class ClusteringEvaluator extends Evaluator with HasPredictionCol with HasFeaturesCol with HasWeightCol with DefaultParamsWritable
Evaluator for clustering results. The metric computes the Silhouette measure using the specified distance measure.
The Silhouette is a measure for the validation of the consistency within clusters. It ranges between 1 and -1, where a value close to 1 means that the points in a cluster are close to the other points in the same cluster and far from the points of the other clusters.
- Annotations
- @Since("2.3.0")
- Source
- ClusteringEvaluator.scala
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- ClusteringEvaluator
- DefaultParamsWritable
- MLWritable
- HasWeightCol
- HasFeaturesCol
- HasPredictionCol
- Evaluator
- Params
- Serializable
- Identifiable
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Instance Constructors
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def $[T](param: Param[T]): T
An alias for
getOrDefault().An alias for
getOrDefault().- Attributes
- protected
- Definition Classes
- Params
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- final def clear(param: Param[_]): ClusteringEvaluator.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- def copy(pMap: ParamMap): ClusteringEvaluator
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
- ClusteringEvaluator → Evaluator → Params
- Annotations
- @Since("2.3.0")
- def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to
defaultParamMap, and explicitly set Params are copied from and toparamMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.- to
the target instance, which should work with the same set of default Params as this source instance
- extra
extra params to be copied to the target's
paramMap- returns
the target instance with param values copied
- Attributes
- protected
- Definition Classes
- Params
- final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
- Attributes
- protected
- Definition Classes
- Params
- val distanceMeasure: Param[String]
param for distance measure to be used in evaluation (supports
"squaredEuclidean"(default),"cosine")param for distance measure to be used in evaluation (supports
"squaredEuclidean"(default),"cosine")- Annotations
- @Since("2.4.0")
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def evaluate(dataset: Dataset[_]): Double
Evaluates model output and returns a scalar metric.
Evaluates model output and returns a scalar metric. The value of isLargerBetter specifies whether larger values are better.
- dataset
a dataset that contains labels/observations and predictions.
- returns
metric
- Definition Classes
- ClusteringEvaluator → Evaluator
- Annotations
- @Since("2.3.0")
- def evaluate(dataset: Dataset[_], paramMap: ParamMap): Double
Evaluates model output and returns a scalar metric.
Evaluates model output and returns a scalar metric. The value of isLargerBetter specifies whether larger values are better.
- dataset
a dataset that contains labels/observations and predictions.
- paramMap
parameter map that specifies the input columns and output metrics
- returns
metric
- Definition Classes
- Evaluator
- 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
- final val featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
- 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 getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- 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
- def getDistanceMeasure: String
- Annotations
- @Since("2.4.0")
- final def getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
- def getMetricName: String
- Annotations
- @Since("2.3.0")
- def getMetrics(dataset: Dataset[_]): ClusteringMetrics
Get a ClusteringMetrics, which can be used to get clustering metrics such as silhouette score.
Get a ClusteringMetrics, which can be used to get clustering metrics such as silhouette score.
- dataset
a dataset that contains labels/observations and predictions.
- returns
ClusteringMetrics
- Annotations
- @Since("3.1.0")
- 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 getPredictionCol: String
- Definition Classes
- HasPredictionCol
- final def getWeightCol: String
- Definition Classes
- HasWeightCol
- 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
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- 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 isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def isLargerBetter: Boolean
Indicates whether the metric returned by
evaluateshould be maximized (true, default) or minimized (false).Indicates whether the metric returned by
evaluateshould be maximized (true, default) or minimized (false). A given evaluator may support multiple metrics which may be maximized or minimized.- Definition Classes
- ClusteringEvaluator → Evaluator
- Annotations
- @Since("2.3.0")
- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
- val metricName: Param[String]
param for metric name in evaluation (supports
"silhouette"(default))param for metric name in evaluation (supports
"silhouette"(default))- Annotations
- @Since("2.3.0")
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- 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.
- final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
- 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(paramPair: ParamPair[_]): ClusteringEvaluator.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
- final def set(param: String, value: Any): ClusteringEvaluator.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
- final def set[T](param: Param[T], value: T): ClusteringEvaluator.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
- final def setDefault(paramPairs: ParamPair[_]*): ClusteringEvaluator.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter
setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.- paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
- Attributes
- protected
- Definition Classes
- Params
- final def setDefault[T](param: Param[T], value: T): ClusteringEvaluator.this.type
Sets a default value for a param.
- def setDistanceMeasure(value: String): ClusteringEvaluator.this.type
- Annotations
- @Since("2.4.0")
- def setFeaturesCol(value: String): ClusteringEvaluator.this.type
- Annotations
- @Since("2.3.0")
- def setMetricName(value: String): ClusteringEvaluator.this.type
- Annotations
- @Since("2.3.0")
- def setPredictionCol(value: String): ClusteringEvaluator.this.type
- Annotations
- @Since("2.3.0")
- def setWeightCol(value: String): ClusteringEvaluator.this.type
- Annotations
- @Since("3.1.0")
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- ClusteringEvaluator → Identifiable → AnyRef → Any
- Annotations
- @Since("3.0.0")
- 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
- ClusteringEvaluator → Identifiable
- Annotations
- @Since("2.3.0")
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- 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
- def write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from HasWeightCol
Inherited from HasFeaturesCol
Inherited from HasPredictionCol
Inherited from Evaluator
Inherited from Params
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.