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- Algorithms
-
-
-Note
-Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, Intel(R) Extension for Scikit-learn*. -All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.
-Classification
-See also Intel(R) oneAPI Data Analytics Library Classification.
-Decision Forest Classification
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Classification Decision Forest.
-Examples:
--
-
Single-Process Decision Forest Classification Default Dense method
-Single-Process Decision Forest Classification Histogram method
-
-
-
- -class daal4py.decision_forest_classification_training -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for Decision forest, double or float
-method (str) – [optional, default: “defaultDense”] Decision forest computation method
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-nTrees (size_t) – [optional, default: -1] Number of trees in the forest. Default is 10
-observationsPerTreeFraction (double) – [optional, default: get_nan64()] Fraction of observations used for a training of one tree, 0 to 1. Default is 1 (sampling with replacement)
-featuresPerNode (size_t) – [optional, default: -1] Number of features tried as possible splits per node. If 0 then sqrt(p) for classification, p/3 for regression, where p is the total number of features.
-maxTreeDepth (size_t) – [optional, default: -1] Maximal tree depth. Default is 0 (unlimited)
-minObservationsInLeafNode (size_t) – [optional, default: -1] Minimal number of observations in a leaf node. Default is 1 for classification, 5 for regression.
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for the random numbers generator used by the algorithms
-impurityThreshold (double) – [optional, default: get_nan64()] Threshold value used as stopping criteria: if the impurity value in the node is smaller than the threshold then the node is not split anymore.
-varImportance (str) – [optional, default: “”] Variable importance computation mode
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-memorySavingMode (bool) – [optional, default: False] If true then use memory saving (but slower) mode
-bootstrap (bool) – [optional, default: False] If true then training set for a tree is a bootstrap of the whole training set
-minObservationsInSplitNode (size_t) – [optional, default: -1] Minimal number of observations in a split node. Default 2
-minWeightFractionInLeafNode (double) – [optional, default: get_nan64()] The minimum weighted fraction of the sum total of weights (of all the input observations) required to be at a leaf node, 0.0 to 0.5. Default is 0.0
-minImpurityDecreaseInSplitNode (double) – [optional, default: get_nan64()] A node will be split if this split induces a decrease of the impurity greater than or equal to the value, non-negative. Default is 0.0
-maxLeafNodes (size_t) – [optional, default: -1] Maximum number of leaf node. Default is 0 (unlimited)
-maxBins (size_t) – [optional, default: -1] Used with ‘hist’ split finding method only. Maximal number of discrete bins to bucket continuous features. Default is 256. Increasing the number results in higher computation costs
-minBinSize (size_t) – [optional, default: -1] Used with ‘hist’ split finding method only. Minimal number of observations in a bin. Default is 5
-splitter (str) – [optional, default: “”] Sets node splitting method. Default is best
-binningStrategy (str) – [optional, default: “”] Used with ‘hist’ split finding method only. Selects the strategy to group data points into bins. Allowed values are ‘quantiles’ (default), ‘averages’
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.decision_forest_classification_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -outOfBagError -
-
-
- Type: -
Numpy array
-
-
-
-
- -outOfBagErrorAccuracy -
-
-
- Type: -
Numpy array
-
-
-
-
- -outOfBagErrorDecisionFunction -
-
-
- Type: -
Numpy array
-
-
-
-
- -outOfBagErrorPerObservation -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportance -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.decision_forest_classification_prediction -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the decision_forest algorithm, double or float
-method (str) – [optional, default: “defaultDense”] decision_forest computation method
-votingMethod (str) – [optional, default: “”]
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (decision_forest_classification_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
Decision Tree Classification
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Classification Decision Tree.
-Examples:
- --
-
- -class daal4py.decision_tree_classification_training -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for Decision tree model-based training, double or float
-method (str) – [optional, default: “defaultDense”] Decision tree training method
-pruning (str) – [optional, default: “”] Pruning method for Decision tree
-maxTreeDepth (size_t) – [optional, default: -1] Maximum tree depth. 0 means unlimited depth.
-minObservationsInLeafNodes (size_t) – [optional, default: -1] Minimum number of observations in the leaf node. Can be any positive number.
-nBins (size_t) – [optional, default: -1] The number of bins used to compute probabilities of the observations belonging to the class. The only supported value for current version of the library is 1.
-splitCriterion (str) – [optional, default: “”] Split criterion for Decision tree classification
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, labels, dataForPruning, labelsForPruning, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-dataForPruning (data_or_file) – Pruning data set
-labelsForPruning (data_or_file) – Labels of the pruning data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.decision_tree_classification_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -class daal4py.decision_tree_classification_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for Decision tree model-based prediction
-method (str) – [optional, default: “defaultDense”] Computation method in the batch processing mode
-pruning (str) – [optional, default: “”] Pruning method for Decision tree
-maxTreeDepth (size_t) – [optional, default: -1] Maximum tree depth. 0 means unlimited depth.
-minObservationsInLeafNodes (size_t) – [optional, default: -1] Minimum number of observations in the leaf node. Can be any positive number.
-nBins (size_t) – [optional, default: -1] The number of bins used to compute probabilities of the observations belonging to the class. The only supported value for current version of the library is 1.
-splitCriterion (str) – [optional, default: “”] Split criterion for Decision tree classification
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (decision_tree_classification_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
Gradient Boosted Classification
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Classification Gradient Boosted Tree.
-Examples:
- --
-
- -class daal4py.gbt_classification_training -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for Gradient Boosted Trees, double or float
-method (str) – [optional, default: “defaultDense”] Gradient Boosted Trees computation method
-loss (str) – [optional, default: “”] Loss function type
-varImportance (str) – [optional, default: “”] 64 bit integer flag VariableImportanceModes that indicates the variable importance computation modes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-splitMethod (str) – [optional, default: “”] Split finding method. Default is exact
-maxIterations (size_t) – [optional, default: -1] Maximal number of iterations of the gradient boosted trees training algorithm. Default is 50
-maxTreeDepth (size_t) – [optional, default: -1] Maximal tree depth, 0 for unlimited. Default is 6
-shrinkage (double) – [optional, default: get_nan64()] Learning rate of the boosting procedure. Scales the contribution of each tree by a factor (0, 1]. Default is 0.3
-minSplitLoss (double) – [optional, default: get_nan64()] Loss regularization parameter. Min loss reduction required to make a further partition on a leaf node of the tree. Range: [0, inf). Default is 0
-lambda (double) – [optional, default: get_nan64()] L2 regularization parameter on weights. Range: [0, inf). Default is 1
-observationsPerTreeFraction (double) – [optional, default: get_nan64()] Fraction of observations used for a training of one tree, sampling without replacement. Range: (0, 1]. Default is 1 (no sampling, entire dataset is used)
-featuresPerNode (size_t) – [optional, default: -1] Number of features tried as possible splits per node. Range : [0, p] where p is the total number of features. Default is 0 (use all features)
-minObservationsInLeafNode (size_t) – [optional, default: -1] Minimal number of observations in a leaf node. Default is 5.
-memorySavingMode (bool) – [optional, default: False] If true then use memory saving (but slower) mode. Default is false
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for the random numbers generator used by the algorithms
-maxBins (size_t) – [optional, default: -1] Used with ‘inexact’ split finding method only. Maximal number of discrete bins to bucket continuous features. Default is 256. Increasing the number results in higher computation costs
-minBinSize (size_t) – [optional, default: -1] Used with ‘inexact’ split finding method only. Minimal number of observations in a bin. Default is 5
-internalOptions (int) – [optional, default: -1] Internal options
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.gbt_classification_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -variableImportanceByCover -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportanceByGain -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportanceByTotalCover -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportanceByTotalGain -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportanceByWeight -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.gbt_classification_prediction -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the gbt algorithm, double or float
-method (str) – [optional, default: “defaultDense”] gradient boosted trees computation method
-nIterations (size_t) – [optional, default: -1] Number of iterations of the trained model to be used for prediction
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (gbt_classification_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
k-Nearest Neighbors (kNN)
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library k-Nearest Neighbors (kNN).
-Examples:
- --
-
- -class daal4py.kdtree_knn_classification_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for KD-tree based kNN model-based training, double or float
-method (str) – [optional, default: “defaultDense”] KD-tree based kNN training method
-k (size_t) – [optional, default: -1] Number of neighbors
-dataUseInModel (str) – [optional, default: “”] The option to enable/disable an usage of the input dataset in kNN model
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for random choosing elements from training dataset
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-voteWeights (str) – [optional, default: “”] Weight function used in prediction
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – [optional, default: None] Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.kdtree_knn_classification_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -class daal4py.kdtree_knn_classification_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for KD-tree based kNN model-based prediction
-method (str) – [optional, default: “defaultDense”] Computation method in the batch processing mode
-k (size_t) – [optional, default: -1] Number of neighbors
-dataUseInModel (str) – [optional, default: “”] The option to enable/disable an usage of the input dataset in kNN model
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for random choosing elements from training dataset
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-voteWeights (str) – [optional, default: “”] Weight function used in prediction
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (kdtree_knn_classification_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
kdtree_knn_classification_prediction_result
-
-
Brute-force k-Nearest Neighbors (kNN)
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library k-Nearest Neighbors (kNN).
--
-
- -class daal4py.bf_knn_classification_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for BF kNN model-based training, double or float
-method (str) – [optional, default: “defaultDense”] BF kNN training method
-k (size_t) – [optional, default: -1] Number of neighbors
-dataUseInModel (str) – [optional, default: “”] The option to enable/disable an usage of the input dataset in kNN model
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-voteWeights (str) – [optional, default: “”] Weight function used in prediction
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for random choosing elements from training dataset
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – [optional, default: None] Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.bf_knn_classification_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -class daal4py.bf_knn_classification_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for BF kNN model-based prediction
-method (str) – [optional, default: “defaultDense”] Computation method in the batch processing mode
-k (size_t) – [optional, default: -1] Number of neighbors
-dataUseInModel (str) – [optional, default: “”] The option to enable/disable an usage of the input dataset in kNN model
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-voteWeights (str) – [optional, default: “”] Weight function used in prediction
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for random choosing elements from training dataset
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (bf_knn_classification_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
bf_knn_classification_prediction_result
-
-
AdaBoost Classification
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Classification AdaBoost.
-Examples:
- --
-
- -class daal4py.adaboost_training -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the AdaBoost, double or float
-method (str) – [optional, default: “defaultDense”] AdaBoost computation method
-weakLearnerTraining (classifier_training_batch__iface__) – [optional, default: None] The algorithm for weak learner model training
-weakLearnerPrediction (classifier_prediction_batch__iface__) – [optional, default: None] The algorithm for prediction based on a weak learner model
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the AdaBoost training algorithm
-maxIterations (size_t) – [optional, default: -1] Maximal number of iterations of the AdaBoost training algorithm
-learningRate (double) – [optional, default: get_nan64()] Multiplier for each classifier to shrink its contribution
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.adaboost_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -weakLearnersErrors -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.adaboost_prediction -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the AdaBoost, double or float
-method (str) – [optional, default: “defaultDense”] AdaBoost computation method
-weakLearnerTraining (classifier_training_batch__iface__) – [optional, default: None] The algorithm for weak learner model training
-weakLearnerPrediction (classifier_prediction_batch__iface__) – [optional, default: None] The algorithm for prediction based on a weak learner model
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the AdaBoost training algorithm
-maxIterations (size_t) – [optional, default: -1] Maximal number of iterations of the AdaBoost training algorithm
-learningRate (double) – [optional, default: get_nan64()] Multiplier for each classifier to shrink its contribution
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (adaboost_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
-
-
- -class daal4py.classifier_prediction_result -
Properties:
--
-
- -logProbabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -prediction -
-
-
- Type: -
Numpy array
-
-
-
-
- -probabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.adaboost_model -
Properties:
--
-
- -Alpha -
-
-
- Type: -
Numpy array
-
-
-
-
- -NFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -NumberOfFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -NumberOfWeakLearners -
-
-
- Type: -
size_t
-
-
-
-
- -WeakLearnerModel(idx) -
-
-
- Type: -
classifier_model (or derived)
-
-
BrownBoost Classification
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Classification BrownBoost.
