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.
22 import daal.algorithms.kmeans
as kmeans
23 import daal.algorithms.kmeans.init
as init
24 from daal
import step1Local, step2Master
25 from daal.data_management
import FileDataSource, DataSourceIface
27 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
28 if utils_folder
not in sys.path:
29 sys.path.insert(0, utils_folder)
30 from utils
import printNumericTable
32 DAAL_PREFIX = os.path.join(
'..',
'data')
35 os.path.join(DAAL_PREFIX,
'distributed',
'kmeans_dense_1.csv'),
36 os.path.join(DAAL_PREFIX,
'distributed',
'kmeans_dense_2.csv'),
37 os.path.join(DAAL_PREFIX,
'distributed',
'kmeans_dense_3.csv'),
38 os.path.join(DAAL_PREFIX,
'distributed',
'kmeans_dense_4.csv')
44 nVectorsInBlock = 2500
46 dataTable = [0] * nBlocks
48 if __name__ ==
"__main__":
50 masterAlgorithm = kmeans.Distributed(step2Master, nClusters, method=kmeans.lloydDense)
53 assignments = [0] * nBlocks
55 masterInitAlgorithm = init.Distributed(step2Master, nClusters, method=init.randomDense)
56 for i
in range(nBlocks):
58 dataSource = FileDataSource(
59 dataFileNames[i], DataSourceIface.doAllocateNumericTable,
60 DataSourceIface.doDictionaryFromContext
64 dataSource.loadDataBlock()
66 dataTable[i] = dataSource.getNumericTable()
69 localInit = init.Distributed(step1Local, nClusters, nBlocks * nVectorsInBlock, i * nVectorsInBlock, method=init.randomDense)
71 localInit.input.set(init.data, dataTable[i])
72 res = localInit.compute()
73 masterInitAlgorithm.input.add(init.partialResults, res)
75 masterInitAlgorithm.compute()
76 res = masterInitAlgorithm.finalizeCompute()
77 centroids = res.get(init.centroids)
79 for it
in range(nIterations):
80 for i
in range(nBlocks):
82 localAlgorithm = kmeans.Distributed(step1Local, nClusters, it == nIterations, method=kmeans.lloydDense)
85 localAlgorithm.input.set(kmeans.data, dataTable[i])
86 localAlgorithm.input.set(kmeans.inputCentroids, centroids)
88 pres = localAlgorithm.compute()
90 masterAlgorithm.input.add(kmeans.partialResults, pres)
92 masterAlgorithm.compute()
93 result = masterAlgorithm.finalizeCompute()
95 centroids = result.get(kmeans.centroids)
96 goalFunction = result.get(kmeans.goalFunction)
98 for i
in range(nBlocks):
100 localAlgorithm = kmeans.Batch(nClusters, 0, method=kmeans.lloydDense)
103 localAlgorithm.input.set(kmeans.data, dataTable[i])
104 localAlgorithm.input.set(kmeans.inputCentroids, centroids)
106 res = localAlgorithm.compute()
108 assignments[i] = res.get(kmeans.assignments)
111 printNumericTable(assignments[0],
"First 10 cluster assignments from 1st node:", 10)
112 printNumericTable(centroids,
"First 10 dimensions of centroids:", 20, 10)
113 printNumericTable(goalFunction,
"Goal function value:")