Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

mn_naive_bayes_dense_batch.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

1 # file: mn_naive_bayes_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 Intel Corporation.
4 #
5 # This software and the related documents are Intel copyrighted materials, and
6 # your use of them is governed by the express license under which they were
7 # provided to you (License). Unless the License provides otherwise, you may not
8 # use, modify, copy, publish, distribute, disclose or transmit this software or
9 # the related documents without Intel's prior written permission.
10 #
11 # This software and the related documents are provided as is, with no express
12 # or implied warranties, other than those that are expressly stated in the
13 # License.
14 #===============================================================================
15 
16 ## <a name="DAAL-EXAMPLE-PY-MULTINOMIAL_NAIVE_BAYES_DENSE_BATCH"></a>
17 ## \example mn_naive_bayes_dense_batch.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms.multinomial_naive_bayes import prediction, training
23 from daal.algorithms import classifier
24 from daal.data_management import (
25  FileDataSource, HomogenNumericTable, MergedNumericTable, DataSourceIface, NumericTableIface
26 )
27 
28 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
29 if utils_folder not in sys.path:
30  sys.path.insert(0, utils_folder)
31 from utils import printNumericTables
32 
33 DAAL_PREFIX = os.path.join('..', 'data')
34 
35 # Input data set parameters
36 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_train_dense.csv')
37 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_test_dense.csv')
38 
39 nFeatures = 20
40 nClasses = 20
41 
42 trainingResult = None
43 predictionResult = None
44 testGroundTruth = None
45 
46 
47 def trainModel():
48  global trainingResult
49 
50  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
51  trainDataSource = FileDataSource(
52  trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
53  DataSourceIface.doDictionaryFromContext
54  )
55 
56  # Create Numeric Tables for training data and labels
57  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
58  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
59  mergedData = MergedNumericTable(trainData, trainGroundTruth)
60 
61  # Retrieve the data from the input file
62  trainDataSource.loadDataBlock(mergedData)
63 
64  # Create an algorithm object to train the Naive Bayes model
65  algorithm = training.Batch(nClasses)
66 
67  # Pass a training data set and dependent values to the algorithm
68  algorithm.input.set(classifier.training.data, trainData)
69  algorithm.input.set(classifier.training.labels, trainGroundTruth)
70 
71  # Build the Naive Bayes model and retrieve the algorithm results
72  trainingResult = algorithm.compute()
73 
74 
75 def testModel():
76  global predictionResult, testGroundTruth
77 
78  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
79  testDataSource = FileDataSource(
80  testDatasetFileName, DataSourceIface.notAllocateNumericTable,
81  DataSourceIface.doDictionaryFromContext
82  )
83 
84  # Create Numeric Tables for testing data and labels
85  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
86  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
87  mergedData = MergedNumericTable(testData, testGroundTruth)
88 
89  # Retrieve the data from input file
90  testDataSource.loadDataBlock(mergedData)
91 
92  # Create an algorithm object to predict Naive Bayes values
93  algorithm = prediction.Batch(nClasses)
94 
95  # Pass a testing data set and the trained model to the algorithm
96  algorithm.input.setTable(classifier.prediction.data, testData)
97  algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
98 
99  # Predict Naive Bayes values (Result class from classifier.prediction)
100  predictionResult = algorithm.compute() # Retrieve the algorithm results
101 
102 def printResults():
103  printNumericTables(
104  testGroundTruth, predictionResult.get(classifier.prediction.prediction),
105  "Ground truth", "Classification results",
106  "NaiveBayes classification results (first 20 observations):", 20, flt64=False
107  )
108 
109 if __name__ == "__main__":
110 
111  trainModel()
112  testModel()
113  printResults()

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