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

pca_metrics_dense_batch.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_metrics_dense_batch.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_METRICS_DENSE_BATCH"></a>
17 ## \example pca_metrics_dense_batch.py
18 
19 import os
20 import sys
21 
22 import daal.algorithms.pca as pca
23 import daal.algorithms.pca.quality_metric_set as quality_metric_set
24 from daal.algorithms.pca.quality_metric import explained_variance
25 from daal.data_management import (
26  DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable,
27  NumericTableIface, BlockDescriptor, readWrite
28 )
29 
30 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
31 if utils_folder not in sys.path:
32  sys.path.insert(0, utils_folder)
33 from utils import printNumericTable
34 
35 datasetFileName = os.path.join('..', 'data', 'batch', 'pca_normalized.csv')
36 nVectors = 1000
37 nComponents = 5
38 
39 qmsResult = None
40 eigenData = None
41 
42 def trainModel():
43  global eigenData
44 
45  # Initialize FileDataSource to retrieve the input data from a .csv file
46  dataSource = FileDataSource(
47  datasetFileName,
48  DataSourceIface.doAllocateNumericTable,
49  DataSourceIface.doDictionaryFromContext
50  )
51 
52  # Retrieve the data from the input file
53  dataSource.loadDataBlock(nVectors)
54 
55  # Create an algorithm for principal component analysis using the SVD method
56  algorithm = pca.Batch(method=pca.svdDense)
57 
58  # Set the algorithm input data
59  algorithm.input.setDataset(pca.data, dataSource.getNumericTable())
60 
61  # Compute results of the PCA algorithm
62  result = algorithm.compute()
63  eigenData = result.get(pca.eigenvalues)
64 
65 def testPcaQuality():
66  global qmsResult
67 
68  # Create a quality metric set object to compute quality metrics of the PCA algorithm
69  qualityMetricSet = quality_metric_set.Batch(nComponents)
70  explainedVariances = explained_variance.Input.downCast(qualityMetricSet.getInputDataCollection().getInput(quality_metric_set.explainedVariancesMetrics))
71  explainedVariances.setInput(explained_variance.eigenvalues, eigenData)
72 
73  # Compute quality metrics
74  qualityMetricSet.compute()
75 
76  # Retrieve the quality metrics
77  qmsResult = qualityMetricSet.getResultCollection()
78 
79 def printResults():
80  print ("Quality metrics for PCA")
81  result = explained_variance.Result.downCast(qmsResult.getResult(quality_metric_set.explainedVariancesMetrics))
82  printNumericTable(result.getResult(explained_variance.explainedVariances), "Explained variances:")
83  printNumericTable(result.getResult(explained_variance.explainedVariancesRatios), "Explained variances ratios:")
84  printNumericTable(result.getResult(explained_variance.noiseVariance), "Noise variance:")
85 
86 if __name__ == "__main__":
87  trainModel()
88  testPcaQuality()
89  printResults()

For more complete information about compiler optimizations, see our Optimization Notice.