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

low_order_moms_dense_distr.py

1 # file: low_order_moms_dense_distr.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
43 
44 
45 import os
46 import sys
47 
48 from daal import step1Local, step2Master
49 from daal.algorithms import low_order_moments
50 from daal.data_management import FileDataSource, DataSourceIface
51 
52 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
53 if utils_folder not in sys.path:
54  sys.path.insert(0, utils_folder)
55 from utils import printNumericTable
56 
57 DAAL_PREFIX = os.path.join('..', 'data')
58 
59 # Input data set parameters
60 nBlocks = 4
61 
62 datasetFileNames = [
63  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_1.csv'),
64  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_2.csv'),
65  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_3.csv'),
66  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_4.csv')
67 ]
68 
69 partialResult = [0] * nBlocks
70 result = None
71 
72 
73 def computestep1Local(block):
74  global partialResult
75 
76  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
77  dataSource = FileDataSource(
78  datasetFileNames[block], DataSourceIface.doAllocateNumericTable,
79  DataSourceIface.doDictionaryFromContext
80  )
81 
82  # Retrieve the data from the input file
83  dataSource.loadDataBlock()
84 
85  # Create algorithm objects to compute low order moments in the distributed processing mode using the default method
86  algorithm = low_order_moments.Distributed(step1Local)
87 
88  # Set input objects for the algorithm
89  algorithm.input.set(low_order_moments.data, dataSource.getNumericTable())
90 
91  # Compute partial low order moments estimates on nodes
92  partialResult[block] = algorithm.compute() # Get the computed partial estimates
93 
94 
95 def computeOnMasterNode():
96  global result
97 
98  # Create algorithm objects to compute low order moments in the distributed processing mode using the default method
99  algorithm = low_order_moments.Distributed(step2Master)
100 
101  # Set input objects for the algorithm
102  for i in range(nBlocks):
103  algorithm.input.add(low_order_moments.partialResults, partialResult[i])
104 
105  # Compute a partial low order moments estimate on the master node from the partial estimates on local nodes
106  algorithm.compute()
107 
108  # Finalize the result in the distributed processing mode
109  result = algorithm.finalizeCompute() # Get the computed low order moments
110 
111 
112 def printResults(res):
113 
114  printNumericTable(res.get(low_order_moments.minimum), "Minimum:")
115  printNumericTable(res.get(low_order_moments.maximum), "Maximum:")
116  printNumericTable(res.get(low_order_moments.sum), "Sum:")
117  printNumericTable(res.get(low_order_moments.sumSquares), "Sum of squares:")
118  printNumericTable(res.get(low_order_moments.sumSquaresCentered), "Sum of squared difference from the means:")
119  printNumericTable(res.get(low_order_moments.mean), "Mean:")
120  printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
121  printNumericTable(res.get(low_order_moments.variance), "Variance:")
122  printNumericTable(res.get(low_order_moments.standardDeviation), "Standard deviation:")
123  printNumericTable(res.get(low_order_moments.variation), "Variation:")
124 
125 if __name__ == "__main__":
126 
127  for i in range(nBlocks):
128  computestep1Local(i)
129 
130  computeOnMasterNode()
131  printResults(result)

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