class KMeansModel extends Model[KMeansModel] with KMeansParams with GeneralMLWritable with HasTrainingSummary[KMeansSummary]
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- KMeansModel
- HasTrainingSummary
- GeneralMLWritable
- MLWritable
- KMeansParams
- HasMaxBlockSizeInMB
- HasSolver
- HasWeightCol
- HasDistanceMeasure
- HasTol
- HasPredictionCol
- HasSeed
- HasFeaturesCol
- HasMaxIter
- Model
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- implicit class LogStringContext extends AnyRef
- Definition Classes
- Logging
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- 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
- def MDC(key: LogKey, value: Any): MDC
- Attributes
- protected
- Definition Classes
- Logging
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- final def clear(param: Param[_]): KMeansModel.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 clusterCenters: Array[Vector]
- Annotations
- @Since("2.0.0")
- def copy(extra: ParamMap): KMeansModel
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
- KMeansModel → Model → Transformer → PipelineStage → Params
- Annotations
- @Since("1.5.0")
- 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
- final val distanceMeasure: Param[String]
Param for The distance measure.
Param for The distance measure. Supported options: 'euclidean' and 'cosine'.
- Definition Classes
- HasDistanceMeasure
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def estimatedSize: Long
For ml connect only.
For ml connect only. Estimate the size of this model in bytes. This is an approximation, the real size might be different. 1, Both driver side memory usage and distributed objects size (like DataFrame, RDD, Graph, Summary) are counted. 2, Lazy vals are not counted, e.g., an auxiliary object used in prediction. 3, The default implementation uses
org.apache.spark.util.SizeEstimator.estimate, some models override the default implementation to achieve more precise estimation. 4, For 3-rd extension, if external languages are used, it is recommended to override this method and return a proper size.- Definition Classes
- KMeansModel → Model
- 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
- final def getDistanceMeasure: String
- Definition Classes
- HasDistanceMeasure
- final def getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
- def getInitMode: String
- Definition Classes
- KMeansParams
- Annotations
- @Since("1.5.0")
- def getInitSteps: Int
- Definition Classes
- KMeansParams
- Annotations
- @Since("1.5.0")
- def getK: Int
- Definition Classes
- KMeansParams
- Annotations
- @Since("1.5.0")
- final def getMaxBlockSizeInMB: Double
- Definition Classes
- HasMaxBlockSizeInMB
- final def getMaxIter: Int
- Definition Classes
- HasMaxIter
- 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 getSeed: Long
- Definition Classes
- HasSeed
- final def getSolver: String
- Definition Classes
- HasSolver
- final def getTol: Double
- Definition Classes
- HasTol
- 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 hasParent: Boolean
Indicates whether this Model has a corresponding parent.
- def hasSummary: Boolean
Indicates whether a training summary exists for this model instance.
Indicates whether a training summary exists for this model instance.
- Definition Classes
- HasTrainingSummary
- Annotations
- @Since("3.0.0")
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- final val initMode: Param[String]
Param for the initialization algorithm.
Param for the initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
- Definition Classes
- KMeansParams
- Annotations
- @Since("1.5.0")
- final val initSteps: IntParam
Param for the number of steps for the k-means|| initialization mode.
Param for the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.
- Definition Classes
- KMeansParams
- Annotations
- @Since("1.5.0")
- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
- def initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
- 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
- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
- def isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
- final val k: IntParam
The number of clusters to create (k).
The number of clusters to create (k). Must be > 1. Note that it is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.
- Definition Classes
- KMeansParams
- Annotations
- @Since("1.5.0")
- def log: Logger
- Attributes
- protected
- Definition Classes
- Logging
- def logBasedOnLevel(level: Level)(f: => MessageWithContext): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logDebug(msg: => String, throwable: Throwable): Unit
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- protected
- Definition Classes
- Logging
- def logDebug(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logDebug(entry: LogEntry): Unit
- Attributes
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- Definition Classes
- Logging
- def logDebug(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(entry: LogEntry, throwable: Throwable): Unit
- Attributes
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- Logging
- def logError(entry: LogEntry): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(msg: => String): Unit
- Attributes
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- Definition Classes
- Logging
- def logInfo(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(entry: LogEntry): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logName: String
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(entry: LogEntry): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(entry: LogEntry, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(entry: LogEntry): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- final val maxBlockSizeInMB: DoubleParam
Param for Maximum memory in MB for stacking input data into blocks.
Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..
- Definition Classes
- HasMaxBlockSizeInMB
- final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
- Definition Classes
- HasMaxIter
- 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 numFeatures: Int
- Annotations
- @Since("3.0.0")
- 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.
- var parent: Estimator[KMeansModel]
The parent estimator that produced this model.
The parent estimator that produced this model.
- Definition Classes
- Model
- Note
For ensembles' component Models, this value can be null.
- def predict(features: Vector): Int
- Annotations
- @Since("3.0.0")
- 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 val seed: LongParam
Param for random seed.
Param for random seed.
- Definition Classes
- HasSeed
- final def set(paramPair: ParamPair[_]): KMeansModel.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): KMeansModel.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): KMeansModel.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[_]*): KMeansModel.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): KMeansModel.this.type
Sets a default value for a param.
- def setFeaturesCol(value: String): KMeansModel.this.type
- Annotations
- @Since("2.0.0")
- def setParent(parent: Estimator[KMeansModel]): KMeansModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
- def setPredictionCol(value: String): KMeansModel.this.type
- Annotations
- @Since("2.0.0")
- final val solver: Param[String]
Param for the name of optimization method used in KMeans.
Param for the name of optimization method used in KMeans. Supported options:
- "auto": Automatically select the solver based on the input schema and sparsity: If input instances are arrays or input vectors are dense, set to "block". Else, set to "row".
- "row": input instances are processed row by row, and triangle-inequality is applied to accelerate the training.
- "block": input instances are stacked to blocks, and GEMM is applied to compute the distances. Default is "auto".
- Definition Classes
- KMeansParams → HasSolver
- Annotations
- @Since("3.4.0")
- def summary: KMeansSummary
Gets summary of model on training set.
Gets summary of model on training set. An exception is thrown if
hasSummaryis false.- Definition Classes
- KMeansModel → HasTrainingSummary
- Annotations
- @Since("2.0.0")
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- KMeansModel → Identifiable → AnyRef → Any
- Annotations
- @Since("3.0.0")
- 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
- def transform(dataset: Dataset[_]): DataFrame
Transforms the input dataset.
Transforms the input dataset.
- Definition Classes
- KMeansModel → Transformer
- Annotations
- @Since("2.0.0")
- def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
- dataset
input dataset
- paramMap
additional parameters, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since("2.0.0")
- def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
- dataset
input dataset
- firstParamPair
the first param pair, overwrite embedded params
- otherParamPairs
other param pairs, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since("2.0.0") @varargs()
- 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
- KMeansModel → PipelineStage
- Annotations
- @Since("1.5.0")
- def transformSchema(schema: StructType, logging: Boolean): StructType
:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
- 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
- KMeansModel → Identifiable
- Annotations
- @Since("1.5.0")
- def validateAndTransformSchema(schema: StructType): StructType
Validates and transforms the input schema.
Validates and transforms the input schema.
- schema
input schema
- returns
output schema
- Attributes
- protected
- Definition Classes
- KMeansParams
- 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 withLogContext(context: Map[String, String])(body: => Unit): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def write: GeneralMLWriter
Returns a org.apache.spark.ml.util.GeneralMLWriter instance for this ML instance.
Returns a org.apache.spark.ml.util.GeneralMLWriter instance for this ML instance.
For KMeansModel, this does NOT currently save the training summary. An option to save summary may be added in the future.
- Definition Classes
- KMeansModel → GeneralMLWritable → MLWritable
- Annotations
- @Since("1.6.0")
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 HasTrainingSummary[KMeansSummary]
Inherited from GeneralMLWritable
Inherited from MLWritable
Inherited from KMeansParams
Inherited from HasMaxBlockSizeInMB
Inherited from HasSolver
Inherited from HasWeightCol
Inherited from HasDistanceMeasure
Inherited from HasTol
Inherited from HasPredictionCol
Inherited from HasSeed
Inherited from HasFeaturesCol
Inherited from HasMaxIter
Inherited from Model[KMeansModel]
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
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.
Members
Parameter setters
Parameter getters
(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.