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

df_cls_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: df_cls_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-DF_CLS_DENSE_BATCH"></a>
17 ## \example df_cls_dense_batch.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms import decision_forest
23 from daal.algorithms.decision_forest.classification import prediction, training
24 from daal.algorithms import classifier
25 from daal.data_management import (
26  FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable,
27  MergedNumericTable, features
28 )
29 
30 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
31 if utils_folder not in sys.path:
32  sys.path.insert(0, utils_folder)
33 from utils import printNumericTable, printNumericTables
34 
35 DAAL_PREFIX = os.path.join('..', 'data')
36 
37 # Input data set parameters
38 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'df_classification_train.csv')
39 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'df_classification_test.csv')
40 
41 nFeatures = 3
42 nClasses = 5
43 
44 # Decision forest parameters
45 nTrees = 10
46 minObservationsInLeafNode = 8
47 
48 # Model object for the decision forest classification algorithm
49 model = None
50 predictionResult = None
51 testGroundTruth = None
52 
53 
54 def trainModel():
55  global model
56 
57  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
58  trainDataSource = FileDataSource(
59  trainDatasetFileName,
60  DataSourceIface.notAllocateNumericTable,
61  DataSourceIface.doDictionaryFromContext
62  )
63 
64  # Create Numeric Tables for training data and labels
65  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
66  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
67  mergedData = MergedNumericTable(trainData, trainGroundTruth)
68 
69  # Retrieve the data from the input file
70  trainDataSource.loadDataBlock(mergedData)
71 
72  # Get the dictionary and update it with additional information about data
73  dict = trainData.getDictionary()
74 
75  # Add a feature type to the dictionary
76  dict[0].featureType = features.DAAL_CONTINUOUS
77  dict[1].featureType = features.DAAL_CONTINUOUS
78  dict[2].featureType = features.DAAL_CATEGORICAL
79 
80  # Create an algorithm object to train the decision forest classification model
81  algorithm = training.Batch(nClasses)
82  algorithm.parameter.nTrees = nTrees
83  algorithm.parameter.minObservationsInLeafNode = minObservationsInLeafNode
84  algorithm.parameter.featuresPerNode = nFeatures
85  algorithm.parameter.varImportance = decision_forest.training.MDI
86  algorithm.parameter.resultsToCompute = decision_forest.training.computeOutOfBagError
87 
88  # Pass the training data set and dependent values to the algorithm
89  algorithm.input.set(classifier.training.data, trainData)
90  algorithm.input.set(classifier.training.labels, trainGroundTruth)
91 
92  # Train the decision forest classification model and retrieve the results of the training algorithm
93  trainingResult = algorithm.compute()
94  model = trainingResult.get(classifier.training.model)
95  printNumericTable(trainingResult.getTable(training.variableImportance), "Variable importance results: ")
96  printNumericTable(trainingResult.getTable(training.outOfBagError), "OOB error: ")
97 
98 def testModel():
99  global testGroundTruth, predictionResult
100 
101  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
102  testDataSource = FileDataSource(
103  testDatasetFileName,
104  DataSourceIface.notAllocateNumericTable,
105  DataSourceIface.doDictionaryFromContext
106  )
107 
108  # Create Numeric Tables for testing data and labels
109  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
110  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
111  mergedData = MergedNumericTable(testData, testGroundTruth)
112 
113  # Retrieve the data from input file
114  testDataSource.loadDataBlock(mergedData)
115 
116  # Get the dictionary and update it with additional information about data
117  dict = testData.getDictionary()
118 
119  # Add a feature type to the dictionary
120  dict[0].featureType = features.DAAL_CONTINUOUS
121  dict[1].featureType = features.DAAL_CONTINUOUS
122  dict[2].featureType = features.DAAL_CATEGORICAL
123 
124  # Create algorithm objects for decision forest classification prediction with the default method
125  algorithm = prediction.Batch(nClasses)
126 
127  # Pass the testing data set and trained model to the algorithm
128  algorithm.input.setTable(classifier.prediction.data, testData)
129  algorithm.input.setModel(classifier.prediction.model, model)
130 
131  # Compute prediction results and retrieve algorithm results
132  # (Result class from classifier.prediction)
133  predictionResult = algorithm.compute()
134 
135 
136 def printResults():
137  printNumericTable(predictionResult.get(classifier.prediction.prediction),"Decision forest prediction results (first 10 rows):",10)
138  printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10);
139 
140 if __name__ == "__main__":
141 
142  trainModel()
143  testModel()
144  printResults()

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