org.apache.spark.ml.clustering

Class KMeansModel

java.lang.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, Identifiable, MLWritable

public class KMeansModel
extends Model<KMeansModel>
implements MLWritable

:: Experimental :: Model fitted by KMeans.

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

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
Vector[]	clusterCenters()
double	computeCost(DataFrame dataset) Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
KMeansModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
java.lang.String	getInitMode()
int	getInitSteps()
int	getK()
Param<java.lang.String>	initMode() Param for the initialization algorithm.
IntParam	initSteps() Param for the number of steps for the k-means|| initialization mode.
IntParam	k() Set the number of clusters to create (k).
static KMeansModel	load(java.lang.String path)
static MLReader<KMeansModel>	read()
DataFrame	transform(DataFrame dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema) Validates and transforms the input schema.
MLWriter	write() Returns an MLWriter 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 org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class java.lang.Object

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

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, validateParams

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.Logging

initializeIfNecessary, initializeLogging, 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(java.lang.String path)

uid

public java.lang.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.

Specified by:

copy in interface Params

Specified by:

copy in class Model<KMeansModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

transform

public DataFrame transform(DataFrame 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 ::

Derives the output schema from the input schema.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

clusterCenters

public Vector[] clusterCenters()

computeCost

public double computeCost(DataFrame dataset)

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 MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

k

public IntParam k()

Set the number of clusters to create (k). Must be > 1. Default: 2.

Returns:

(undocumented)

getK

public int getK()

initMode

public Param<java.lang.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)

getInitMode

public java.lang.String getInitMode()

initSteps

public IntParam initSteps()

Param for the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 5 is almost always enough. Must be > 0. Default: 5.

Returns:

(undocumented)

getInitSteps

public int getInitSteps()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema)

Validates and transforms the input schema.

Parameters:

schema - input schema

Returns:

output schema