Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

dt_reg_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: dt_reg_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 ## <a name="DAAL-EXAMPLE-PY-DT_REG_DENSE_BATCH"></a>
17 ## \example dt_reg_dense_batch.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms.decision_tree.regression import prediction, training
23 from daal.data_management import (
24  FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable, MergedNumericTable
25 )
26 
27 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
28 if utils_folder not in sys.path:
29  sys.path.insert(0, utils_folder)
30 from utils import printNumericTables
31 
32 DAAL_PREFIX = os.path.join('..', 'data')
33 
34 # Input data set parameters
35 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'decision_tree_train.csv')
36 pruneDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'decision_tree_prune.csv')
37 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'decision_tree_test.csv')
38 
39 nFeatures = 5
40 
41 # Model object for the decision tree regression algorithm
42 model = None
43 predictionResult = None
44 testGroundTruth = None
45 
46 
47 def trainModel():
48  global model
49 
50  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
51  trainDataSource = FileDataSource(
52  trainDatasetFileName,
53  DataSourceIface.notAllocateNumericTable,
54  DataSourceIface.doDictionaryFromContext
55  )
56 
57  # Create Numeric Tables for training data and labels
58  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
59  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
60  mergedData = MergedNumericTable(trainData, trainGroundTruth)
61 
62  # Retrieve the data from the input file
63  trainDataSource.loadDataBlock(mergedData)
64 
65  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
66  pruneDataSource = FileDataSource(
67  pruneDatasetFileName,
68  DataSourceIface.notAllocateNumericTable,
69  DataSourceIface.doDictionaryFromContext
70  )
71 
72  # Create Numeric Tables for pruning data and labels
73  pruneData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
74  pruneGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
75  pruneMergedData = MergedNumericTable(pruneData, pruneGroundTruth)
76 
77  # Retrieve the data from the input file
78  pruneDataSource.loadDataBlock(pruneMergedData)
79 
80  # Create an algorithm object to train the decision tree regression model
81  algorithm = training.Batch()
82 
83  # Pass the training data set and dependent values to the algorithm
84  algorithm.input.set(training.data, trainData)
85  algorithm.input.set(training.dependentVariables, trainGroundTruth)
86  algorithm.input.set(training.dataForPruning, pruneData)
87  algorithm.input.set(training.dependentVariablesForPruning, pruneGroundTruth)
88 
89  # Train the decision tree regression model and retrieve the results of the training algorithm
90  trainingResult = algorithm.compute()
91  model = trainingResult.get(training.model)
92 
93 def testModel():
94  global testGroundTruth, predictionResult
95 
96  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
97  testDataSource = FileDataSource(
98  testDatasetFileName,
99  DataSourceIface.notAllocateNumericTable,
100  DataSourceIface.doDictionaryFromContext
101  )
102 
103  # Create Numeric Tables for testing data and labels
104  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
105  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
106  mergedData = MergedNumericTable(testData, testGroundTruth)
107 
108  # Retrieve the data from input file
109  testDataSource.loadDataBlock(mergedData)
110 
111  # Create algorithm objects for decision tree regression prediction with the default method
112  algorithm = prediction.Batch()
113 
114  # Pass the testing data set and trained model to the algorithm
115  #print("Number of columns: {}".format(testData.getNumberOfColumns()))
116  algorithm.input.setTable(prediction.data, testData)
117  algorithm.input.setModel(prediction.model, model)
118 
119  # Compute prediction results and retrieve algorithm results
120  predictionResult = algorithm.compute()
121 
122 
123 def printResults():
124 
125  printNumericTables(testGroundTruth, predictionResult.get(prediction.prediction),
126  "Ground truth", "Regression results",
127  "Decision tree regression results (first 20 observations):",
128  20, flt64=False)
129 
130 if __name__ == "__main__":
131 
132  trainModel()
133  testModel()
134  printResults()

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