Module hummingbird.ml.operator_converters.sklearn
All scikit-learn operators converters are stored under this package.
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# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
All scikit-learn operators converters are stored under this package.
"""
Sub-modules
hummingbird.ml.operator_converters.sklearn.array_feature_extractor
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Converters for scikit-learn feature selectors: SelectKBest, SelectPercentile, VarianceThreshold.
hummingbird.ml.operator_converters.sklearn.decision_tree
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Converters for scikit-learn decision-tree-based models: DecisionTree, RandomForest and ExtraTrees.
hummingbird.ml.operator_converters.sklearn.decomposition
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Converters for scikit-learn matrix decomposition transformers: PCA, KernelPCA, TruncatedSVD, FastICA.
hummingbird.ml.operator_converters.sklearn.discretizer
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Converter for scikit-learn discretizers: Binarizer and KBinsDiscretizer.
hummingbird.ml.operator_converters.sklearn.gbdt
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Converters for Sklearn's GradientBoosting models.
hummingbird.ml.operator_converters.sklearn.iforest
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Converters for scikit-learn isolation forest.
hummingbird.ml.operator_converters.sklearn.imputer
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Converter for scikit-learn Imputers: SimpleImputer and MissingIndicator
hummingbird.ml.operator_converters.sklearn.linear
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Converters for scikit-learn linear models: LinearRegression, LogisticRegression, LinearSVC, SGDClassifier, LogisticRegressionCV.
hummingbird.ml.operator_converters.sklearn.mlp
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Converters for scikit-learn MLP models: MLPClassifier, MLPRegressor
hummingbird.ml.operator_converters.sklearn.nb
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Converters for scikit-learn Naive Bayes models: BernoulliNB, GaussianNB, MultinomialNB
hummingbird.ml.operator_converters.sklearn.normalizer
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Converter for scikit-learn Normalizer.
hummingbird.ml.operator_converters.sklearn.one_hot_encoder
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Converter for scikit-learn one hot encoder.
hummingbird.ml.operator_converters.sklearn.pipeline
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Converters for operators necessary for supporting scikit-learn Pipelines.
hummingbird.ml.operator_converters.sklearn.poly_features
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Converter for scikit-learn PolynomialFeatures.
hummingbird.ml.operator_converters.sklearn.scaler
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Converters for scikit-learn scalers: RobustScaler, MaxAbsScaler, MinMaxScaler, StandardScaler.
hummingbird.ml.operator_converters.sklearn.sv
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Converters for scikit-learn SV models: SVC, NuSVC. (LinearSVC is covered by linear_classifier.py).