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

brownboost_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: brownboost_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-BROWNBOOST_BATCH"></a>
17 ## \example brownboost_dense_batch.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms.brownboost import prediction, training
23 from daal.algorithms import classifier
24 from daal.data_management import (
25  FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable, MergedNumericTable
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', 'brownboost_train.csv')
37 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'brownboost_test.csv')
38 
39 nFeatures = 20
40 
41 trainingResult = None
42 predictionResult = None
43 testGroundTruth = None
44 
45 
46 def trainModel():
47  global trainingResult
48 
49  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
50  trainDataSource = FileDataSource(
51  trainDatasetFileName,
52  DataSourceIface.notAllocateNumericTable,
53  DataSourceIface.doDictionaryFromContext
54  )
55  # Create Numeric Tables for training data and labels
56  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
57  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
58  mergedData = MergedNumericTable(trainData, trainGroundTruth)
59 
60  # Retrieve the data from the input file
61  trainDataSource.loadDataBlock(mergedData)
62 
63  # Create an algorithm object to train the BrownBoost model
64  algorithm = training.Batch()
65 
66  # Pass the training data set and dependent values to the algorithm
67  algorithm.input.set(classifier.training.data, trainData)
68  algorithm.input.set(classifier.training.labels, trainGroundTruth)
69 
70  # Train the BrownBoost model and retrieve the results of the training algorithm
71  trainingResult = algorithm.compute()
72 
73 
74 def testModel():
75  global testGroundTruth, predictionResult
76 
77  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
78  testDataSource = FileDataSource(
79  testDatasetFileName,
80  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 BrownBoost 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 
104 def printResults():
105  printNumericTables(
106  testGroundTruth,
107  predictionResult.get(classifier.prediction.prediction),
108  "Ground truth", "Classification results",
109  "BrownBoost classification results (first 20 observations):", 20
110  )
111 
112 if __name__ == "__main__":
113 
114  trainModel()
115  testModel()
116  printResults()

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