Deprecation Notice: With the introduction of
daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL
pip and
conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.
Note: To find daal4py examples, refer to daal4py documentation
or browse github
repository.
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 DataSourceIface, FileDataSource
26 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
27 if utils_folder
not in sys.path:
28 sys.path.insert(0, utils_folder)
29 from utils
import printNumericTables, createSparseTable
32 data_dir = os.path.join(
'..',
'data',
'batch')
33 trainDatasetFileName = os.path.join(data_dir,
'svm_multi_class_train_csr.csv')
34 trainLabelsFileName = os.path.join(data_dir,
'svm_multi_class_train_labels.csv')
35 testDatasetFileName = os.path.join(data_dir,
'svm_multi_class_test_csr.csv')
36 testLabelsFileName = os.path.join(data_dir,
'svm_multi_class_test_labels.csv')
40 trainingAlg = training.Batch()
41 predictionAlg = prediction.Batch()
44 kernel = kernel_function.linear.Batch(method=kernel_function.linear.fastCSR)
47 predictionResult =
None
48 testGroundTruth =
None
55 trainLabelsDataSource = FileDataSource(
56 trainLabelsFileName, DataSourceIface.doAllocateNumericTable,
57 DataSourceIface.doDictionaryFromContext
61 trainData = createSparseTable(trainDatasetFileName)
64 trainLabelsDataSource.loadDataBlock()
67 algorithm = multi_class_classifier.training.Batch(nClasses)
69 algorithm.parameter.training = trainingAlg
70 algorithm.parameter.prediction = predictionAlg
73 algorithm.input.set(classifier.training.data, trainData)
74 algorithm.input.set(classifier.training.labels, trainLabelsDataSource.getNumericTable())
78 trainingResult = algorithm.compute()
82 global predictionResult
85 testData = createSparseTable(testDatasetFileName)
88 algorithm = multi_class_classifier.prediction.Batch(nClasses)
90 algorithm.parameter.training = trainingAlg
91 algorithm.parameter.prediction = predictionAlg
94 algorithm.input.setTable(classifier.prediction.data, testData)
95 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
99 predictionResult = algorithm.compute()
105 testLabelsDataSource = FileDataSource(
106 testLabelsFileName, DataSourceIface.doAllocateNumericTable,
107 DataSourceIface.doDictionaryFromContext
110 testLabelsDataSource.loadDataBlock()
111 testGroundTruth = testLabelsDataSource.getNumericTable()
114 testGroundTruth, predictionResult.get(classifier.prediction.prediction),
115 "Ground truth",
"Classification results",
116 "Multi-class SVM classification sample program results (first 20 observations):",
120 if __name__ ==
"__main__":
121 trainingAlg.parameter.cacheSize = 100000000
122 trainingAlg.parameter.kernel = kernel
123 predictionAlg.parameter.kernel = kernel