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

gbt_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: gbt_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-GBT_CLS_DENSE_BATCH"></a>
17 ## \example gbt_cls_dense_batch.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms import gbt
23 from daal.algorithms.gbt.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 # Gradient boosted trees parameters
45 maxIterations = 40
46 minObservationsInLeafNode = 8
47 
48 # Model object for the gradient boosted trees 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 gradient boosted trees classification model
81  algorithm = training.Batch(nClasses)
82  algorithm.parameter().maxIterations = maxIterations
83  algorithm.parameter().minObservationsInLeafNode = minObservationsInLeafNode
84  algorithm.parameter().featuresPerNode = nFeatures
85 
86  # Pass the training data set and dependent values to the algorithm
87  algorithm.input.set(classifier.training.data, trainData)
88  algorithm.input.set(classifier.training.labels, trainGroundTruth)
89 
90  # Train the gradient boosted trees classification model and retrieve the results of the training algorithm
91  trainingResult = algorithm.compute()
92  model = trainingResult.get(classifier.training.model)
93 
94 def testModel():
95  global testGroundTruth, predictionResult
96 
97  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
98  testDataSource = FileDataSource(
99  testDatasetFileName,
100  DataSourceIface.notAllocateNumericTable,
101  DataSourceIface.doDictionaryFromContext
102  )
103 
104  # Create Numeric Tables for testing data and labels
105  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
106  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
107  mergedData = MergedNumericTable(testData, testGroundTruth)
108 
109  # Retrieve the data from input file
110  testDataSource.loadDataBlock(mergedData)
111 
112  # Get the dictionary and update it with additional information about data
113  dict = testData.getDictionary()
114 
115  # Add a feature type to the dictionary
116  dict[0].featureType = features.DAAL_CONTINUOUS
117  dict[1].featureType = features.DAAL_CONTINUOUS
118  dict[2].featureType = features.DAAL_CATEGORICAL
119 
120  # Create algorithm objects for gradient boosted trees classification prediction with the default method
121  algorithm = prediction.Batch(nClasses)
122 
123  # Pass the testing data set and trained model to the algorithm
124  algorithm.input.setTable(classifier.prediction.data, testData)
125  algorithm.input.setModel(classifier.prediction.model, model)
126 
127  # Compute prediction results and retrieve algorithm results
128  # (Result class from classifier.prediction)
129  predictionResult = algorithm.compute()
130 
131 
132 def printResults():
133 
134  printNumericTable(predictionResult.get(classifier.prediction.prediction),"Gragient boosted trees prediction results (first 10 rows):",10)
135  printNumericTable(testGroundTruth,"Ground truth (first 10 rows):",10)
136 
137 if __name__ == "__main__":
138 
139  trainModel()
140  testModel()
141  printResults()

For more complete information about compiler optimizations, see our Optimization Notice.