Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

mn_naive_bayes_csr_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_csr_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_CSR_BATCH"></a>
17 ## \example mn_naive_bayes_csr_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 FileDataSource, DataSourceIface
25 
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
30 
31 DAAL_PREFIX = os.path.join('..', 'data')
32 
33 # Input data set parameters
34 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_train_csr.csv')
35 trainGroundTruthFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_train_labels.csv')
36 
37 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_test_csr.csv')
38 testGroundTruthFileName = os.path.join(DAAL_PREFIX, 'batch', 'naivebayes_test_labels.csv')
39 
40 nTrainObservations = 8000
41 nTestObservations = 2000
42 nClasses = 20
43 
44 trainingResult = None
45 predictionResult = None
46 
47 
48 def trainModel():
49  global trainingResult
50 
51  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
52  trainGroundTruthSource = FileDataSource(
53  trainGroundTruthFileName,
54  DataSourceIface.doAllocateNumericTable,
55  DataSourceIface.doDictionaryFromContext
56  )
57 
58  # Retrieve the data from input files
59  trainData = createSparseTable(trainDatasetFileName)
60  trainGroundTruthSource.loadDataBlock(nTrainObservations)
61 
62  # Create an algorithm object to train the Naive Bayes model
63  algorithm = training.Batch(nClasses, method=training.fastCSR)
64 
65  # Pass a training data set and dependent values to the algorithm
66  algorithm.input.set(classifier.training.data, trainData)
67  algorithm.input.set(classifier.training.labels, trainGroundTruthSource.getNumericTable())
68 
69  # Build the Naive Bayes model and retrieve the algorithm results
70  trainingResult = algorithm.compute()
71 
72 
73 def testModel():
74  global predictionResult
75 
76  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
77  testData = createSparseTable(testDatasetFileName)
78 
79  # Create an algorithm object to predict Naive Bayes values
80  algorithm = prediction.Batch(nClasses, method=prediction.fastCSR)
81 
82  # Pass a testing data set and the trained model to the algorithm
83  algorithm.input.setTable(classifier.prediction.data, testData)
84  algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
85 
86  # Predict Naive Bayes values and retrieve the algorithm results (Result class from classifier.prediction)
87  predictionResult = algorithm.compute()
88 
89 
90 def printResults():
91 
92  testGroundTruth = FileDataSource(
93  testGroundTruthFileName,
94  DataSourceIface.doAllocateNumericTable,
95  DataSourceIface.doDictionaryFromContext
96  )
97 
98  testGroundTruth.loadDataBlock(nTestObservations)
99 
100  printNumericTables(
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
105  )
106 
107 if __name__ == "__main__":
108 
109  trainModel()
110  testModel()
111  printResults()

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