org.apache.spark.ml.clustering

Class KMeansModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.clustering.KMeansModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, GeneralMLWritable, Identifiable, MLWritable

public class KMeansModel
extends Model<KMeansModel>
implements GeneralMLWritable

Model fitted by KMeans.

param: parentModel a model trained by spark.mllib.clustering.KMeans.

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
static Params	clear(Param<?> param)
Vector[]	clusterCenters()
double	computeCost(Dataset<?> dataset) Deprecated. This method is deprecated and will be removed in 3.0.0. Use ClusteringEvaluator instead. You can also get the cost on the training dataset in the summary.
KMeansModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static Param<String>	distanceMeasure()
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static Param<String>	featuresCol()
static <T> scala.Option<T>	get(Param<T> param)
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getDistanceMeasure()
static String	getFeaturesCol()
static String	getInitMode()
String	getInitMode()
static int	getInitSteps()
int	getInitSteps()
static int	getK()
int	getK()
static int	getMaxIter()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
static long	getSeed()
static double	getTol()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	hasParent()
boolean	hasSummary() Return true if there exists summary of model.
static Param<String>	initMode()
Param<String>	initMode() Param for the initialization algorithm.
static IntParam	initSteps()
IntParam	initSteps() Param for the number of steps for the k-means|| initialization mode.
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static IntParam	k()
IntParam	k() The number of clusters to create (k).
static KMeansModel	load(String path)
static IntParam	maxIter()
static Param<?>[]	params()
static void	parent_$eq(Estimator<M> x$1)
static Estimator<M>	parent()
static Param<String>	predictionCol()
static MLReader<KMeansModel>	read()
static void	save(String path)
static LongParam	seed()
static <T> Params	set(Param<T> param, T value)
KMeansModel	setFeaturesCol(String value)
static M	setParent(Estimator<M> parent)
KMeansModel	setPredictionCol(String value)
KMeansSummary	summary() Gets summary of model on training set.
static DoubleParam	tol()
static String	toString()
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema) Validates and transforms the input schema.
GeneralMLWriter	write() Returns a GeneralMLWriter instance for this ML instance.

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

hasParent, parent, setParent

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

transform, transform, transform

Methods inherited from class Object

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

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

getMaxIter, maxIter

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

featuresCol, getFeaturesCol

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

getSeed, seed

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

getPredictionCol, predictionCol

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

getTol, tol

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

distanceMeasure, getDistanceMeasure

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.MLWritable

save

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

initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

read

public static MLReader<KMeansModel> read()

load

public static KMeansModel load(String path)

toString

public static String toString()

params

public static Param<?>[] params()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
                                   T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

parent

public static Estimator<M> parent()

parent_$eq

public static void parent_$eq(Estimator<M> x$1)

setParent

public static M setParent(Estimator<M> parent)

hasParent

public static boolean hasParent()

maxIter

public static final IntParam maxIter()

getMaxIter

public static final int getMaxIter()

featuresCol

public static final Param<String> featuresCol()

getFeaturesCol

public static final String getFeaturesCol()

seed

public static final LongParam seed()

getSeed

public static final long getSeed()

predictionCol

public static final Param<String> predictionCol()

getPredictionCol

public static final String getPredictionCol()

tol

public static final DoubleParam tol()

getTol

public static final double getTol()

distanceMeasure

public static final Param<String> distanceMeasure()

getDistanceMeasure

public static final String getDistanceMeasure()

k

public static final IntParam k()

getK

public static int getK()

initMode

public static final Param<String> initMode()

getInitMode

public static String getInitMode()

initSteps

public static final IntParam initSteps()

getInitSteps

public static int getInitSteps()

save

public static void save(String path)
                 throws java.io.IOException

Throws:

java.io.IOException

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 KMeansModel 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 Model<KMeansModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

setFeaturesCol

public KMeansModel setFeaturesCol(String value)

setPredictionCol

public KMeansModel setPredictionCol(String value)

transform

public Dataset<Row> transform(Dataset<?> dataset)

Description copied from class: Transformer

Transforms the input dataset.

Specified by:

transform in class Transformer

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

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)

clusterCenters

public Vector[] clusterCenters()

computeCost

public double computeCost(Dataset<?> dataset)

Deprecated. This method is deprecated and will be removed in 3.0.0. Use ClusteringEvaluator instead. You can also get the cost on the training dataset in the summary.

Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

write

public GeneralMLWriter write()

Returns a GeneralMLWriter instance for this ML instance.

For KMeansModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

Specified by:

write in interface GeneralMLWritable

Specified by:

write in interface MLWritable

Returns:

(undocumented)

hasSummary

public boolean hasSummary()

Return true if there exists summary of model.

Returns:

(undocumented)

summary

public KMeansSummary summary()

Gets summary of model on training set. An exception is thrown if trainingSummary == None.

Returns:

(undocumented)

getInitMode

public String getInitMode()

getInitSteps

public int getInitSteps()

getK

public int getK()

initMode

public Param<String> initMode()

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||.

Returns:

(undocumented)

initSteps

public IntParam initSteps()

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.

Returns:

(undocumented)

k

public IntParam k()

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.

Returns:

(undocumented)

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema)

Validates and transforms the input schema.

Parameters:

schema - input schema

Returns:

output schema