BisectingKMeans¶
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class
pyspark.ml.clustering.
BisectingKMeans
(featuresCol='features', predictionCol='prediction', maxIter=20, seed=None, k=4, minDivisibleClusterSize=1.0, distanceMeasure='euclidean', weightCol=None)[source]¶ 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. The algorithm starts from a single cluster that contains all points. Iteratively it finds divisible clusters on the bottom level and bisects each of them using k-means, until there are k leaf clusters in total or no leaf clusters are divisible. The bisecting steps of clusters on the same level are grouped together to increase parallelism. If bisecting all divisible clusters on the bottom level would result more than k leaf clusters, larger clusters get higher priority.
>>> from pyspark.ml.linalg import Vectors >>> data = [(Vectors.dense([0.0, 0.0]), 2.0), (Vectors.dense([1.0, 1.0]), 2.0), ... (Vectors.dense([9.0, 8.0]), 2.0), (Vectors.dense([8.0, 9.0]), 2.0)] >>> df = spark.createDataFrame(data, ["features", "weighCol"]) >>> bkm = BisectingKMeans(k=2, minDivisibleClusterSize=1.0) >>> bkm.setMaxIter(10) BisectingKMeans... >>> bkm.getMaxIter() 10 >>> bkm.clear(bkm.maxIter) >>> bkm.setSeed(1) BisectingKMeans... >>> bkm.setWeightCol("weighCol") BisectingKMeans... >>> bkm.getSeed() 1 >>> bkm.clear(bkm.seed) >>> model = bkm.fit(df) >>> model.getMaxIter() 20 >>> model.setPredictionCol("newPrediction") BisectingKMeansModel... >>> model.predict(df.head().features) 0 >>> centers = model.clusterCenters() >>> len(centers) 2 >>> model.computeCost(df) 2.0 >>> model.hasSummary True >>> summary = model.summary >>> summary.k 2 >>> summary.clusterSizes [2, 2] >>> summary.trainingCost 4.000... >>> transformed = model.transform(df).select("features", "newPrediction") >>> rows = transformed.collect() >>> rows[0].newPrediction == rows[1].newPrediction True >>> rows[2].newPrediction == rows[3].newPrediction True >>> bkm_path = temp_path + "/bkm" >>> bkm.save(bkm_path) >>> 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) >>> model2.hasSummary False >>> model.clusterCenters()[0] == model2.clusterCenters()[0] array([ True, True], dtype=bool) >>> model.clusterCenters()[1] == model2.clusterCenters()[1] array([ True, True], dtype=bool)
New in version 2.0.0.
Methods
Attributes
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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copy
(extra=None)¶ Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.
- Parameters
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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extractParamMap
(extra=None)¶ Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
- Parameters
extra – extra param values
- Returns
merged param map
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fit
(dataset, params=None)¶ Fits a model to the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.
- Returns
fitted model(s)
New in version 1.3.0.
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fitMultiple
(dataset, paramMaps)¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.
New in version 2.3.0.
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getDistanceMeasure
()¶ Gets the value of distanceMeasure or its default value.
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getFeaturesCol
()¶ Gets the value of featuresCol or its default value.
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getK
()¶ Gets the value of k or its default value.
New in version 2.0.0.
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getMaxIter
()¶ Gets the value of maxIter or its default value.
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getMinDivisibleClusterSize
()¶ Gets the value of minDivisibleClusterSize or its default value.
New in version 2.0.0.
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getOrDefault
(param)¶ Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
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getParam
(paramName)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
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getSeed
()¶ Gets the value of seed or its default value.
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getWeightCol
()¶ Gets the value of weightCol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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setDistanceMeasure
(value)[source]¶ Sets the value of
distanceMeasure
.New in version 2.4.0.
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setFeaturesCol
(value)[source]¶ Sets the value of
featuresCol
.New in version 2.0.0.
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setMinDivisibleClusterSize
(value)[source]¶ Sets the value of
minDivisibleClusterSize
.New in version 2.0.0.
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setParams
(self, featuresCol='features', predictionCol='prediction', maxIter=20, seed=None, k=4, minDivisibleClusterSize=1.0, distanceMeasure='euclidean', weightCol=None)[source]¶ Sets params for BisectingKMeans.
New in version 2.0.0.
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setPredictionCol
(value)[source]¶ Sets the value of
predictionCol
.New in version 2.0.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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distanceMeasure
= Param(parent='undefined', name='distanceMeasure', doc="the distance measure. Supported options: 'euclidean' and 'cosine'.")¶
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featuresCol
= Param(parent='undefined', name='featuresCol', doc='features column name.')¶
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k
= Param(parent='undefined', name='k', doc='The desired number of leaf clusters. Must be > 1.')¶
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maxIter
= Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶
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minDivisibleClusterSize
= Param(parent='undefined', name='minDivisibleClusterSize', doc='The minimum number of points (if >= 1.0) or the minimum proportion of points (if < 1.0) of a divisible cluster.')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
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seed
= Param(parent='undefined', name='seed', doc='random seed.')¶
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weightCol
= Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')¶
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