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.
 ## \example
 import os
 import sys
 from daal.algorithms.svm import training, prediction
 from daal.algorithms import classifier, kernel_function, multi_class_classifier
 from daal.data_management import DataSourceIface, FileDataSource
 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, createSparseTable
 # Input data set parameters
 data_dir = os.path.join('..', 'data', 'batch')
 trainDatasetFileName = os.path.join(data_dir, 'svm_multi_class_train_csr.csv')
 trainLabelsFileName = os.path.join(data_dir, 'svm_multi_class_train_labels.csv')
 testDatasetFileName = os.path.join(data_dir, 'svm_multi_class_test_csr.csv')
 testLabelsFileName = os.path.join(data_dir, 'svm_multi_class_test_labels.csv')
 nClasses = 5
 trainingAlg = training.Batch()
 predictionAlg = prediction.Batch()
 # Parameters for the SVM kernel function
 kernel = kernel_function.linear.Batch(method=kernel_function.linear.fastCSR)
 trainingResult = None
 predictionResult = None
 testGroundTruth = None
 def trainModel():
     global trainingResult
     # Initialize FileDataSource to retrieve the input data from a .csv file
     trainLabelsDataSource = FileDataSource(
         trainLabelsFileName, DataSourceIface.doAllocateNumericTable,
     # Create numeric table for training data
     trainData = createSparseTable(trainDatasetFileName)
     # Retrieve the data from the input file
     # Create an algorithm object to train the multi-class SVM model
     algorithm = = trainingAlg
     algorithm.parameter.prediction = predictionAlg
     # Pass a training data set and dependent values to the algorithm
     algorithm.input.set(, trainData)
     algorithm.input.set(, trainLabelsDataSource.getNumericTable())
     # Build the multi-class SVM model and retrieve the algorithm results
     # (Result class from
     trainingResult = algorithm.compute()
 def testModel():
     global predictionResult
     # Create Numeric Tables for testing data
     testData = createSparseTable(testDatasetFileName)
     # Create an algorithm object to predict multi-class SVM values
     algorithm = multi_class_classifier.prediction.Batch(nClasses) = trainingAlg
     algorithm.parameter.prediction = predictionAlg
     # 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 multi-class SVM values and retrieve the algorithm results
     # (Result class from classifier.prediction)
     predictionResult = algorithm.compute()
 def printResults():
     # Initialize FileDataSource to retrieve the test data from a .csv file
     testLabelsDataSource = FileDataSource(
         testLabelsFileName, DataSourceIface.doAllocateNumericTable,
     # Retrieve the data from input file
     testGroundTruth = testLabelsDataSource.getNumericTable()
         testGroundTruth, predictionResult.get(classifier.prediction.prediction),
         "Ground truth", "Classification results",
         "Multi-class SVM classification sample program results (first 20 observations):",
         20, flt64=False
 if __name__ == "__main__":
     trainingAlg.parameter.cacheSize = 100000000
     trainingAlg.parameter.kernel = kernel
     predictionAlg.parameter.kernel = kernel
For more complete information about compiler optimizations, see our Optimization Notice.
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