Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

ridge_reg_norm_eq_dense_distr.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

1 # file: ridge_reg_norm_eq_dense_distr.py
2 #===============================================================================
3 # Copyright 2014-2019 Intel Corporation.
4 #
5 # This software and the related documents are Intel copyrighted materials, and
6 # your use of them is governed by the express license under which they were
7 # provided to you (License). Unless the License provides otherwise, you may not
8 # use, modify, copy, publish, distribute, disclose or transmit this software or
9 # the related documents without Intel's prior written permission.
10 #
11 # This software and the related documents are provided as is, with no express
12 # or implied warranties, other than those that are expressly stated in the
13 # License.
14 #===============================================================================
15 
16 #
17 # ! Content:
18 # ! Python example of ridge regression in the distributed processing mode.
19 # !
20 # ! The program trains the multiple ridge regression model on a training
21 # ! datasetFileName with the normal equations method and computes regression
22 # ! for the test data.
23 # !*****************************************************************************
24 
25 #
26 ## <a name="DAAL-EXAMPLE-PY-RIDGE_REGRESSION_NORM_EQ_DISTRIBUTED"></a>
27 ## \example ridge_reg_norm_eq_dense_distr.py
28 #
29 
30 import os
31 import sys
32 
33 from daal import step1Local, step2Master
34 from daal.algorithms.ridge_regression import training, prediction
35 from daal.data_management import DataSource, FileDataSource, NumericTable, HomogenNumericTable, MergedNumericTable
36 
37 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
38 if utils_folder not in sys.path:
39  sys.path.insert(0, utils_folder)
40 from utils import printNumericTable
41 
42 trainDatasetFileNames = [
43  os.path.join("..", "data", "distributed", "linear_regression_train_1.csv"),
44  os.path.join("..", "data", "distributed", "linear_regression_train_2.csv"),
45  os.path.join("..", "data", "distributed", "linear_regression_train_3.csv"),
46  os.path.join("..", "data", "distributed", "linear_regression_train_4.csv"),
47 ]
48 
49 
50 testDatasetFileName = os.path.join("..", "data", "distributed", "linear_regression_test.csv")
51 
52 nBlocks = 4
53 nFeatures = 10 # Number of features in training and testing data sets
54 nDependentVariables = 2 # Number of dependent variables that correspond to each observation
55 
56 
57 def trainModel():
58  # Create an algorithm object to build the final ridge regression model on the master node
59  masterAlgorithm = training.Distributed(step=step2Master)
60 
61  for i in range(nBlocks):
62  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
63  trainDataSource = FileDataSource(trainDatasetFileNames[i],
64  DataSource.notAllocateNumericTable,
65  DataSource.doDictionaryFromContext)
66 
67  # Create Numeric Tables for training data and variables
68  trainData = HomogenNumericTable(nFeatures, 0, NumericTable.doNotAllocate)
69  trainDependentVariables = HomogenNumericTable(nDependentVariables, 0, NumericTable.doNotAllocate)
70  mergedData = MergedNumericTable(trainData, trainDependentVariables)
71 
72  # Retrieve the data from input file
73  trainDataSource.loadDataBlock(mergedData)
74 
75  # Create an algorithm object to train the ridge regression model based on the local-node data
76  localAlgorithm = training.Distributed(step=step1Local)
77 
78  # Pass a training data set and dependent values to the algorithm
79  localAlgorithm.input.set(training.data, trainData)
80  localAlgorithm.input.set(training.dependentVariables, trainDependentVariables)
81 
82  # Train the ridge regression model on the local-node data
83  presult = localAlgorithm.compute()
84 
85  # Set the local ridge regression model as input for the master-node algorithm
86  masterAlgorithm.input.add(training.partialModels, presult)
87 
88 
89  # Merge and finalize the ridge regression model on the master node
90  masterAlgorithm.compute()
91 
92  # Retrieve the algorithm results
93  trainingResult = masterAlgorithm.finalizeCompute()
94 
95  printNumericTable(trainingResult.get(training.model).getBeta(), "Ridge Regression coefficients:")
96  return trainingResult
97 
98 
99 def testModel(trainingResult):
100  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
101  testDataSource = FileDataSource(testDatasetFileName,
102  DataSource.doAllocateNumericTable,
103  DataSource.doDictionaryFromContext)
104 
105  # Create Numeric Tables for testing data and ground truth values
106  testData = HomogenNumericTable(nFeatures, 0, NumericTable.doNotAllocate)
107  testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTable.doNotAllocate)
108  mergedData = MergedNumericTable(testData, testGroundTruth)
109 
110  # Load the data from the data file
111  testDataSource.loadDataBlock(mergedData)
112 
113  # Create an algorithm object to predict values of ridge regression
114  algorithm = prediction.Batch()
115 
116  # Pass a testing data set and the trained model to the algorithm
117  algorithm.input.setTable(prediction.data, testData)
118  algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
119 
120  # Predict values of ridge regression and retrieve the algorithm results
121  predictionResult = algorithm.compute()
122 
123  printNumericTable(predictionResult.get(prediction.prediction), "Ridge Regression prediction results: (first 10 rows):", 10)
124  printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)
125 
126 
127 if __name__ == "__main__":
128  trainingResult = trainModel()
129  testModel(trainingResult)

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