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

ridge_reg_norm_eq_dense_batch.py

1 # file: ridge_reg_norm_eq_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of ridge regression in the batch processing mode.
45 # !
46 # ! The program trains the ridge regression model on a training
47 # ! datasetFileName with the normal equations method and computes regression
48 # ! for the test data.
49 # !*****************************************************************************
50 
51 #
52 
55 
56 import os
57 import sys
58 
59 from daal.algorithms.ridge_regression import training, prediction
60 from daal.data_management import DataSource, FileDataSource, NumericTable, HomogenNumericTable, MergedNumericTable
61 
62 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
63 if utils_folder not in sys.path:
64  sys.path.insert(0, utils_folder)
65 from utils import printNumericTable
66 
67 # Input data set parameters
68 trainDatasetFileName = os.path.join("..", "data", "batch", "linear_regression_train.csv")
69 testDatasetFileName = os.path.join("..", "data", "batch", "linear_regression_test.csv")
70 
71 nFeatures = 10 # Number of features in training and testing data sets
72 nDependentVariables = 2 # Number of dependent variables that correspond to each observation
73 
74 
75 def trainModel():
76  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
77  trainDataSource = FileDataSource(trainDatasetFileName,
78  DataSource.notAllocateNumericTable,
79  DataSource.doDictionaryFromContext)
80 
81  # Create Numeric Tables for training data and dependent variables
82  trainData = HomogenNumericTable(nFeatures, 0, NumericTable.doNotAllocate)
83  trainDependentVariables = HomogenNumericTable(nDependentVariables, 0, NumericTable.doNotAllocate)
84  mergedData = MergedNumericTable(trainData, trainDependentVariables)
85 
86  # Retrieve the data from input file
87  trainDataSource.loadDataBlock(mergedData)
88 
89  # Create an algorithm object to train the ridge regression model with the normal equations method
90  algorithm = training.Batch()
91 
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  # Build the ridge regression model and etrieve the algorithm results
97  trainingResult = algorithm.compute()
98 
99  printNumericTable(trainingResult.get(training.model).getBeta(), "Ridge Regression coefficients:")
100  return trainingResult
101 
102 
103 def testModel(trainingResult):
104  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
105  testDataSource = FileDataSource(testDatasetFileName,
106  DataSource.doAllocateNumericTable,
107  DataSource.doDictionaryFromContext)
108 
109  # Create Numeric Tables for testing data and ground truth values
110  testData = HomogenNumericTable(nFeatures, 0, NumericTable.doNotAllocate)
111  testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTable.doNotAllocate)
112  mergedData = MergedNumericTable(testData, testGroundTruth)
113 
114  # Load the data from the data file
115  testDataSource.loadDataBlock(mergedData)
116 
117  # Create an algorithm object to predict values of ridge regression
118  algorithm = prediction.Batch()
119 
120  # Pass a testing data set and the trained model to the algorithm
121  algorithm.input.setTable(prediction.data, testData)
122  algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
123 
124  # Predict values of ridge regression and retrieve the algorithm results
125  predictionResult = algorithm.compute()
126 
127  printNumericTable(predictionResult.get(prediction.prediction),
128  "Ridge Regression prediction results: (first 10 rows):", 10)
129  printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)
130 
131 
132 if __name__ == "__main__":
133  trainingResult = trainModel()
134  testModel(trainingResult)

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