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

adaboost_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: adaboost_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-ADABOOST_BATCH"></a>
17 ## \example adaboost_dense_batch.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms.adaboost import prediction, training
23 from daal.algorithms import classifier
24 from daal.data_management import (
25  FileDataSource, DataSourceIface, 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 DAAL_PREFIX = os.path.join('..', 'data')
34 
35 # Input data set parameters
36 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'adaboost_train.csv')
37 
38 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'adaboost_test.csv')
39 
40 nFeatures = 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 AdaBoost model
65  algorithm = training.Batch()
66 
67  # Pass the 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  # Train the AdaBoost model and retrieve the results of the training algorithm
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 algorithm objects for AdaBoost prediction with the default method
93  algorithm = prediction.Batch()
94 
95  # Pass the testing data set and 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  # Compute prediction results and retrieve algorithm results
100  # (Result class from classifier.prediction)
101  predictionResult = algorithm.compute()
102 
103 def printResults():
104  printNumericTables(
105  testGroundTruth, predictionResult.get(classifier.prediction.prediction),
106  "Ground truth", "Classification results",
107  "AdaBoost classification results (first 20 observations):", 20, flt64=False
108  )
109 
110 if __name__ == "__main__":
111 
112  trainModel()
113  testModel()
114  printResults()

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