KMeans¶

class pyspark.ml.clustering.KMeans(*, featuresCol: str = 'features', predictionCol: str = 'prediction', k: int = 2, initMode: str = 'k-means||', initSteps: int = 2, tol: float = 0.0001, maxIter: int = 20, seed: Optional[int] = None, distanceMeasure: str = 'euclidean', weightCol: Optional[str] = None, solver: str = 'auto', maxBlockSizeInMB: float = 0.0)[source]¶

K-means clustering with a k-means++ like initialization mode (the k-means|| algorithm by Bahmani et al).

New in version 1.5.0.

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"])
>>> kmeans = KMeans(k=2)
>>> kmeans.setSeed(1)
KMeans...
>>> kmeans.setWeightCol("weighCol")
KMeans...
>>> kmeans.setMaxIter(10)
KMeans...
>>> kmeans.getMaxIter()
10
>>> kmeans.clear(kmeans.maxIter)
>>> kmeans.getSolver()
'auto'
>>> model = kmeans.fit(df)
>>> model.getMaxBlockSizeInMB()
0.0
>>> model.getDistanceMeasure()
'euclidean'
>>> model.setPredictionCol("newPrediction")
KMeansModel...
>>> model.predict(df.head().features)
0
>>> centers = model.clusterCenters()
>>> len(centers)
2
>>> transformed = model.transform(df).select("features", "newPrediction")
>>> rows = transformed.collect()
>>> rows[0].newPrediction == rows[1].newPrediction
True
>>> rows[2].newPrediction == rows[3].newPrediction
True
>>> model.hasSummary
True
>>> summary = model.summary
>>> summary.k
2
>>> summary.clusterSizes
[2, 2]
>>> summary.trainingCost
4.0
>>> kmeans_path = temp_path + "/kmeans"
>>> kmeans.save(kmeans_path)
>>> kmeans2 = KMeans.load(kmeans_path)
>>> kmeans2.getK()
2
>>> model_path = temp_path + "/kmeans_model"
>>> model.save(model_path)
>>> model2 = KMeansModel.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.
`getInitMode`()	Gets the value of initMode
`getInitSteps`()	Gets the value of initSteps
`getK`()	Gets the value of k
`getMaxBlockSizeInMB`()	Gets the value of maxBlockSizeInMB or its default value.
`getMaxIter`()	Gets the value of maxIter 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.
`getSolver`()	Gets the value of solver or its default value.
`getTol`()	Gets the value of tol 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`.
`setInitMode`(value)	Sets the value of `initMode`.
`setInitSteps`(value)	Sets the value of `initSteps`.
`setK`(value)	Sets the value of `k`.
`setMaxBlockSizeInMB`(value)	Sets the value of `maxBlockSizeInMB`.
`setMaxIter`(value)	Sets the value of `maxIter`.
`setParams`(self, \*[, featuresCol, …])	Sets params for KMeans.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setSeed`(value)	Sets the value of `seed`.
`setSolver`(value)	Sets the value of `solver`.
`setTol`(value)	Sets the value of `tol`.
`setWeightCol`(value)	Sets the value of `weightCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`distanceMeasure`
`featuresCol`
`initMode`
`initSteps`
`k`
`maxBlockSizeInMB`
`maxIter`
`params`	Returns all params ordered by name.
`predictionCol`
`seed`
`solver`
`tol`
`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.

New in version 1.3.0.

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

:py:class:`Transformer` or a list ofpy:class: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.

New in version 2.3.0.

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.

getInitMode() → str¶: Gets the value of initMode

New in version 1.5.0.

getInitSteps() → int¶: Gets the value of initSteps

New in version 1.5.0.

getK() → int¶: Gets the value of k

New in version 1.5.0.

getMaxBlockSizeInMB() → float¶: Gets the value of maxBlockSizeInMB or its default value.

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

getSolver() → str¶: Gets the value of solver or its default value.

getTol() → float¶: Gets the value of tol 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.KMeans [source]¶: Sets the value of distanceMeasure.

New in version 2.4.0.

setFeaturesCol(value: str) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of featuresCol.

New in version 1.5.0.

setInitMode(value: str) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of initMode.

New in version 1.5.0.

setInitSteps(value: int) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of initSteps.

New in version 1.5.0.

setK(value: int) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of k.

New in version 1.5.0.

setMaxBlockSizeInMB(value: float) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of maxBlockSizeInMB.

New in version 3.4.0.

setMaxIter(value: int) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of maxIter.

New in version 1.5.0.

setParams(self, \*, featuresCol="features", predictionCol="prediction", k=2, initMode="k-means||", initSteps=2, tol=1e-4, maxIter=20, seed=None, distanceMeasure="euclidean", weightCol=None, solver="auto", maxBlockSizeInMB=0.0)[source]¶: Sets params for KMeans.

New in version 1.5.0.

setPredictionCol(value: str) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of predictionCol.

New in version 1.5.0.

setSeed(value: int) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of seed.

New in version 1.5.0.

setSolver(value: str) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of solver.

New in version 3.4.0.

setTol(value: float) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of tol.

New in version 1.5.0.

setWeightCol(value: str) → pyspark.ml.clustering.KMeans [source]¶: Sets the value of weightCol.

New in version 3.0.0.

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.')¶

initMode = Param(parent='undefined', name='initMode', doc='The initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++')¶

initSteps = Param(parent='undefined', name='initSteps', doc='The number of steps for k-means|| initialization mode. Must be > 0.')¶

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

maxBlockSizeInMB = Param(parent='undefined', name='maxBlockSizeInMB', doc='maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0.')¶

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

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.')¶

solver = Param(parent='undefined', name='solver', doc='The solver algorithm for optimization. Supported options: auto, row, block.')¶

tol = Param(parent='undefined', name='tol', doc='the convergence tolerance for iterative algorithms (>= 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.')¶

BisectingKMeansSummary

KMeansModel