-Examples:
- --
-
- -class daal4py.brownboost_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for BrownBoost, double or float
-method (str) – [optional, default: “defaultDense”] BrownBoost computation method
-weakLearnerTraining (classifier_training_batch__iface__) – [optional, default: None] The algorithm for weak learner model training
-weakLearnerPrediction (classifier_prediction_batch__iface__) – [optional, default: None] The algorithm for prediction based on a weak learner model
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the BrownBoost training algorithm
-maxIterations (size_t) – [optional, default: -1] Maximal number of iterations of the BrownBoost training algorithm
-newtonRaphsonAccuracyThreshold (double) – [optional, default: get_nan64()] Accuracy threshold for Newton-Raphson iterations in the BrownBoost training algorithm
-newtonRaphsonMaxIterations (size_t) – [optional, default: -1] Maximal number of Newton-Raphson iterations in the BrownBoost training algorithm
-degenerateCasesThreshold (double) – [optional, default: get_nan64()] Threshold needed to avoid degenerate cases in the BrownBoost training algorithm
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.brownboost_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the BrownBoost algorithm, double or float
-method (str) – [optional, default: “defaultDense”] BrownBoost computation method
-weakLearnerTraining (classifier_training_batch__iface__) – [optional, default: None] The algorithm for weak learner model training
-weakLearnerPrediction (classifier_prediction_batch__iface__) – [optional, default: None] The algorithm for prediction based on a weak learner model
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the BrownBoost training algorithm
-maxIterations (size_t) – [optional, default: -1] Maximal number of iterations of the BrownBoost training algorithm
-newtonRaphsonAccuracyThreshold (double) – [optional, default: get_nan64()] Accuracy threshold for Newton-Raphson iterations in the BrownBoost training algorithm
-newtonRaphsonMaxIterations (size_t) – [optional, default: -1] Maximal number of Newton-Raphson iterations in the BrownBoost training algorithm
-degenerateCasesThreshold (double) – [optional, default: get_nan64()] Threshold needed to avoid degenerate cases in the BrownBoost training algorithm
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (brownboost_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
-
-
- -class daal4py.classifier_prediction_result -
Properties:
--
-
- -logProbabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -prediction -
-
-
- Type: -
Numpy array
-
-
-
-
- -probabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.brownboost_model -
Properties:
--
-
- -Alpha -
-
-
- Type: -
Numpy array
-
-
-
-
- -NFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -NumberOfFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -NumberOfWeakLearners -
-
-
- Type: -
size_t
-
-
-
-
- -WeakLearnerModel(idx) -
-
-
- Type: -
classifier_model (or derived)
-
-
LogitBoost Classification
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Classification LogitBoost.
-Examples:
- --
-
- -class daal4py.logitboost_training -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for LogitBoost, double or float
-method (str) – [optional, default: “friedman”] LogitBoost computation method
-weakLearnerTraining (regression_training_batch__iface__) – [optional, default: None] The algorithm for weak learner model training
-weakLearnerPrediction (regression_prediction_batch__iface__) – [optional, default: None] The algorithm for prediction based on a weak learner model
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the LogitBoost training algorithm
-maxIterations (size_t) – [optional, default: -1] Maximal number of terms in additive regression
-weightsDegenerateCasesThreshold (double) – [optional, default: get_nan64()] Threshold to avoid degenerate cases when calculating weights W
-responsesDegenerateCasesThreshold (double) – [optional, default: get_nan64()] Threshold to avoid degenerate cases when calculating responses Z
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.logitboost_prediction -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the LogitBoost algorithm, double or float
-method (str) – [optional, default: “defaultDense”] LogitBoost computation method
-weakLearnerTraining (regression_training_batch__iface__) – [optional, default: None] The algorithm for weak learner model training
-weakLearnerPrediction (regression_prediction_batch__iface__) – [optional, default: None] The algorithm for prediction based on a weak learner model
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the LogitBoost training algorithm
-maxIterations (size_t) – [optional, default: -1] Maximal number of terms in additive regression
-weightsDegenerateCasesThreshold (double) – [optional, default: get_nan64()] Threshold to avoid degenerate cases when calculating weights W
-responsesDegenerateCasesThreshold (double) – [optional, default: get_nan64()] Threshold to avoid degenerate cases when calculating responses Z
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (logitboost_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
-
-
- -class daal4py.classifier_prediction_result -
Properties:
--
-
- -logProbabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -prediction -
-
-
- Type: -
Numpy array
-
-
-
-
- -probabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.logitboost_model -
Properties:
--
-
- -Iterations -
-
-
- Type: -
size_t
-
-
-
-
- -NFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -NumberOfFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -NumberOfWeakLearners -
-
-
- Type: -
size_t
-
-
-
-
- -WeakLearnerModel(idx) -
-
-
- Type: -
regression_model (or derived)
-
-
Stump Weak Learner Classification
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Classification Weak Learner Stump.
-Examples:
- --
-
- -class daal4py.stump_classification_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the the decision stump training method, double or float
-method (str) – [optional, default: “defaultDense”] Decision stump training method
-splitCriterion (str) – [optional, default: “”] Split criterion for stump classification
-varImportance (str) – [optional, default: “”] Variable importance computation mode
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.stump_classification_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -variableImportance -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.stump_classification_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the decision stump prediction algorithm, double or float
-method (str) – [optional, default: “defaultDense”] Decision stump model-based prediction method
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (stump_classification_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
-
-
- -class daal4py.classifier_prediction_result -
Properties:
--
-
- -logProbabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -prediction -
-
-
- Type: -
Numpy array
-
-
-
-
- -probabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.stump_classification_model -
Properties:
--
-
- -LeftValue -
-
-
- Type: -
double
-
-
-
-
- -NFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -NumberOfFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -RightValue -
-
-
- Type: -
double
-
-
-
-
- -SplitFeature -
-
-
- Type: -
size_t
-
-
-
-
- -SplitValue -
-
-
- Type: -
double
-
-
Multinomial Naive Bayes
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Naive Bayes.
-Examples:
- --
-
- -class daal4py.multinomial_naive_bayes_training -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for multinomial naive Bayes training, double or float
-method (str) – [optional, default: “defaultDense”] Computation method
-priorClassEstimates (array) – [optional, default: None] Prior class estimates
-alpha (array) – [optional, default: None] Imagined occurrences of the each word
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-streaming (bool) – [optional, default: False] enable streaming
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.multinomial_naive_bayes_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -class daal4py.multinomial_naive_bayes_prediction -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for prediction based on the multinomial naive Bayes model, double or float
-method (str) – [optional, default: “defaultDense”] Multinomial naive Bayes prediction method
-priorClassEstimates (array) – [optional, default: None] Prior class estimates
-alpha (array) – [optional, default: None] Imagined occurrences of the each word
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (multinomial_naive_bayes_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
Support Vector Machine (SVM)
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library SVM.
-Note: For the labels parameter, data is formatted as -1s and 1s
-Examples:
- --
-
- -class daal4py.svm_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the SVM training algorithm, double or float
-method (str) – [optional, default: “boser”] SVM training method
-C (double) – [optional, default: get_nan64()] Upper bound in constraints of the quadratic optimization problem
-accuracyThreshold (double) – [optional, default: get_nan64()] Training accuracy
-tau (double) – [optional, default: get_nan64()] Tau parameter of the working set selection scheme
-maxIterations (size_t) – [optional, default: -1] Maximal number of iterations for the algorithm
-cacheSize (size_t) – [optional, default: -1] Size of cache in bytes to store values of the kernel matrix. A non-zero value enables use of a cache optimization technique
-doShrinking (bool) – [optional, default: False] Flag that enables use of the shrinking optimization technique
-shrinkingStep (size_t) – [optional, default: -1] Number of iterations between the steps of shrinking optimization technique
-kernel (kernel_function_kerneliface__iface__) – [optional, default: None] Kernel function
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.svm_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”]
-method (str) – [optional, default: “defaultDense”]
-C (double) – [optional, default: get_nan64()] Upper bound in constraints of the quadratic optimization problem
-accuracyThreshold (double) – [optional, default: get_nan64()] Training accuracy
-tau (double) – [optional, default: get_nan64()] Tau parameter of the working set selection scheme
-maxIterations (size_t) – [optional, default: -1] Maximal number of iterations for the algorithm
-cacheSize (size_t) – [optional, default: -1] Size of cache in bytes to store values of the kernel matrix. A non-zero value enables use of a cache optimization technique
-doShrinking (bool) – [optional, default: False] Flag that enables use of the shrinking optimization technique
-shrinkingStep (size_t) – [optional, default: -1] Number of iterations between the steps of shrinking optimization technique
-kernel (kernel_function_kerneliface__iface__) – [optional, default: None] Kernel function
-nClasses (size_t) – [optional, default: -1] Number of classes
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (svm_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
-
-
- -class daal4py.classifier_prediction_result -
Properties:
--
-
- -logProbabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -prediction -
-
-
- Type: -
Numpy array
-
-
-
-
- -probabilities -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.svm_model -
Properties:
--
-
- -Bias -
-
-
- Type: -
double
-
-
-
-
- -ClassificationCoefficients -
-
-
- Type: -
Numpy array
-
-
-
-
- -NFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -NumberOfFeatures -
-
-
- Type: -
size_t
-
-
-
-
- -SupportIndices -
-
-
- Type: -
Numpy array
-
-
-
-
- -SupportVectors -
-
-
- Type: -
Numpy array
-
-
Logistic Regression
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Logistic Regression.
-Examples:
- --
-
- -class daal4py.logistic_regression_training -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for logistic regression, double or float
-method (str) – [optional, default: “defaultDense”] logistic regression computation method
-interceptFlag (bool) – [optional, default: False] Whether the intercept needs to be computed
-penaltyL1 (float) – [optional, default: get_nan32()] L1 regularization coefficient. Default is 0 (not applied)
-penaltyL2 (float) – [optional, default: get_nan32()] L2 regularization coefficient. Default is 0 (not applied)
-optimizationSolver (optimization_solver_iterative_solver_batch__iface__) – [optional, default: None] Default is sgd momentum solver
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, labels, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Training data set
-labels (data_or_file) – Labels of the training data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.logistic_regression_prediction -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the logistic regression algorithm, double or float
-method (str) – [optional, default: “defaultDense”] logistic regression computation method
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data set
-model (logistic_regression_modelptr) – Input model trained by the classification algorithm
-
-- Return type: -
- - -
Regression
-See also Intel(R) oneAPI Data Analytics Library Regression.
-Decision Forest Regression
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Regression Decision Forest.
-Examples:
- --
-
- -class daal4py.decision_forest_regression_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for decision forest model-based training, double or float
-method (str) – [optional, default: “defaultDense”] decision forest training method
-nTrees (size_t) – [optional, default: -1] Number of trees in the forest. Default is 10
-observationsPerTreeFraction (double) – [optional, default: get_nan64()] Fraction of observations used for a training of one tree, 0 to 1. Default is 1 (sampling with replacement)
-featuresPerNode (size_t) – [optional, default: -1] Number of features tried as possible splits per node. If 0 then sqrt(p) for classification, p/3 for regression, where p is the total number of features.
-maxTreeDepth (size_t) – [optional, default: -1] Maximal tree depth. Default is 0 (unlimited)
-minObservationsInLeafNode (size_t) – [optional, default: -1] Minimal number of observations in a leaf node. Default is 1 for classification, 5 for regression.
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for the random numbers generator used by the algorithms
-impurityThreshold (double) – [optional, default: get_nan64()] Threshold value used as stopping criteria: if the impurity value in the node is smaller than the threshold then the node is not split anymore.
