47 from daal
import step1Local, step2Master
48 from daal.algorithms
import classifier
49 from daal.algorithms.multinomial_naive_bayes
import training, prediction
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')
79 nTrainVectorsInBlock = 8000
80 nTestObservations = 2000
83 predictionResult =
None
84 trainData = [0] * nBlocks
89 global trainData, trainingResult
91 masterAlgorithm = training.Distributed(step2Master, nClasses, method=training.fastCSR)
93 for i
in range(nBlocks):
95 trainData[i] = createSparseTable(trainDatasetFileNames[i])
98 trainLabelsSource = FileDataSource(
99 trainGroundTruthFileNames[i], DataSourceIface.doAllocateNumericTable,
100 DataSourceIface.doDictionaryFromContext
104 trainLabelsSource.loadDataBlock(nTrainVectorsInBlock)
107 localAlgorithm = training.Distributed(step1Local, nClasses, method=training.fastCSR)
110 localAlgorithm.input.set(classifier.training.data, trainData[i])
111 localAlgorithm.input.set(classifier.training.labels, trainLabelsSource.getNumericTable())
115 masterAlgorithm.input.add(training.partialModels, localAlgorithm.compute())
118 masterAlgorithm.compute()
119 trainingResult = masterAlgorithm.finalizeCompute()
123 global predictionResult, testData
126 testData = createSparseTable(testDatasetFileName)
129 algorithm = prediction.Batch(nClasses, method=prediction.fastCSR)
132 algorithm.input.setTable(classifier.prediction.data, testData)
133 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
136 predictionResult = algorithm.compute()
141 testGroundTruth = FileDataSource(
142 testGroundTruthFileName, DataSourceIface.doAllocateNumericTable,
143 DataSourceIface.doDictionaryFromContext
145 testGroundTruth.loadDataBlock(nTestObservations)
148 testGroundTruth.getNumericTable(),
149 predictionResult.get(classifier.prediction.prediction),
150 "Ground truth",
"Classification results",
151 "NaiveBayes classification results (first 20 observations):", 20, 15, flt64=
False
154 if __name__ ==
"__main__":