22 from daal.algorithms.svm
import training, prediction
23 from daal.algorithms
import classifier, kernel_function, multi_class_classifier
24 from daal.data_management
import (
25 FileDataSource, DataSourceIface, HomogenNumericTable, MergedNumericTable, NumericTableIface
28 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
29 if utils_folder
not in sys.path:
30 sys.path.insert(0, utils_folder)
31 from utils
import printNumericTables
33 DAAL_PREFIX = os.path.join(
'..',
'data')
36 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'svm_multi_class_train_dense.csv')
38 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'svm_multi_class_test_dense.csv')
43 trainingBatch = training.Batch()
44 predictionBatch = prediction.Batch()
47 predictionResult =
None
48 kernelBatch = kernel_function.linear.Batch()
49 testGroundTruth =
None
56 trainDataSource = FileDataSource(
58 DataSourceIface.notAllocateNumericTable,
59 DataSourceIface.doDictionaryFromContext
63 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
64 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
65 mergedData = MergedNumericTable(trainData, trainGroundTruth)
68 trainDataSource.loadDataBlock(mergedData)
71 algorithm = multi_class_classifier.training.Batch(nClasses)
73 algorithm.parameter.training = trainingBatch
74 algorithm.parameter.prediction = predictionBatch
77 algorithm.input.set(classifier.training.data, trainData)
78 algorithm.input.set(classifier.training.labels, trainGroundTruth)
82 trainingResult = algorithm.compute()
86 global predictionResult, testGroundTruth
89 testDataSource = FileDataSource(
91 DataSourceIface.doAllocateNumericTable,
92 DataSourceIface.doDictionaryFromContext
96 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
97 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
98 mergedData = MergedNumericTable(testData, testGroundTruth)
101 testDataSource.loadDataBlock(mergedData)
104 algorithm = multi_class_classifier.prediction.Batch(nClasses)
106 algorithm.parameter.training = trainingBatch
107 algorithm.parameter.prediction = predictionBatch
110 algorithm.input.setTable(classifier.prediction.data, testData)
111 algorithm.input.setModel(classifier.prediction.model,
112 trainingResult.get(classifier.training.model))
116 predictionResult = algorithm.compute()
123 predictionResult.get(classifier.prediction.prediction),
124 "Ground truth",
"Classification results",
125 "Multi-class SVM classification sample program results (first 20 observations):", 20, flt64=
False
128 if __name__ ==
"__main__":
130 trainingBatch.parameter.cacheSize = 100000000
131 trainingBatch.parameter.kernel = kernelBatch
132 predictionBatch.parameter.kernel = kernelBatch