-varImportance (str) – [optional, default: “”] Variable importance computation mode
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-memorySavingMode (bool) – [optional, default: False] If true then use memory saving (but slower) mode
-bootstrap (bool) – [optional, default: False] If true then training set for a tree is a bootstrap of the whole training set
-minObservationsInSplitNode (size_t) – [optional, default: -1] Minimal number of observations in a split node. Default 2
-minWeightFractionInLeafNode (double) – [optional, default: get_nan64()] The minimum weighted fraction of the sum total of weights (of all the input observations) required to be at a leaf node, 0.0 to 0.5. Default is 0.0
-minImpurityDecreaseInSplitNode (double) – [optional, default: get_nan64()] A node will be split if this split induces a decrease of the impurity greater than or equal to the value, non-negative. Default is 0.0
-maxLeafNodes (size_t) – [optional, default: -1] Maximum number of leaf node. Default is 0 (unlimited)
-maxBins (size_t) – [optional, default: -1] Used with ‘hist’ split finding method only. Maximal number of discrete bins to bucket continuous features. Default is 256. Increasing the number results in higher computation costs
-minBinSize (size_t) – [optional, default: -1] Used with ‘hist’ split finding method only. Minimal number of observations in a bin. Default is 5
-splitter (str) – [optional, default: “”] Sets node splitting method. Default is best
-binningStrategy (str) – [optional, default: “”] Used with ‘hist’ split finding method only. Selects the strategy to group data points into bins. Allowed values are ‘quantiles’ (default), ‘averages’
-
-
-
-
- -compute(data, dependentVariable, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-dependentVariable (data_or_file) – Values of the dependent variable for the input data
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.decision_forest_regression_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -outOfBagError -
-
-
- Type: -
Numpy array
-
-
-
-
- -outOfBagErrorPerObservation -
-
-
- Type: -
Numpy array
-
-
-
-
- -outOfBagErrorPrediction -
-
-
- Type: -
Numpy array
-
-
-
-
- -outOfBagErrorR2 -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportance -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.decision_forest_regression_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for decision forest model-based prediction
-method (str) – [optional, default: “defaultDense”] Computation method in the batch processing mode
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-model (decision_forest_regression_modelptr) – Trained decision tree model
-
-- Return type: -
- - -
Decision Tree Regression
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Regression Decision Tree.
-Examples:
- --
-
- -class daal4py.decision_tree_regression_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for Decision tree model-based training, double or float
-method (str) – [optional, default: “defaultDense”] Decision tree training method
-pruning (str) – [optional, default: “”] Pruning method for Decision tree
-maxTreeDepth (size_t) – [optional, default: -1] Maximum tree depth. 0 means unlimited depth.
-minObservationsInLeafNodes (size_t) – [optional, default: -1] Minimum number of observations in the leaf node. Can be any positive number.
-
-
-
-
- -compute(data, dependentVariables, dataForPruning, dependentVariablesForPruning, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-dependentVariables (data_or_file) – Values of the dependent variable for the input data
-dataForPruning (data_or_file) – Pruning data set
-dependentVariablesForPruning (data_or_file) – Labels of the pruning data set
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set
-
-- Return type: -
- - -
-
-
- -class daal4py.decision_tree_regression_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -class daal4py.decision_tree_regression_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for Decision tree model-based prediction
-method (str) – [optional, default: “defaultDense”] Computation method in the batch processing mode
-pruning (str) – [optional, default: “”] Pruning method for Decision tree
-maxTreeDepth (size_t) – [optional, default: -1] Maximum tree depth. 0 means unlimited depth.
-minObservationsInLeafNodes (size_t) – [optional, default: -1] Minimum number of observations in the leaf node. Can be any positive number.
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-model (decision_tree_regression_modelptr) – Trained decision tree model
-
-- Return type: -
- - -
Gradient Boosted Regression
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Regression Gradient Boosted Tree.
-Examples:
- --
-
- -class daal4py.gbt_regression_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for model-based training, double or float
-method (str) – [optional, default: “defaultDense”] gradient boosted trees training method
-loss (str) – [optional, default: “”] Loss function type
-varImportance (str) – [optional, default: “”] 64 bit integer flag VariableImportanceModes that indicates the variable importance computation modes
-splitMethod (str) – [optional, default: “”] Split finding method. Default is exact
-maxIterations (size_t) – [optional, default: -1] Maximal number of iterations of the gradient boosted trees training algorithm. Default is 50
-maxTreeDepth (size_t) – [optional, default: -1] Maximal tree depth, 0 for unlimited. Default is 6
-shrinkage (double) – [optional, default: get_nan64()] Learning rate of the boosting procedure. Scales the contribution of each tree by a factor (0, 1]. Default is 0.3
-minSplitLoss (double) – [optional, default: get_nan64()] Loss regularization parameter. Min loss reduction required to make a further partition on a leaf node of the tree. Range: [0, inf). Default is 0
-lambda (double) – [optional, default: get_nan64()] L2 regularization parameter on weights. Range: [0, inf). Default is 1
-observationsPerTreeFraction (double) – [optional, default: get_nan64()] Fraction of observations used for a training of one tree, sampling without replacement. Range: (0, 1]. Default is 1 (no sampling, entire dataset is used)
-featuresPerNode (size_t) – [optional, default: -1] Number of features tried as possible splits per node. Range : [0, p] where p is the total number of features. Default is 0 (use all features)
-minObservationsInLeafNode (size_t) – [optional, default: -1] Minimal number of observations in a leaf node. Default is 5.
-memorySavingMode (bool) – [optional, default: False] If true then use memory saving (but slower) mode. Default is false
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for the random numbers generator used by the algorithms
-maxBins (size_t) – [optional, default: -1] Used with ‘inexact’ split finding method only. Maximal number of discrete bins to bucket continuous features. Default is 256. Increasing the number results in higher computation costs
-minBinSize (size_t) – [optional, default: -1] Used with ‘inexact’ split finding method only. Minimal number of observations in a bin. Default is 5
-internalOptions (int) – [optional, default: -1] Internal options
-
-
-
-
- -compute(data, dependentVariable) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-dependentVariable (data_or_file) – Values of the dependent variable for the input data
-
-- Return type: -
- - -
-
-
- -class daal4py.gbt_regression_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -variableImportanceByCover -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportanceByGain -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportanceByTotalCover -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportanceByTotalGain -
-
-
- Type: -
Numpy array
-
-
-
-
- -variableImportanceByWeight -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.gbt_regression_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for model-based prediction
-method (str) – [optional, default: “defaultDense”] Computation method in the batch processing mode
-nIterations (size_t) – [optional, default: -1] Number of iterations of the trained model to be uses for prediction
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-model (gbt_regression_modelptr) – Trained gradient boosted trees model
-
-- Return type: -
- - -
Linear Regression
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Linear Regression.
-Examples:
- --
-
- -class daal4py.linear_regression_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for linear regression model-based training, double or float
-method (str) – [optional, default: “normEqDense”] Linear regression training method
-interceptFlag (bool) – [optional, default: False] Flag that indicates whether the intercept needs to be computed
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-streaming (bool) – [optional, default: False] enable streaming
-
-
-
-
- -compute(data, dependentVariables) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-dependentVariables (data_or_file) – Values of the dependent variable for the input data
-
-- Return type: -
- - -
-
-
- -class daal4py.linear_regression_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for linear regression model-based prediction
-method (str) – [optional, default: “defaultDense”] Computation method in the batch processing mode
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-model (linear_regression_modelptr) – Trained linear regression model
-
-- Return type: -
- - -
Least Absolute Shrinkage and Selection Operator
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Least Absolute Shrinkage and Selection Operator.
-Examples:
- --
-
- -class daal4py.lasso_regression_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for lasso regression model-based training, double or float
-method (str) – [optional, default: “defaultDense”] LASSO regression training method
-lassoParameters (array) – [optional, default: None] Numeric table that contains values of lasso parameters
-optimizationSolver (optimization_solver_iterative_solver_batch__iface__) – [optional, default: None] Default is coordinate descent solver
-dataUseInComputation (str) – [optional, default: “”] The flag allows to corrupt input data
-optResultToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the optional results to compute
-interceptFlag (bool) – [optional, default: False] Flag that indicates whether the intercept needs to be computed
-
-
-
-
- -compute(data, dependentVariables, weights, gramMatrix) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-dependentVariables (data_or_file) – Values of the dependent variable for the input data
-weights (data_or_file) – [optional, default: None] NumericTable of size 1 x n with weights of samples. Applied for all method
-gramMatrix (data_or_file) – [optional, default: None] NumericTable of size p x p with last iteration number. Applied for all method
-
-- Return type: -
- - -
-
-
- -class daal4py.lasso_regression_training_result -
Properties:
--
-
- -gramMatrixId -
-
-
- Type: -
Numpy array
-
-
-
-
- -model -
-
-
- Type: -
- - -
-
-
- -class daal4py.lasso_regression_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for lasso regression model-based prediction
-method (str) – [optional, default: “defaultDense”]
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-model (lasso_regression_modelptr) – Trained lasso regression model
-
-- Return type: -
- - -
Ridge Regression
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Ridge Regression.
-Examples:
- --
-
- -class daal4py.ridge_regression_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for ridge regression model-based training, double or float
-method (str) – [optional, default: “normEqDense”] Ridge regression training method
-ridgeParameters (array) – [optional, default: None] Numeric table that contains values of ridge parameters
-interceptFlag (bool) – [optional, default: False] Flag that indicates whether the intercept needs to be computed
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-streaming (bool) – [optional, default: False] enable streaming
-
-
-
-
- -compute(data, dependentVariables) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-dependentVariables (data_or_file) – Values of the dependent variable for the input data
-
-- Return type: -
- - -
-
-
- -class daal4py.ridge_regression_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for ridge regression model-based prediction
-method (str) – [optional, default: “defaultDense”] Computation method in the batch processing mode
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-model (ridge_regression_modelptr) – Trained ridge regression model
-
-- Return type: -
- - -
Stump Regression
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Regression Stump.
-Examples:
- --
-
- -class daal4py.stump_regression_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the the decision stump training method, double or float
-method (str) – [optional, default: “defaultDense”] Decision stump training method
-varImportance (str) – [optional, default: “”] Variable importance mode. Variable importance computation is not supported for current version of the library
-
-
-
-
- -compute(data, dependentVariables, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-dependentVariables (data_or_file) – Values of the dependent variable for the input data
-weights (data_or_file) – [optional, default: None] Optional. Weights of the observations in the training data set. Some values are skipped for backward compatibility.
-
-- Return type: -
- - -
-
-
- -class daal4py.stump_regression_training_result -
Properties:
--
-
- -model -
-
-
- Type: -
- - -
-
-
- -variableImportance -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.stump_regression_prediction -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the decision stump prediction algorithm, double or float
-method (str) – [optional, default: “defaultDense”] Decision stump model-based prediction method
-varImportance (str) – [optional, default: “”] Variable importance mode. Variable importance computation is not supported for current version of the library
-
-
-
-
- -compute(data, model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-model (stump_regression_modelptr) – Trained regression model
-
-- Return type: -
- - -
Principal Component Analysis (PCA)
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library PCA.
-Examples:
- --
-
- -class daal4py.pca -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for PCA, double or float
-method (str) – [optional, default: “correlationDense”] PCA computation method
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-nComponents (size_t) – [optional, default: -1] number of components for reduced implementation
-isDeterministic (bool) – [optional, default: False] sign flip if required
-doScale (bool) – [optional, default: False] scaling if required
-isCorrelation (bool) – [optional, default: False] correlation is provided
-normalization (normalization_zscore_batchimpl__iface__) – [optional, default: None] Pointer to batch covariance
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-
-
-
-
- -compute(data, correlation) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-correlation (data_or_file) – [optional, default: None] Input correlation table
-
-- Return type: -
- - -
-
-
- -class daal4py.pca_result -
Properties:
--
-
- -dataForTransform -
-
-
- Type: -
Numpy array
-
-
-
-
- -eigenvalues -
-
-
- Type: -
Numpy array
-
-
-
-
- -eigenvectors -
-
-
- Type: -
Numpy array
-
-
-
-
- -means -
-
-
- Type: -
Numpy array
-
-
-
-
- -variances -
-
-
- Type: -
Numpy array
-
-
Principal Component Analysis (PCA) Transform
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library PCA Transform.
