22 from daal.algorithms.multinomial_naive_bayes
import prediction, training
23 from daal.algorithms
import classifier
24 from daal.data_management
import FileDataSource, DataSourceIface
26 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
27 if utils_folder
not in sys.path:
28 sys.path.insert(0, utils_folder)
29 from utils
import printNumericTables, createSparseTable
31 DAAL_PREFIX = os.path.join(
'..',
'data')
34 trainDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_csr.csv')
35 trainGroundTruthFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_train_labels.csv')
37 testDatasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_csr.csv')
38 testGroundTruthFileName = os.path.join(DAAL_PREFIX,
'batch',
'naivebayes_test_labels.csv')
40 nTrainObservations = 8000
41 nTestObservations = 2000
45 predictionResult =
None
52 trainGroundTruthSource = FileDataSource(
53 trainGroundTruthFileName,
54 DataSourceIface.doAllocateNumericTable,
55 DataSourceIface.doDictionaryFromContext
59 trainData = createSparseTable(trainDatasetFileName)
60 trainGroundTruthSource.loadDataBlock(nTrainObservations)
63 algorithm = training.Batch(nClasses, method=training.fastCSR)
66 algorithm.input.set(classifier.training.data, trainData)
67 algorithm.input.set(classifier.training.labels, trainGroundTruthSource.getNumericTable())
70 trainingResult = algorithm.compute()
74 global predictionResult
77 testData = createSparseTable(testDatasetFileName)
80 algorithm = prediction.Batch(nClasses, method=prediction.fastCSR)
83 algorithm.input.setTable(classifier.prediction.data, testData)
84 algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
87 predictionResult = algorithm.compute()
92 testGroundTruth = FileDataSource(
93 testGroundTruthFileName,
94 DataSourceIface.doAllocateNumericTable,
95 DataSourceIface.doDictionaryFromContext
98 testGroundTruth.loadDataBlock(nTestObservations)
101 testGroundTruth.getNumericTable(),
102 predictionResult.get(classifier.prediction.prediction),
103 "Ground truth",
"Classification results",
104 "NaiveBayes classification results (first 20 observations):", 20, 15, flt64=
False
107 if __name__ ==
"__main__":