BisectingKMeansModel¶

class pyspark.ml.clustering.BisectingKMeansModel(java_model: Optional[JavaObject] = None)¶

Model fitted by BisectingKMeans.

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

clusterCenters() → List[numpy.ndarray]¶: Get the cluster centers, represented as a list of NumPy arrays.

computeCost(dataset: pyspark.sql.dataframe.DataFrame) → float¶: Computes the sum of squared distances between the input points and their corresponding cluster centers.

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

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

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).

predict(value: pyspark.ml.linalg.Vector) → int¶: Predict label for the given features.

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.

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

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

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶

Transforms the input dataset with optional parameters.

Parameters

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

Returns

pyspark.sql.DataFrame: transformed dataset

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.')¶

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

k: pyspark.ml.param.Param[int] = 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: pyspark.ml.param.Param[float] = 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.

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.')¶

BisectingKMeans

BisectingKMeansSummary