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

lin_reg_norm_eq_dense_online.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: lin_reg_norm_eq_dense_online.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-LINEAR_REGRESSION_NORM_EQ_ONLINE"></a>
17 ## \example lin_reg_norm_eq_dense_online.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms.linear_regression import training, prediction
23 from daal.data_management import (
24  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
25 )
26 
27 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
28 if utils_folder not in sys.path:
29  sys.path.insert(0, utils_folder)
30 from utils import printNumericTable
31 
32 DAAL_PREFIX = os.path.join('..', 'data')
33 
34 # Input data set parameters
35 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'online', 'linear_regression_train.csv')
36 testDatasetFileName = os.path.join(DAAL_PREFIX, 'online', 'linear_regression_test.csv')
37 
38 nTrainVectorsInBlock = 250
39 
40 nFeatures = 10 # Number of features in training and testing data sets
41 nDependentVariables = 2 # Number of dependent variables that correspond to each observation
42 
43 trainingResult = None
44 predictionResult = 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 dependent variables
57  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
58  trainDependentVariables = HomogenNumericTable(
59  nDependentVariables, 0, NumericTableIface.doNotAllocate
60  )
61  mergedData = MergedNumericTable(trainData, trainDependentVariables)
62 
63  # Create an algorithm object to train the multiple linear regression model
64  algorithm = training.Online()
65 
66  while(trainDataSource.loadDataBlock(nTrainVectorsInBlock, mergedData) == nTrainVectorsInBlock):
67  # Pass a training data set and dependent values to the algorithm
68  algorithm.input.set(training.data, trainData)
69  algorithm.input.set(training.dependentVariables, trainDependentVariables)
70 
71  # Update the multiple linear regression model
72  algorithm.compute()
73 
74  # Finalize the multiple linear regression model and retrieve the results
75  trainingResult = algorithm.finalizeCompute()
76 
77  printNumericTable(trainingResult.get(training.model).getBeta(), "Linear Regression coefficients:")
78 
79 
80 def testModel():
81  global trainingResult, predictionResult
82 
83  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
84  testDataSource = FileDataSource(
85  testDatasetFileName, DataSourceIface.doAllocateNumericTable,
86  DataSourceIface.doDictionaryFromContext
87  )
88 
89  # Create Numeric Tables for testing data and ground truth values
90  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
91  testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTableIface.doNotAllocate)
92  mergedData = MergedNumericTable(testData, testGroundTruth)
93 
94  # Retrieve the data from the input file
95  testDataSource.loadDataBlock(mergedData)
96 
97  # Create an algorithm object to predict values of multiple linear regression
98  algorithm = prediction.Batch()
99 
100  # Pass a testing data set and the trained model to the algorithm
101  algorithm.input.setTable(prediction.data, testData)
102  algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
103 
104  # Predict values of multiple linear regression and retrieve the algorithm results
105  predictionResult = algorithm.compute()
106  printNumericTable(predictionResult.get(prediction.prediction), "Linear Regression prediction results: (first 10 rows):", 10)
107  printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)
108 
109 if __name__ == "__main__":
110 
111  trainModel()
112  testModel()

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