Package 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:
Serializable, org.apache.spark.internal.Logging, GaussianMixtureParams, Params, HasAggregationDepth, HasFeaturesCol, HasMaxIter, HasPredictionCol, HasProbabilityCol, HasSeed, HasTol, HasWeightCol, DefaultParamsWritable, Identifiable, MLWritable, scala.Serializable
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:
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 Details

    • GaussianMixture

      public GaussianMixture(String uid)

    • GaussianMixture

      public GaussianMixture()

  • Method Details

    • 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)