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

qr_dense_distr.py

1 # file: qr_dense_distr.py
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40 #===============================================================================
41 
42 
44 
45 import os
46 import sys
47 
48 from daal import step1Local, step2Master, step3Local
49 from daal.algorithms import qr
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', 'qr_1.csv'),
64  os.path.join(DAAL_PREFIX, 'distributed', 'qr_2.csv'),
65  os.path.join(DAAL_PREFIX, 'distributed', 'qr_3.csv'),
66  os.path.join(DAAL_PREFIX, 'distributed', 'qr_4.csv')
67 ]
68 
69 dataFromStep1ForStep2 = [0] * nBlocks
70 dataFromStep1ForStep3 = [0] * nBlocks
71 dataFromStep2ForStep3 = [0] * nBlocks
72 R = None
73 Qi = [0] * nBlocks
74 
75 
76 def computestep1Local(block):
77  global dataFromStep1ForStep2, dataFromStep1ForStep3
78 
79  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
80  dataSource = FileDataSource(
81  datasetFileNames[block],
82  DataSourceIface.doAllocateNumericTable,
83  DataSourceIface.doDictionaryFromContext
84  )
85 
86  # Retrieve the input data
87  dataSource.loadDataBlock()
88 
89  # Create an algorithm to compute QR decomposition on the local node
90  algorithm = qr.Distributed(step1Local)
91 
92  algorithm.input.set(qr.data, dataSource.getNumericTable())
93 
94  # Compute QR decomposition and get OnlinePartialResult class from daal.algorithms.qr
95  pres = algorithm.compute()
96 
97  dataFromStep1ForStep2[block] = pres.get(qr.outputOfStep1ForStep2)
98  dataFromStep1ForStep3[block] = pres.get(qr.outputOfStep1ForStep3)
99 
100 
101 def computeOnMasterNode():
102  global R, dataFromStep2ForStep3
103 
104  # Create an algorithm to compute QR decomposition on the master node
105  algorithm = qr.Distributed(step2Master)
106 
107  for i in range(nBlocks):
108  algorithm.input.add(qr.inputOfStep2FromStep1, i, dataFromStep1ForStep2[i])
109 
110  # Compute QR decomposition and get DistributedPartialResult class from daal.algorithms.qr
111  pres = algorithm.compute()
112 
113  for i in range(nBlocks):
114  dataFromStep2ForStep3[i] = pres.getCollection(qr.outputOfStep2ForStep3, i)
115 
116  res = algorithm.finalizeCompute()
117  R = res.get(qr.matrixR)
118 
119 
120 def finalizeComputestep1Local(block):
121  global Qi
122 
123  # Create an algorithm to compute QR decomposition on the master node
124  algorithm = qr.Distributed(step3Local)
125 
126  algorithm.input.set(qr.inputOfStep3FromStep1, dataFromStep1ForStep3[block])
127  algorithm.input.set(qr.inputOfStep3FromStep2, dataFromStep2ForStep3[block])
128 
129  # Compute QR decomposition
130  algorithm.compute()
131 
132  res = algorithm.finalizeCompute()
133 
134  Qi[block] = res.get(qr.matrixQ)
135 
136 if __name__ == "__main__":
137 
138  for i in range(nBlocks):
139  computestep1Local(i)
140 
141  computeOnMasterNode()
142 
143  for i in range(nBlocks):
144  finalizeComputestep1Local(i)
145 
146  # Print the results
147  printNumericTable(Qi[0], "Part of orthogonal matrix Q from 1st node:", 10)
148  printNumericTable(R, "Triangular matrix R:")

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