48 from daal.algorithms.svm
import training, prediction
49 from daal.algorithms
import classifier, kernel_function, multi_class_classifier
50 from daal.data_management
import (
51 FileDataSource, DataSourceIface, 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
59 DAAL_PREFIX = os.path.join(
'..',
'data')
62 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'svm_multi_class_train_dense.csv')
64 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'svm_multi_class_test_dense.csv')
69 trainingBatch = training.Batch()
70 predictionBatch = prediction.Batch()
73 predictionResult =
None 74 kernelBatch = kernel_function.linear.Batch()
75 testGroundTruth =
None 82 trainDataSource = FileDataSource(
84 DataSourceIface.notAllocateNumericTable,
85 DataSourceIface.doDictionaryFromContext
89 trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
90 trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
91 mergedData = MergedNumericTable(trainData, trainGroundTruth)
94 trainDataSource.loadDataBlock(mergedData)
97 algorithm = multi_class_classifier.training.Batch(nClasses)
99 algorithm.parameter.training = trainingBatch
100 algorithm.parameter.prediction = predictionBatch
103 algorithm.input.set(classifier.training.data, trainData)
104 algorithm.input.set(classifier.training.labels, trainGroundTruth)
108 trainingResult = algorithm.compute()
112 global predictionResult, testGroundTruth
115 testDataSource = FileDataSource(
117 DataSourceIface.doAllocateNumericTable,
118 DataSourceIface.doDictionaryFromContext
122 testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
123 testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
124 mergedData = MergedNumericTable(testData, testGroundTruth)
127 testDataSource.loadDataBlock(mergedData)
130 algorithm = multi_class_classifier.prediction.Batch(nClasses)
132 algorithm.parameter.training = trainingBatch
133 algorithm.parameter.prediction = predictionBatch
136 algorithm.input.setTable(classifier.prediction.data, testData)
137 algorithm.input.setModel(classifier.prediction.model,
138 trainingResult.get(classifier.training.model))
142 predictionResult = algorithm.compute()
149 predictionResult.get(classifier.prediction.prediction),
150 "Ground truth",
"Classification results",
151 "Multi-class SVM classification sample program results (first 20 observations):", 20, flt64=
False 154 if __name__ ==
"__main__":
156 trainingBatch.parameter.cacheSize = 100000000
157 trainingBatch.parameter.kernel = kernelBatch
158 predictionBatch.parameter.kernel = kernelBatch