48 from daal.algorithms.multinomial_naive_bayes
import prediction, training
49 from daal.algorithms
import classifier
50 from daal.data_management
import FileDataSource, DataSourceIface
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
57 DAAL_PREFIX = os.path.join(
'..',
'data')
60 trainDatasetFileNames = [
61 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv'),
62 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv'),
63 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv'),
64 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv')
67 trainGroundTruthFileNames = [
68 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv'),
69 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv'),
70 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv'),
71 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv')
74 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_csr.csv')
75 testGroundTruthFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_labels.csv')
77 nTrainVectorsInBlock = 8000
78 nTestObservations = 2000
83 predictionResult =
None
84 trainData = [0] * nBlocks
89 global trainData, trainingResult
92 algorithm = training.Online(nClasses, method=training.fastCSR)
94 for i
in range(nBlocks):
96 trainData[i] = createSparseTable(trainDatasetFileNames[i])
97 trainLabelsSource = FileDataSource(
98 trainGroundTruthFileNames[i], DataSourceIface.doAllocateNumericTable,
99 DataSourceIface.doDictionaryFromContext
102 trainLabelsSource.loadDataBlock(nTrainVectorsInBlock)
105 algorithm.input.set(classifier.training.data, trainData[i])
106 algorithm.input.set(classifier.training.labels, trainLabelsSource.getNumericTable())
112 trainingResult = algorithm.finalizeCompute()
116 global predictionResult, testData
119 testData = createSparseTable(testDatasetFileName)
122 algorithm = prediction.Batch(nClasses, method=prediction.fastCSR)
125 algorithm.input.setTable(classifier.prediction.data, testData)
126 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
129 predictionResult = algorithm.compute()
134 testGroundTruth = FileDataSource(
135 testGroundTruthFileName, DataSourceIface.doAllocateNumericTable,
136 DataSourceIface.doDictionaryFromContext
138 testGroundTruth.loadDataBlock(nTestObservations)
141 testGroundTruth.getNumericTable(),
142 predictionResult.get(classifier.prediction.prediction),
143 "Ground truth",
"Classification results",
144 "NaiveBayes classification results (first 20 observations):", 20, 15, flt64=
False
147 if __name__ ==
"__main__":