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

lin_reg_norm_eq_dense_batch.py

1 # file: lin_reg_norm_eq_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 Intel Corporation.
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6 # your use of them is governed by the express license under which they were
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9 # the related documents without Intel's prior written permission.
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13 # License.
14 #===============================================================================
15 
16 ## <a name="DAAL-EXAMPLE-PY-LINEAR_REGRESSION_NORM_EQ_BATCH"></a>
17 ## \example lin_reg_norm_eq_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(nDependentVariables, 0, NumericTableIface.doNotAllocate)
57  mergedData = MergedNumericTable(trainData, trainDependentVariables)
58 
59  # Retrieve the data from input file
60  trainDataSource.loadDataBlock(mergedData)
61 
62  # Create an algorithm object to train the multiple linear regression model with the normal equations method
63  algorithm = training.Batch()
64 
65  # Pass a training data set and dependent values to the algorithm
66  algorithm.input.set(training.data, trainData)
67  algorithm.input.set(training.dependentVariables, trainDependentVariables)
68 
69  # Build the multiple linear regression model and retrieve the algorithm results
70  trainingResult = algorithm.compute()
71  printNumericTable(trainingResult.get(training.model).getBeta(), "Linear Regression coefficients:")
72 
73 
74 def testModel():
75  global trainingResult, predictionResult
76 
77  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
78  testDataSource = FileDataSource(
79  testDatasetFileName, DataSourceIface.doAllocateNumericTable,
80  DataSourceIface.doDictionaryFromContext
81  )
82 
83  # Create Numeric Tables for testing data and ground truth values
84  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
85  testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTableIface.doNotAllocate)
86  mergedData = MergedNumericTable(testData, testGroundTruth)
87 
88  # Load the data from the data file
89  testDataSource.loadDataBlock(mergedData)
90 
91  # Create an algorithm object to predict values of multiple linear regression
92  algorithm = prediction.Batch()
93 
94  # Pass a testing data set and the trained model to the algorithm
95  algorithm.input.setTable(prediction.data, testData)
96  algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
97 
98  # Predict values of multiple linear regression and retrieve the algorithm results
99  predictionResult = algorithm.compute()
100  printNumericTable(predictionResult.get(prediction.prediction), "Linear Regression prediction results: (first 10 rows):", 10)
101  printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)
102 
103 if __name__ == "__main__":
104 
105  trainModel()
106  testModel()

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