Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

mn_naive_bayes_dense_batch.py

1 # file: mn_naive_bayes_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
43 
44 
45 import os
46 import sys
47 
48 from daal.algorithms.multinomial_naive_bayes import prediction, training
49 from daal.algorithms import classifier
50 from daal.data_management import (
51  FileDataSource, HomogenNumericTable, MergedNumericTable, DataSourceIface, NumericTableIface
52 )
53 
54 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
55 if utils_folder not in sys.path:
56  sys.path.insert(0, utils_folder)
57 from utils import printNumericTables
58 
59 DAAL_PREFIX = os.path.join('..', 'data')
60 
61 # Input data set parameters
62 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_train_dense.csv')
63 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_test_dense.csv')
64 
65 nFeatures = 20
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  # Retrieve the data from the input file
88  trainDataSource.loadDataBlock(mergedData)
89 
90  # Create an algorithm object to train the Naive Bayes model
91  algorithm = training.Batch(nClasses)
92 
93  # Pass a training data set and dependent values to the algorithm
94  algorithm.input.set(classifier.training.data, trainData)
95  algorithm.input.set(classifier.training.labels, trainGroundTruth)
96 
97  # Build the Naive Bayes model and retrieve the algorithm results
98  trainingResult = algorithm.compute()
99 
100 
101 def testModel():
102  global predictionResult, testGroundTruth
103 
104  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
105  testDataSource = FileDataSource(
106  testDatasetFileName, DataSourceIface.notAllocateNumericTable,
107  DataSourceIface.doDictionaryFromContext
108  )
109 
110  # Create Numeric Tables for testing data and labels
111  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
112  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
113  mergedData = MergedNumericTable(testData, testGroundTruth)
114 
115  # Retrieve the data from input file
116  testDataSource.loadDataBlock(mergedData)
117 
118  # Create an algorithm object to predict Naive Bayes values
119  algorithm = prediction.Batch(nClasses)
120 
121  # Pass a testing data set and the trained model to the algorithm
122  algorithm.input.setTable(classifier.prediction.data, testData)
123  algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
124 
125  # Predict Naive Bayes values (Result class from classifier.prediction)
126  predictionResult = algorithm.compute() # Retrieve the algorithm results
127 
128 def printResults():
129  printNumericTables(
130  testGroundTruth, predictionResult.get(classifier.prediction.prediction),
131  "Ground truth", "Classification results",
132  "NaiveBayes classification results (first 20 observations):", 20, flt64=False
133  )
134 
135 if __name__ == "__main__":
136 
137  trainModel()
138  testModel()
139  printResults()

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