22 from daal.algorithms.multinomial_naive_bayes
import prediction, training
23 from daal.algorithms
import classifier
24 from daal.data_management
import FileDataSource, DataSourceIface
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
31 DAAL_PREFIX = os.path.join(
'..',
'data')
34 trainDatasetFileNames = [
35 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv'),
36 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv'),
37 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv'),
38 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv')
41 trainGroundTruthFileNames = [
42 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv'),
43 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv'),
44 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv'),
45 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv')
48 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_csr.csv')
49 testGroundTruthFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_labels.csv')
51 nTrainVectorsInBlock = 8000
52 nTestObservations = 2000
57 predictionResult =
None
58 trainData = [0] * nBlocks
63 global trainData, trainingResult
66 algorithm = training.Online(nClasses, method=training.fastCSR)
68 for i
in range(nBlocks):
70 trainData[i] = createSparseTable(trainDatasetFileNames[i])
71 trainLabelsSource = FileDataSource(
72 trainGroundTruthFileNames[i], DataSourceIface.doAllocateNumericTable,
73 DataSourceIface.doDictionaryFromContext
76 trainLabelsSource.loadDataBlock(nTrainVectorsInBlock)
79 algorithm.input.set(classifier.training.data, trainData[i])
80 algorithm.input.set(classifier.training.labels, trainLabelsSource.getNumericTable())
86 trainingResult = algorithm.finalizeCompute()
90 global predictionResult, testData
93 testData = createSparseTable(testDatasetFileName)
96 algorithm = prediction.Batch(nClasses, method=prediction.fastCSR)
99 algorithm.input.setTable(classifier.prediction.data, testData)
100 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
103 predictionResult = algorithm.compute()
108 testGroundTruth = FileDataSource(
109 testGroundTruthFileName, DataSourceIface.doAllocateNumericTable,
110 DataSourceIface.doDictionaryFromContext
112 testGroundTruth.loadDataBlock(nTestObservations)
115 testGroundTruth.getNumericTable(),
116 predictionResult.get(classifier.prediction.prediction),
117 "Ground truth",
"Classification results",
118 "NaiveBayes classification results (first 20 observations):", 20, 15, flt64=
False
121 if __name__ ==
"__main__":