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, org.apache.spark.internal.Logging, KMeansParams, Params, HasDistanceMeasure, HasFeaturesCol, HasMaxBlockSizeInMB, HasMaxIter, HasPredictionCol, HasSeed, HasSolver, HasTol, HasWeightCol, GeneralMLWritable, HasTrainingSummary<KMeansSummary>, Identifiable, MLWritable

public class KMeansModel
extends Model<KMeansModel>
implements KMeansParams, GeneralMLWritable, HasTrainingSummary<KMeansSummary>

Model fitted by KMeans.

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

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

Modifier and Type	Method and Description
Vector[]	clusterCenters()
KMeansModel	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.
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 KMeansModel	load(String path)
DoubleParam	maxBlockSizeInMB() Param for Maximum memory in MB for stacking input data into blocks.
IntParam	maxIter() Param for maximum number of iterations (&gt;= 0).
int	numFeatures()
int	predict(Vector features)
Param<String>	predictionCol() Param for prediction column name.
static MLReader<KMeansModel>	read()
LongParam	seed() Param for random seed.
KMeansModel	setFeaturesCol(String value)
KMeansModel	setPredictionCol(String value)
Param<String>	solver() Param for the name of optimization method used in KMeans.
KMeansSummary	summary() Gets summary of model on training set.
DoubleParam	tol() Param for the convergence tolerance for iterative algorithms (&gt;= 0).
String	toString()
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
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.
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 org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, 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.shared.HasSolver

getSolver

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

getMaxBlockSizeInMB

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, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

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

save

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

hasSummary, setSummary

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

Method Detail

read

public static MLReader<KMeansModel> read()

load

public static KMeansModel load(String path)

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)

solver

public final Param<String> solver()

Description copied from interface: KMeansParams

Param for the name of optimization method used in KMeans. Supported options: - "auto": Automatically select the solver based on the input schema and sparsity: If input instances are arrays or input vectors are dense, set to "block". Else, set to "row". - "row": input instances are processed row by row, and triangle-inequality is applied to accelerate the training. - "block": input instances are stacked to blocks, and GEMM is applied to compute the distances. Default is "auto".

Specified by:

solver in interface KMeansParams

Specified by:

solver in interface HasSolver

Returns:

(undocumented)

maxBlockSizeInMB

public final DoubleParam maxBlockSizeInMB()

Description copied from interface: HasMaxBlockSizeInMB

Param for 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 &gt;= 0..

Specified by:

maxBlockSizeInMB in interface HasMaxBlockSizeInMB

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)

numFeatures

public int numFeatures()

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

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)

predict

public int predict(Vector features)

clusterCenters

public Vector[] clusterCenters()

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)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object

summary

public KMeansSummary summary()

Gets summary of model on training set. An exception is thrown if hasSummary is false.

Specified by:

summary in interface HasTrainingSummary<KMeansSummary>

Returns:

(undocumented)