-Examples:
- --
-
- -class daal4py.pca_transform -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”]
-method (str) – [optional, default: “defaultDense”]
-nComponents (size_t) – [optional, default: -1]
-
-
-
-
- -compute(data, eigenvectors, dataForTransform) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-eigenvectors (data_or_file) – Transformation matrix of eigenvectors
-dataForTransform (dict_numerictableptr) – Data for transform
-
-- Return type: -
- - -
K-Means Clustering
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library K-Means Clustering.
-Examples:
- -K-Means Initialization
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library K-Means Initialization.
--
-
- -class daal4py.kmeans_init -
-
-
- Parameters: -
-
-
nClusters (size_t) – Number of clusters
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of initial clusters for K-Means algorithm, double or float
-method (str) – [optional, default: “defaultDense”] Method of computing initial clusters for the algorithm
-nTrials (size_t) – [optional, default: -1] Kmeans++ only. The number of trials to generate all clusters but the first initial cluster.
-oversamplingFactor (double) – [optional, default: get_nan64()] Kmeans|| only. A fraction of nClusters being chosen in each of nRounds of kmeans||.L = nClusters* oversamplingFactor points are sampled in a round.
-nRounds (size_t) – [optional, default: -1] Kmeans|| only. Number of rounds for k-means||. (oversamplingFactor*nRounds) > 1 is a requirement.
-engine (engines_batchbase__iface__) – [optional, default: None] Engine to be used for generating random numbers for the initialization
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
K-Means
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library K-Means Computation.
--
-
- -class daal4py.kmeans -
-
-
- Parameters: -
-
-
nClusters (size_t) – Number of clusters
-maxIterations (size_t) – Number of iterations
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of K-Means, double or float
-method (str) – [optional, default: “lloydDense”] Computation method of the algorithm
-accuracyThreshold (double) – [optional, default: get_nan64()] Threshold for the termination of the algorithm
-gamma (double) – [optional, default: get_nan64()] Weight used in distance computation for categorical features
-distanceType (str) – [optional, default: “”] Distance used in the algorithm
-resultsToEvaluate (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-assignFlag (bool) – [optional, default: False] Do data points assignment :param bool distributed: [optional, default: False] enable distributed computation (SPMD)
-
-
-
-
- -compute(data, inputCentroids) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-inputCentroids (data_or_file) – Initial centroids for the algorithm
-
-- Return type: -
- - -
Density-Based Spatial Clustering of Applications with Noise
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Density-Based Spatial Clustering of Applications with Noise.
-Examples:
- --
-
- -class daal4py.dbscan -
-
-
- Parameters: -
-
-
epsilon (double) – Radius of neighborhood
-minObservations (size_t) – Minimal total weight of observations in neighborhood of core observation
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of DBSCAN, double or float
-method (str) – [optional, default: “defaultDense”] Computation method of the algorithm
-memorySavingMode (bool) – [optional, default: False] If true then use memory saving (but slower) mode
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-blockIndex (size_t) – [optional, default: -1] Unique identifier of block initially passed for computation on the local node
-nBlocks (size_t) – [optional, default: -1] Number of blocks initially passed for computation on all nodes
-leftBlocks (size_t) – [optional, default: -1] Number of blocks that will process observations with value of selected split feature lesser than selected split value
-rightBlocks (size_t) – [optional, default: -1] Number of blocks that will process observations with value of selected split feature greater than selected split value
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-
-
-
-
- -compute(data, weights) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-weights (data_or_file) – [optional, default: None] Input weights of observations
-
-- Return type: -
- - -
Outlier Detection
-Multivariate Outlier Detection
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Multivariate Outlier Detection.
-Examples:
- --
-
- -class daal4py.multivariate_outlier_detection -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the multivariate outlier detection, double or float
-method (str) – [optional, default: “defaultDense”] Multivariate outlier detection computation method
-
-
-
-
- -compute(data, location, scatter, threshold) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-location (data_or_file) – [optional, default: None] Vector of mean estimates of size 1 x p
-scatter (data_or_file) – [optional, default: None] Measure of spread, the variance-covariance matrix of size p x p
-threshold (data_or_file) – [optional, default: None] Limit that defines the outlier region, the array of size 1 x 1 containing a non-negative number
-
-- Return type: -
- - -
Univariate Outlier Detection
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Univariate Outlier Detection.
-Examples:
- --
-
- -class daal4py.univariate_outlier_detection -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the univariate outlier detection algorithm, double or float
-method (str) – [optional, default: “defaultDense”] univariate outlier detection computation method
-
-
-
-
- -compute(data, location, scatter, threshold) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-location (data_or_file) – [optional, default: None] Vector of mean estimates of size 1 x p
-scatter (data_or_file) – [optional, default: None] Measure of spread, the array of standard deviations of size 1 x p
-threshold (data_or_file) – [optional, default: None] Limit that defines the outlier region, the array of non-negative numbers of size 1 x p
-
-- Return type: -
- - -
Multivariate Bacon Outlier Detection
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Multivariate Bacon Outlier Detection.
-Examples:
- --
-
- -class daal4py.bacon_outlier_detection -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the BACON outlier detection, double or float
-method (str) – [optional, default: “defaultDense”] BACON outlier detection computation method
-initMethod (str) – [optional, default: “”] Initialization method
-alpha (double) – [optional, default: get_nan64()] One-tailed probability that defines the (1 - lpha) quantile of the chi^2 distribution with p degrees of freedom. Recommended value: lpha / n, where n is the number of observations.
-toleranceToConverge (double) – [optional, default: get_nan64()] Stopping criterion: the algorithm is terminated if the size of the basic subset is changed by less than the threshold
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Optimization Solvers
-Objective Functions
-Mean Squared Error Algorithm (MSE)
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library MSE.
-Examples:
--
-
- -
- -
- -
-
-
- -class daal4py.optimization_solver_mse -
-
-
- Parameters: -
-
-
numberOfTerms (size_t) – The number of terms in the function
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the Mean squared error objective function, double or float
-method (str) – [optional, default: “defaultDense”] The Mean squared error objective function computation method
-interceptFlag (bool) – [optional, default: False] Whether the intercept needs to be computed. Default is true
-penaltyL1 (array) – [optional, default: None] L1 regularization coefficients. Default is 0 (not applied)
-penaltyL2 (array) – [optional, default: None] L2 regularization coefficients. Default is 0 (not applied)
-batchIndices (array) – [optional, default: None] Numeric table of size 1 x m where m is batch size that represent a batch of indices used to compute the function results, e.g., value of the sum of the functions. If no indices are provided, all terms will be used in the computations.
-featureId (size_t) – [optional, default: -1] The feature index to compute part of gradient/hessian/proximal projection
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, dependentVariables, argument, weights, gramMatrix) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Numeric table of size n x p with data
-dependentVariables (data_or_file) – Numeric table of size n x 1 with dependent variables
-argument (data_or_file) – Numeric table of size 1 x p with input argument of the objective function
-weights (data_or_file) – NumericTable of size 1 x n with samples weights. Applied for all method
-gramMatrix (data_or_file) – NumericTable of size p x p with last iteration number. Applied for all method
-
-- Return type: -
optimization_solver_objective_function_result
-
-
-
-
- -setup(data, dependentVariables, argument, weights, gramMatrix) -
Setup (partial) input data for using algorithm object in other algorithms.
--
-
- Parameters: -
-
-
data (data_or_file) – Numeric table of size n x p with data
-dependentVariables (data_or_file) – Numeric table of size n x 1 with dependent variables
-argument (data_or_file) – Numeric table of size 1 x p with input argument of the objective function
-weights (data_or_file) – NumericTable of size 1 x n with samples weights. Applied for all method
-gramMatrix (data_or_file) – NumericTable of size p x p with last iteration number. Applied for all method
-
-- Return type: -
None
-
-
-
-
- -daal4py.optimization_solver_mse_result -
alias of
-optimization_solver_objective_function_result
Logistic Loss
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Logistic Loss.
-Examples:
--
-
- -
-
-
- -class daal4py.optimization_solver_logistic_loss -
-
-
- Parameters: -
-
-
numberOfTerms (size_t) – The number of terms in the function
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the Logistic loss objective function, double or float
-method (str) – [optional, default: “defaultDense”] The Logistic loss objective function computation method
-interceptFlag (bool) – [optional, default: False] Whether the intercept needs to be computed. Default is true
-penaltyL1 (float) – [optional, default: get_nan32()] L1 regularization coefficient. Default is 0 (not applied)
-penaltyL2 (float) – [optional, default: get_nan32()] L2 regularization coefficient. Default is 0 (not applied)
-batchIndices (array) – [optional, default: None] Numeric table of size 1 x m where m is batch size that represent a batch of indices used to compute the function results, e.g., value of the sum of the functions. If no indices are provided, all terms will be used in the computations.
-featureId (size_t) – [optional, default: -1] The feature index to compute part of gradient/hessian/proximal projection
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, dependentVariables, argument) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Numeric table of size n x p with data
-dependentVariables (data_or_file) – Numeric table of size n x 1 with dependent variables
-argument (data_or_file) – Numeric table of size 1 x p with input argument of the objective function
-
-- Return type: -
optimization_solver_objective_function_result
-
-
-
-
- -setup(data, dependentVariables, argument) -
Setup (partial) input data for using algorithm object in other algorithms.
--
-
- Parameters: -
-
-
data (data_or_file) – Numeric table of size n x p with data
-dependentVariables (data_or_file) – Numeric table of size n x 1 with dependent variables
-argument (data_or_file) – Numeric table of size 1 x p with input argument of the objective function
-
-- Return type: -
None
-
-
-
-
- -daal4py.optimization_solver_logistic_loss_result -
alias of
-optimization_solver_objective_function_result
Cross-entropy Loss
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Cross Entropy Loss.
-Examples:
--
-
- -
-
-
- -class daal4py.optimization_solver_cross_entropy_loss -
-
-
- Parameters: -
-
-
nClasses (size_t) – Number of classes (different values of dependent variable)
-numberOfTerms (size_t) – The number of terms in the function
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the Cross-entropy loss objective function, double or float
-method (str) – [optional, default: “defaultDense”] The Cross-entropy loss objective function computation method
-interceptFlag (bool) – [optional, default: False] Whether the intercept needs to be computed. Default is true
-penaltyL1 (float) – [optional, default: get_nan32()] L1 regularization coefficient. Default is 0 (not applied)
-penaltyL2 (float) – [optional, default: get_nan32()] L2 regularization coefficient. Default is 0 (not applied)
-batchIndices (array) – [optional, default: None] Numeric table of size 1 x m where m is batch size that represent a batch of indices used to compute the function results, e.g., value of the sum of the functions. If no indices are provided, all terms will be used in the computations.
-featureId (size_t) – [optional, default: -1] The feature index to compute part of gradient/hessian/proximal projection
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-
-
-
-
- -compute(data, dependentVariables, argument) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Numeric table of size n x p with data
-dependentVariables (data_or_file) – Numeric table of size n x 1 with dependent variables
-argument (data_or_file) – Numeric table of size 1 x p with input argument of the objective function
-
-- Return type: -
optimization_solver_objective_function_result
-
-
-
-
- -setup(data, dependentVariables, argument) -
Setup (partial) input data for using algorithm object in other algorithms.
--
-
- Parameters: -
-
-
data (data_or_file) – Numeric table of size n x p with data
-dependentVariables (data_or_file) – Numeric table of size n x 1 with dependent variables
-argument (data_or_file) – Numeric table of size 1 x p with input argument of the objective function
-
-- Return type: -
None
-
-
-
-
- -daal4py.optimization_solver_cross_entropy_loss_result -
alias of
-optimization_solver_objective_function_result
Iterative Solvers
-Stochastic Gradient Descent Algorithm
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library SGD.
