Supported Algorithms

Note

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

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

on CPU

Classification

Algorithm	Parameters	Data formats
sklearn.svm.SVC	kernel must be one of ["linear", "rbf", "poly", "sigmoid"]	No limitations
sklearn.svm.NuSVC	kernel must be one of ["linear", "rbf", "poly", "sigmoid"]	No limitations
sklearn.ensemble.RandomForestClassifier	All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != 'gini' n_estimators > 6024	Multi-output and sparse data are not supported. Number of classes must be at least 2.
sklearn.ensemble.ExtraTreesClassifier	All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != 'gini' n_estimators > 6024	Multi-output and sparse data are not supported. Number of classes must be at least 2.
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. Number of classes must be at least 2.
sklearn.linear_model.LogisticRegression	All parameters are supported except: solver not in ['lbfgs', 'newton-cg'] l1_ratio != 0 dual = True sample_weight != None class_weight != None Solver 'newton-cg' with fit_intercept = False is not supported	Sparse data is not supported.

Regression

Algorithm	Parameters	Data formats
sklearn.svm.SVR	kernel must be one of ["linear", "rbf", "poly", "sigmoid"]	No limitations
sklearn.svm.NuSVR	kernel must be one of ["linear", "rbf", "poly", "sigmoid"]	No limitations
sklearn.ensemble.RandomForestRegressor	All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != 'squared_error' n_estimators > 6024	Multi-output and sparse data are not supported
sklearn.ensemble.ExtraTreesRegressor	All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != 'squared_error' n_estimators > 6024	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 (this is supported through the class sklearn.linear_model.ElasticNet)	Only dense data is supported.
sklearn.linear_model.Ridge	All parameters are supported except: solver != 'auto' sample_weight != None positive = True (this is supported through the class sklearn.linear_model.ElasticNet) alpha must be a scalar	Only dense data is supported.
sklearn.linear_model.ElasticNet	All parameters are supported except: sample_weight != None	Sparse data is not supported.
sklearn.linear_model.Lasso	All parameters are supported except: sample_weight != None	Sparse data is not 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 verbose = True will only print results from the last iteration, and will only print inertia numbers, not ‘convergence achieved’ messages.	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 method != "barnes_hut" Refer to TSNE acceleration details to learn more.	Sparse data with init = "pca" 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

GPU support

Classification

Algorithm	Parameters	Data formats
sklearn.svm.SVC	All parameters are supported except: kernel not in ["linear", "rbf"] 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 n_estimators > 6024	Multi-output and sparse data are not supported. Number of classes must be at least 2.
sklearn.ensemble.ExtraTreesClassifier	All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != 'gini' oob_score = True sample_weight != None n_estimators > 6024	Multi-output and sparse data are not supported. Number of classes must be at least 2.
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. Number of classes must be at least 2.
sklearn.linear_model.LogisticRegression	All parameters are supported except: solver != 'newton-cg' class_weight != None sample_weight != None dual = True intercept_scaling != 1 warm_start = True l1_ratio != 0 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 != 'squared_error' oob_score = True sample_weight != None n_estimators > 6024	Multi-output and sparse data are not supported
sklearn.ensemble.ExtraTreesRegressor	All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != 'squared_error' oob_score = True sample_weight != None n_estimators > 6024	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 verbose = True will only print results from the last iteration, and will only print inertia numbers, not ‘convergence achieved’ messages.	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 n_estimators > 6024	Multi-output and sparse data are not supported. Number of classes must be at least 2.
sklearn.ensemble.ExtraTreesClassifier	All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != 'gini' oob_score = True sample_weight != None n_estimators > 6024	Multi-output and sparse data are not supported. Number of classes must be at least 2.
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. Number of classes must be at least 2.
sklearn.linear_model.LogisticRegression	All parameters are supported except: solver != 'newton-cg' class_weight != None sample_weight != None dual = True intercept_scaling != 1 warm_start = True l1_ratio != 0 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 != 'squared_error' oob_score = True sample_weight != None n_estimators > 6024	Multi-output and sparse data are not supported
sklearn.ensemble.ExtraTreesRegressor	All parameters are supported except: warm_start = True ccp_alpha != 0 criterion != 'squared_error' oob_score = True sample_weight != None n_estimators > 6024	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 verbose = True will only print results from the last iteration, and will only print inertia numbers, not ‘convergence achieved’ messages.	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.