47 from daal.algorithms.multinomial_naive_bayes
import prediction, training
48 from daal.algorithms
import classifier
49 from daal.data_management
import FileDataSource, DataSourceIface
51 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
52 if utils_folder
not in sys.path:
53 sys.path.insert(0, utils_folder)
54 from utils
import printNumericTables, createSparseTable
56 DAAL_PREFIX = os.path.join(
'..',
'data')
59 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv')
60 trainGroundTruthFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv')
62 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_csr.csv')
63 testGroundTruthFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_labels.csv')
65 nTrainObservations = 8000
66 nTestObservations = 2000
70 predictionResult =
None
77 trainGroundTruthSource = FileDataSource(
78 trainGroundTruthFileName,
79 DataSourceIface.doAllocateNumericTable,
80 DataSourceIface.doDictionaryFromContext
84 trainData = createSparseTable(trainDatasetFileName)
85 trainGroundTruthSource.loadDataBlock(nTrainObservations)
88 algorithm = training.Batch(nClasses, method=training.fastCSR)
91 algorithm.input.set(classifier.training.data, trainData)
92 algorithm.input.set(classifier.training.labels, trainGroundTruthSource.getNumericTable())
95 trainingResult = algorithm.compute()
99 global predictionResult
102 testData = createSparseTable(testDatasetFileName)
105 algorithm = prediction.Batch(nClasses, method=prediction.fastCSR)
108 algorithm.input.setTable(classifier.prediction.data, testData)
109 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
112 predictionResult = algorithm.compute()
117 testGroundTruth = FileDataSource(
118 testGroundTruthFileName,
119 DataSourceIface.doAllocateNumericTable,
120 DataSourceIface.doDictionaryFromContext
123 testGroundTruth.loadDataBlock(nTestObservations)
126 testGroundTruth.getNumericTable(),
127 predictionResult.get(classifier.prediction.prediction),
128 "Ground truth",
"Classification results",
129 "NaiveBayes classification results (first 20 observations):", 20, 15, flt64=
False
132 if __name__ ==
"__main__":