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 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv')
61 trainGroundTruthFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv')
63 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_csr.csv')
64 testGroundTruthFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_labels.csv')
66 nTrainObservations = 8000
67 nTestObservations = 2000
71 predictionResult =
None 78 trainGroundTruthSource = FileDataSource(
79 trainGroundTruthFileName,
80 DataSourceIface.doAllocateNumericTable,
81 DataSourceIface.doDictionaryFromContext
85 trainData = createSparseTable(trainDatasetFileName)
86 trainGroundTruthSource.loadDataBlock(nTrainObservations)
89 algorithm = training.Batch(nClasses, method=training.fastCSR)
92 algorithm.input.set(classifier.training.data, trainData)
93 algorithm.input.set(classifier.training.labels, trainGroundTruthSource.getNumericTable())
96 trainingResult = algorithm.compute()
100 global predictionResult
103 testData = createSparseTable(testDatasetFileName)
106 algorithm = prediction.Batch(nClasses, method=prediction.fastCSR)
109 algorithm.input.setTable(classifier.prediction.data, testData)
110 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
113 predictionResult = algorithm.compute()
118 testGroundTruth = FileDataSource(
119 testGroundTruthFileName,
120 DataSourceIface.doAllocateNumericTable,
121 DataSourceIface.doDictionaryFromContext
124 testGroundTruth.loadDataBlock(nTestObservations)
127 testGroundTruth.getNumericTable(),
128 predictionResult.get(classifier.prediction.prediction),
129 "Ground truth",
"Classification results",
130 "NaiveBayes classification results (first 20 observations):", 20, 15, flt64=
False 133 if __name__ ==
"__main__":