Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

kdtree_knn_dense_batch.py

1 # file: kdtree_knn_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
43 
44 
45 import os
46 import sys
47 
48 from daal.algorithms.kdtree_knn_classification import training, prediction
49 from daal.algorithms import classifier
50 from daal.data_management import (
51  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
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 printNumericTables
58 
59 DAAL_PREFIX = os.path.join('..', 'data')
60 
61 # Input data set parameters
62 trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'k_nearest_neighbors_train.csv')
63 testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'k_nearest_neighbors_test.csv')
64 
65 nFeatures = 5
66 
67 trainingResult = None
68 predictionResult = None
69 
70 
71 def trainModel():
72  global trainingResult
73 
74  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
75  trainDataSource = FileDataSource(
76  trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
77  DataSourceIface.doDictionaryFromContext
78  )
79 
80  # Create Numeric Tables for training data and dependent variables
81  trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
82  trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
83  mergedData = MergedNumericTable(trainData, trainGroundTruth)
84 
85  # Retrieve the data from input file
86  trainDataSource.loadDataBlock(mergedData)
87 
88  # Create an algorithm object to train the KD-tree based kNN model
89  algorithm = training.Batch()
90 
91  # Pass a training data set and dependent values to the algorithm
92  algorithm.input.set(classifier.training.data, trainData)
93  algorithm.input.set(classifier.training.labels, trainGroundTruth)
94 
95  # Train the KD-tree based kNN model
96  trainingResult = algorithm.compute()
97 
98 
99 def testModel():
100  global trainingResult, predictionResult
101 
102  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
103  testDataSource = FileDataSource(
104  testDatasetFileName, DataSourceIface.doAllocateNumericTable,
105  DataSourceIface.doDictionaryFromContext
106  )
107 
108  # Create Numeric Tables for testing data and ground truth values
109  testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
110  testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
111  mergedData = MergedNumericTable(testData, testGroundTruth)
112 
113  # Load the data from the data file
114  testDataSource.loadDataBlock(mergedData)
115 
116  # Create algorithm objects for KD-tree based kNN prediction with the default method
117  algorithm = prediction.Batch()
118 
119  # Pass the testing data set and trained model to the algorithm
120  algorithm.input.setTable(classifier.prediction.data, testData)
121  algorithm.input.setModel(classifier.prediction.model, trainingResult.get(classifier.training.model))
122 
123  # Compute prediction results
124  predictionResult = algorithm.compute()
125  printNumericTables(
126  testGroundTruth, predictionResult.get(classifier.prediction.prediction),
127  "Ground truth", "Classification results",
128  "KD-tree based kNN classification results (first 20 observations):", 20, flt64=False
129  )
130 
131 if __name__ == "__main__":
132 
133  trainModel()
134  testModel()

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