-Examples:
- --
-
- -class daal4py.optimization_solver_sgd -
-
-
- Parameters: -
-
-
function (optimization_solver_sum_of_functions_batch__iface__) – Objective function represented as sum of functions
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the Stochastic gradient descent algorithm,
-method (str) – [optional, default: “defaultDense”] Stochastic gradient descent computation method
-batchIndices (array) – [optional, default: None] Numeric table that represents 32 bit integer indices of terms in the objective function. If no indices are provided, the implementation will generate random indices.
-learningRateSequence (array) – [optional, default: None] Numeric table that contains values of the learning rate sequence
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for random generation of 32 bit integer indices of terms in the objective function.
-nIterations (size_t) – [optional, default: -1] Maximal number of iterations of the algorithm
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the algorithm. The algorithm terminates when this accuracy is achieved
-optionalResultRequired (bool) – [optional, default: False] Indicates whether optional result is required
-batchSize (size_t) – [optional, default: -1] Number of batch indices to compute the stochastic gradient. If batchSize is equal to the number of terms in objective function then no random sampling is performed, and all terms are used to calculate the gradient. This parameter is ignored if batchIndices is provided.
-conservativeSequence (array) – [optional, default: None] Numeric table of values of the conservative coefficient sequence
-innerNIterations (size_t) – [optional, default: -1]
-momentum (double) – [optional, default: get_nan64()] Momentum value
-
-
-
-
- -compute(inputArgument) -
Do the actual computation on provided input data.
--
-
- Parameters: -
inputArgument (data_or_file) – Initial value to start optimization
-
-- Return type: -
- - -
Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library LBFGS.
-Examples:
--
-
- -
-
-
- -class daal4py.optimization_solver_lbfgs -
-
-
- Parameters: -
-
-
function (optimization_solver_sum_of_functions_batch__iface__) – Objective function represented as sum of functions
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the LBFGS algorithm,
-method (str) – [optional, default: “defaultDense”] LBFGS computation method
-m (size_t) – [optional, default: -1] Memory parameter of LBFGS. The maximum number of correction pairs that define the approximation of inverse Hessian matrix.
-L (size_t) – [optional, default: -1] The number of iterations between the curvature estimates calculations
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for random choosing terms from objective function.
-batchIndices (array) – [optional, default: None]
-correctionPairBatchSize (size_t) – [optional, default: -1] Number of observations to compute the sub-sampled Hessian for correction pairs computation
-correctionPairBatchIndices (array) – [optional, default: None]
-stepLengthSequence (array) – [optional, default: None]
-nIterations (size_t) – [optional, default: -1] Maximal number of iterations of the algorithm
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the algorithm. The algorithm terminates when this accuracy is achieved
-optionalResultRequired (bool) – [optional, default: False] Indicates whether optional result is required
-batchSize (size_t) – [optional, default: -1] Number of batch indices to compute the stochastic gradient. If batchSize is equal to the number of terms in objective function then no random sampling is performed, and all terms are used to calculate the gradient. This parameter is ignored if batchIndices is provided.
-
-
-
-
- -compute(inputArgument) -
Do the actual computation on provided input data.
--
-
- Parameters: -
inputArgument (data_or_file) – Initial value to start optimization
-
-- Return type: -
- - -
Adaptive Subgradient Method
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library AdaGrad.
-Examples:
--
-
- -
-
-
- -class daal4py.optimization_solver_adagrad -
-
-
- Parameters: -
-
-
function (optimization_solver_sum_of_functions_batch__iface__) – Objective function represented as sum of functions
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the Adaptive gradient descent algorithm,
-method (str) – [optional, default: “defaultDense”] Adaptive gradient descent computation method
-batchIndices (array) – [optional, default: None] Numeric table that represents 32 bit integer indices of terms in the objective function. If no indices are provided, the implementation will generate random indices.
-learningRate (array) – [optional, default: None] Numeric table that contains value of the learning rate
-degenerateCasesThreshold (double) – [optional, default: get_nan64()] Value needed to avoid degenerate cases in square root computing.
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for random generation of 32 bit integer indices of terms in the objective function.
-nIterations (size_t) – [optional, default: -1] Maximal number of iterations of the algorithm
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the algorithm. The algorithm terminates when this accuracy is achieved
-optionalResultRequired (bool) – [optional, default: False] Indicates whether optional result is required
-batchSize (size_t) – [optional, default: -1] Number of batch indices to compute the stochastic gradient. If batchSize is equal to the number of terms in objective function then no random sampling is performed, and all terms are used to calculate the gradient. This parameter is ignored if batchIndices is provided.
-
-
-
-
- -compute(inputArgument) -
Do the actual computation on provided input data.
--
-
- Parameters: -
inputArgument (data_or_file) – Initial value to start optimization
-
-- Return type: -
- - -
Stochastic Average Gradient Descent
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Stochastic Average Gradient Descent SAGA.
-Examples:
- --
-
- -class daal4py.optimization_solver_saga -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the Stochastic average gradient descent algorithm,
-method (str) – [optional, default: “defaultDense”] Stochastic average gradient descent computation method
-batchIndices (array) – [optional, default: None] Numeric table that represents 32 bit integer indices of terms in the objective function. If no indices are provided, the implementation will generate random indices.
-learningRateSequence (array) – [optional, default: None] Numeric table that contains value of the learning rate
-engine (engines_batchbase__iface__) – [optional, default: None] Engine for random generation of 32 bit integer indices of terms in the objective function.
-function (optimization_solver_sum_of_functions_batch__iface__) – [optional, default: None] Objective function represented as sum of functions
-nIterations (size_t) – [optional, default: -1] Maximal number of iterations of the algorithm
-accuracyThreshold (double) – [optional, default: get_nan64()] Accuracy of the algorithm. The algorithm terminates when this accuracy is achieved
-optionalResultRequired (bool) – [optional, default: False] Indicates whether optional result is required
-batchSize (size_t) – [optional, default: -1] Number of batch indices to compute the stochastic gradient. If batchSize is equal to the number of terms in objective function then no random sampling is performed, and all terms are used to calculate the gradient. This parameter is ignored if batchIndices is provided.
-
-
-
-
- -compute(inputArgument, gradientsTable) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
inputArgument (data_or_file) – Initial value to start optimization
-gradientsTable (data_or_file) – Numeric table of size p x 1 with the values of G, where each value is an accumulated sum of squares of corresponding gradient’s coordinate values.
-
-- Return type: -
- - -
Distances
-Cosine Distance Matrix
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Cosine Distance.
-Examples:
- --
-
- -class daal4py.cosine_distance -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the cosine distance, double or float
-method (str) – [optional, default: “defaultDense”] Cosine distance computation method
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Correlation Distance Matrix
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Correlation Distance.
-Examples:
- --
-
- -class daal4py.correlation_distance -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the correlation distance algorithm, double or float
-method (str) – [optional, default: “defaultDense”] Correlation distance computation method
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Expectation-Maximization (EM)
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Expectation-Maximization.
-Initialization for the Gaussian Mixture Model
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Expectation-Maximization Initialization.
-Examples:
- --
-
- -class daal4py.em_gmm_init -
-
-
- Parameters: -
-
-
nComponents (size_t) – Number of components in the Gaussian mixture model
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of initial values for the EM for GMM algorithm, double or float
-method (str) – [optional, default: “defaultDense”]
-nTrials (size_t) – [optional, default: -1] Number of trials of short EM runs
-nIterations (size_t) – [optional, default: -1] Number of iterations in every short EM run
-accuracyThreshold (double) – [optional, default: get_nan64()] Threshold for the termination of the algorithm
-covarianceStorage (str) – [optional, default: “”] Type of covariance in the Gaussian mixture model.
-engine (engines_batchbase__iface__) – [optional, default: None] Engine to be used for randomly generating data points to start the initialization of short EM
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
EM algorithm for the Gaussian Mixture Model
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Expectation-Maximization for the Gaussian Mixture Model.
-Examples:
- --
-
- -class daal4py.em_gmm -
-
-
- Parameters: -
-
-
nComponents (size_t) – Number of components in the Gaussian mixture model
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the EM for GMM algorithm, double or float
-method (str) – [optional, default: “defaultDense”] EM for GMM computation method
-maxIterations (size_t) – [optional, default: -1] Maximal number of iterations of the algorithm.
-accuracyThreshold (double) – [optional, default: get_nan64()] Threshold for the termination of the algorithm.
-regularizationFactor (double) – [optional, default: get_nan64()] Factor for covariance regularization in case of ill-conditional data
-covarianceStorage (str) – [optional, default: “”] Type of covariance in the Gaussian mixture model.
-
-
-
-
- -compute(data, inputWeights, inputMeans, inputCovariances) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table
-inputWeights (data_or_file) – Input weights
-inputMeans (data_or_file) – Input means
-inputCovariances (list_numerictableptr) – Collection of input covariances
-
-- Return type: -
- - -
QR Decomposition
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library QR Decomposition.
-QR Decomposition (without pivoting)
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library QR Decomposition without pivoting.
-Examples:
- --
-
- -class daal4py.qr -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the QR decomposition algorithm, double or float
-method (str) – [optional, default: “defaultDense”] Computation method of the algorithm
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-streaming (bool) – [optional, default: False] enable streaming
-
-
Pivoted QR Decomposition
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Pivoted QR Decomposition.
-Examples:
- --
-
- -class daal4py.pivoted_qr -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of the pivoted QR algorithm, double or float
-method (str) – [optional, default: “defaultDense”] Computation method
-permutedColumns (array) – [optional, default: None] On entry, if i-th element of permutedColumns != 0, * the i-th column of input matrix is moved to the beginning of Data * P before * the computation, and fixed in place during the computation. * If i-th element of permutedColumns = 0, the i-th column of input data * is a free column (that is, it may be interchanged during the * computation with any other free column).
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Normalization
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Normalization.
-Z-Score
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Z-Score.
-Examples:
- --
-
- -class daal4py.normalization_zscore -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the z-score normalization, double or float
-method (str) – [optional, default: “defaultDense”] Z-score normalization computation method
-resultsToCompute (str) – [optional, default: “”] 64 bit integer flag that indicates the results to compute
-doScale (bool) – [optional, default: False] boolean flag that indicates the mode of computation. If true both centering and scaling, otherwise only centering.
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Min-Max
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Min-Max.
-Examples:
- --
-
- -class daal4py.normalization_minmax -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the min-max normalization, double or float
-method (str) – [optional, default: “defaultDense”] Min-max normalization computation method
-lowerBound (double) – [optional, default: get_nan64()] The lower bound of the features value will be obtained during normalization.
-upperBound (double) – [optional, default: get_nan64()] The upper bound of the features value will be obtained during normalization.
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Random Number Engines
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Engines.
- - -mt19937
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library mt19937.
--
-
- -class daal4py.engines_mt19937 -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of mt19937 engine, double or float
-method (str) – [optional, default: “defaultDense”] Computation method of the engine
-seed (size_t) – [optional, default: -1] seed
-
-
-
-
- -compute(tableToFill) -
Do the actual computation on provided input data.
--
-
- Parameters: -
tableToFill (data_or_file) – Input table to fill with random numbers
-
-- Return type: -
- - -
-
-
- -daal4py.engines_mt19937_result -
alias of
-engines_result
mt2203
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library mt2203.
--
-
- -class daal4py.engines_mt2203 -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of mt2203 engine, double or float
-method (str) – [optional, default: “defaultDense”] Computation method of the engine
-seed (size_t) – [optional, default: -1] seed
-
-
-
-
- -compute(tableToFill) -
Do the actual computation on provided input data.
--
-
- Parameters: -
tableToFill (data_or_file) – Input table to fill with random numbers
-
-- Return type: -
- - -
-
-
- -daal4py.engines_mt2203_result -
alias of
-engines_result
mcg59
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library mcg59.
--
-
- -class daal4py.engines_mcg59 -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of mcg59 engine, double or float
-method (str) – [optional, default: “defaultDense”] Computation method of the engine
-seed (size_t) – [optional, default: -1] seed
-
-
-
-
- -compute(tableToFill) -
Do the actual computation on provided input data.
