org.apache.spark.ml.clustering

Class KMeans

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<KMeansModel>

org.apache.spark.ml.clustering.KMeans

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, KMeansParams, Params, HasDistanceMeasure, HasFeaturesCol, HasMaxIter, HasPredictionCol, HasSeed, HasTol, HasWeightCol, DefaultParamsWritable, Identifiable, MLWritable

public class KMeans
extends Estimator<KMeansModel>
implements KMeansParams, DefaultParamsWritable

K-means clustering with support for k-means|| initialization proposed by Bahmani et al.

See Also:

Bahmani et al., Scalable k-means++., Serialized Form

Constructor Summary

Constructors

Constructor and Description
KMeans()
KMeans(String uid)

Method Summary

Modifier and Type	Method and Description
KMeans	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<String>	distanceMeasure() Param for The distance measure.
Param<String>	featuresCol() Param for features column name.
KMeansModel	fit(Dataset<?> dataset) Fits a model to the input data.
Param<String>	initMode() Param for the initialization algorithm.
IntParam	initSteps() Param for the number of steps for the k-means|| initialization mode.
IntParam	k() The number of clusters to create (k).
static KMeans	load(String path)
IntParam	maxIter() Param for maximum number of iterations (&gt;= 0).
Param<String>	predictionCol() Param for prediction column name.
static MLReader<T>	read()
LongParam	seed() Param for random seed.
KMeans	setDistanceMeasure(String value)
KMeans	setFeaturesCol(String value)
KMeans	setInitMode(String value)
KMeans	setInitSteps(int value)
KMeans	setK(int value)
KMeans	setMaxIter(int value)
KMeans	setPredictionCol(String value)
KMeans	setSeed(long value)
KMeans	setTol(double value)
KMeans	setWeightCol(String value) Sets the value of param weightCol.
DoubleParam	tol() Param for the convergence tolerance for iterative algorithms (&gt;= 0).
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.
String	uid() An immutable unique ID for the object and its derivatives.
Param<String>	weightCol() Param for weight column name.

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.clustering.KMeansParams

getInitMode, getInitSteps, getK, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter

getMaxIter

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasSeed

getSeed

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasTol

getTol

Methods inherited from interface org.apache.spark.ml.param.shared.HasDistanceMeasure

getDistanceMeasure

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable

write

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Constructor Detail

KMeans

public KMeans(String uid)

KMeans

public KMeans()

Method Detail

load

public static KMeans load(String path)

read

public static MLReader<T> read()

k

public final IntParam k()

Description copied from interface: KMeansParams

The number of clusters to create (k). Must be &gt; 1. Note that it is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.

Specified by:

k in interface KMeansParams

Returns:

(undocumented)

initMode

public final Param<String> initMode()

Description copied from interface: KMeansParams

Param for 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++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.

Specified by:

initMode in interface KMeansParams

Returns:

(undocumented)

initSteps

public final IntParam initSteps()

Description copied from interface: KMeansParams

Param for the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Must be &gt; 0. Default: 2.

Specified by:

initSteps in interface KMeansParams

Returns:

(undocumented)

weightCol

public final Param<String> weightCol()

Description copied from interface: HasWeightCol

Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

Specified by:

weightCol in interface HasWeightCol

Returns:

(undocumented)

distanceMeasure

public final Param<String> distanceMeasure()

Description copied from interface: HasDistanceMeasure

Param for The distance measure. Supported options: 'euclidean' and 'cosine'.

Specified by:

distanceMeasure in interface HasDistanceMeasure

Returns:

(undocumented)

tol

public final DoubleParam tol()

Description copied from interface: HasTol

Param for the convergence tolerance for iterative algorithms (&gt;= 0).

Specified by:

tol in interface HasTol

Returns:

(undocumented)

predictionCol

public final Param<String> predictionCol()

Description copied from interface: HasPredictionCol

Param for prediction column name.

Specified by:

predictionCol in interface HasPredictionCol

Returns:

(undocumented)

seed

public final LongParam seed()

Description copied from interface: HasSeed

Param for random seed.

Specified by:

seed in interface HasSeed

Returns:

(undocumented)

featuresCol

public final Param<String> featuresCol()

Description copied from interface: HasFeaturesCol

Param for features column name.

Specified by:

featuresCol in interface HasFeaturesCol

Returns:

(undocumented)

maxIter

public final IntParam maxIter()

Description copied from interface: HasMaxIter

Param for maximum number of iterations (&gt;= 0).

Specified by:

maxIter in interface HasMaxIter

Returns:

(undocumented)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

copy

public KMeans copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Estimator<KMeansModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

setFeaturesCol

public KMeans setFeaturesCol(String value)

setPredictionCol

public KMeans setPredictionCol(String value)

setK

public KMeans setK(int value)

setInitMode

public KMeans setInitMode(String value)

setDistanceMeasure

public KMeans setDistanceMeasure(String value)

setInitSteps

public KMeans setInitSteps(int value)

setMaxIter

public KMeans setMaxIter(int value)

setTol

public KMeans setTol(double value)

setSeed

public KMeans setSeed(long value)

setWeightCol

public KMeans setWeightCol(String value)

Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

Parameters:

value - (undocumented)

Returns:

(undocumented)

fit

public KMeansModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<KMeansModel>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

Check transform validity and derive the output schema from the input schema.

We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)