Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

cov_dense_distr.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: cov_dense_distr.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-COVARIANCE_DENSE_DISTRIBUTED"></a>
17 ## \example cov_dense_distr.py
18 
19 import os
20 import sys
21 
22 from daal import step1Local, step2Master
23 from daal.algorithms import covariance
24 from daal.data_management import FileDataSource, DataSourceIface
25 
26 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
27 if utils_folder not in sys.path:
28  sys.path.insert(0, utils_folder)
29 from utils import printNumericTable
30 
31 DAAL_PREFIX = os.path.join('..', 'data')
32 
33 # Input data set parameters
34 nBlocks = 4
35 
36 datasetFileNames = [
37  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_1.csv'),
38  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_2.csv'),
39  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_3.csv'),
40  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_4.csv')
41 ]
42 
43 partialResult = [0] * nBlocks
44 result = None
45 
46 
47 def computestep1Local(block):
48  global partialResult
49 
50  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
51  dataSource = FileDataSource(
52  datasetFileNames[block], DataSourceIface.doAllocateNumericTable,
53  DataSourceIface.doDictionaryFromContext
54  )
55 
56  # Retrieve the data from the input file
57  dataSource.loadDataBlock()
58 
59  # Create algorithm objects to compute a dense variance-covariance matrix in the distributed processing mode using the default method
60  algorithm = covariance.Distributed(step1Local)
61 
62  # Set input objects for the algorithm
63  algorithm.input.set(covariance.data, dataSource.getNumericTable())
64 
65  # Compute partial estimates on local nodes
66  partialResult[block] = algorithm.compute() # Get the computed partial estimates
67 
68 
69 def computeOnMasterNode():
70  global result
71 
72  # Create algorithm objects to compute a dense variance-covariance matrix in the distributed processing mode using the default method
73  algorithm = covariance.Distributed(step2Master)
74 
75  # Set input objects for the algorithm
76  for i in range(nBlocks):
77  algorithm.input.add(covariance.partialResults, partialResult[i])
78 
79  # Compute a partial estimate on the master node from the partial estimates on local nodes
80  algorithm.compute()
81 
82  # Finalize the result in the distributed processing mode
83  result = algorithm.finalizeCompute() # Get the computed dense variance-covariance matrix
84 
85 if __name__ == "__main__":
86 
87  for i in range(nBlocks):
88  computestep1Local(i)
89 
90  computeOnMasterNode()
91 
92  printNumericTable(result.get(covariance.covariance), "Covariance matrix:")
93  printNumericTable(result.get(covariance.mean), "Mean vector:")

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