--
-
- Parameters: -
tableToFill (data_or_file) – Input table to fill with random numbers
-
-- Return type: -
- - -
-
-
- -daal4py.engines_mcg59_result -
alias of
-engines_result
Distributions
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Distributions.
-Bernoulli
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Bernoulli Distribution.
-Examples:
- --
-
- -class daal4py.distributions_bernoulli -
-
-
- Parameters: -
-
-
p (double) – Success probability of a trial, value from [0.0; 1.0]
-fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of bernoulli distribution, double or float
-method (str) – [optional, default: “defaultDense”] Computation method of the distribution
-engine (engines_batchbase__iface__) – [optional, default: None] Pointer to the engine
-
-
-
-
- -compute(tableToFill) -
Do the actual computation on provided input data.
--
-
- Parameters: -
tableToFill (data_or_file) – Input table to fill with random numbers
-
-- Return type: -
distributions_result
-
-
-
-
- -daal4py.distributions_bernoulli_result -
alias of
-distributions_result
Normal
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Normal Distribution.
-Examples:
- --
-
- -class daal4py.distributions_normal -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of normal distribution, double or float
-method (str) – [optional, default: “defaultDense”] Computation method of the distribution
-a (double) – [optional, default: get_nan64()] Mean
-sigma (double) – [optional, default: get_nan64()] Standard deviation
-engine (engines_batchbase__iface__) – [optional, default: None] Pointer to the engine
-
-
-
-
- -compute(tableToFill) -
Do the actual computation on provided input data.
--
-
- Parameters: -
tableToFill (data_or_file) – Input table to fill with random numbers
-
-- Return type: -
distributions_result
-
-
-
-
- -daal4py.distributions_normal_result -
alias of
-distributions_result
Uniform
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Uniform Distribution.
-Examples:
- --
-
- -class daal4py.distributions_uniform -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of uniform distribution, double or float
-method (str) – [optional, default: “defaultDense”] Computation method of the distribution
-a (double) – [optional, default: get_nan64()] Left bound a
-b (double) – [optional, default: get_nan64()] Right bound b
-engine (engines_batchbase__iface__) – [optional, default: None] Pointer to the engine
-
-
-
-
- -compute(tableToFill) -
Do the actual computation on provided input data.
--
-
- Parameters: -
tableToFill (data_or_file) – Input table to fill with random numbers
-
-- Return type: -
distributions_result
-
-
-
-
- -daal4py.distributions_uniform_result -
alias of
-distributions_result
Association Rules
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Association Rules.
-Examples:
- --
-
- -class daal4py.association_rules -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the association rules algorithm, double or float
-method (str) – [optional, default: “apriori”] Association rules algorithm computation method
-minSupport (double) – [optional, default: get_nan64()] Minimum support 0.0 <= minSupport < 1.0
-minConfidence (double) – [optional, default: get_nan64()] Minimum confidence 0.0 <= minConfidence < 1.0
-nUniqueItems (size_t) – [optional, default: -1] Number of unique items
-nTransactions (size_t) – [optional, default: -1] Number of transactions
-discoverRules (bool) – [optional, default: False] Flag. If true, association rules are built from large itemsets
-itemsetsOrder (str) – [optional, default: “”] Format of the resulting itemsets
-rulesOrder (str) – [optional, default: “”] Format of the resulting association rules
-minItemsetSize (size_t) – [optional, default: -1] Minimum number of items in a large itemset
-maxItemsetSize (size_t) – [optional, default: -1] Maximum number of items in a large itemset. Set to zero to not limit the upper boundary for the size of large itemsets
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
-
-
- -class daal4py.association_rules_result -
Properties:
--
-
- -antecedentItemsets -
-
-
- Type: -
Numpy array
-
-
-
-
- -confidence -
-
-
- Type: -
Numpy array
-
-
-
-
- -consequentItemsets -
-
-
- Type: -
Numpy array
-
-
-
-
- -largeItemsets -
-
-
- Type: -
Numpy array
-
-
-
-
- -largeItemsetsSupport -
-
-
- Type: -
Numpy array
-
-
Cholesky Decomposition
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Cholesky Decomposition.
-Examples:
- --
-
- -class daal4py.cholesky -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the Cholesky decomposition algorithm,
-method (str) – [optional, default: “defaultDense”] Cholesky decomposition computation method
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Correlation and Variance-Covariance Matrices
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Correlation and Variance-Covariance Matrices.
-Examples:
- --
-
- -class daal4py.covariance -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of the correlation or variance-covariance matrix, double or float
-method (str) – [optional, default: “defaultDense”] Computation method
-outputMatrixType (str) – [optional, default: “”] Type of the computed matrix
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-streaming (bool) – [optional, default: False] enable streaming
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Implicit Alternating Least Squares (implicit ALS)
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Implicit Alternating Least Squares.
-Examples:
- --
-
- -class daal4py.implicit_als_training -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for implicit ALS model training, double or float
-method (str) – [optional, default: “defaultDense”] Implicit ALS training method
-nFactors (size_t) – [optional, default: -1] Number of factors
-maxIterations (size_t) – [optional, default: -1] Maximum number of iterations of the implicit ALS training algorithm
-alpha (double) – [optional, default: get_nan64()] Confidence parameter of the implicit ALS training algorithm
-lambda (double) – [optional, default: get_nan64()] Regularization parameter
-preferenceThreshold (double) – [optional, default: get_nan64()] Threshold used to define preference values
-
-
-
-
- -compute(data, inputModel) -
Do the actual computation on provided input data.
--
-
- Parameters: -
-
-
data (data_or_file) – Input data table that contains ratings
-inputModel (implicit_als_modelptr) – Initial model that contains initialized factors
-
-- Return type: -
- - -
-
-
- -class daal4py.implicit_als_model -
Properties:
--
-
- -ItemsFactors -
-
-
- Type: -
Numpy array
-
-
-
-
- -UsersFactors -
-
-
- Type: -
Numpy array
-
-
-
-
- -class daal4py.implicit_als_prediction_ratings -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for implicit ALS model-based prediction, double or float
-method (str) – [optional, default: “defaultDense”] Implicit ALS prediction method
-nFactors (size_t) – [optional, default: -1] Number of factors
-maxIterations (size_t) – [optional, default: -1] Maximum number of iterations of the implicit ALS training algorithm
-alpha (double) – [optional, default: get_nan64()] Confidence parameter of the implicit ALS training algorithm
-lambda (double) – [optional, default: get_nan64()] Regularization parameter
-preferenceThreshold (double) – [optional, default: get_nan64()] Threshold used to define preference values
-
-
-
-
- -compute(model) -
Do the actual computation on provided input data.
--
-
- Parameters: -
model (implicit_als_modelptr) – Input model trained by the ALS algorithm
-
-- Return type: -
- - -
Moments of Low Order
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Moments of Low Order.
-Examples:
- --
-
- -class daal4py.low_order_moments -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations of the low order moments, double or float
-method (str) – [optional, default: “defaultDense”] Computation method of the algorithm
-estimatesToCompute (str) – [optional, default: “”] Estimates to be computed by the algorithm
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-streaming (bool) – [optional, default: False] enable streaming
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
-
-
- -class daal4py.low_order_moments_result -
Properties:
--
-
- -maximum -
-
-
- Type: -
Numpy array
-
-
-
-
- -mean -
-
-
- Type: -
Numpy array
-
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-
- -minimum -
-
-
- Type: -
Numpy array
-
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-
-
- -secondOrderRawMoment -
-
-
- Type: -
Numpy array
-
-
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-
- -standardDeviation -
-
-
- Type: -
Numpy array
-
-
-
-
- -sum -
-
-
- Type: -
Numpy array
-
-
-
-
- -sumSquares -
-
-
- Type: -
Numpy array
-
-
-
-
- -sumSquaresCentered -
-
-
- Type: -
Numpy array
-
-
-
-
- -variance -
-
-
- Type: -
Numpy array
-
-
-
-
- -variation -
-
-
- Type: -
Numpy array
-
-
Quantiles
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Quantiles.
-Examples:
- --
-
- -class daal4py.quantiles -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the quantile algorithms, double or float
-method (str) – [optional, default: “defaultDense”] Quantiles computation method
-quantileOrders (array) – [optional, default: None] Numeric table with quantile orders. Default value is 0.5 (median)
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Singular Value Decomposition (SVD)
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library SVD.
-Examples:
- --
-
- -class daal4py.svd -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the SVD algorithm, double or float
-method (str) – [optional, default: “defaultDense”] SVD computation method
-leftSingularMatrix (str) – [optional, default: “”] Format of the matrix of left singular vectors >
-rightSingularMatrix (str) – [optional, default: “”] Format of the matrix of right singular vectors >
-distributed (bool) – [optional, default: False] enable distributed computation (SPMD)
-streaming (bool) – [optional, default: False] enable streaming
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Sorting
-Parameters and semantics are described in Intel(R) oneAPI Data Analytics Library Sorting.
-Examples:
- --
-
- -class daal4py.sorting -
-
-
- Parameters: -
-
-
fptype (str) – [optional, default: “double”] Data type to use in intermediate computations for the sorting, double or float
-method (str) – [optional, default: “defaultDense”] Sorting computation method
-
-
-
-
- -compute(data) -
Do the actual computation on provided input data.
--
-
- Parameters: -
data (data_or_file) – Input data table
-
-- Return type: -
- - -
Trees
--
-
- -daal4py.getTreeState(model, i=0, n_classes=1) -
Examples:
- -Redirecting...
+The daal4py algorithms documentation has moved. You should be redirected automatically to the API reference. If not, please visit:
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-Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, Intel(R) Extension for Scikit-learn*. -All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.
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-Contents:
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-
- About daal4py - -
- Data - -
- Model Builders - -
- Supported Algorithms
-
-
- Classification -
- Regression -
- Principal Component Analysis (PCA) -
- K-Means Clustering -
- Density-Based Spatial Clustering of Applications with Noise -
- Outlier Detection -
- Optimization Solvers -
- Distances -
- Expectation-Maximization (EM) -
- QR Decomposition -
- Normalization -
- Random Number Engines -
- Distributions -
- Association Rules -
- Cholesky Decomposition -
- Correlation and Variance-Covariance Matrices -
- Implicit Alternating Least Squares (implicit ALS) -
- Moments of Low Order -
- Quantiles -
- Singular Value Decomposition (SVD) -
- Sorting -
- Trees -
- - Distributed Mode - -
- Streaming Mode - -
- Examples - -
- Scikit-Learn API - -
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-All array arguments to compute functions and to algorithm constructors can be -provided in different formats. daal4py will automatically do its best to work on -the provided data with minimal overhead, most notably without copying the data.
-Numpy Arrays
-daal4py can directly handle all types of numpy arrays with numerical data -without copying the entire data. Arrays can be homogeneous (e.g. simple dtype) or -heterogeneous (structured array) as well as contiguous or non-contiguous.
-Pandas DataFrames
-daal4py directly accepts pandas DataFrames with columns of numerical data. No -extra full copy is required.
-SciPy Sparse CSR Matrix
-daal4py can directly handle matrices of type scipy.sparse.csr_matrix without -copying the entire data.
-Note: some algorithms can be configured to use an optimized compute path for CSR -data. It is required to explicitly specify the CSR method, otherwise the default -and less efficient method is used.
-CSV Files
-The compute functions daal4py’s algorithms additionally accept -CSV-filenames. Internally, daal4py will use DAAL’s fast CSV reader to create -contiguous homogeneous tables.
-Redirecting...
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+ - \ No newline at end of file + diff --git a/daal4py/examples.html b/daal4py/examples.html old mode 100755 new mode 100644 index 7a83945..54d979c --- a/daal4py/examples.html +++ b/daal4py/examples.html @@ -1,287 +1,14 @@ - + - - - -
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-Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, Intel(R) Extension for Scikit-learn*. -All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.
-Below are examples on how to utilize daal4py for various usage styles.
