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 DataSourceIface, FileDataSource
52 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
53 if utils_folder
not in sys.path:
54 sys.path.insert(0, utils_folder)
55 from utils
import printNumericTables, createSparseTable
58 data_dir = os.path.join(
'..',
'data',
'batch')
59 trainDatasetFileName = os.path.join(data_dir,
'svm_multi_class_train_csr.csv')
60 trainLabelsFileName = os.path.join(data_dir,
'svm_multi_class_train_labels.csv')
61 testDatasetFileName = os.path.join(data_dir,
'svm_multi_class_test_csr.csv')
62 testLabelsFileName = os.path.join(data_dir,
'svm_multi_class_test_labels.csv')
66 trainingAlg = training.Batch()
67 predictionAlg = prediction.Batch()
70 kernel = kernel_function.linear.Batch(method=kernel_function.linear.fastCSR)
73 predictionResult =
None 74 testGroundTruth =
None 81 trainLabelsDataSource = FileDataSource(
82 trainLabelsFileName, DataSourceIface.doAllocateNumericTable,
83 DataSourceIface.doDictionaryFromContext
87 trainData = createSparseTable(trainDatasetFileName)
90 trainLabelsDataSource.loadDataBlock()
93 algorithm = multi_class_classifier.training.Batch(nClasses)
95 algorithm.parameter.training = trainingAlg
96 algorithm.parameter.prediction = predictionAlg
99 algorithm.input.set(classifier.training.data, trainData)
100 algorithm.input.set(classifier.training.labels, trainLabelsDataSource.getNumericTable())
104 trainingResult = algorithm.compute()
108 global predictionResult
111 testData = createSparseTable(testDatasetFileName)
114 algorithm = multi_class_classifier.prediction.Batch(nClasses)
116 algorithm.parameter.training = trainingAlg
117 algorithm.parameter.prediction = predictionAlg
120 algorithm.input.setTable(classifier.prediction.data, testData)
121 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
125 predictionResult = algorithm.compute()
131 testLabelsDataSource = FileDataSource(
132 testLabelsFileName, DataSourceIface.doAllocateNumericTable,
133 DataSourceIface.doDictionaryFromContext
136 testLabelsDataSource.loadDataBlock()
137 testGroundTruth = testLabelsDataSource.getNumericTable()
140 testGroundTruth, predictionResult.get(classifier.prediction.prediction),
141 "Ground truth",
"Classification results",
142 "Multi-class SVM classification sample program results (first 20 observations):",
146 if __name__ ==
"__main__":
147 trainingAlg.parameter.cacheSize = 100000000
148 trainingAlg.parameter.kernel = kernel
149 predictionAlg.parameter.kernel = kernel