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

pca_cor_dense_distr.py

1 # file: pca_cor_dense_distr.py
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40 #===============================================================================
41 
42 
44 
45 import os
46 import sys
47 
48 import numpy as np
49 
50 from daal import step1Local, step2Master
51 from daal.algorithms import pca
52 from daal.data_management import FileDataSource, DataSourceIface
53 
54 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
55 if utils_folder not in sys.path:
56  sys.path.insert(0, utils_folder)
57 from utils import printNumericTable
58 
59 DAAL_PREFIX = os.path.join('..', 'data')
60 
61 # Input data set parameters
62 nBlocks = 4
63 nVectorsInBlock = 250
64 nFeatures = None
65 
66 dataFileNames = [
67  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_1.csv'),
68  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_2.csv'),
69  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_3.csv'),
70  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_4.csv')
71 ]
72 
73 if __name__ == "__main__":
74 
75  # Create an algorithm for principal component analysis using the correlation method on the master node
76  masterAlgorithm = pca.Distributed(step2Master,fptype=np.float64)
77 
78  for i in range(nBlocks):
79  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
80  dataSource = FileDataSource(
81  dataFileNames[i], DataSourceIface.doAllocateNumericTable,
82  DataSourceIface.doDictionaryFromContext
83  )
84 
85  # Retrieve the input data
86  dataSource.loadDataBlock(nVectorsInBlock)
87 
88  # Create an algorithm for principal component analysis using the correlation method on the local node
89  localAlgorithm = pca.Distributed(step1Local,fptype=np.float64)
90 
91  # Set the input data to the algorithm
92  localAlgorithm.input.setDataset(pca.data, dataSource.getNumericTable())
93 
94  # Compute PCA decomposition
95  # Set local partial results as input for the master-node algorithm
96  masterAlgorithm.input.add(pca.partialResults, localAlgorithm.compute())
97 
98  # Merge and finalize PCA decomposition on the master node
99  masterAlgorithm.compute()
100  result = masterAlgorithm.finalizeCompute()
101 
102  # Print the results
103  printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
104  printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")

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