Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

out_detect_uni_dense_batch.py

1 # file: out_detect_uni_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 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-OUTLIER_DETECTION_UNIVARIATE_BATCH"></a>
17 ## \example out_detect_uni_dense_batch.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms import univariate_outlier_detection
23 from daal.data_management import FileDataSource, DataSourceIface
24 
25 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
26 if utils_folder not in sys.path:
27  sys.path.insert(0, utils_folder)
28 from utils import printNumericTable
29 
30 DAAL_PREFIX = os.path.join('..', 'data')
31 
32 # Input data set parameters
33 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'outlierdetection.csv')
34 
35 if __name__ == "__main__":
36 
37  # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
38  dataSource = FileDataSource(
39  datasetFileName, DataSourceIface.doAllocateNumericTable,
40  DataSourceIface.doDictionaryFromContext
41  )
42 
43  # Retrieve the data from the input file
44  dataSource.loadDataBlock()
45 
46  nFeatures = dataSource.getNumberOfColumns()
47 
48  algorithm = univariate_outlier_detection.Batch()
49 
50  algorithm.input.set(univariate_outlier_detection.data, dataSource.getNumericTable())
51 
52  # Compute outliers and get the computed results
53  res = algorithm.compute()
54 
55  printNumericTable(dataSource.getNumericTable(), "Input data")
56  printNumericTable(res.get(univariate_outlier_detection.weights), "Outlier detection result (univariate)")

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