Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

lin_reg_qr_dense_online.py

1 # file: lin_reg_qr_dense_online.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
43 
44 
45 import os
46 import sys
47 
48 from daal.algorithms.linear_regression import training, prediction
49 from daal.data_management import (
50  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
51 )
52 
53 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
54 if utils_folder not in sys.path:
55  sys.path.insert(0, utils_folder)
56 from utils import printNumericTable
57 
58 DAAL_PREFIX = os.path.join('..', 'data')
59 
60 # Input data set parameters
61 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'online', 'linear_regression_train.csv')
62 testDatasetFileName = os.path.join(DAAL_PREFIX, 'online', 'linear_regression_test.csv')
63 
64 nTrainVectorsInBlock = 250
65 
66 nFeatures = 10 # Number of features in training and testing data sets
67 nDependentVariables = 2 # Number of dependent variables that correspond to each observation
68 
69 trainingResult = None
70 predictionResult = None
71 
72 
73 def trainModel():
74  global trainingResult
75 
76  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
77  trainDataSource = FileDataSource(
78  trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
79  DataSourceIface.doDictionaryFromContext
80  )
81 
82  # Create Numeric Tables for training data and dependent variables
83  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
84  trainDependentVariables = HomogenNumericTable(
85  nDependentVariables, 0, NumericTableIface.doNotAllocate
86  )
87  mergedData = MergedNumericTable(trainData, trainDependentVariables)
88 
89  # Create an algorithm object to train the multiple linear regression model
90  algorithm = training.Online(method=training.qrDense)
91 
92  while(trainDataSource.loadDataBlock(nTrainVectorsInBlock, mergedData) == nTrainVectorsInBlock):
93  # Pass a training data set and dependent values to the algorithm
94  algorithm.input.set(training.data, trainData)
95  algorithm.input.set(training.dependentVariables, trainDependentVariables)
96 
97  # Update the multiple linear regression model
98  algorithm.compute()
99 
100  # Finalize the multiple linear regression model and retrieve the algorithm results
101  trainingResult = algorithm.finalizeCompute()
102  printNumericTable(trainingResult.get(training.model).getBeta(), "Linear Regression coefficients:")
103 
104 
105 def testModel():
106  global trainingResult, predictionResult
107 
108  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
109  testDataSource = FileDataSource(
110  testDatasetFileName, DataSourceIface.doAllocateNumericTable,
111  DataSourceIface.doDictionaryFromContext
112  )
113 
114  # Create Numeric Tables for testing data and ground truth values
115  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
116  testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTableIface.doNotAllocate)
117  mergedData = MergedNumericTable(testData, testGroundTruth)
118 
119  # Retrieve the data from the input file
120  testDataSource.loadDataBlock(mergedData)
121 
122  # Create an algorithm object to predict values of multiple linear regression
123  algorithm = prediction.Batch()
124 
125  # Pass a testing data set and the trained model to the algorithm
126  algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
127  algorithm.input.setTable(prediction.data, testData)
128 
129  # Predict values of multiple linear regression and retrieve the algorithm results
130  predictionResult = algorithm.compute()
131  printNumericTable(predictionResult.get(prediction.prediction), "Linear Regression prediction results: (first 10 rows):", 10)
132  printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)
133 
134 if __name__ == "__main__":
135 
136  trainModel()
137  testModel()

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