratio != 0 and l1_ratio != None

  • Only binary classification is supported

No limitations

Regression

Algorithm

Parameters

Data formats

sklearn.ensemble.RandomForestRegressor

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'mse'

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesRegressor

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'mse'

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsRegressor

All parameters are supported except:

  • algorithm != 'brute'

  • weights = 'callable'

  • metric != 'euclidean' or 'minkowski' with p != 2

Only dense data is supported

sklearn.linear_model.Ridge

All parameters are supported except:

  • solver != 'auto'

  • sample_weight != None

  • positive = True

  • alpha must be a scalar

Only dense data is supported.

sklearn.linear_model.LinearRegression

All parameters are supported except:

  • sample_weight != None

  • positive = True

Only dense data is supported.

Clustering

Algorithm

Parameters

Data formats

sklearn.cluster.KMeans

All parameters are supported except:

  • algorithm != 'lloyd' (‘elkan’ falls back to ‘lloyd’)

  • n_clusters = 1

  • sample_weight must be None, constant, or equal weights

  • init = 'k-means++' falls back to CPU

No limitations

sklearn.cluster.DBSCAN

All parameters are supported except:

  • metric != 'euclidean'

  • algorithm not in ['brute', 'auto']

Only dense data is supported

Dimensionality Reduction

Algorithm

Parameters

Data formats

sklearn.decomposition.PCA

All parameters are supported except:

  • svd_solver not in ['full', 'covariance_eigh', 'onedal_svd']

  • For scikit-learn < 1.5: 'full' solver is automatically mapped to 'covariance_eigh'

Sparse data is not supported

Nearest Neighbors

Algorithm

Parameters

Data formats

sklearn.neighbors.NearestNeighbors

All parameters are supported except:

  • algorithm != 'brute'

  • weights = 'callable'

  • metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported

Other Tasks

Algorithm

Parameters

Data formats

sklearn.covariance.EmpiricalCovariance

All parameters are supported

Only dense data is supported

sklearnex.basic_statistics.BasicStatistics

All parameters are supported

Supported data formats:

  • Dense data

  • CSR sparse matrices

  • Sample weights not supported for CSR data format

SPMD Support

See also

SPMD (multi-GPU distributed mode)

Classification

Algorithm

Parameters & Methods

Data formats

sklearn.ensemble.RandomForestClassifier

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'gini'

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesClassifier

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'gini'

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsClassifier

All parameters are supported except:

  • algorithm != 'brute'

  • weights = 'callable'

  • metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

  • predict_proba method not supported

Only dense data is supported

sklearn.linear_model.LogisticRegression

All parameters are supported except:

  • solver != 'newton-cg'

  • class_weight != None

  • sample_weight != None

  • penalty != 'l2'

  • dual = True

  • intercept_scaling != 1

  • multi_class != 'multinomial'

  • warm_start = True

  • l1_ratio != None

  • Only binary classification is supported

No limitations

Regression

Algorithm

Parameters & Methods

Data formats

sklearn.ensemble.RandomForestRegressor

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'mse'

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

sklearn.ensemble.ExtraTreesRegressor

All parameters are supported except:

  • warm_start = True

  • ccp_alpha != 0

  • criterion != 'mse'

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

sklearn.neighbors.KNeighborsRegressor

All parameters are supported except:

  • algorithm != 'brute'

  • weights = 'callable'

  • metric != 'euclidean' or 'minkowski' with p != 2

Only dense data is supported

sklearn.linear_model.LinearRegression

All parameters are supported except:

  • sample_weight != None

  • positive = True

Only dense data is supported.

Clustering

Algorithm

Parameters & Methods

Data formats

sklearn.cluster.KMeans

All parameters are supported except:

  • algorithm != 'lloyd' (‘elkan’ falls back to ‘lloyd’)

  • n_clusters = 1

  • sample_weight must be None, constant, or equal weights

  • init = 'k-means++' falls back to CPU

No limitations

sklearn.cluster.DBSCAN

All parameters are supported except:

  • metric != 'euclidean'

  • algorithm not in ['brute', 'auto']

Only dense data is supported

Dimensionality Reduction

Algorithm

Parameters & Methods

Data formats

sklearn.decomposition.PCA

All parameters are supported except:

  • svd_solver not in ['full', 'covariance_eigh', 'onedal_svd']

  • For scikit-learn < 1.5: 'full' solver is automatically mapped to 'covariance_eigh'

Sparse data is not supported

Nearest Neighbors

Algorithm

Parameters

Data formats

sklearn.neighbors.NearestNeighbors

All parameters are supported except:

  • algorithm != 'brute'

  • weights = 'callable'

  • metric not in ['euclidean', 'manhattan', 'minkowski', 'chebyshev', 'cosine']

Only dense data is supported

Other Tasks

Algorithm

Parameters

Data formats

sklearn.covariance.EmpiricalCovariance

All parameters are supported

Only dense data is supported

sklearnex.basic_statistics.BasicStatistics

All parameters are supported

Supported data formats:

  • Dense data

  • CSR sparse matrices

  • Sample weights not supported for CSR data format

Scikit-learn Tests

Monkey-patched scikit-learn classes and functions passes scikit-learn’s own test suite, with few exceptions, specified in deselected_tests.yaml.

See the file scikit-learn-tests.md for instructions about how to execute the scikit-learn test suite under patching.