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

ridge_reg_norm_eq_dense_batch.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_batch.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 batch processing mode.
19 # !
20 # ! The program trains the 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_BATCH"></a>
27 ## \example ridge_reg_norm_eq_dense_batch.py
28 #
29 
30 import os
31 import sys
32 
33 from daal.algorithms.ridge_regression import training, prediction
34 from daal.data_management import DataSource, FileDataSource, NumericTable, HomogenNumericTable, MergedNumericTable
35 
36 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
37 if utils_folder not in sys.path:
38  sys.path.insert(0, utils_folder)
39 from utils import printNumericTable
40 
41 # Input data set parameters
42 trainDatasetFileName = os.path.join("..", "data", "batch", "linear_regression_train.csv")
43 testDatasetFileName = os.path.join("..", "data", "batch", "linear_regression_test.csv")
44 
45 nFeatures = 10 # Number of features in training and testing data sets
46 nDependentVariables = 2 # Number of dependent variables that correspond to each observation
47 
48 
49 def trainModel():
50  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
51  trainDataSource = FileDataSource(trainDatasetFileName,
52  DataSource.notAllocateNumericTable,
53  DataSource.doDictionaryFromContext)
54 
55  # Create Numeric Tables for training data and dependent variables
56  trainData = HomogenNumericTable(nFeatures, 0, NumericTable.doNotAllocate)
57  trainDependentVariables = HomogenNumericTable(nDependentVariables, 0, NumericTable.doNotAllocate)
58  mergedData = MergedNumericTable(trainData, trainDependentVariables)
59 
60  # Retrieve the data from input file
61  trainDataSource.loadDataBlock(mergedData)
62 
63  # Create an algorithm object to train the ridge regression model with the normal equations method
64  algorithm = training.Batch()
65 
66  # Pass a training data set and dependent values to the algorithm
67  algorithm.input.set(training.data, trainData)
68  algorithm.input.set(training.dependentVariables, trainDependentVariables)
69 
70  # Build the ridge regression model and etrieve the algorithm results
71  trainingResult = algorithm.compute()
72 
73  printNumericTable(trainingResult.get(training.model).getBeta(), "Ridge Regression coefficients:")
74  return trainingResult
75 
76 
77 def testModel(trainingResult):
78  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
79  testDataSource = FileDataSource(testDatasetFileName,
80  DataSource.doAllocateNumericTable,
81  DataSource.doDictionaryFromContext)
82 
83  # Create Numeric Tables for testing data and ground truth values
84  testData = HomogenNumericTable(nFeatures, 0, NumericTable.doNotAllocate)
85  testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTable.doNotAllocate)
86  mergedData = MergedNumericTable(testData, testGroundTruth)
87 
88  # Load the data from the data file
89  testDataSource.loadDataBlock(mergedData)
90 
91  # Create an algorithm object to predict values of ridge regression
92  algorithm = prediction.Batch()
93 
94  # Pass a testing data set and the trained model to the algorithm
95  algorithm.input.setTable(prediction.data, testData)
96  algorithm.input.setModel(prediction.model, trainingResult.get(training.model))
97 
98  # Predict values of ridge regression and retrieve the algorithm results
99  predictionResult = algorithm.compute()
100 
101  printNumericTable(predictionResult.get(prediction.prediction),
102  "Ridge Regression prediction results: (first 10 rows):", 10)
103  printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)
104 
105 
106 if __name__ == "__main__":
107  trainingResult = trainModel()
108  testModel(trainingResult)

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