BisectingKMeans¶

class pyspark.ml.clustering.BisectingKMeans(*, featuresCol: str = 'features', predictionCol: str = 'prediction', maxIter: int = 20, seed: Optional[int] = None, k: int = 4, minDivisibleClusterSize: float = 1.0, distanceMeasure: str = 'euclidean', weightCol: Optional[str] = None)¶

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.

Examples

>>> 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)
>>> model.transform(df).take(1) == model2.transform(df).take(1)
True

Methods

`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with the same uid and some extra params.
`explainParam`(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
`explainParams`()	Returns the documentation of all params with their optionally default values and user-supplied values.
`extractParamMap`([extra])	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.
`fit`(dataset[, params])	Fits a model to the input dataset with optional parameters.
`fitMultiple`(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
`getDistanceMeasure`()	Gets the value of distanceMeasure or its default value.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getK`()	Gets the value of k or its default value.
`getMaxIter`()	Gets the value of maxIter or its default value.
`getMinDivisibleClusterSize`()	Gets the value of minDivisibleClusterSize or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getParam`(paramName)	Gets a param by its name.
`getPredictionCol`()	Gets the value of predictionCol or its default value.
`getSeed`()	Gets the value of seed or its default value.
`getWeightCol`()	Gets the value of weightCol or its default value.
`hasDefault`(param)	Checks whether a param has a default value.
`hasParam`(paramName)	Tests whether this instance contains a param with a given (string) name.
`isDefined`(param)	Checks whether a param is explicitly set by user or has a default value.
`isSet`(param)	Checks whether a param is explicitly set by user.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`read`()	Returns an MLReader instance for this class.
`save`(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
`set`(param, value)	Sets a parameter in the embedded param map.
`setDistanceMeasure`(value)	Sets the value of `distanceMeasure`.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setK`(value)	Sets the value of `k`.
`setMaxIter`(value)	Sets the value of `maxIter`.
`setMinDivisibleClusterSize`(value)	Sets the value of `minDivisibleClusterSize`.
`setParams`(self, \*[, featuresCol, …])	Sets params for BisectingKMeans.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setSeed`(value)	Sets the value of `seed`.
`setWeightCol`(value)	Sets the value of `weightCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`distanceMeasure`
`featuresCol`
`k`
`maxIter`
`minDivisibleClusterSize`
`params`	Returns all params ordered by name.
`predictionCol`
`seed`
`weightCol`

Methods Documentation

clear(param: pyspark.ml.param.Param) → None¶: Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP¶

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

extradict, optional: Extra parameters to copy to the new instance

Returns

JavaParams: Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.Param]) → str¶: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str¶: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap¶

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

extradict, optional: extra param values

Returns

dict: merged param map

fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]¶

Fits a model to the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramsdict or list or tuple, optional: 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

Transformer or a list of Transformer: fitted model(s)

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]¶

Fits a model to the input dataset for each param map in paramMaps.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramMapscollections.abc.Sequence: A Sequence of param maps.

Returns

_FitMultipleIterator: 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.

getDistanceMeasure() → str¶: Gets the value of distanceMeasure or its default value.

getFeaturesCol() → str¶: Gets the value of featuresCol or its default value.

getK() → int¶: Gets the value of k or its default value.

getMaxIter() → int¶: Gets the value of maxIter or its default value.

getMinDivisibleClusterSize() → float¶: Gets the value of minDivisibleClusterSize or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getParam(paramName: str) → pyspark.ml.param.Param ¶: Gets a param by its name.

getPredictionCol() → str¶: Gets the value of predictionCol or its default value.

getSeed() → int¶: Gets the value of seed or its default value.

getWeightCol() → str¶: Gets the value of weightCol or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param has a default value.

hasParam(paramName: str) → bool¶: Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL¶: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]¶: Returns an MLReader instance for this class.

save(path: str) → None¶: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None¶: Sets a parameter in the embedded param map.

setDistanceMeasure(value: str) → pyspark.ml.clustering.BisectingKMeans ¶: Sets the value of distanceMeasure.

setFeaturesCol(value: str) → pyspark.ml.clustering.BisectingKMeans ¶: Sets the value of featuresCol.

setK(value: int) → pyspark.ml.clustering.BisectingKMeans ¶: Sets the value of k.

setMaxIter(value: int) → pyspark.ml.clustering.BisectingKMeans ¶: Sets the value of maxIter.

setMinDivisibleClusterSize(value: float) → pyspark.ml.clustering.BisectingKMeans ¶: Sets the value of minDivisibleClusterSize.

setParams(self, \*, featuresCol="features", predictionCol="prediction", maxIter=20, seed=None, k=4, minDivisibleClusterSize=1.0, distanceMeasure="euclidean", weightCol=None)¶: Sets params for BisectingKMeans.

setPredictionCol(value: str) → pyspark.ml.clustering.BisectingKMeans ¶: Sets the value of predictionCol.

setSeed(value: int) → pyspark.ml.clustering.BisectingKMeans ¶: Sets the value of seed.

setWeightCol(value: str) → pyspark.ml.clustering.BisectingKMeans ¶: Sets the value of weightCol.

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

distanceMeasure = Param(parent='undefined', name='distanceMeasure', doc="the distance measure. Supported options: 'euclidean' and 'cosine'.")¶

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')¶

k = Param(parent='undefined', name='k', doc='The desired number of leaf clusters. Must be > 1.')¶

maxIter = Param(parent='undefined', name='maxIter', doc='max number of iterations (>= 0).')¶

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.')¶

params¶: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶

seed = Param(parent='undefined', name='seed', doc='random seed.')¶

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.')¶

FMClassificationTrainingSummary

BisectingKMeansModel