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

low_order_moms_csr_distr.py

1 # file: low_order_moms_csr_distr.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 
17 
18 
19 import os
20 import sys
21 
22 from daal import step1Local, step2Master
23 from daal.algorithms import low_order_moments
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, createSparseTable
29 
30 DAAL_PREFIX = os.path.join('..', 'data')
31 
32 # Input data set parameters
33 nBlocks = 4
34 
35 datasetFileNames = [
36  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_1.csv'),
37  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_2.csv'),
38  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_3.csv'),
39  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_4.csv')
40 ]
41 
42 partialResult = [0] * nBlocks
43 result = None
44 
45 
46 def computestep1Local(block):
47 
48  dataTable = createSparseTable(datasetFileNames[block])
49 
50  # Create algorithm objects to compute low order moments in the distributed processing mode using the default method
51  algorithm = low_order_moments.Distributed(step1Local, method=low_order_moments.fastCSR)
52 
53  # Set input objects for the algorithm
54  algorithm.input.set(low_order_moments.data, dataTable)
55 
56  # Compute partial low order moments estimates on nodes
57  partialResult[block] = algorithm.compute() # Get the computed partial estimates
58 
59 
60 def computeOnMasterNode():
61  global result
62 
63  # Create algorithm objects to compute low order moments in the distributed processing mode using the default method
64  algorithm = low_order_moments.Distributed(step2Master, method=low_order_moments.fastCSR)
65 
66  # Set input objects for the algorithm
67  for i in range(nBlocks):
68  algorithm.input.add(low_order_moments.partialResults, partialResult[i])
69 
70  # Compute a partial low order moments estimate on the master node from the partial estimates on local nodes
71  algorithm.compute()
72 
73  # Finalize the result in the distributed processing mode and get the computed low order moments
74  result = algorithm.finalizeCompute()
75 
76 
77 def printResults(res):
78 
79  printNumericTable(res.get(low_order_moments.minimum), "Minimum:")
80  printNumericTable(res.get(low_order_moments.maximum), "Maximum:")
81  printNumericTable(res.get(low_order_moments.sum), "Sum:")
82  printNumericTable(res.get(low_order_moments.sumSquares), "Sum of squares:")
83  printNumericTable(res.get(low_order_moments.sumSquaresCentered), "Sum of squared difference from the means:")
84  printNumericTable(res.get(low_order_moments.mean), "Mean:")
85  printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
86  printNumericTable(res.get(low_order_moments.variance), "Variance:")
87  printNumericTable(res.get(low_order_moments.standardDeviation), "Standard deviation:")
88  printNumericTable(res.get(low_order_moments.variation), "Variation:")
89 
90 if __name__ == "__main__":
91  for block in range(nBlocks):
92  computestep1Local(block)
93 
94  computeOnMasterNode()
95  printResults(result)

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