-General usage
-Building models from Gradient Boosting frameworks
- -Principal Component Analysis (PCA) Transform
- -Singular Value Decomposition (SVD)
- -Moments of Low Order
- -Correlation and Variance-Covariance Matrices
- -Decision Forest Classification
--
-
Single-Process Decision Forest Classification Default Dense method
-Single-Process Decision Forest Classification Histogram method
-
Decision Tree Classification
- -Gradient Boosted Classification
- -k-Nearest Neighbors (kNN)
- -Multinomial Naive Bayes
- -Support Vector Machine (SVM)
- -Logistic Regression
- -Decision Forest Regression
- -Gradient Boosted Regression
- -Linear Regression
- -Ridge Regression
- -K-Means Clustering
- -Multivariate Outlier Detection
- -Univariate Outlier Detection
- -Optimization Solvers-Mean Squared Error Algorithm (MSE)
- -Logistic Loss
- -Stochastic Gradient Descent Algorithm
--
-
- -
- -
Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm
- -Adaptive Subgradient Method
- -Cosine Distance Matrix
- -Correlation Distance Matrix
- -Trees
- -Redirecting...
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- Fast, Scalable and Easy Machine Learning With DAAL4PY
-
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-Note
-Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, Intel(R) Extension for Scikit-learn*. -All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.
-Daal4py makes your Machine Learning algorithms in Python lightning fast and easy to use. It provides -highly configurable Machine Learning kernels, some of which support streaming input data and/or can -be easily and efficiently scaled out to clusters of workstations. Internally it uses Intel(R) -oneAPI Data Analytics Library to deliver the best performance.
-Designed for Data Scientists and Framework Designers
-daal4py was created to give data scientists the easiest way to utilize Intel(R) oneAPI Data Analytics -Library powerful machine learning building blocks directly in a high-productivity manner. A -simplified API gives high-level abstractions to the user with minimal boilerplate, allowing for -quick to write and easy to maintain code when utilizing Jupyter Notebooks. For scaling capabilities, -daal4py also provides the ability to do distributed machine learning, giving a quick way to scale -out. Its streaming mode provides a flexible mechanism for processing large amounts of data and/or -non-contiguous input data.
-For framework designers, daal4py has been fashioned to be built under other frameworks from both an -API and feature perspective. The machine learning models split the training and inference classes, -allowing the model to be exported and serialized if desired. This design also gives the flexibility -to work directly with the model and associated primitives, allowing one to customize the behavior of -the model itself. The daal4py package can be built with customized algorithm loadouts, allowing for -a smaller footprint of dependencies when necessary.
-API Design and usage
-As an example of the type of API that would be used in a data science context, -the linear regression workflow is showcased below:
-import daal4py as d4p
-# train, test, and target are Pandas dataframes
-
-d4p_lm = d4p.linear_regression_training(interceptFlag=True)
-lm_trained = d4p_lm.compute(train, target)
-
-lm_predictor_component = d4p.linear_regression_prediction()
-result = lm_predictor_component.compute(test, lm_trained.model)
-In the example above, it can be seen that model is divided into training and -prediction. This gives flexibility when writing custom grid searches and custom -functions that modify model behavior or use it as a parameter. Daal4py also -allows for direct usage of NumPy arrays and pandas DataFrames instead of oneDAL -NumericTables, which allow for better integration with the pandas/NumPy/SciPy stack.
-Daal4py machine learning algorithms are constructed with a rich set of -parameters. Assuming we want to find the initial set of centroids for kmeans, -we first create an algorithm and configure it for 10 clusters using the ‘PlusPlus’ method:
-kmi = kmeans_init(10, method="plusPlusDense")
-Assuming we have all our data in a CSV file we can now call it:
-result = kmi.compute('data.csv')
-Our result will hold the computed centroids in the ‘centroids’ attribute:
-print(result.centroids)
-The full example could look like this:
-from daal4py import kmeans_init
-result = kmeans_init(10, method="plusPlusDense").compute('data.csv')
-print(result.centroids)
-One can even run this on a cluster by simply -adding initializing/finalizing the network and adding a keyword-parameter:
-from daal4py import daalinit, daalfini, kmeans_init
-daalinit()
-result = kmeans_init(10, method="plusPlusDense", distributed=True).compute(my_file)
-daalfini()
-Last but not least, daal4py allows getting input data from streams:
-from daal4py import svd
-algo = svd(streaming=True)
-for input in stream_or_filelist:
- algo.compute(input)
-result = algo.finalize()
-oneAPI and GPU support in daal4py
-daal4py oneAPI and GPU support is deprecated. Use scikit-learn-intelex -instead.
-Daal4py’s Design
-The design of daal4py utilizes several different technologies to deliver Intel(R) oneAPI Data -Analytics Library performance in a flexible design to Data Scientists and Framework designers. The -package uses Jinja templates to generate Cython-wrapped oneDAL C++ headers, with Cython as a bridge -between the generated oneDAL code and the Python layer. This design allows for quicker development -cycles and acts as a reference design to those looking to tailor their build of daal4py. Cython -also allows for good Python behavior, both for compatibility to different frameworks and for -pickling and serialization.
-Built for Performance
-Besides superior (e.g. close to native C++ Intel(R) oneAPI Data Analytics Library) performance on a -single node, the distribution mechanics of daal4py provides excellent strong and weak scaling. It -nicely handles distributing a fixed input size on increasing clusters sizes (strong scaling: orange) -which addresses possible response time requirements. It also scales with growing input size (weak -scaling: yellow) which is needed if the data no longer fits into memory of a single node.
-
-On a 32-node cluster (1280 cores) daal4py computed linear regression -of 2.15 TB of data in 1.18 seconds and 68.66 GB of data in less than -48 milliseconds.
-
-On a 32-node cluster (1280 cores) daal4py computed K-Means (10 -clusters) of 1.12 TB of data in 107.4 seconds and 35.76 GB of data -in 4.8 seconds.
-Configuration: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, EIST/Turbo on 2 -sockets, 20 cores per socket, 192 GB RAM, 16 nodes connected with Infiniband, -Oracle Linux Server release 7.4, using 64-bit floating point numbers
-Getting daal4py
-daal4py is available at the Python Package Index, -on Anaconda Cloud in Conda Forge channel -and in Intel channel. -Sources and build instructions are available in -daal4py repository.
-The daal4py package is available via same distribution channels and platforms as scikit-learn-intelex. -See -scikit-learn-intelex requirements <https://intel.github.io/scikit-learn-intelex/latest/system-requirements.html> _
--
-
Install from PyPI:
---pip install daal4py -
-Install from Anaconda Cloud: Conda-Forge channel:
---сonda install daal4py -c conda-forge -
-Install using conda from the Intel repository:
---conda install daal4py -c https://software.repos.intel.com/python/conda/ -
-
We recommend to use PyPi. If you are using Intel® Distribution for Python, -we recommend using conda from the Intel Repository. -In other cases, use Anaconda Cloud: conda-forge channel.
-Overview
-All algorithms in daal4py work the same way:
--
-
Instantiate and parameterize
-Run/compute on input data
-
The below tables list the accepted arguments. Those with no default (None) are
-required arguments. All other arguments with defaults are optional and can be
-provided as keyword arguments (like optarg=77). Each algorithm returns a
-class-like object with properties as its result.
For algorithms with training and prediction, simply extract the model
-property from the result returned by the training and pass it in as the (second)
-input argument.
Note that all input objects and the result/model properties are native types, -e.g. standard types (integer, float, Numpy arrays, Pandas DataFrames, -…). Additionally, if you provide the name of a csv-file as an input argument -daal4py will work on the entire file content.
-Scikit-Learn API and patching
-
-
-Tip
-We recommend using -the ‘scikit-learn-intelex package patching <https://intel.github.io/scikit-learn-intelex/latest/what-is-patching.html>’ _ for the scikit-learn patching.
-daal4py exposes some oneDAL solvers using a scikit-learn compatible API.
-daal4py can furthermore monkey-patch the sklearn package to use the DAAL
-solvers as drop-in replacement without any code change.
Please refer to the section on scikit-learn API and patching -for more details.
-Redirecting...
+The daal4py documentation has moved. You should be redirected automatically. If not, please visit:
+https://uxlfoundation.github.io/scikit-learn-intelex/2025.5/about_daal4py.html
+Note: daal4py is now part of the scikit-learn-intelex package and has been deprecated in favor of sklearnex.
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- Model Builders for the Gradient Boosting Frameworks
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-Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, Intel(R) Extension for Scikit-learn*. -All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.
-Introduction
-Gradient boosting on decision trees is one of the most accurate and efficient -machine learning algorithms for classification and regression. -The most popular implementations of it are:
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XGBoost*
-LightGBM*
-CatBoost*
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daal4py Model Builders deliver the accelerated -models inference of those frameworks. The inference is performed by the oneDAL GBT implementation tuned -for the best performance on the Intel(R) Architecture.
-
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-Note
-Currently, experimental support for XGBoost* and LightGBM* categorical data is not supported. -For the model conversion to work with daal4py, convert non-numeric data to numeric data -before training and converting the model.
-Conversion
-The first step is to convert already trained model. The -API usage for different frameworks is the same:
-XGBoost:
-import daal4py as d4p
-d4p_model = d4p.mb.convert_model(xgb_model)
-LightGBM:
-import daal4py as d4p
-d4p_model = d4p.mb.convert_model(lgb_model)
-CatBoost:
-import daal4py as d4p
-d4p_model = d4p.mb.convert_model(cb_model)
-
-
-Note
-Convert model only once and then use it for the inference.
-Classification and Regression Inference
-The API is the same for classification and regression inference.
-Based on the original model passed to the convert_model(), d4p_prediction is either the classification or regression output.
----d4p_prediction = d4p_model.predict(test_data) -
Here, the predict() method of d4p_model is being used to make predictions on the test_data dataset.
-The d4p_prediction variable stores the predictions made by the predict() method.
SHAP Value Calculation for Regression Models
-SHAP contribution and interaction value calculation are natively supported by models created with daal4py Model Builders.
-For these models, the predict() method takes additional keyword arguments:
----d4p_model.predict(test_data, pred_contribs=True) # for SHAP contributions -d4p_model.predict(test_data, pred_interactions=True) # for SHAP interactions -
The returned prediction has the shape:
----
-- -
(n_rows, n_features + 1)for SHAP contributions- -
(n_rows, n_features + 1, n_features + 1)for SHAP interactions
Here, n_rows is the number of rows (i.e., observations) in
-test_data, and n_features is the number of features in the dataset.
The prediction result for SHAP contributions includes a feature attribution value for each feature and a bias term for each observation.
-The prediction result for SHAP interactions comprises (n_features + 1) x (n_features + 1) values for all possible
-feature combinations, along with their corresponding bias terms.
-
-Note
-The shapes of SHAP contributions and interactions are consistent with the XGBoost results. -In contrast, the SHAP Python package drops bias terms, resulting -in SHAP contributions (SHAP interactions) with one fewer column (one fewer column and row) per observation.
-Scikit-learn-style Estimators
-You can also use the scikit-learn-style classes GBTDAALClassifier and GBTDAALRegressor to convert and infer your models. For example:
from daal4py.sklearn.ensemble import GBTDAALRegressor
-reg = xgb.XGBRegressor()
-reg.fit(X, y)
-d4p_predt = GBTDAALRegressor.convert_model(reg).predict(X)
-Limitations
-Model Builders support only base inference with prediction and probabilities prediction. The functionality is to be extended. -Therefore, there are the following limitations: -- The categorical features are not supported for conversion and prediction. -- The multioutput models are not supported for conversion and prediction. -- SHAP values can be calculated for regression models only.
-Examples
-Model Builders models conversion
- -Articles and Blog Posts
- -Redirecting...
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+Serving GBT Models from Other Libraries
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- Scaling on Distributed Memory (Multiprocessing)
-
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-Note
-Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, Intel(R) Extension for Scikit-learn*. -All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.
-It’s Easy
-daal4py operates in SPMD style (Single Program Multiple Data), which means your -program is executed on several processes (e.g. similar to MPI). The use of MPI is -not required for daal4py’s SPMD-mode to work, all necessary communication and -synchronization happens under the hood of daal4py. It is possible to use daal4py and -mpi4py in the same program, though.
