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	No limitations

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 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 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.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.