48 from daal.algorithms.svm
import training, prediction
49 from daal.algorithms
import kernel_function, classifier
50 from daal.data_management
import (
51 DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
54 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
55 if utils_folder
not in sys.path:
56 sys.path.insert(0, utils_folder)
57 from utils
import printNumericTables
60 DATA_PREFIX = os.path.join(
'..',
'data',
'batch')
62 trainDatasetFileName = os.path.join(DATA_PREFIX,
'svm_two_class_train_dense.csv')
63 testDatasetFileName = os.path.join(DATA_PREFIX,
'svm_two_class_test_dense.csv')
68 kernel = kernel_function.linear.Batch()
72 predictionResult =
None
73 testGroundTruth =
None
80 trainDataSource = FileDataSource(
81 trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
82 DataSourceIface.doDictionaryFromContext
86 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
87 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
88 mergedData = MergedNumericTable(trainData, trainGroundTruth)
91 trainDataSource.loadDataBlock(mergedData)
94 algorithm = training.Batch()
96 algorithm.parameter.kernel = kernel
97 algorithm.parameter.cacheSize = 600000000
100 algorithm.input.set(classifier.training.data, trainData)
101 algorithm.input.set(classifier.training.labels, trainGroundTruth)
104 trainingResult = algorithm.compute()
108 global predictionResult, testGroundTruth
111 testDataSource = FileDataSource(
112 testDatasetFileName, DataSourceIface.notAllocateNumericTable,
113 DataSourceIface.doDictionaryFromContext
117 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
118 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
119 mergedData = MergedNumericTable(testData, testGroundTruth)
122 testDataSource.loadDataBlock(mergedData)
125 algorithm = prediction.Batch()
127 algorithm.parameter.kernel = kernel
130 algorithm.input.setTable(classifier.prediction.data, testData)
131 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
137 predictionResult = algorithm.getResult()
143 testGroundTruth, predictionResult.get(classifier.prediction.prediction),
144 "Ground truth\t",
"Classification results",
145 "SVM classification results (first 20 observations):", 20, flt64=
False
148 if __name__ ==
"__main__":