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

lin_reg_qr_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: lin_reg_qr_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-LINEAR_REGRESSION_QR_BATCH"></a>
17 ## \example lin_reg_qr_dense_batch.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, 'batch', 'linear_regression_train.csv')
36 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'linear_regression_test.csv')
37 
38 nFeatures = 10 # Number of features in training and testing data sets
39 nDependentVariables = 2 # Number of dependent variables that correspond to each observation
40 
41 trainingResult = None
42 predictionResult = None
43 
44 
45 def trainModel():
46  global trainingResult
47 
48  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
49  trainDataSource = FileDataSource(
50  trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
51  DataSourceIface.doDictionaryFromContext
52  )
53 
54  # Create Numeric Tables for training data and dependent variables
55  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
56  trainDependentVariables = HomogenNumericTable(
57  nDependentVariables, 0, NumericTableIface.doNotAllocate
58  )
59  mergedData = MergedNumericTable(trainData, trainDependentVariables)
60 
61  # Retrieve the data from input file
62  trainDataSource.loadDataBlock(mergedData)
63 
64  # Create an algorithm object to train the multiple linear regression model with a QR decomposition-based method
65  algorithm = training.Batch(method=training.qrDense)
66 
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  # Build the multiple linear regression model and retrieve the algorithm results
72  trainingResult = algorithm.compute()
73  printNumericTable(trainingResult.get(training.model).getBeta(), "Linear Regression coefficients:")
74 
75 
76 def testModel():
77  global predictionResult
78 
79  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
80  testDataSource = FileDataSource(
81  testDatasetFileName, DataSourceIface.doAllocateNumericTable,
82  DataSourceIface.doDictionaryFromContext
83  )
84 
85  # Create Numeric Tables for testing data and ground truth values
86  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
87  testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTableIface.doNotAllocate)
88  mergedData = MergedNumericTable(testData, testGroundTruth)
89 
90  testDataSource.loadDataBlock(mergedData)
91 
92  # Create an algorithm object to predict values of multiple linear regression
93  algorithm = prediction.Batch()
94 
95  # Pass a testing data set and the trained model to the algorithm
96  algorithm.input.setTable(prediction.data, testData)
97  algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
98 
99  # Predict values of multiple linear regression and retrieve the algorithm results
100  predictionResult = algorithm.compute()
101  printNumericTable(predictionResult.get(prediction.prediction), "Linear Regression prediction results: (first 10 rows):", 10)
102  printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)
103 
104 if __name__ == "__main__":
105 
106  trainModel()
107  testModel()

For more complete information about compiler optimizations, see our Optimization Notice.