24 from daal.algorithms.multinomial_naive_bayes
import prediction, training
25 from daal.algorithms
import classifier
26 from daal.data_management
import FileDataSource, DataSourceIface
28 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
29 if utils_folder
not in sys.path:
30 sys.path.insert(0, utils_folder)
31 from utils
import printNumericTables, createSparseTable
33 DAAL_PREFIX = os.path.join(
'..',
'data')
36 trainDatasetFileNames = [
37 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv'),
38 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv'),
39 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv'),
40 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv')
43 trainGroundTruthFileNames = [
44 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv'),
45 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv'),
46 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv'),
47 os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv')
50 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_csr.csv')
51 testGroundTruthFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_labels.csv')
53 nTrainVectorsInBlock = 8000
54 nTestObservations = 2000
59 predictionResult =
None
60 trainData = [0] * nBlocks
65 global trainData, trainingResult
68 algorithm = training.Online(nClasses, method=training.fastCSR)
70 for i
in range(nBlocks):
72 trainData[i] = createSparseTable(trainDatasetFileNames[i])
73 trainLabelsSource = FileDataSource(
74 trainGroundTruthFileNames[i], DataSourceIface.doAllocateNumericTable,
75 DataSourceIface.doDictionaryFromContext
78 trainLabelsSource.loadDataBlock(nTrainVectorsInBlock)
81 algorithm.input.set(classifier.training.data, trainData[i])
82 algorithm.input.set(classifier.training.labels, trainLabelsSource.getNumericTable())
88 trainingResult = algorithm.finalizeCompute()
92 global predictionResult, testData
95 testData = createSparseTable(testDatasetFileName)
98 algorithm = prediction.Batch(nClasses, method=prediction.fastCSR)
101 algorithm.input.setTable(classifier.prediction.data, testData)
102 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
105 predictionResult = algorithm.compute()
110 testGroundTruth = FileDataSource(
111 testGroundTruthFileName, DataSourceIface.doAllocateNumericTable,
112 DataSourceIface.doDictionaryFromContext
114 testGroundTruth.loadDataBlock(nTestObservations)
117 testGroundTruth.getNumericTable(),
118 predictionResult.get(classifier.prediction.prediction),
119 "Ground truth",
"Classification results",
120 "NaiveBayes classification results (first 20 observations):", 20, 15, flt64=
False
123 if __name__ ==
"__main__":