org.apache.spark.ml.clustering

Class GaussianMixture

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.clustering.GaussianMixture

All Implemented Interfaces:

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

public class GaussianMixture
extends Estimator<GaussianMixtureModel>
implements GaussianMixtureParams, DefaultParamsWritable

Gaussian Mixture clustering.

This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated "mixing" weights specifying each's contribution to the composite.

Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than convergenceTol, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum.

See Also:

Serialized Form

Note:

This algorithm is limited in its number of features since it requires storing a covariance matrix which has size quadratic in the number of features. Even when the number of features does not exceed this limit, this algorithm may perform poorly on high-dimensional data. This is due to high-dimensional data (a) making it difficult to cluster at all (based on statistical/theoretical arguments) and (b) numerical issues with Gaussian distributions.

Constructor Summary

Constructors

Constructor and Description
GaussianMixture()
GaussianMixture(String uid)

Method Summary

Modifier and Type	Method and Description
IntParam	aggregationDepth() Param for suggested depth for treeAggregate (>= 2).
GaussianMixture	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<String>	featuresCol() Param for features column name.
GaussianMixtureModel	fit(Dataset<?> dataset) Fits a model to the input data.
IntParam	k() Number of independent Gaussians in the mixture model.
static GaussianMixture	load(String path)
IntParam	maxIter() Param for maximum number of iterations (&gt;= 0).
Param<String>	predictionCol() Param for prediction column name.
Param<String>	probabilityCol() Param for Column name for predicted class conditional probabilities.
static MLReader<T>	read()
LongParam	seed() Param for random seed.
GaussianMixture	setAggregationDepth(int value)
GaussianMixture	setFeaturesCol(String value)
GaussianMixture	setK(int value)
GaussianMixture	setMaxIter(int value)
GaussianMixture	setPredictionCol(String value)
GaussianMixture	setProbabilityCol(String value)
GaussianMixture	setSeed(long value)
GaussianMixture	setTol(double value)
GaussianMixture	setWeightCol(String value)
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.GaussianMixtureParams

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.HasWeightCol

getWeightCol

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

getProbabilityCol

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

getTol

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

getAggregationDepth

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

GaussianMixture

public GaussianMixture(String uid)

GaussianMixture

public GaussianMixture()

Method Detail

load

public static GaussianMixture load(String path)

read

public static MLReader<T> read()

k

public final IntParam k()

Description copied from interface: GaussianMixtureParams

Number of independent Gaussians in the mixture model. Must be greater than 1. Default: 2.

Specified by:

k in interface GaussianMixtureParams

Returns:

(undocumented)

aggregationDepth

public final IntParam aggregationDepth()

Description copied from interface: HasAggregationDepth

Param for suggested depth for treeAggregate (&gt;= 2).

Specified by:

aggregationDepth in interface HasAggregationDepth

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)

probabilityCol

public final Param<String> probabilityCol()

Description copied from interface: HasProbabilityCol

Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Specified by:

probabilityCol in interface HasProbabilityCol

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)

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 GaussianMixture 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<GaussianMixtureModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

setFeaturesCol

public GaussianMixture setFeaturesCol(String value)

setPredictionCol

public GaussianMixture setPredictionCol(String value)

setProbabilityCol

public GaussianMixture setProbabilityCol(String value)

setWeightCol

public GaussianMixture setWeightCol(String value)

setK

public GaussianMixture setK(int value)

setMaxIter

public GaussianMixture setMaxIter(int value)

setTol

public GaussianMixture setTol(double value)

setSeed

public GaussianMixture setSeed(long value)

setAggregationDepth

public GaussianMixture setAggregationDepth(int value)

fit

public GaussianMixtureModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<GaussianMixtureModel>

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)