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

svm_two_class_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: svm_two_class_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-SVM_TWO_CLASS_DENSE_BATCH"></a>
17 ## \example svm_two_class_dense_batch.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms.svm import training, prediction
23 from daal.algorithms import kernel_function, classifier
24 from daal.data_management import (
25  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, 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 # Input data set parameters
34 DATA_PREFIX = os.path.join('..', 'data', 'batch')
35 
36 trainDatasetFileName = os.path.join(DATA_PREFIX, 'svm_two_class_train_dense.csv')
37 testDatasetFileName = os.path.join(DATA_PREFIX, 'svm_two_class_test_dense.csv')
38 
39 nFeatures = 20
40 
41 # Parameters for the SVM kernel function
42 kernel = kernel_function.linear.Batch()
43 
44 # Model object for the SVM algorithm
45 trainingResult = None
46 predictionResult = None
47 testGroundTruth = None
48 
49 
50 def trainModel():
51  global trainingResult
52 
53  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
54  trainDataSource = FileDataSource(
55  trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
56  DataSourceIface.doDictionaryFromContext
57  )
58 
59  # Create Numeric Tables for training data and labels
60  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
61  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
62  mergedData = MergedNumericTable(trainData, trainGroundTruth)
63 
64  # Retrieve the data from the input file
65  trainDataSource.loadDataBlock(mergedData)
66 
67  # Create an algorithm object to train the SVM model
68  algorithm = training.Batch()
69 
70  algorithm.parameter.kernel = kernel
71  algorithm.parameter.cacheSize = 600000000
72 
73  # Pass a training data set and dependent values to the algorithm
74  algorithm.input.set(classifier.training.data, trainData)
75  algorithm.input.set(classifier.training.labels, trainGroundTruth)
76 
77  # Build the SVM model
78  trainingResult = algorithm.compute()
79 
80 
81 def testModel():
82  global predictionResult, testGroundTruth
83 
84  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
85  testDataSource = FileDataSource(
86  testDatasetFileName, DataSourceIface.notAllocateNumericTable,
87  DataSourceIface.doDictionaryFromContext
88  )
89 
90  # Create Numeric Tables for testing data and labels
91  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
92  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
93  mergedData = MergedNumericTable(testData, testGroundTruth)
94 
95  # Retrieve the data from input file
96  testDataSource.loadDataBlock(mergedData)
97 
98  # Create an algorithm object to predict SVM values
99  algorithm = prediction.Batch()
100 
101  algorithm.parameter.kernel = kernel
102 
103  # Pass a testing data set and the trained model to the algorithm
104  algorithm.input.setTable(classifier.prediction.data, testData)
105  algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
106 
107  # Predict SVM values
108  algorithm.compute()
109 
110  # Retrieve the algorithm results
111  predictionResult = algorithm.getResult()
112 
113 
114 def printResults():
115 
116  printNumericTables(
117  testGroundTruth, predictionResult.get(classifier.prediction.prediction),
118  "Ground truth\t", "Classification results",
119  "SVM classification results (first 20 observations):", 20, flt64=False
120  )
121 
122 if __name__ == "__main__":
123 
124  trainModel()
125  testModel()
126  printResults()

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