Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

lin_reg_norm_eq_dense_online.py

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40 
41 ## <a name="DAAL-EXAMPLE-PY-LINEAR_REGRESSION_NORM_EQ_ONLINE"></a>
42 ## \example lin_reg_norm_eq_dense_online.py
43 
44 import os
45 import sys
46 
47 from daal.algorithms.linear_regression import training, prediction
48 from daal.data_management import (
49  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
50 )
51 
52 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
53 if utils_folder not in sys.path:
54  sys.path.insert(0, utils_folder)
55 from utils import printNumericTable
56 
57 DAAL_PREFIX = os.path.join('..', 'data')
58 
59 # Input data set parameters
60 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'online', 'linear_regression_train.csv')
61 testDatasetFileName = os.path.join(DAAL_PREFIX, 'online', 'linear_regression_test.csv')
62 
63 nTrainVectorsInBlock = 250
64 
65 nFeatures = 10 # Number of features in training and testing data sets
66 nDependentVariables = 2 # Number of dependent variables that correspond to each observation
67 
68 trainingResult = None
69 predictionResult = None
70 
71 
72 def trainModel():
73  global trainingResult
74 
75  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
76  trainDataSource = FileDataSource(
77  trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
78  DataSourceIface.doDictionaryFromContext
79  )
80 
81  # Create Numeric Tables for training data and dependent variables
82  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
83  trainDependentVariables = HomogenNumericTable(
84  nDependentVariables, 0, NumericTableIface.doNotAllocate
85  )
86  mergedData = MergedNumericTable(trainData, trainDependentVariables)
87 
88  # Create an algorithm object to train the multiple linear regression model
89  algorithm = training.Online()
90 
91  while(trainDataSource.loadDataBlock(nTrainVectorsInBlock, mergedData) == nTrainVectorsInBlock):
92  # Pass a training data set and dependent values to the algorithm
93  algorithm.input.set(training.data, trainData)
94  algorithm.input.set(training.dependentVariables, trainDependentVariables)
95 
96  # Update the multiple linear regression model
97  algorithm.compute()
98 
99  # Finalize the multiple linear regression model and retrieve the results
100  trainingResult = algorithm.finalizeCompute()
101 
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.setTable(prediction.data, testData)
127  algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
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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