~~~~~~~~~~~~~~~~~~~ cuML API Reference ~~~~~~~~~~~~~~~~~~~ .. role:: py(code) :language: python :class: highlight Module Configuration ==================== .. _output-data-type-configuration: Output Data Type Configuration ------------------------------ .. autofunction:: cuml.common.memory_utils.set_global_output_type .. autofunction:: cuml.common.memory_utils.using_output_type .. _verbosity-levels: Verbosity Levels ---------------- cuML follows a verbosity model similar to Scikit-learn's: The verbose parameter can be a boolean, or a numeric value, and higher numeric values mean more verbosity. The exact values can be set directly, or through the cuml.common.logger module, and they are: .. list-table:: Verbosity Levels :widths: 25 25 50 :header-rows: 1 * - Numeric value - cuml.common.logger value - Verbosity level * - 0 - cuml.common.logger.level_off - Disables all log messages * - 1 - cuml.common.logger.level_critical - Enables only critical messages * - 2 - cuml.common.logger.level_error - Enables all messages up to and including errors. * - 3 - cuml.common.logger.level_warn - Enables all messages up to and including warnings. * - 4 or False - cuml.common.logger.level_info - Enables all messages up to and including information messages. * - 5 or True - cuml.common.logger.level_debug - Enables all messages up to and including debug messages. * - 6 - cuml.common.logger.level_trace - Enables all messages up to and including trace messages. Preprocessing, Metrics, and Utilities ===================================== Model Selection and Data Splitting ---------------------------------- .. autofunction:: cuml.preprocessing.model_selection.train_test_split Feature and Label Encoding (Single-GPU) --------------------------------------- .. autoclass:: cuml.preprocessing.LabelEncoder.LabelEncoder :members: .. autoclass:: cuml.preprocessing.LabelBinarizer :members: .. autofunction:: cuml.preprocessing.label_binarize .. autoclass:: cuml.preprocessing.OneHotEncoder :members: .. autoclass:: cuml.preprocessing.TargetEncoder.TargetEncoder :members: Text Preprocessing (Single-GPU) --------------------------------------- .. autoclass:: cuml.preprocessing.text.stem.PorterStemmer :members: Feature and Label Encoding (Dask-based Multi-GPU) ------------------------------------------------- .. autoclass:: cuml.dask.preprocessing.LabelBinarizer :members: .. autoclass:: cuml.dask.preprocessing.OneHotEncoder :members: Feature Extraction (Single-GPU) ------------------------------- .. autoclass:: cuml.feature_extraction.text.CountVectorizer :members: .. autoclass:: cuml.feature_extraction.text.HashingVectorizer :members: .. autoclass:: cuml.feature_extraction.text.TfidfVectorizer :members: Feature Extraction (Dask-based Multi-GPU) ----------------------------------------- .. autoclass:: cuml.dask.feature_extraction.text.TfidfTransformer :members: Dataset Generation (Single-GPU) ------------------------------- .. glossary:: random_state Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. .. autofunction:: cuml.datasets.make_blobs .. autofunction:: cuml.datasets.make_classification .. autofunction:: cuml.datasets.make_regression .. autofunction:: cuml.datasets.make_arima Dataset Generation (Dask-based Multi-GPU) ----------------------------------------- .. automodule:: cuml.dask.datasets.blobs :members: .. automodule:: cuml.dask.datasets.classification :members: .. automodule:: cuml.dask.datasets.regression :members: Array Wrappers (Internal API) ----------------------------- .. autoclass:: cuml.common.CumlArray :members: Metrics (regression, classification, and distance) --------------------------------------------------- .. automodule:: cuml.metrics.regression :members: .. automodule:: cuml.metrics.accuracy :members: .. autofunction:: cuml.metrics.confusion_matrix .. autofunction:: cuml.metrics.log_loss .. autofunction:: cuml.metrics.roc_auc_score .. autofunction:: cuml.metrics.precision_recall_curve .. automodule:: cuml.metrics.pairwise_distances :members: Metrics (clustering and trustworthiness) ---------------------------------------- .. automodule:: cuml.metrics.trustworthiness :members: .. automodule:: cuml.metrics.cluster.adjusted_rand_index :members: .. automodule:: cuml.metrics.cluster.entropy :members: .. automodule:: cuml.metrics.cluster.homogeneity_score :members: .. automodule:: cuml.metrics.cluster.silhouette_score :members: .. automodule:: cuml.metrics.cluster.completeness_score :members: .. automodule:: cuml.metrics.cluster.mutual_info_score :members: Benchmarking ------------- .. automodule:: cuml.benchmark.algorithms :members: .. automodule:: cuml.benchmark.runners :members: .. automodule:: cuml.benchmark.datagen :members: Regression and Classification ============================= Linear Regression ----------------- .. autoclass:: cuml.LinearRegression :members: Logistic Regression ------------------- .. autoclass:: cuml.LogisticRegression :members: Ridge Regression ---------------- .. autoclass:: cuml.Ridge :members: Lasso Regression ---------------- .. autoclass:: cuml.Lasso :members: ElasticNet Regression --------------------- .. autoclass:: cuml.ElasticNet :members: Mini Batch SGD Classifier ------------------------- .. autoclass:: cuml.MBSGDClassifier :members: Mini Batch SGD Regressor ------------------------ .. autoclass:: cuml.MBSGDRegressor :members: Multiclass Classification ------------------------- .. autoclass:: cuml.multiclass.MulticlassClassifier :members: .. autoclass:: cuml.multiclass.OneVsOneClassifier :members: .. autoclass:: cuml.multiclass.OneVsRestClassifier :members: Mutinomial Naive Bayes ---------------------- .. autoclass:: cuml.MultinomialNB :members: Stochastic Gradient Descent --------------------------- .. autoclass:: cuml.SGD :members: Random Forest ------------- .. autoclass:: cuml.ensemble.RandomForestClassifier :members: .. autoclass:: cuml.ensemble.RandomForestRegressor :members: Forest Inferencing ------------------ .. autoclass:: cuml.ForestInference :members: Coordinate Descent ------------------ .. autoclass:: cuml.CD :members: Quasi-Newton ------------ .. autoclass:: cuml.QN :members: Support Vector Machines ------------------------ .. autoclass:: cuml.svm.SVC :members: .. autoclass:: cuml.svm.SVR :members: Nearest Neighbors Classification -------------------------------- .. autoclass:: cuml.neighbors.KNeighborsClassifier :members: :noindex: Nearest Neighbors Regression ---------------------------- .. autoclass:: cuml.neighbors.KNeighborsRegressor :members: :noindex: Clustering ========== K-Means Clustering -------------------- .. autoclass:: cuml.KMeans :members: DBSCAN ------- .. autoclass:: cuml.DBSCAN :members: Agglomerative Clustering ------------------------ .. autoclass:: cuml.AgglomerativeClustering :members: Dimensionality Reduction and Manifold Learning ============================================== Principal Component Analysis ----------------------------- .. autoclass:: cuml.PCA :members: Incremental PCA --------------- .. autoclass:: cuml.IncrementalPCA :members: Truncated SVD -------------- .. autoclass:: cuml.TruncatedSVD :members: UMAP ------------- .. autoclass:: cuml.UMAP :members: Random Projections ------------------ .. autoclass:: cuml.random_projection.GaussianRandomProjection :members: .. autoclass:: cuml.random_projection.SparseRandomProjection :members: .. autofunction:: cuml.random_projection.johnson_lindenstrauss_min_dim TSNE ------------- .. autoclass:: cuml.TSNE :members: Neighbors ========== Nearest Neighbors ----------------- .. autoclass:: cuml.neighbors.NearestNeighbors :members: Nearest Neighbors Classification -------------------------------- .. autoclass:: cuml.neighbors.KNeighborsClassifier :members: Nearest Neighbors Regression -------------------------------- .. autoclass:: cuml.neighbors.KNeighborsRegressor :members: Time Series ============ HoltWinters ------------- .. autoclass:: cuml.ExponentialSmoothing :members: ARIMA ----- .. autoclass:: cuml.tsa.ARIMA :members: .. autoclass:: cuml.tsa.auto_arima.AutoARIMA :members: Model Explainability ==================== SHAP Kernel Explainer --------------------- .. autoclass:: cuml.explainer.KernelExplainer :members: SHAP Permutation Explainer -------------------------- .. autoclass:: cuml.explainer.PermutationExplainer :members: Multi-Node, Multi-GPU Algorithms ================================ DBSCAN Clustering -------------------- .. autoclass:: cuml.dask.cluster.DBSCAN :members: K-Means Clustering -------------------- .. autoclass:: cuml.dask.cluster.KMeans :members: Nearest Neighbors ----------------- .. autoclass:: cuml.dask.neighbors.NearestNeighbors :members: .. autoclass:: cuml.dask.neighbors.KNeighborsRegressor :members: .. autoclass:: cuml.dask.neighbors.KNeighborsClassifier :members: Principal Component Analysis ----------------------------- .. autoclass:: cuml.dask.decomposition.PCA :members: Random Forest ------------- .. autoclass:: cuml.dask.ensemble.RandomForestClassifier :members: .. autoclass:: cuml.dask.ensemble.RandomForestRegressor :members: Truncated SVD -------------- .. autoclass:: cuml.dask.decomposition.TruncatedSVD :members: Manifold -------- .. autoclass:: cuml.dask.manifold.UMAP :members: Linear Models ------------- .. autoclass:: cuml.dask.linear_model.LinearRegression :members: .. autoclass:: cuml.dask.linear_model.Ridge :members: .. autoclass:: cuml.dask.linear_model.Lasso :members: .. autoclass:: cuml.dask.linear_model.ElasticNet :members: Naive Bayes ----------- .. autoclass:: cuml.dask.naive_bayes.MultinomialNB :members: Solvers ------- .. autoclass:: cuml.dask.solvers.CD :members: Dask Base Classes and Mixins ---------------------------- .. autoclass:: cuml.dask.common.base.BaseEstimator :members: .. autoclass:: cuml.dask.common.base.DelayedParallelFunc :members: .. autoclass:: cuml.dask.common.base.DelayedPredictionMixin :members: .. autoclass:: cuml.dask.common.base.DelayedTransformMixin :members: .. autoclass:: cuml.dask.common.base.DelayedInverseTransformMixin :members: Experimental ============ .. warning:: The `cuml.experimental` module contains features that are still under development. It is not recommended to depend on features in this module as they may change in future releases. .. note:: Due to the nature of this module, it is not imported by default by the root `cuml` package. Each `experimental` submodule must be imported separately. Preprocessing ------------- .. automodule:: cuml.experimental.preprocessing :members: Binarizer, KBinsDiscretizer, MaxAbsScaler, MinMaxScaler, Normalizer, RobustScaler, SimpleImputer, StandardScaler, add_dummy_feature, binarize, minmax_scale, normalize, PolynomialFeatures, robust_scale, scale Linear Models ------------- .. autoclass:: cuml.experimental.linear_model.Lars :members: