Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

pca_cor_dense_distr.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

1 # file: pca_cor_dense_distr.py
2 #===============================================================================
3 # Copyright 2014-2019 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 ## <a name="DAAL-EXAMPLE-PY-PCA_CORRELATION_DENSE_DISTRIBUTED"></a>
17 ## \example pca_cor_dense_distr.py
18 
19 import os
20 import sys
21 
22 import numpy as np
23 
24 from daal import step1Local, step2Master
25 from daal.algorithms import pca
26 from daal.data_management import FileDataSource, DataSourceIface
27 
28 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
29 if utils_folder not in sys.path:
30  sys.path.insert(0, utils_folder)
31 from utils import printNumericTable
32 
33 DAAL_PREFIX = os.path.join('..', 'data')
34 
35 # Input data set parameters
36 nBlocks = 4
37 nVectorsInBlock = 250
38 nFeatures = None
39 
40 dataFileNames = [
41  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_1.csv'),
42  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_2.csv'),
43  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_3.csv'),
44  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_4.csv')
45 ]
46 
47 if __name__ == "__main__":
48 
49  # Create an algorithm for principal component analysis using the correlation method on the master node
50  masterAlgorithm = pca.Distributed(step2Master,fptype=np.float64)
51 
52  for i in range(nBlocks):
53  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
54  dataSource = FileDataSource(
55  dataFileNames[i], DataSourceIface.doAllocateNumericTable,
56  DataSourceIface.doDictionaryFromContext
57  )
58 
59  # Retrieve the input data
60  dataSource.loadDataBlock(nVectorsInBlock)
61 
62  # Create an algorithm for principal component analysis using the correlation method on the local node
63  localAlgorithm = pca.Distributed(step1Local,fptype=np.float64)
64 
65  # Set the input data to the algorithm
66  localAlgorithm.input.setDataset(pca.data, dataSource.getNumericTable())
67 
68  # Compute PCA decomposition
69  # Set local partial results as input for the master-node algorithm
70  masterAlgorithm.input.add(pca.partialResults, localAlgorithm.compute())
71 
72  # Merge and finalize PCA decomposition on the master node
73  masterAlgorithm.compute()
74  result = masterAlgorithm.finalizeCompute()
75 
76  # Print the results
77  printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
78  printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")

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