Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

gbt_reg_dense_batch.py

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40 
41 
42 
43 
44 import os
45 import sys
46 
47 from daal.algorithms import gbt
48 from daal.algorithms.gbt.regression import prediction, training
49 from daal.data_management import (
50  FileDataSource, DataSourceIface, NumericTableIface,
51  HomogenNumericTable, MergedNumericTable, data_feature_utils
52 )
53 
54 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
55 if utils_folder not in sys.path:
56  sys.path.insert(0, utils_folder)
57 from utils import printNumericTable
58 
59 DAAL_PREFIX = os.path.join('..', 'data')
60 
61 # Input data set parameters
62 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'df_regression_train.csv')
63 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'df_regression_test.csv')
64 
65 nFeatures = 13
66 
67 # Gradient boosted trees parameters
68 maxIterations = 40
69 
70 # Model object for the gradient boosted trees regression algorithm
71 model = None
72 predictionResult = None
73 testGroundTruth = None
74 
75 
76 def trainModel():
77  global model
78 
79  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
80  trainDataSource = FileDataSource(
81  trainDatasetFileName,
82  DataSourceIface.notAllocateNumericTable,
83  DataSourceIface.doDictionaryFromContext
84  )
85 
86  # Create Numeric Tables for training data and labels
87  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
88  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
89  mergedData = MergedNumericTable(trainData, trainGroundTruth)
90 
91  # Retrieve the data from the input file
92  trainDataSource.loadDataBlock(mergedData)
93 
94  # Get the dictionary and update it with additional information about data
95  dict = trainData.getDictionary()
96 
97  # Add a feature type to the dictionary
98  dict[3].featureType = data_feature_utils.DAAL_CATEGORICAL
99 
100  # Create an algorithm object to train the gradient boosted trees regression model
101  algorithm = training.Batch()
102  algorithm.parameter().maxIterations = maxIterations
103 
104  # Pass the training data set and dependent values to the algorithm
105  algorithm.input.set(training.data, trainData)
106  algorithm.input.set(training.dependentVariable, trainGroundTruth)
107 
108  # Train the gradient boosted trees regression model and retrieve the results of the training algorithm
109  trainingResult = algorithm.compute()
110  model = trainingResult.get(training.model)
111 
112 def testModel():
113  global testGroundTruth, predictionResult
114 
115  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
116  testDataSource = FileDataSource(
117  testDatasetFileName,
118  DataSourceIface.notAllocateNumericTable,
119  DataSourceIface.doDictionaryFromContext
120  )
121 
122  # Create Numeric Tables for testing data and labels
123  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
124  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
125  mergedData = MergedNumericTable(testData, testGroundTruth)
126 
127  # Retrieve the data from input file
128  testDataSource.loadDataBlock(mergedData)
129 
130  # Get the dictionary and update it with additional information about data
131  dict = testData.getDictionary()
132 
133  # Add a feature type to the dictionary
134  dict[3].featureType = data_feature_utils.DAAL_CATEGORICAL
135 
136  # Create algorithm objects for the gradient boosted trees regression prediction with the default method
137  algorithm = prediction.Batch()
138 
139  # Pass the testing data set and trained model to the algorithm
140  algorithm.input.setTable(prediction.data, testData)
141  algorithm.input.set(prediction.model, model)
142 
143  # Compute prediction results and retrieve algorithm results
144  predictionResult = algorithm.compute()
145 
146 
147 def printResults():
148 
149  printNumericTable(
150  predictionResult.get(prediction.prediction),
151  "Gradient boosted trees prediction results (first 10 rows):", 10
152  )
153  printNumericTable(
154  testGroundTruth,
155  "Ground truth (first 10 rows):", 10
156  )
157 
158 if __name__ == "__main__":
159 
160  trainModel()
161  testModel()
162  printResults()

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