Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

mn_naive_bayes_dense_online.py

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40 
41 ## <a name="DAAL-EXAMPLE-PY-MULTINOMIAL_NAIVE_BAYES_DENSE_ONLINE"></a>
42 ## \example mn_naive_bayes_dense_online.py
43 
44 import os
45 import sys
46 
47 from daal.algorithms.multinomial_naive_bayes import prediction, training
48 from daal.algorithms import classifier
49 from daal.data_management import (
50  FileDataSource, DataSourceIface, HomogenNumericTable, MergedNumericTable, NumericTableIface
51 )
52 
53 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
54 if utils_folder not in sys.path:
55  sys.path.insert(0, utils_folder)
56 from utils import printNumericTables
57 
58 DAAL_PREFIX = os.path.join('..', 'data')
59 
60 # Input data set parameters
61 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_train_dense.csv')
62 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_test_dense.csv')
63 
64 nFeatures = 20
65 nTrainVectorsInBlock = 2000
66 nClasses = 20
67 
68 trainingResult = None
69 predictionResult = None
70 testGroundTruth = None
71 
72 
73 def trainModel():
74  global trainingResult
75 
76  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
77  trainDataSource = FileDataSource(
78  trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
79  DataSourceIface.doDictionaryFromContext
80  )
81 
82  # Create Numeric Tables for training data and labels
83  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
84  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
85  mergedData = MergedNumericTable(trainData, trainGroundTruth)
86 
87  # Create an algorithm object to train the Naive Bayes model
88  algorithm = training.Online(nClasses)
89 
90  while(trainDataSource.loadDataBlock(nTrainVectorsInBlock, mergedData) == nTrainVectorsInBlock):
91  # Pass a training data set and dependent values to the algorithm
92  algorithm.input.set(classifier.training.data, trainData)
93  algorithm.input.set(classifier.training.labels, trainGroundTruth)
94 
95  # Build the Naive Bayes model
96  algorithm.compute()
97 
98  # Finalize the Naive Bayes model
99  trainingResult = algorithm.finalizeCompute() # Retrieve the algorithm results
100 
101 
102 def testModel():
103  global predictionResult, testGroundTruth
104 
105  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
106  testDataSource = FileDataSource(
107  testDatasetFileName, DataSourceIface.notAllocateNumericTable,
108  DataSourceIface.doDictionaryFromContext
109  )
110 
111  # Create Numeric Tables for testing data and labels
112  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
113  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
114  mergedData = MergedNumericTable(testData, testGroundTruth)
115 
116  # Retrieve the data from input file
117  testDataSource.loadDataBlock(mergedData)
118 
119  # Create an algorithm object to predict Naive Bayes values
120  algorithm = prediction.Batch(nClasses)
121 
122  # Pass a testing data set and the trained model to the algorithm
123  algorithm.input.setTable(classifier.prediction.data, testData)
124  algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
125 
126  # Predict Naive Bayes values (Result class from classifier.prediction)
127  predictionResult = algorithm.compute() # Retrieve the algorithm results
128 
129 
130 def printResults():
131 
132  printNumericTables(
133  testGroundTruth, predictionResult.get(classifier.prediction.prediction),
134  "Ground truth", "Classification results",
135  "NaiveBayes classification results (first 20 observations):", 20, flt64=False
136  )
137 
138 if __name__ == "__main__":
139 
140  trainModel()
141  testModel()
142  printResults()

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