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

pca_svd_dense_distr.py

1 # file: pca_svd_dense_distr.py
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40 #===============================================================================
41 
42 
44 
45 import os
46 import sys
47 
48 import daal
49 from daal.algorithms import pca
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 nVectorsInBlock = 250
62 
63 dataFileNames = [
64  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_1.csv'),
65  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_2.csv'),
66  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_3.csv'),
67  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_4.csv')
68 ]
69 
70 if __name__ == "__main__":
71 
72  # Create an algorithm for principal component analysis using the SVD method on the master node
73  masterAlgorithm = pca.Distributed(step=daal.step2Master, method=pca.svdDense)
74 
75  for i in range(nBlocks):
76  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
77  dataSource = FileDataSource(
78  dataFileNames[i], DataSourceIface.doAllocateNumericTable,
79  DataSourceIface.doDictionaryFromContext
80  )
81 
82  # Retrieve the input data
83  dataSource.loadDataBlock(nVectorsInBlock)
84 
85  # Create an algorithm for principal component analysis using the SVD method on the local node
86  localAlgorithm = pca.Distributed(step=daal.step1Local, method=pca.svdDense)
87 
88  # Set the input data to the algorithm
89  localAlgorithm.input.setDataset(pca.data, dataSource.getNumericTable())
90 
91  # Compute PCA decomposition
92  # Set local partial results as input for the master-node algorithm
93  masterAlgorithm.input.add(pca.partialResults, localAlgorithm.compute())
94 
95  # Merge and finalize PCA decomposition on the master node
96  masterAlgorithm.compute()
97  result = masterAlgorithm.finalizeCompute()
98 
99  # Print the results
100  printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
101  printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")

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