org.apache.spark.ml.clustering

Class BisectingKMeansModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.clustering.BisectingKMeansModel

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, BisectingKMeansParams, Params, HasDistanceMeasure, HasFeaturesCol, HasMaxIter, HasPredictionCol, HasSeed, HasWeightCol, HasTrainingSummary<BisectingKMeansSummary>, Identifiable, MLWritable

public class BisectingKMeansModel
extends Model<BisectingKMeansModel>
implements BisectingKMeansParams, MLWritable, HasTrainingSummary<BisectingKMeansSummary>

Model fitted by BisectingKMeans.

param: parentModel a model trained by BisectingKMeans.

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()
double	computeCost(Dataset<?> dataset) Deprecated. This method is deprecated and will be removed in future versions. Use ClusteringEvaluator instead. You can also get the cost on the training dataset in the summary.
BisectingKMeansModel	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.
IntParam	k() The desired number of leaf clusters.
static BisectingKMeansModel	load(String path)
IntParam	maxIter() Param for maximum number of iterations (&gt;= 0).
DoubleParam	minDivisibleClusterSize() The minimum number of points (if greater than or equal to 1.0) or the minimum proportion of points (if less than 1.0) of a divisible cluster (default: 1.0).
int	numFeatures()
int	predict(Vector features)
Param<String>	predictionCol() Param for prediction column name.
static MLReader<BisectingKMeansModel>	read()
LongParam	seed() Param for random seed.
BisectingKMeansModel	setFeaturesCol(String value)
BisectingKMeansModel	setPredictionCol(String value)
BisectingKMeansSummary	summary() Gets summary of model on training set.
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.
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

params

Methods inherited from class Object

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

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

getK, getMinDivisibleClusterSize, 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.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, 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<BisectingKMeansModel> read()

load

public static BisectingKMeansModel load(String path)

k

public final IntParam k()

Description copied from interface: BisectingKMeansParams

The desired number of leaf clusters. Must be &gt; 1. Default: 4. The actual number could be smaller if there are no divisible leaf clusters.

Specified by:

k in interface BisectingKMeansParams

Returns:

(undocumented)

minDivisibleClusterSize

public final DoubleParam minDivisibleClusterSize()

Description copied from interface: BisectingKMeansParams

The minimum number of points (if greater than or equal to 1.0) or the minimum proportion of points (if less than 1.0) of a divisible cluster (default: 1.0).

Specified by:

minDivisibleClusterSize in interface BisectingKMeansParams

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)

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 BisectingKMeansModel 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<BisectingKMeansModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

setFeaturesCol

public BisectingKMeansModel setFeaturesCol(String value)

setPredictionCol

public BisectingKMeansModel 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()

computeCost

public double computeCost(Dataset<?> dataset)

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

Computes the sum of squared distances between the input points and their corresponding cluster centers.

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)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object

summary

public BisectingKMeansSummary summary()

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

Specified by:

summary in interface HasTrainingSummary<BisectingKMeansSummary>

Returns:

(undocumented)