Repository: spark Updated Branches: refs/heads/branch-2.0 20a07e443 -> 7feb79085
[MINOR][DOC][ML] ml.clustering scala & python api doc sync ## What changes were proposed in this pull request? Since we done Scala API audit for ml.clustering at #13148, we should also fix and update the corresponding Python API docs to keep them in sync. ## How was this patch tested? Docs change, no tests. Author: Yanbo Liang <yblia...@gmail.com> Closes #13291 from yanboliang/spark-15361-followup. (cherry picked from commit 594484cd8343f870c53fbc829ed4fb889016a8cf) Signed-off-by: Nick Pentreath <ni...@za.ibm.com> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/7feb7908 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/7feb7908 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/7feb7908 Branch: refs/heads/branch-2.0 Commit: 7feb7908563431090eabdee1369531dc1ba3e35b Parents: 20a07e4 Author: Yanbo Liang <yblia...@gmail.com> Authored: Tue May 31 14:56:43 2016 -0700 Committer: Nick Pentreath <ni...@za.ibm.com> Committed: Tue May 31 14:56:51 2016 -0700 ---------------------------------------------------------------------- python/pyspark/ml/clustering.py | 35 +++++++++++++++++++++++++---------- 1 file changed, 25 insertions(+), 10 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/7feb7908/python/pyspark/ml/clustering.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py index a457904..92df19e 100644 --- a/python/pyspark/ml/clustering.py +++ b/python/pyspark/ml/clustering.py @@ -64,6 +64,21 @@ class GaussianMixture(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIte .. note:: Experimental GaussianMixture clustering. + This class performs expectation maximization for multivariate Gaussian + Mixture Models (GMMs). A GMM represents a composite distribution of + independent Gaussian distributions with associated "mixing" weights + specifying each's contribution to the composite. + + Given a set of sample points, this class will maximize the log-likelihood + for a mixture of k Gaussians, iterating until the log-likelihood changes by + less than convergenceTol, or until it has reached the max number of iterations. + While this process is generally guaranteed to converge, it is not guaranteed + to find a global optimum. + + Note: For high-dimensional data (with many features), this algorithm may perform poorly. + This is due to high-dimensional data (a) making it difficult to cluster at all + (based on statistical/theoretical arguments) and (b) numerical issues with + Gaussian distributions. >>> from pyspark.ml.linalg import Vectors @@ -118,8 +133,8 @@ class GaussianMixture(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIte .. versionadded:: 2.0.0 """ - k = Param(Params._dummy(), "k", "number of clusters to create", - typeConverter=TypeConverters.toInt) + k = Param(Params._dummy(), "k", "Number of independent Gaussians in the mixture model. " + + "Must be > 1.", typeConverter=TypeConverters.toInt) @keyword_only def __init__(self, featuresCol="features", predictionCol="prediction", k=2, @@ -227,15 +242,15 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol .. versionadded:: 1.5.0 """ - k = Param(Params._dummy(), "k", "number of clusters to create", + k = Param(Params._dummy(), "k", "The number of clusters to create. Must be > 1.", typeConverter=TypeConverters.toInt) initMode = Param(Params._dummy(), "initMode", - "the initialization algorithm. This can be either \"random\" to " + + "The initialization algorithm. This can be either \"random\" to " + "choose random points as initial cluster centers, or \"k-means||\" " + "to use a parallel variant of k-means++", typeConverter=TypeConverters.toString) - initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode", - typeConverter=TypeConverters.toInt) + initSteps = Param(Params._dummy(), "initSteps", "The number of steps for k-means|| " + + "initialization mode. Must be > 0.", typeConverter=TypeConverters.toInt) @keyword_only def __init__(self, featuresCol="features", predictionCol="prediction", k=2, @@ -380,11 +395,11 @@ class BisectingKMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIte .. versionadded:: 2.0.0 """ - k = Param(Params._dummy(), "k", "number of clusters to create", + k = Param(Params._dummy(), "k", "The desired number of leaf clusters. Must be > 1.", typeConverter=TypeConverters.toInt) minDivisibleClusterSize = Param(Params._dummy(), "minDivisibleClusterSize", - "the minimum number of points (if >= 1.0) " + - "or the minimum proportion", + "The minimum number of points (if >= 1.0) or the minimum " + + "proportion of points (if < 1.0) of a divisible cluster.", typeConverter=TypeConverters.toFloat) @keyword_only @@ -661,7 +676,7 @@ class LDA(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed, HasCheckpointInter .. versionadded:: 2.0.0 """ - k = Param(Params._dummy(), "k", "number of topics (clusters) to infer", + k = Param(Params._dummy(), "k", "The number of topics (clusters) to infer. Must be > 1.", typeConverter=TypeConverters.toInt) optimizer = Param(Params._dummy(), "optimizer", "Optimizer or inference algorithm used to estimate the LDA model. 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