24 from daal.algorithms.svm
import training, prediction
25 from daal.algorithms
import classifier, kernel_function, multi_class_classifier
26 from daal.data_management
import (
27 FileDataSource, DataSourceIface, 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
35 DAAL_PREFIX = os.path.join(
'..',
'data')
38 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'svm_multi_class_train_dense.csv')
40 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'svm_multi_class_test_dense.csv')
45 trainingBatch = training.Batch()
46 predictionBatch = prediction.Batch()
49 predictionResult =
None
50 kernelBatch = kernel_function.linear.Batch()
51 testGroundTruth =
None
58 trainDataSource = FileDataSource(
60 DataSourceIface.notAllocateNumericTable,
61 DataSourceIface.doDictionaryFromContext
65 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
66 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
67 mergedData = MergedNumericTable(trainData, trainGroundTruth)
70 trainDataSource.loadDataBlock(mergedData)
73 algorithm = multi_class_classifier.training.Batch(nClasses)
75 algorithm.parameter.training = trainingBatch
76 algorithm.parameter.prediction = predictionBatch
79 algorithm.input.set(classifier.training.data, trainData)
80 algorithm.input.set(classifier.training.labels, trainGroundTruth)
84 trainingResult = algorithm.compute()
88 global predictionResult, testGroundTruth
91 testDataSource = FileDataSource(
93 DataSourceIface.doAllocateNumericTable,
94 DataSourceIface.doDictionaryFromContext
98 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
99 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
100 mergedData = MergedNumericTable(testData, testGroundTruth)
103 testDataSource.loadDataBlock(mergedData)
106 algorithm = multi_class_classifier.prediction.Batch(nClasses)
108 algorithm.parameter.training = trainingBatch
109 algorithm.parameter.prediction = predictionBatch
112 algorithm.input.setTable(classifier.prediction.data, testData)
113 algorithm.input.setModel(classifier.prediction.model,
114 trainingResult.get(classifier.training.model))
118 predictionResult = algorithm.compute()
125 predictionResult.get(classifier.prediction.prediction),
126 "Ground truth",
"Classification results",
127 "Multi-class SVM classification sample program results (first 20 observations):", 20, flt64=
False
130 if __name__ ==
"__main__":
132 trainingBatch.parameter.cacheSize = 100000000
133 trainingBatch.parameter.kernel = kernelBatch
134 predictionBatch.parameter.kernel = kernelBatch