-Only very minimal changes are needed to your daal4py code to allow daal4py to -run on a cluster of workstations. Initialize the distribution engine:
-daalinit()
-Add the distribution parameter to the algorithm construction:
-kmi = kmeans_init(10, method="plusPlusDense", distributed=True)
-When calling the actual computation each process expects an input file or input -array/DataFrame. Your program needs to tell each process which -file/array/DataFrame it should operate on. Like with other SPMD programs this is -usually done conditionally on the process id/rank (‘daal4py.my_procid()’). Assume -we have one file for each process, all having the same prefix ‘file’ and being -suffixed by a number. The code could then look like this:
-result = kmi.compute("file{}.csv", daal4py.my_procid())
-The result of the computation will now be available on all processes.
-Finally stop the distribution engine:
-daalfini()
-That’s all for the python code:
-from daal4py import daalinit, daalfini, kmeans_init
-daalinit()
-kmi = kmeans_init(10, method="plusPlusDense", distributed=True)
-result = kmi.compute("file{}.csv", daal4py.my_procid())
-daalfini()
-To actually get it executed on several processes use standard MPI mechanics, -like:
-mpirun -n 4 python ./kmeans.py
-The binaries provided by Intel use the Intel® MPI library, but -daal4py can also be compiled for any other MPI implementation.
-Supported Algorithms and Examples
-The following algorithms support distribution:
--
-
PCA (pca)
--
-
- -
-SVD (svd)
--
-
- -
-Linear Regression Training (linear_regression_training)
- -
-Ridge Regression Training (ridge_regression_training)
- -
-Multinomial Naive Bayes Training (multinomial_naive_bayes_training)
--
-
- -
-K-Means (kmeans_init and kmeans)
--
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- -
-Correlation and Variance-Covariance Matrices (covariance)
--
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- -
-Moments of Low Order (low_order_moments)
- -
-QR Decomposition (qr)
--
-
- -
-
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+Distributed Mode (daal4py, CPU)
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"weaklearnerserrors (daal4py.adaboost_training_result attribute)": [[0, "daal4py.adaboost_training_result.weakLearnersErrors"]], "weights (daal4py.bacon_outlier_detection_result attribute)": [[0, "daal4py.bacon_outlier_detection_result.weights"]], "weights (daal4py.em_gmm_init_result attribute)": [[0, "daal4py.em_gmm_init_result.weights"]], "weights (daal4py.em_gmm_result attribute)": [[0, "daal4py.em_gmm_result.weights"]], "weights (daal4py.multivariate_outlier_detection_result attribute)": [[0, "daal4py.multivariate_outlier_detection_result.weights"]], "weights (daal4py.univariate_outlier_detection_result attribute)": [[0, "daal4py.univariate_outlier_detection_result.weights"]]}}) - diff --git a/daal4py/sklearn.html b/daal4py/sklearn.html old mode 100755 new mode 100644 index 0a39506..6d8d192 --- a/daal4py/sklearn.html +++ b/daal4py/sklearn.html @@ -1,367 +1,15 @@ - + - - - -
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- Scikit-Learn API and patching
-Python interface to efficient Intel(R) oneAPI Data Analytics Library provided by daal4py allows one -to create scikit-learn compatible estimators, transformers, clusterers, etc. powered by oneDAL which -are nearly as efficient as native programs.
-Deprecation Notice
-Scikit-learn patching functionality in daal4py was deprecated and moved to a separate -package, Intel(R) Extension for Scikit-learn*. -All future patches will be available only in Intel(R) Extension for Scikit-learn*. -Please use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.
-oneDAL accelerated scikit-learn
-daal4py can dynamically patch scikit-learn estimators to use Intel(R) oneAPI Data Analytics Library -as the underlying solver, while getting the same solution faster.
-It is possible to enable those patches without editing the code of a scikit-learn application by -using the following commandline flag:
-python -m daal4py my_application.py
-If you are using Scikit-Learn from Intel® Distribution for Python, then
-you can enable daal4py patches through an environment variable. To do this, set USE_DAAL4PY_SKLEARN to one of the values
-True, '1', 'y', 'yes', 'Y', 'YES', 'Yes', 'true', 'True' or 'TRUE' as shown below.
On Linux and Mac OS:
-export USE_DAAL4PY_SKLEARN=1
-On Windows:
-set USE_DAAL4PY_SKLEARN=1
-To disable daal4py patches, set the USE_DAAL4PY_SKLEARN environment variable to 0.
Patches can also be enabled programmatically:
-import daal4py.sklearn
-daal4py.sklearn.patch_sklearn()
-It is possible to undo the patch with:
-daal4py.sklearn.unpatch_sklearn()
-Applying the monkey patch will impact the following existing scikit-learn -algorithms:
-Task |
-Functionality |
-Parameters support |
-Data support |
-
|---|---|---|---|
Classification |
-SVC |
-All parameters except |
-No limitations. |
-
Classification |
-RandomForestClassifier |
-All parameters except |
-Multi-output, sparse data and out-of-bag score are not supported. |
-
Classification |
-KNeighborsClassifier |
-All parameters except |
-Multi-output and sparse data is not supported. |
-
Classification |
-LogisticRegression |
-All parameters except |
-Only dense data is supported. |
-
Regression |
-RandomForestRegressor |
-All parameters except |
-Multi-output, sparse data and out-of-bag score are not supported. |
-
Regression |
-KNeighborsRegressor |
-All parameters except |
-Multi-output and sparse data is not supported. |
-
Regression |
-LinearRegression |
-All parameters except |
-Only dense data is supported, #observations should be >= #features. |
-
Regression |
-Ridge |
-All parameters except |
-Only dense data is supported, #observations should be >= #features. |
-
Regression |
-ElasticNet |
-All parameters except |
-Multi-output and sparse data is not supported, #observations should be >= #features. |
-
Regression |
-Lasso |
-All parameters except |
-Multi-output and sparse data is not supported, #observations should be >= #features. |
-
Clustering |
-KMeans |
-All parameters except |
-No limitations. |
-
Clustering |
-DBSCAN |
-All parameters except |
-Only dense data is supported. |
-
Dimensionality reduction |
-PCA |
-All parameters except |
-Sparse data is not supported. |
-
Unsupervised |
-NearestNeighbors |
-All parameters except |
-Sparse data is not supported. |
-
Other |
-train_test_split |
-All parameters are supported. |
-Only dense data is supported. |
-
Other |
-assert_all_finite |
-All parameters are supported. |
-Only dense data is supported. |
-
Other |
-pairwise_distance |
-With metric=``cosine`` and |
-Only dense data is supported. |
-
Other |
-roc_auc_score |
-Parameters |
-No limitations. |
-
Monkey-patched scikit-learn classes and functions passes scikit-learn’s own test -suite, with few exceptions, specified in deselected_tests.yaml.
-In particular the tests execute check_estimator -on all added and monkey-patched classes, which are discovered by means of -introspection. This assures scikit-learn API compatibility of all -daal4py.sklearn classes.
-
-
-Note
-daal4py supports optimizations for the last four versions of scikit-learn. -The latest release of daal4py-2021.1 supports scikit-learn 0.21.X, 0.22.X, 0.23.X and 0.24.X.
-scikit-learn verbose
-To find out which implementation of the algorithm is currently used, -set the environment variable.
-On Linux and Mac OS:
-export IDP_SKLEARN_VERBOSE=INFO
-On Windows:
-set IDP_SKLEARN_VERBOSE=INFO
-During the calls that use Intel-optimized scikit-learn, you will receive additional print statements -that indicate which implementation is being called. -These print statements are only available for scikit-learn algorithms with daal4py patches.
-For example, for DBSCAN you get one of these print statements depending on which implementation is used:
-INFO: sklearn.cluster.DBSCAN.fit: running accelerated version on CPU
-INFO: sklearn.cluster.DBSCAN.fit: fallback to original Scikit-learn
-scikit-learn API
-The daal4py.sklearn package contains scikit-learn compatible API which
-implement a subset of scikit-learn algorithms using Intel(R) oneAPI Data Analytics Library.
Currently, these include:
--
-
daal4py.sklearn.neighbors.KNeighborsClassifier
-daal4py.sklearn.neighbors.KNeighborsRegressor
-daal4py.sklearn.neighbors.NearestNeighbors
-daal4py.sklearn.tree.DecisionTreeClassifier
-daal4py.sklearn.ensemble.RandomForestClassifier
-daal4py.sklearn.ensemble.RandomForestRegressor
-daal4py.sklearn.ensemble.AdaBoostClassifier
-daal4py.sklearn.cluster.KMeans
-daal4py.sklearn.cluster.DBSCAN
-daal4py.sklearn.decomposition.PCA
-daal4py.sklearn.linear_model.Ridge
-daal4py.sklearn.svm.SVC
-daal4py.sklearn.linear_model.logistic_regression_path
-daal4py.sklearn.linear_model.LogisticRegression
-daal4py.sklearn.linear_model.ElasticNet
-daal4py.sklearn.linear_model.Lasso
-daal4py.sklearn.model_selection._daal_train_test_split
-daal4py.sklearn.metrics._daal_roc_auc_score
-
These classes are always available, whether the scikit-learn itself has been -patched, or not. For example:
-import daal4py.sklearn
-daal4py.sklearn.unpatch_sklearn()
-import sklearn.datasets, sklearn.svm
-
-digits = sklearn.datasets.load_digits()
-X, y = digits.data, digits.target
-
-clf_d = daal4py.sklearn.svm.SVC(kernel='rbf', gamma='scale', C = 0.5).fit(X, y)
-clf_v = sklearn.svm.SVC(kernel='rbf', gamma='scale', C =0.5).fit(X, y)
-
-clf_d.score(X, y) # output: 0.9905397885364496
-clf_v.score(X, y) # output: 0.9905397885364496
-Redirecting...
+The scikit-learn integration documentation has moved. You should be redirected automatically. If not, please visit:
+Patching Utilities for scikit-learn
+Note: Use sklearnex for scikit-learn acceleration. daal4py sklearn module is deprecated.
- \ No newline at end of file + diff --git a/daal4py/streaming.html b/daal4py/streaming.html old mode 100755 new mode 100644 index 1a253f7..d109918 --- a/daal4py/streaming.html +++ b/daal4py/streaming.html @@ -1,217 +1,14 @@ - + - - - -
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- Streaming Data
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-Note
-Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, Intel(R) Extension for Scikit-learn*. -All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.
-For large quantities of data it might be impossible to provide all input data at -once. This might be because the data resides in multiple files and merging it is -to costly (or not feasible in other ways). In other cases the data is simply too -large to be loaded completely into memory. Or, the data might come in as an -actual stream. daal4py’s streaming mode allows you to process such data.
-Besides supporting certain use cases, streaming also allows interleaving I/O -operations with computation.
-daal4py’s streaming mode is as easy as follows:
--
-
When constructing the algorithm configure it with
-streaming=True:--algo = daal4py.svd(streaming=True) -
-Repeat calling
-compute(input-data)with chunks of your input (arrays, DataFrames or -files):--for f in input_files: - algo.compute(f) -
-When done with inputting, call
-finalize()to obtain the result:--result = algo.finalize() -
-
The streaming algorithms also accept arrays and DataFrames as input, e.g. the -data can come from a stream rather than from multiple files. Here is an example -which simulates a data stream using a generator which reads a file in chunks: -SVD reading stream of data
-Supported Algorithms and Examples
-The following algorithms support streaming:
--
-
SVD (svd)
--
-
- -
-Linear Regression Training (linear_regression_training)
- -
-Ridge Regression Training (ridge_regression_training)
- -
-Multinomial Naive Bayes Training (multinomial_naive_bayes_training)
--
-
- -
-Moments of Low Order
- -
-Covariance
--
-
- -
-QR
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-
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+The streaming mode documentation has moved. You should be redirected automatically. If not, please visit:
+ - \ No newline at end of file +