BisectingKMeansModel#

class pyspark.ml.clustering.BisectingKMeansModel(java_model=None)[source]#

Model fitted by BisectingKMeans.

New in version 2.0.0.

Methods

`clear`(param)	Clears a param from the param map if it has been explicitly set.
`clusterCenters`()	Get the cluster centers, represented as a list of NumPy arrays.
`computeCost`(dataset)	Computes the sum of squared distances between the input points and their corresponding cluster centers.
`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.
`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).
`predict`(value)	Predict label for the given features.
`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.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`distanceMeasure`
`featuresCol`
`hasSummary`	Indicates whether a training summary exists for this model instance.
`k`
`maxIter`
`minDivisibleClusterSize`
`params`	Returns all params ordered by name.
`predictionCol`
`seed`
`summary`	Gets summary (cluster assignments, cluster sizes) of the model trained on the training set.
`weightCol`

Methods Documentation

clear(param)#: Clears a param from the param map if it has been explicitly set.

clusterCenters()[source]#: Get the cluster centers, represented as a list of NumPy arrays.

New in version 2.0.0.

computeCost(dataset)[source]#: Computes the sum of squared distances between the input points and their corresponding cluster centers.

Deprecated since version 3.0.0: It will be removed in future versions. Use ClusteringEvaluator instead. You can also get the cost on the training dataset in the summary.

New in version 2.0.0.

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

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

Returns

JavaParams: Copy of this instance

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=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

extradict, optional: extra param values

Returns

dict: merged param map

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.

New in version 2.0.0.

getMaxIter()#: Gets the value of maxIter or its default value.

getMinDivisibleClusterSize()#: Gets the value of minDivisibleClusterSize or its default value.

New in version 2.0.0.

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.

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.

classmethod load(path)#: Reads an ML instance from the input path, a shortcut of read().load(path).

predict(value)[source]#: Predict label for the given features.

New in version 3.0.0.

classmethod 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.

setFeaturesCol(value)[source]#: Sets the value of featuresCol.

New in version 3.0.0.

setPredictionCol(value)[source]#: Sets the value of predictionCol.

New in version 3.0.0.

transform(dataset, params=None)#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame: transformed dataset

write()#: 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.')#

hasSummary#: Indicates whether a training summary exists for this model instance.

New in version 2.1.0.

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.')#

summary#: Gets summary (cluster assignments, cluster sizes) of the model trained on the training set. An exception is thrown if no summary exists.

New in version 2.1.0.

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.')#

uid#: A unique id for the object.