svm_multi_class_dense_batch.py

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.

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 # file: svm_multi_class_dense_batch.py
 #===============================================================================
 # 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.
 #===============================================================================
 
 ## <a name="DAAL-EXAMPLE-PY-SVM_MULTI_CLASS_DENSE_BATCH"></a>
 ## \example svm_multi_class_dense_batch.py
 
 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 (
     FileDataSource, DataSourceIface, HomogenNumericTable, MergedNumericTable, NumericTableIface
 )
 
 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
 
 DAAL_PREFIX = os.path.join('..', 'data')
 
 # Input data set parameters
 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'svm_multi_class_train_dense.csv')
 
 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'svm_multi_class_test_dense.csv')
 
 nFeatures = 20
 nClasses = 5
 
 trainingBatch = training.Batch()
 predictionBatch = prediction.Batch()
 
 trainingResult = None
 predictionResult = None
 kernelBatch = kernel_function.linear.Batch()
 testGroundTruth = None
 
 
 def trainModel():
     global trainingResult
 
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
     trainDataSource = FileDataSource(
         trainDatasetFileName,
         DataSourceIface.notAllocateNumericTable,
         DataSourceIface.doDictionaryFromContext
     )
 
     # 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
     trainDataSource.loadDataBlock(mergedData)
 
     # Create an algorithm object to train the multi-class SVM model
     algorithm = multi_class_classifier.training.Batch(nClasses)
 
     algorithm.parameter.training = trainingBatch
     algorithm.parameter.prediction = predictionBatch
 
     # Pass a training data set and dependent values to the algorithm
     algorithm.input.set(classifier.training.data, trainData)
     algorithm.input.set(classifier.training.labels, trainGroundTruth)
 
     # Build the multi-class SVM model
     # and retrieve Result class from multi_class_classifier.training
     trainingResult = algorithm.compute()
 
 
 def testModel():
     global predictionResult, testGroundTruth
 
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
     testDataSource = FileDataSource(
         testDatasetFileName,
         DataSourceIface.doAllocateNumericTable,
         DataSourceIface.doDictionaryFromContext
     )
 
     # Create Numeric Tables for testing data and labels
     testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
     testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
     mergedData = MergedNumericTable(testData, testGroundTruth)
 
     # Retrieve the data from input file
     testDataSource.loadDataBlock(mergedData)
 
     # Create an algorithm object to predict multi-class SVM values
     algorithm = multi_class_classifier.prediction.Batch(nClasses)
 
     algorithm.parameter.training = trainingBatch
     algorithm.parameter.prediction = predictionBatch
 
     # Pass a testing data set and the trained model to the algorithm
     algorithm.input.setTable(classifier.prediction.data, testData)
     algorithm.input.setModel(classifier.prediction.model,
                              trainingResult.get(classifier.training.model))
 
     # Predict multi-class SVM values
     # and retrieve Result class from classifier.prediction
     predictionResult = algorithm.compute()  # Retrieve the algorithm results
 
 
 def printResults():
 
     printNumericTables(
         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__":
 
     trainingBatch.parameter.cacheSize = 100000000
     trainingBatch.parameter.kernel = kernelBatch
     predictionBatch.parameter.kernel = kernelBatch
 
     trainModel()
     testModel()
     printResults()
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