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

out_detect_mult_dense_batch.py

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40 
41 ## <a name="DAAL-EXAMPLE-PY-OUTLIER_DETECTION_MULTIVARIATE_DENSE_BATCH"></a>
42 ## \example out_detect_mult_dense_batch.py
43 
44 import os
45 import sys
46 
47 from daal.algorithms import multivariate_outlier_detection
48 from daal.data_management import FileDataSource, DataSourceIface
49 
50 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
51 if utils_folder not in sys.path:
52  sys.path.insert(0, utils_folder)
53 from utils import printNumericTables
54 
55 DAAL_PREFIX = os.path.join('..', 'data')
56 
57 # Input data set parameters
58 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'outlierdetection.csv')
59 
60 if __name__ == "__main__":
61 
62  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
63  dataSource = FileDataSource(
64  datasetFileName, DataSourceIface.doAllocateNumericTable,
65  DataSourceIface.doDictionaryFromContext
66  )
67 
68  # Retrieve the data from the input file
69  dataSource.loadDataBlock()
70 
71  # Create an algorithm to detect outliers using the default method
72  algorithm = multivariate_outlier_detection.Batch()
73 
74  algorithm.input.set(multivariate_outlier_detection.data, dataSource.getNumericTable())
75 
76  # Compute outliers and get the computed results
77  res = algorithm.compute()
78 
79  printNumericTables(
80  dataSource.getNumericTable(),
81  res.get(multivariate_outlier_detection.weights),
82  "Input data", "Weights",
83  "Outlier detection result (Default method)"
84  )

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