kmeans_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.

 # file: kmeans_dense_distr.py
 #===============================================================================
 # Copyright 2014-2019 Intel Corporation.
 #
 # This software and the related documents are Intel copyrighted  materials,  and
 # your use of  them is  governed by the  express license  under which  they were
 # provided to you (License).  Unless the License provides otherwise, you may not
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
 #
 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 #===============================================================================
 
 ## <a name="DAAL-EXAMPLE-PY-KMEANS_DENSE_DISTRIBUTED"></a>
 ## \example kmeans_dense_distr.py
 
 import os
 import sys
 
 import daal.algorithms.kmeans as kmeans
 import daal.algorithms.kmeans.init as init
 from daal import step1Local, step2Master
 from daal.data_management import FileDataSource, DataSourceIface
 
 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
 if utils_folder not in sys.path:
     sys.path.insert(0, utils_folder)
 from utils import printNumericTable
 
 DAAL_PREFIX = os.path.join('..', 'data')
 
 dataFileNames = [
     os.path.join(DAAL_PREFIX, 'distributed', 'kmeans_dense_1.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'kmeans_dense_2.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'kmeans_dense_3.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'kmeans_dense_4.csv')
 ]
 
 nClusters = 20
 nIterations = 5
 nBlocks = 4
 nVectorsInBlock = 2500
 
 dataTable = [0] * nBlocks
 
 if __name__ == "__main__":
 
     masterAlgorithm = kmeans.Distributed(step2Master, nClusters, method=kmeans.lloydDense)
 
     centroids = None
     assignments = [0] * nBlocks
 
     masterInitAlgorithm = init.Distributed(step2Master, nClusters, method=init.randomDense)
     for i in range(nBlocks):
         # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
         dataSource = FileDataSource(
             dataFileNames[i], DataSourceIface.doAllocateNumericTable,
             DataSourceIface.doDictionaryFromContext
         )
 
         # Retrieve the data from the input file
         dataSource.loadDataBlock()
 
         dataTable[i] = dataSource.getNumericTable()
 
         # Create an algorithm object for the K-Means algorithm
         localInit = init.Distributed(step1Local, nClusters, nBlocks * nVectorsInBlock, i * nVectorsInBlock, method=init.randomDense)
 
         localInit.input.set(init.data, dataTable[i])
         res = localInit.compute()
         masterInitAlgorithm.input.add(init.partialResults, res)
 
     masterInitAlgorithm.compute()
     res = masterInitAlgorithm.finalizeCompute()
     centroids = res.get(init.centroids)
 
     for it in range(nIterations):
         for i in range(nBlocks):
             # Create an algorithm object for the K-Means algorithm
             localAlgorithm = kmeans.Distributed(step1Local, nClusters, it == nIterations, method=kmeans.lloydDense)
 
             # Set the input data to the algorithm
             localAlgorithm.input.set(kmeans.data,           dataTable[i])
             localAlgorithm.input.set(kmeans.inputCentroids, centroids)
 
             pres = localAlgorithm.compute()
 
             masterAlgorithm.input.add(kmeans.partialResults, pres)
 
         masterAlgorithm.compute()
         result = masterAlgorithm.finalizeCompute()
 
         centroids = result.get(kmeans.centroids)
         goalFunction = result.get(kmeans.goalFunction)
 
     for i in range(nBlocks):
         # Create an algorithm object for the K-Means algorithm
         localAlgorithm = kmeans.Batch(nClusters, 0, method=kmeans.lloydDense)
 
         # Set the input data to the algorithm
         localAlgorithm.input.set(kmeans.data,           dataTable[i])
         localAlgorithm.input.set(kmeans.inputCentroids, centroids)
 
         res = localAlgorithm.compute()
 
         assignments[i] = res.get(kmeans.assignments)
 
     # Print the clusterization results
     printNumericTable(assignments[0], "First 10 cluster assignments from 1st node:", 10)
     printNumericTable(centroids, "First 10 dimensions of centroids:", 20, 10)
     printNumericTable(goalFunction,   "Goal function value:")
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