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https://issues.apache.org/jira/browse/SPARK-14174?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15947114#comment-15947114
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Nick Pentreath commented on SPARK-14174:
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The actual fix in the PR is pretty small - essentially just adding an
{{rdd.sample}} call (similar to the old {{mllib}} gradient descent impl). So if
we can see some good speed improvements on a relatively large class of input
datasets, this seems like an easy win. From the performance tests above it
seems like there's a significant win even for low-dimensional vectors. For
higher dimensions the improvement may be as large or perhaps larger.
[~podongfeng] it may be best to add a few different cases to the performance
tests to illustrate the behavior for different cases (and if not for certain
cases, we should document that):
# small dimension, dense
# high dimension, dense
# small dimension, sparse
# high dimension, sparse
[~rnowling] do you have time to check out the PR here? It seems similar in
spirit to what you had done and just uses the built-in RDD sampling (which I
think [~derrickburns] mentioned in SPARK-2308).
> Accelerate KMeans via Mini-Batch EM
> -----------------------------------
>
> Key: SPARK-14174
> URL: https://issues.apache.org/jira/browse/SPARK-14174
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: zhengruifeng
>
> The MiniBatchKMeans is a variant of the KMeans algorithm which uses
> mini-batches to reduce the computation time, while still attempting to
> optimise the same objective function. Mini-batches are subsets of the input
> data, randomly sampled in each training iteration. These mini-batches
> drastically reduce the amount of computation required to converge to a local
> solution. In contrast to other algorithms that reduce the convergence time of
> k-means, mini-batch k-means produces results that are generally only slightly
> worse than the standard algorithm.
> I have implemented mini-batch kmeans in Mllib, and the acceleration is realy
> significant.
> The MiniBatch KMeans is named XMeans in following lines.
> {code}
> val path = "/tmp/mnist8m.scale"
> val data = MLUtils.loadLibSVMFile(sc, path)
> val vecs = data.map(_.features).persist()
> val km = KMeans.train(data=vecs, k=10, maxIterations=10, runs=1,
> initializationMode="k-means||", seed=123l)
> km.computeCost(vecs)
> res0: Double = 3.317029898599564E8
> val xm = XMeans.train(data=vecs, k=10, maxIterations=10, runs=1,
> initializationMode="k-means||", miniBatchFraction=0.1, seed=123l)
> xm.computeCost(vecs)
> res1: Double = 3.3169865959604424E8
> val xm2 = XMeans.train(data=vecs, k=10, maxIterations=10, runs=1,
> initializationMode="k-means||", miniBatchFraction=0.01, seed=123l)
> xm2.computeCost(vecs)
> res2: Double = 3.317195831216454E8
> {code}
> The above three training all reached the max number of iterations 10.
> We can see that the WSSSEs are almost the same. While their speed perfermence
> have significant difference:
> {code}
> KMeans 2876sec
> MiniBatch KMeans (fraction=0.1) 263sec
> MiniBatch KMeans (fraction=0.01) 90sec
> {code}
> With appropriate fraction, the bigger the dataset is, the higher speedup is.
> The data used above have 8,100,000 samples, 784 features. It can be
> downloaded here
> (https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist8m.scale.bz2)
> Comparison of the K-Means and MiniBatchKMeans on sklearn :
> http://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html#example-cluster-plot-mini-batch-kmeans-py
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