svm_two_class_csr_batch.py

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 # file: svm_two_class_csr_batch.py
 #===============================================================================
 # Copyright 2014-2019 Intel Corporation.
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 # 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.
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 # 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_TWO_CLASS_CSR_BATCH"></a>
 ## \example svm_two_class_csr_batch.py
 
 import os
 import sys
 
 from daal.algorithms.svm import training, prediction
 from daal.algorithms import kernel_function, 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_PREFIX = os.path.join('..', 'data', 'batch')
 
 trainDatasetFileName = os.path.join(DATA_PREFIX, 'svm_two_class_train_csr.csv')
 trainLabelsFileName = os.path.join(DATA_PREFIX, 'svm_two_class_train_labels.csv')
 testDatasetFileName = os.path.join(DATA_PREFIX, 'svm_two_class_test_csr.csv')
 testLabelsFileName = os.path.join(DATA_PREFIX, 'svm_two_class_test_labels.csv')
 
 # Parameters for the SVM kernel function
 kernel = kernel_function.linear.Batch(method=kernel_function.linear.fastCSR)
 
 # Model object for the SVM algorithm
 trainingResult = None
 predictionResult = None
 
 
 def trainModel():
     global trainingResult
 
     # Initialize FileDataSource to retrieve the input data from a .csv file
     trainLabelsDataSource = FileDataSource(
         trainLabelsFileName, DataSourceIface.doAllocateNumericTable,
         DataSourceIface.doDictionaryFromContext
     )
 
     # Create numeric table for training data
     trainData = createSparseTable(trainDatasetFileName)
 
     # Retrieve the data from the input file
     trainLabelsDataSource.loadDataBlock()
 
     # Create an algorithm object to train the SVM model
     algorithm = training.Batch()
 
     algorithm.parameter.kernel = kernel
     algorithm.parameter.cacheSize = 40000000
 
     # Pass a training data set and dependent values to the algorithm
     algorithm.input.set(classifier.training.data, trainData)
     algorithm.input.set(classifier.training.labels, trainLabelsDataSource.getNumericTable())
 
     # Build the SVM model
     trainingResult = algorithm.compute()
 
 
 def testModel():
     global predictionResult
 
     # Create Numeric Tables for testing data
     testData = createSparseTable(testDatasetFileName)
 
     # Create an algorithm object to predict SVM values
     algorithm = prediction.Batch()
 
     algorithm.parameter.kernel = kernel
 
     # 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 SVM values
     algorithm.compute()
 
     # Retrieve the algorithm results
     predictionResult = algorithm.getResult()
 
 
 def printResults():
 
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
     testLabelsDataSource = FileDataSource(
         testLabelsFileName, DataSourceIface.doAllocateNumericTable,
         DataSourceIface.doDictionaryFromContext
     )
     # Retrieve the data from input file
     testLabelsDataSource.loadDataBlock()
     testGroundTruth = testLabelsDataSource.getNumericTable()
 
     printNumericTables(
         testGroundTruth, predictionResult.get(classifier.prediction.prediction),
         "Ground truth\t", "Classification results",
         "SVM classification results (first 20 observations):", 20, flt64=False
     )
 
 if __name__ == "__main__":
 
     trainModel()
     testModel()
     printResults()
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