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

log_reg_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: log_reg_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-LOG_REG_DENSE_BATCH"></a>
17 ## \example log_reg_dense_batch.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms import logistic_regression
23 from daal.algorithms.logistic_regression import prediction, training
24 from daal.algorithms import classifier
25 from daal.data_management import (
26  FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable,
27  MergedNumericTable, features
28 )
29 
30 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
31 if utils_folder not in sys.path:
32  sys.path.insert(0, utils_folder)
33 from utils import printNumericTable, printNumericTables
34 
35 DAAL_PREFIX = os.path.join('..', 'data')
36 
37 # Input data set parameters
38 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'logreg_train.csv')
39 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'logreg_test.csv')
40 
41 nFeatures = 6
42 nClasses = 5
43 
44 # Model object for the logistic regression algorithm
45 model = None
46 predictionResult = None
47 testGroundTruth = None
48 
49 def trainModel():
50  global model
51 
52  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
53  trainDataSource = FileDataSource(
54  trainDatasetFileName,
55  DataSourceIface.notAllocateNumericTable,
56  DataSourceIface.doDictionaryFromContext
57  )
58 
59  # Create Numeric Tables for training data and labels
60  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
61  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
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 logistic regression model
68  algorithm = training.Batch(nClasses)
69 
70  # Pass the training data set and dependent values to the algorithm
71  algorithm.input.set(classifier.training.data, trainData)
72  algorithm.input.set(classifier.training.labels, trainGroundTruth)
73  algorithm.parameter().penaltyL1=0.1;
74  algorithm.parameter().penaltyL2=0.1;
75 
76  # Train the logistic regression model and retrieve the results of the training algorithm
77  trainingResult = algorithm.compute()
78  model = trainingResult.get(classifier.training.model)
79  printNumericTable(model.getBeta(), "Logistic Regression coefficients:")
80 
81 def testModel():
82  global testGroundTruth, predictionResult
83 
84  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
85  testDataSource = FileDataSource(
86  testDatasetFileName,
87  DataSourceIface.notAllocateNumericTable,
88  DataSourceIface.doDictionaryFromContext
89  )
90 
91  # Create Numeric Tables for testing data and labels
92  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
93  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
94  mergedData = MergedNumericTable(testData, testGroundTruth)
95 
96  # Retrieve the data from input file
97  testDataSource.loadDataBlock(mergedData)
98 
99  # Create algorithm objects for logistic regression prediction with the default method
100  algorithm = prediction.Batch(nClasses)
101 
102  # Pass the testing data set and trained model to the algorithm
103  algorithm.input.setTable(classifier.prediction.data, testData)
104  algorithm.input.setModel(classifier.prediction.model, model)
105  algorithm.parameter().resultsToCompute |= logistic_regression.prediction.computeClassesProbabilities | logistic_regression.prediction.computeClassesLogProbabilities
106 
107  # Compute prediction results and retrieve algorithm results
108  # (Result class from classifier.prediction)
109  predictionResult = algorithm.compute()
110 
111 
112 def printResults():
113 
114  printNumericTable(predictionResult.get(classifier.prediction.prediction),"Logistic regression prediction results (first 10 rows):",10)
115  printNumericTable(testGroundTruth,"Ground truth (first 10 rows):",10)
116  printNumericTable(predictionResult.get(logistic_regression.prediction.probabilities),"Logistic regression prediction probabilities (first 10 rows):",10)
117  printNumericTable(predictionResult.get(logistic_regression.prediction.logProbabilities),"Logistic regression prediction log probabilities (first 10 rows):",10)
118 
119 if __name__ == "__main__":
120  trainModel()
121  testModel()
122  printResults()

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