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

lin_reg_norm_eq_dense_distr.py

1 # file: lin_reg_norm_eq_dense_distr.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
44 
45 import os
46 import sys
47 
48 from daal import step1Local, step2Master
49 from daal.algorithms.linear_regression import training, prediction
50 from daal.data_management import (
51  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable,NumericTableIface
52 )
53 
54 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
55 if utils_folder not in sys.path:
56  sys.path.insert(0, utils_folder)
57 from utils import printNumericTable
58 
59 DAAL_PREFIX = os.path.join('..', 'data')
60 
61 trainDatasetFileNames = [
62  os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_train_1.csv'),
63  os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_train_2.csv'),
64  os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_train_3.csv'),
65  os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_train_4.csv')
66 ]
67 
68 testDatasetFileName = os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_test.csv')
69 
70 nBlocks = 4
71 
72 nFeatures = 10 # Number of features in training and testing data sets
73 nDependentVariables = 2 # Number of dependent variables that correspond to each observation
74 
75 trainingResult = None
76 predictionResult = None
77 
78 
79 def trainModel():
80  global trainingResult
81 
82  # Create an algorithm object to build the final multiple linear regression model on the master node
83  masterAlgorithm = training.Distributed(step2Master)
84 
85  for i in range(nBlocks):
86  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
87  trainDataSource = FileDataSource(
88  trainDatasetFileNames[i], DataSourceIface.notAllocateNumericTable,
89  DataSourceIface.doDictionaryFromContext
90  )
91 
92  # Create Numeric Tables for training data and variables
93  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
94  trainDependentVariables = HomogenNumericTable(
95  nDependentVariables, 0, NumericTableIface.doNotAllocate
96  )
97  mergedData = MergedNumericTable(trainData, trainDependentVariables)
98 
99  # Retrieve the data from input file
100  trainDataSource.loadDataBlock(mergedData)
101 
102  # Create an algorithm object to train the multiple linear regression model based on the local-node data
103  localAlgorithm = training.Distributed(step1Local)
104 
105  # Pass a training data set and dependent values to the algorithm
106  localAlgorithm.input.set(training.data, trainData)
107  localAlgorithm.input.set(training.dependentVariables, trainDependentVariables)
108 
109  # Train the multiple linear regression model on the local-node data
110  # Set the local multiple linear regression model as input for the master-node algorithm
111  masterAlgorithm.input.add(training.partialModels, localAlgorithm.compute())
112 
113  # Merge and finalize the multiple linear regression model on the master node
114  masterAlgorithm.compute()
115 
116  # Retrieve the algorithm results
117  trainingResult = masterAlgorithm.finalizeCompute()
118  printNumericTable(trainingResult.get(training.model).getBeta(), "Linear Regression coefficients:")
119 
120 
121 def testModel():
122  global trainingResult, predictionResult
123 
124  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
125  testDataSource = FileDataSource(
126  testDatasetFileName, DataSourceIface.doAllocateNumericTable,
127  DataSourceIface.doDictionaryFromContext
128  )
129 
130  # Create Numeric Tables for testing data and ground truth values
131  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
132  testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTableIface.doNotAllocate)
133  mergedData = MergedNumericTable(testData, testGroundTruth)
134 
135  # Retrieve the data from the input file
136  testDataSource.loadDataBlock(mergedData)
137 
138  # Create an algorithm object to predict values of multiple linear regression
139  algorithm = prediction.Batch()
140 
141  # Pass a testing data set and the trained model to the algorithm
142  algorithm.input.setTable(prediction.data, testData)
143  algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
144 
145  # Predict values of multiple linear regression and retrieve the algorithm results
146  predictionResult = algorithm.compute()
147  printNumericTable(predictionResult.get(prediction.prediction), "Linear Regression prediction results: (first 10 rows):", 10)
148  printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)
149 
150 if __name__ == "__main__":
151 
152  trainModel()
153  testModel()

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