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.
29 from daal.algorithms
import kmeans
30 import daal.algorithms.kmeans.init
31 from daal.data_management
import HomogenNumericTable, FileDataSource, DataSource, BlockDescriptor, readOnly
33 DAAL_PREFIX = os.path.join(
'..',
'data')
35 datasetFileName = os.path.join(DAAL_PREFIX,
'batch',
'kmeans_init_dense.csv')
39 cAccuracyThreshold = 0.01
42 def getSingleValue(pTbl, ntype):
43 block = BlockDescriptor(ntype=ntype)
44 pTbl.getBlockOfRows(0, 1, readOnly, block)
45 value = block.getArray().flatten()[0]
46 pTbl.releaseBlockOfRows(block)
50 def runKmeans(inputData, nClusters, method, methodName, oversamplingFactor = -1.0):
52 init = kmeans.init.Batch(nClusters, fptype=np.float32, method=method)
53 init.input.set(kmeans.init.data, inputData)
54 if oversamplingFactor > 0:
55 init.parameter.oversamplingFactor = oversamplingFactor
56 if method == kmeans.init.parallelPlusDense:
57 print(
"K-means init parameters: method = " + methodName +
", oversamplingFactor = "
58 + str(init.parameter.oversamplingFactor) +
", nRounds = " + str(init.parameter.nRounds))
60 print(
"K-means init parameters: method = " + methodName)
62 centroids = init.compute().get(kmeans.init.centroids)
65 algorithm = kmeans.Batch(nClusters, nMaxIterations)
67 algorithm.input.set(kmeans.data, inputData)
68 algorithm.input.set(kmeans.inputCentroids, centroids)
69 algorithm.parameter.accuracyThreshold = cAccuracyThreshold
70 print(
"K-means algorithm parameters: maxIterations = " + str(algorithm.parameter.maxIterations)
71 +
", accuracyThreshold = " + str(algorithm.parameter.accuracyThreshold))
72 res = algorithm.compute()
75 goalFunc = getSingleValue(res.get(kmeans.objectiveFunction), ntype=np.float32)
76 nIterations = getSingleValue(res.get(kmeans.nIterations), ntype=np.intc)
77 print(
"K-means algorithm results: Objective function value = " + str(goalFunc*1e-6)
78 +
"*1E+6, number of iterations = " + str(nIterations) +
"\n")
81 if __name__ ==
"__main__":
83 inputData = HomogenNumericTable(ntype=np.float32)
84 dataSource = FileDataSource(datasetFileName,
85 DataSource.notAllocateNumericTable,
86 DataSource.doDictionaryFromContext)
89 dataSource.loadDataBlock(inputData)
91 runKmeans(inputData, nClusters, kmeans.init.deterministicDense,
"deterministicDense")
92 runKmeans(inputData, nClusters, kmeans.init.randomDense,
"randomDense")
93 runKmeans(inputData, nClusters, kmeans.init.plusPlusDense,
"plusPlusDense")
94 runKmeans(inputData, nClusters, kmeans.init.parallelPlusDense,
"parallelPlusDense", 0.5)
95 runKmeans(inputData, nClusters, kmeans.init.parallelPlusDense,
"parallelPlusDense", 2.0)