class GaussianMixture extends Estimator[GaussianMixtureModel] with GaussianMixtureParams with 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.
- Annotations
- @Since("2.0.0")
- Source
- GaussianMixture.scala
- 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.
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- GaussianMixture
- DefaultParamsWritable
- MLWritable
- GaussianMixtureParams
- HasAggregationDepth
- HasTol
- HasProbabilityCol
- HasWeightCol
- HasPredictionCol
- HasSeed
- HasFeaturesCol
- HasMaxIter
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Identifiable
- AnyRef
- Any
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- Public
- Protected
Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
- final val featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
- final val k: IntParam
Number of independent Gaussians in the mixture model.
Number of independent Gaussians in the mixture model. Must be greater than 1. Default: 2.
- Definition Classes
- GaussianMixtureParams
- Annotations
- @Since("2.0.0")
- final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
- Definition Classes
- HasMaxIter
- final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
- final val probabilityCol: Param[String]
Param for Column name for predicted class conditional probabilities.
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.
- Definition Classes
- HasProbabilityCol
- final val seed: LongParam
Param for random seed.
Param for random seed.
- Definition Classes
- HasSeed
- final val tol: DoubleParam
Param for the convergence tolerance for iterative algorithms (>= 0).
Param for the convergence tolerance for iterative algorithms (>= 0).
- Definition Classes
- HasTol
- final val weightCol: Param[String]
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
- Definition Classes
- HasWeightCol
Members
- implicit class LogStringContext extends AnyRef
- Definition Classes
- Logging
- final def clear(param: Param[_]): GaussianMixture.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
- def copy(extra: ParamMap): GaussianMixture
Creates a copy of this instance with the same UID and some extra 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().- Definition Classes
- GaussianMixture → Estimator → PipelineStage → Params
- Annotations
- @Since("2.0.0")
- def explainParam(param: Param[_]): String
Explains a param.
Explains a param.
- param
input param, must belong to this instance.
- returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
- Definition Classes
- Params
- def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam().- Definition Classes
- Params
- final def extractParamMap(): ParamMap
extractParamMapwith no extra values.extractParamMapwith no extra values.- Definition Classes
- Params
- final def extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
- Definition Classes
- Params
- def fit(dataset: Dataset[_]): GaussianMixtureModel
Fits a model to the input data.
Fits a model to the input data.
- Definition Classes
- GaussianMixture → Estimator
- Annotations
- @Since("2.0.0")
- def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GaussianMixtureModel]
Fits multiple models to the input data with multiple sets of parameters.
Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.
- dataset
input dataset
- paramMaps
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted models, matching the input parameter maps
- Definition Classes
- Estimator
- Annotations
- @Since("2.0.0")
- def fit(dataset: Dataset[_], paramMap: ParamMap): GaussianMixtureModel
Fits a single model to the input data with provided parameter map.
Fits a single model to the input data with provided parameter map.
- dataset
input dataset
- paramMap
Parameter map. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since("2.0.0")
- def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GaussianMixtureModel
Fits a single model to the input data with optional parameters.
Fits a single model to the input data with optional parameters.
- dataset
input dataset
- firstParamPair
the first param pair, overrides embedded params
- otherParamPairs
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since("2.0.0") @varargs()
- final def get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
- Definition Classes
- Params
- final def getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
- final def getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
- Definition Classes
- Params
- def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
- final def hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
- Definition Classes
- Params
- def hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
- Definition Classes
- Params
- final def isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
- Definition Classes
- Params
- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
- lazy val params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
- Definition Classes
- Params
- Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
- def save(path: String): Unit
Saves this ML instance to the input path, a shortcut of
write.save(path).Saves this ML instance to the input path, a shortcut of
write.save(path).- Definition Classes
- MLWritable
- Annotations
- @Since("1.6.0") @throws("If the input path already exists but overwrite is not enabled.")
- final def set[T](param: Param[T], value: T): GaussianMixture.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
- def toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
- def transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during
transformSchemaand raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate().Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- GaussianMixture → PipelineStage
- Annotations
- @Since("2.0.0")
- val uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
- Definition Classes
- GaussianMixture → Identifiable
- Annotations
- @Since("2.0.0")
- def write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Parameter setters
- def setFeaturesCol(value: String): GaussianMixture.this.type
- Annotations
- @Since("2.0.0")
- def setK(value: Int): GaussianMixture.this.type
- Annotations
- @Since("2.0.0")
- def setMaxIter(value: Int): GaussianMixture.this.type
- Annotations
- @Since("2.0.0")
- def setPredictionCol(value: String): GaussianMixture.this.type
- Annotations
- @Since("2.0.0")
- def setProbabilityCol(value: String): GaussianMixture.this.type
- Annotations
- @Since("2.0.0")
- def setSeed(value: Long): GaussianMixture.this.type
- Annotations
- @Since("2.0.0")
- def setTol(value: Double): GaussianMixture.this.type
- Annotations
- @Since("2.0.0")
- def setWeightCol(value: String): GaussianMixture.this.type
- Annotations
- @Since("3.0.0")
Parameter getters
- final def getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
- def getK: Int
- Definition Classes
- GaussianMixtureParams
- Annotations
- @Since("2.0.0")
- final def getMaxIter: Int
- Definition Classes
- HasMaxIter
- final def getPredictionCol: String
- Definition Classes
- HasPredictionCol
- final def getProbabilityCol: String
- Definition Classes
- HasProbabilityCol
- final def getSeed: Long
- Definition Classes
- HasSeed
- final def getTol: Double
- Definition Classes
- HasTol
- final def getWeightCol: String
- Definition Classes
- HasWeightCol
(expert-only) Parameters
A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
- final val aggregationDepth: IntParam
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).
- Definition Classes
- HasAggregationDepth
(expert-only) Parameter setters
- def setAggregationDepth(value: Int): GaussianMixture.this.type
- Annotations
- @Since("3.0.0")
(expert-only) Parameter getters
- final def getAggregationDepth: Int
- Definition Classes
- HasAggregationDepth