Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

 # file:
 # Copyright 2014-2019 Intel Corporation.
 # This software and the related documents are Intel copyrighted  materials,  and
 # your use of  them is  governed by the  express license  under which  they were
 # provided to you (License).  Unless the License provides otherwise, you may not
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 # !  Content:
 # !    Python example of two-class support vector machine (SVM) quality metrics
 # !
 # !*****************************************************************************
 ## \example
 import os
 import sys
 from daal.algorithms import kernel_function
 from daal.algorithms.classifier.quality_metric import binary_confusion_matrix
 from daal.algorithms import svm
 from daal.algorithms import classifier
 from daal.data_management import (
     DataSourceIface, FileDataSource, readOnly, BlockDescriptor,
     HomogenNumericTable, NumericTableIface, MergedNumericTable
 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
 if utils_folder not in sys.path:
     sys.path.insert(0, utils_folder)
 from utils import printNumericTables, printNumericTable
 # Input data set parameters
 DATA_PREFIX = os.path.join('..', 'data', 'batch')
 trainDatasetFileName = os.path.join(DATA_PREFIX, 'svm_two_class_train_dense.csv')
 testDatasetFileName = os.path.join(DATA_PREFIX, 'svm_two_class_test_dense.csv')
 nFeatures = 20
 # Parameters for the SVM kernel function
 kernel = kernel_function.linear.Batch()
 # Model object for the SVM algorithm
 trainingResult = None
 predictionResult = None
 qualityMetricSetResult = None
 predictedLabels = None
 groundTruthLabels = None
 def trainModel():
     global trainingResult
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
     trainDataSource = FileDataSource(
         trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
     # Create Numeric Tables for training data and labels
     trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
     trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
     mergedData = MergedNumericTable(trainData, trainGroundTruth)
     # Retrieve the data from the input file
     # Create an algorithm object to train the SVM model
     algorithm =
     algorithm.parameter.kernel = kernel
     algorithm.parameter.cacheSize = 600000000
     # Pass a training data set and dependent values to the algorithm
     algorithm.input.set(, trainData)
     algorithm.input.set(, trainGroundTruth)
     # Build the SVM model and get the algorithm results
     trainingResult = algorithm.compute()
 def testModel():
     global predictionResult, groundTruthLabels
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
     testDataSource = FileDataSource(
         testDatasetFileName, DataSourceIface.doAllocateNumericTable,
     # Create Numeric Tables for testing data and labels
     testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
     groundTruthLabels = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
     mergedData = MergedNumericTable(testData, groundTruthLabels)
     # Retrieve the data from input file
     # Create an algorithm object to predict SVM values
     algorithm = svm.prediction.Batch()
     algorithm.parameter.kernel = kernel
     # Pass a testing data set and the trained model to the algorithm
     algorithm.input.setTable(, testData)
     algorithm.input.setModel(classifier.prediction.model, trainingResult.get(
     # Predict SVM values
     # returns Result class from daal.algorithms.classifier.prediction
     predictionResult = algorithm.compute()
 def testModelQuality():
     global predictedLabels, qualityMetricSetResult, groundTruthLabels
     # Retrieve predicted labels
     predictedLabels = predictionResult.get(classifier.prediction.prediction)
     # Create a quality metric set object to compute quality metrics of the SVM algorithm
     qualityMetricSet = svm.quality_metric_set.Batch()
     input = qualityMetricSet.getInputDataCollection().getInput(svm.quality_metric_set.confusionMatrix)
     input.set(binary_confusion_matrix.predictedLabels,   predictedLabels)
     input.set(binary_confusion_matrix.groundTruthLabels, groundTruthLabels)
     # Compute quality metrics and get the quality metrics
     # returns ResultCollection class from svm.quality_metric_set
     qualityMetricSetResult = qualityMetricSet.compute()
 def printResults():
     # Print the classification results
         groundTruthLabels, predictedLabels,
         "Ground truth", "Classification results",
         "SVM classification results (first 20 observations):", 20, interval=15, flt64=False
     # Print the quality metrics
     qualityMetricResult = qualityMetricSetResult.getResult(svm.quality_metric_set.confusionMatrix)
     printNumericTable(qualityMetricResult.get(binary_confusion_matrix.confusionMatrix), "Confusion matrix:")
     block = BlockDescriptor()
     qualityMetricsTable = qualityMetricResult.get(binary_confusion_matrix.binaryMetrics)
     qualityMetricsTable.getBlockOfRows(0, 1, readOnly, block)
     qualityMetricsData = block.getArray().flatten()
     print("Accuracy:      {0:.3f}".format(qualityMetricsData[binary_confusion_matrix.accuracy]))
     print("Precision:     {0:.3f}".format(qualityMetricsData[binary_confusion_matrix.precision]))
     print("Recall:        {0:.3f}".format(qualityMetricsData[binary_confusion_matrix.recall]))
     print("F-score:       {0:.3f}".format(qualityMetricsData[binary_confusion_matrix.fscore]))
     print("Specificity:   {0:.3f}".format(qualityMetricsData[binary_confusion_matrix.specificity]))
     print("AUC:           {0:.3f}".format(qualityMetricsData[binary_confusion_matrix.AUC]))
 if __name__ == "__main__":
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