Supported Algorithms

Applying Intel® Extension for Scikit-learn* impacts the following scikit-learn algorithms:

on CPU

Classification

Algorithm

Parameters

Data formats

SVC

All parameters are supported

No limitations

NuSVC

All parameters are supported

No limitations

RandomForestClassifier

All parameters are supported except:

  • warm_start = True

  • cpp_alpha != 0

  • criterion != ‘gini’

Multi-output and sparse data are not supported

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

LogisticRegression

All parameters are supported except:

  • solver not in [‘lbfgs’, ‘newton-cg’]

  • class_weight != None

  • sample_weight != None

Only dense data is supported

Regression

Algorithm

Parameters

Data formats

SVR

All parameters are supported

No limitations

NuSVR

All parameters are supported

No limitations

RandomForestRegressor

All parameters are supported except:

  • warm_start = True

  • cpp_alpha != 0

  • criterion != ‘mse’

Multi-output and sparse data are not supported

KNeighborsRegressor

All parameters are supported except:

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

Multi-output and sparse data are not supported

LinearRegression

All parameters are supported except:

  • normalize != False

  • sample_weight != None

Only dense data is supported, #observations should be >= #features.

Ridge

All parameters are supported except:

  • normalize != False

  • solver != ‘auto’

  • sample_weight != None

Only dense data is supported, #observations should be >= #features.

ElasticNet

All parameters are supported except:

  • sample_weight != None

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

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

KMeans

All parameters are supported except:

  • precompute_distances

  • sample_weight != None

No limitations

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

PCA

All parameters are supported except:

  • svd_solver not in [‘full’, ‘covariance_eigh’]

Sparse data is not supported

TSNE

All parameters are supported except:

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

Refer to TSNE acceleration details to learn more.

Sparse data is not supported

Nearest Neighbors

Algorithm

Parameters

Data formats

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

train_test_split

All parameters are supported

Only dense data is supported

assert_all_finite

All parameters are supported

Only dense data is supported

pairwise_distance

All parameters are supported except:

  • metric not in [‘cosine’, ‘correlation’]

Only dense data is supported

roc_auc_score

All parameters are supported except:

  • average != None

  • sample_weight != None

  • max_fpr != None

  • multi_class != None

No limitations

on GPU

Classification

Algorithm

Parameters

Data formats

SVC

All parameters are supported except:

  • kernel = ‘sigmoid_poly’

  • class_weight != None

Only binary dense data is supported

RandomForestClassifier

All parameters are supported except:

  • warm_start = True

  • cpp_alpha != 0

  • criterion != ‘gini’

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

KNeighborsClassifier

All parameters are supported except:

  • algorithm != ‘brute’

  • weights = ‘callable’

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

Only dense data is supported

LogisticRegression

All parameters are supported except:

  • solver != ‘newton-cg’

  • class_weight != None

  • sample_weight != None

  • penalty != ‘l2’

Only dense data is supported

Regression

Algorithm

Parameters

Data formats

RandomForestRegressor

All parameters are supported except:

  • warm_start = True

  • cpp_alpha != 0

  • criterion != ‘mse’

  • oob_score = True

  • sample_weight != None

Multi-output and sparse data are not supported

KNeighborsRegressor

All parameters are supported except:

  • algorithm != ‘brute’

  • weights = ‘callable’

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

Only dense data is supported

LinearRegression

All parameters are supported except:

  • normalize != False

  • sample_weight != None

Only dense data is supported, #observations should be >= #features.

Clustering

Algorithm

Parameters

Data formats

KMeans

All parameters are supported except:

  • precompute_distances

  • sample_weight != None

Init = ‘k-means++’ fallbacks to CPU.

Sparse data is not supported

DBSCAN

All parameters are supported except:

  • metric != ‘euclidean’

  • algorithm not in [‘brute’, ‘auto’]

Only dense data is supported

Dimensionality reduction

Algorithm

Parameters

Data formats

PCA

All parameters are supported except:

  • svd_solver not in [‘full’, ‘covariance_eigh’]

Sparse data is not supported

Nearest Neighbors

Algorithm

Parameters

Data formats

NearestNeighbors

All parameters are supported except:

  • algorithm != ‘brute’

  • weights = ‘callable’

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

Only dense data is supported

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.

The results of the entire latest scikit-learn test suite with Intel® Extension for Scikit-learn*: CircleCI.