Repository: spark Updated Branches: refs/heads/master e1d3f8010 -> 2224861f2
[SPARK-24439][ML][PYTHON] Add distanceMeasure to BisectingKMeans in PySpark ## What changes were proposed in this pull request? add distanceMeasure to BisectingKMeans in Python. ## How was this patch tested? added doctest and also manually tested it. Author: Huaxin Gao <huax...@us.ibm.com> Closes #21557 from huaxingao/spark-24439. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2224861f Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2224861f Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2224861f Branch: refs/heads/master Commit: 2224861f2f93830d736b625c9a4cb72c918512b2 Parents: e1d3f80 Author: Huaxin Gao <huax...@us.ibm.com> Authored: Thu Jun 28 14:07:28 2018 -0700 Committer: Bryan Cutler <cutl...@gmail.com> Committed: Thu Jun 28 14:07:28 2018 -0700 ---------------------------------------------------------------------- python/pyspark/ml/clustering.py | 35 ++++++++++++++------ .../pyspark/ml/param/_shared_params_code_gen.py | 4 ++- python/pyspark/ml/param/shared.py | 24 ++++++++++++++ 3 files changed, 51 insertions(+), 12 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/2224861f/python/pyspark/ml/clustering.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py index 4aa1cf8..6d77baf 100644 --- a/python/pyspark/ml/clustering.py +++ b/python/pyspark/ml/clustering.py @@ -349,8 +349,8 @@ class KMeansModel(JavaModel, JavaMLWritable, JavaMLReadable): @inherit_doc -class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol, HasSeed, - JavaMLWritable, JavaMLReadable): +class KMeans(JavaEstimator, HasDistanceMeasure, HasFeaturesCol, HasPredictionCol, HasMaxIter, + HasTol, HasSeed, JavaMLWritable, JavaMLReadable): """ K-means clustering with a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al). @@ -406,9 +406,6 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol typeConverter=TypeConverters.toString) initSteps = Param(Params._dummy(), "initSteps", "The number of steps for k-means|| " + "initialization mode. Must be > 0.", typeConverter=TypeConverters.toInt) - distanceMeasure = Param(Params._dummy(), "distanceMeasure", "The distance measure. " + - "Supported options: 'euclidean' and 'cosine'.", - typeConverter=TypeConverters.toString) @keyword_only def __init__(self, featuresCol="features", predictionCol="prediction", k=2, @@ -544,8 +541,8 @@ class BisectingKMeansModel(JavaModel, JavaMLWritable, JavaMLReadable): @inherit_doc -class BisectingKMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasSeed, - JavaMLWritable, JavaMLReadable): +class BisectingKMeans(JavaEstimator, HasDistanceMeasure, HasFeaturesCol, HasPredictionCol, + HasMaxIter, HasSeed, JavaMLWritable, JavaMLReadable): """ A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark. @@ -585,6 +582,8 @@ class BisectingKMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIte >>> bkm2 = BisectingKMeans.load(bkm_path) >>> bkm2.getK() 2 + >>> bkm2.getDistanceMeasure() + 'euclidean' >>> model_path = temp_path + "/bkm_model" >>> model.save(model_path) >>> model2 = BisectingKMeansModel.load(model_path) @@ -607,10 +606,10 @@ class BisectingKMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIte @keyword_only def __init__(self, featuresCol="features", predictionCol="prediction", maxIter=20, - seed=None, k=4, minDivisibleClusterSize=1.0): + seed=None, k=4, minDivisibleClusterSize=1.0, distanceMeasure="euclidean"): """ __init__(self, featuresCol="features", predictionCol="prediction", maxIter=20, \ - seed=None, k=4, minDivisibleClusterSize=1.0) + seed=None, k=4, minDivisibleClusterSize=1.0, distanceMeasure="euclidean") """ super(BisectingKMeans, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.clustering.BisectingKMeans", @@ -622,10 +621,10 @@ class BisectingKMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIte @keyword_only @since("2.0.0") def setParams(self, featuresCol="features", predictionCol="prediction", maxIter=20, - seed=None, k=4, minDivisibleClusterSize=1.0): + seed=None, k=4, minDivisibleClusterSize=1.0, distanceMeasure="euclidean"): """ setParams(self, featuresCol="features", predictionCol="prediction", maxIter=20, \ - seed=None, k=4, minDivisibleClusterSize=1.0) + seed=None, k=4, minDivisibleClusterSize=1.0, distanceMeasure="euclidean") Sets params for BisectingKMeans. """ kwargs = self._input_kwargs @@ -659,6 +658,20 @@ class BisectingKMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIte """ return self.getOrDefault(self.minDivisibleClusterSize) + @since("2.4.0") + def setDistanceMeasure(self, value): + """ + Sets the value of :py:attr:`distanceMeasure`. + """ + return self._set(distanceMeasure=value) + + @since("2.4.0") + def getDistanceMeasure(self): + """ + Gets the value of `distanceMeasure` or its default value. + """ + return self.getOrDefault(self.distanceMeasure) + def _create_model(self, java_model): return BisectingKMeansModel(java_model) http://git-wip-us.apache.org/repos/asf/spark/blob/2224861f/python/pyspark/ml/param/_shared_params_code_gen.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py index 6e9e0a3..e45ba84 100644 --- a/python/pyspark/ml/param/_shared_params_code_gen.py +++ b/python/pyspark/ml/param/_shared_params_code_gen.py @@ -162,7 +162,9 @@ if __name__ == "__main__": "fitting. If set to true, then all sub-models will be available. Warning: For large " + "models, collecting all sub-models can cause OOMs on the Spark driver.", "False", "TypeConverters.toBoolean"), - ("loss", "the loss function to be optimized.", None, "TypeConverters.toString")] + ("loss", "the loss function to be optimized.", None, "TypeConverters.toString"), + ("distanceMeasure", "the distance measure. Supported options: 'euclidean' and 'cosine'.", + "'euclidean'", "TypeConverters.toString")] code = [] for name, doc, defaultValueStr, typeConverter in shared: http://git-wip-us.apache.org/repos/asf/spark/blob/2224861f/python/pyspark/ml/param/shared.py ---------------------------------------------------------------------- diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index 08408ee..618f5bf 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -790,3 +790,27 @@ class DecisionTreeParams(Params): """ return self.getOrDefault(self.cacheNodeIds) + +class HasDistanceMeasure(Params): + """ + Mixin for param distanceMeasure: the distance measure. Supported options: 'euclidean' and 'cosine'. + """ + + distanceMeasure = Param(Params._dummy(), "distanceMeasure", "the distance measure. Supported options: 'euclidean' and 'cosine'.", typeConverter=TypeConverters.toString) + + def __init__(self): + super(HasDistanceMeasure, self).__init__() + self._setDefault(distanceMeasure='euclidean') + + def setDistanceMeasure(self, value): + """ + Sets the value of :py:attr:`distanceMeasure`. + """ + return self._set(distanceMeasure=value) + + def getDistanceMeasure(self): + """ + Gets the value of distanceMeasure or its default value. + """ + return self.getOrDefault(self.distanceMeasure) + --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
