24 from daal.algorithms.svm
import training, prediction
25 from daal.algorithms
import kernel_function, classifier
26 from daal.data_management
import (
27 DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
30 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
31 if utils_folder
not in sys.path:
32 sys.path.insert(0, utils_folder)
33 from utils
import printNumericTables
36 DATA_PREFIX = os.path.join(
'..',
'data',
'batch')
38 trainDatasetFileName = os.path.join(DATA_PREFIX,
'svm_two_class_train_dense.csv')
39 testDatasetFileName = os.path.join(DATA_PREFIX,
'svm_two_class_test_dense.csv')
44 kernel = kernel_function.linear.Batch()
48 predictionResult =
None
49 testGroundTruth =
None
56 trainDataSource = FileDataSource(
57 trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
58 DataSourceIface.doDictionaryFromContext
62 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
63 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
64 mergedData = MergedNumericTable(trainData, trainGroundTruth)
67 trainDataSource.loadDataBlock(mergedData)
70 algorithm = training.Batch()
72 algorithm.parameter.kernel = kernel
73 algorithm.parameter.cacheSize = 600000000
76 algorithm.input.set(classifier.training.data, trainData)
77 algorithm.input.set(classifier.training.labels, trainGroundTruth)
80 trainingResult = algorithm.compute()
84 global predictionResult, testGroundTruth
87 testDataSource = FileDataSource(
88 testDatasetFileName, DataSourceIface.notAllocateNumericTable,
89 DataSourceIface.doDictionaryFromContext
93 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
94 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
95 mergedData = MergedNumericTable(testData, testGroundTruth)
98 testDataSource.loadDataBlock(mergedData)
101 algorithm = prediction.Batch()
103 algorithm.parameter.kernel = kernel
106 algorithm.input.setTable(classifier.prediction.data, testData)
107 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
113 predictionResult = algorithm.getResult()
119 testGroundTruth, predictionResult.get(classifier.prediction.prediction),
120 "Ground truth\t",
"Classification results",
121 "SVM classification results (first 20 observations):", 20, flt64=
False
124 if __name__ ==
"__main__":