Class/Object

org.apache.spark.ml.clustering

KMeansModel

Related Docs: object KMeansModel | package clustering

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class KMeansModel extends Model[KMeansModel] with KMeansParams with MLWritable

Model fitted by KMeans.

Annotations
@Since( "1.5.0" )
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Inherited
  1. KMeansModel
  2. MLWritable
  3. KMeansParams
  4. HasTol
  5. HasPredictionCol
  6. HasSeed
  7. HasFeaturesCol
  8. HasMaxIter
  9. Model
  10. Transformer
  11. PipelineStage
  12. Logging
  13. Params
  14. Serializable
  15. Serializable
  16. Identifiable
  17. AnyRef
  18. Any
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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
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  2. final def ##(): Int

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    Definition Classes
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  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
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  5. final def asInstanceOf[T0]: T0

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    Definition Classes
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  6. final def clear(param: Param[_]): KMeansModel.this.type

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    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def clusterCenters: Array[Vector]

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    @Since( "2.0.0" )
  9. def computeCost(dataset: Dataset[_]): Double

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    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.

    Annotations
    @Since( "2.0.0" )
  10. def copy(extra: ParamMap): KMeansModel

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    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
    KMeansModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.5.0" )
  11. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    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 to paramMap. 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
  12. final def defaultCopy[T <: Params](extra: ParamMap): T

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    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
  13. final def eq(arg0: AnyRef): Boolean

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  14. def equals(arg0: Any): Boolean

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  15. def explainParam(param: Param[_]): String

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    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
  16. def explainParams(): String

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    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  17. final def extractParamMap(): ParamMap

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  18. final def extractParamMap(extra: ParamMap): ParamMap

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    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
  19. final val featuresCol: Param[String]

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    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  20. def finalize(): Unit

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    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  21. final def get[T](param: Param[T]): Option[T]

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    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  22. final def getClass(): Class[_]

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  23. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  24. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  25. def getInitMode: String

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    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  26. def getInitSteps: Int

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    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  27. def getK: Int

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    Definition Classes
    KMeansParams
    Annotations
    @Since( "1.5.0" )
  28. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  29. final def getOrDefault[T](param: Param[T]): T

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    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
  30. def getParam(paramName: String): Param[Any]

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  31. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  32. final def getSeed: Long

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    Definition Classes
    HasSeed
  33. final def getTol: Double

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    Definition Classes
    HasTol
  34. final def hasDefault[T](param: Param[T]): Boolean

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  35. def hasParam(paramName: String): Boolean

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    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
  36. def hasParent: Boolean

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    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  37. def hasSummary: Boolean

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    Return true if there exists summary of model.

    Return true if there exists summary of model.

    Annotations
    @Since( "2.0.0" )
  38. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  39. final val initMode: Param[String]

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    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" )
  40. final val initSteps: IntParam

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    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" )
  41. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Logging
  42. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Logging
  43. final def isDefined(param: Param[_]): Boolean

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    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
  44. final def isInstanceOf[T0]: Boolean

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    Any
  45. final def isSet(param: Param[_]): Boolean

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  46. def isTraceEnabled(): Boolean

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    protected
    Definition Classes
    Logging
  47. final val k: IntParam

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    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" )
  48. def log: Logger

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    Logging
  49. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  50. def logDebug(msg: ⇒ String): Unit

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    Logging
  51. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  52. def logError(msg: ⇒ String): Unit

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    Logging
  53. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  54. def logInfo(msg: ⇒ String): Unit

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    Logging
  55. def logName: String

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    Logging
  56. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  57. def logTrace(msg: ⇒ String): Unit

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    Definition Classes
    Logging
  58. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    protected
    Definition Classes
    Logging
  59. def logWarning(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  60. final val maxIter: IntParam

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    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  61. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  62. final def notify(): Unit

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  63. final def notifyAll(): Unit

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  64. lazy val params: Array[Param[_]]

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    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.

  65. var parent: Estimator[KMeansModel]

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    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.

  66. final val predictionCol: Param[String]

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    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  67. def save(path: String): Unit

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    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( ... )
  68. final val seed: LongParam

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    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  69. final def set(paramPair: ParamPair[_]): KMeansModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  70. final def set(param: String, value: Any): KMeansModel.this.type

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    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
  71. final def set[T](param: Param[T], value: T): KMeansModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  72. final def setDefault(paramPairs: ParamPair[_]*): KMeansModel.this.type

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    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
  73. final def setDefault[T](param: Param[T], value: T): KMeansModel.this.type

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    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  74. def setFeaturesCol(value: String): KMeansModel.this.type

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    Annotations
    @Since( "2.0.0" )
  75. def setParent(parent: Estimator[KMeansModel]): KMeansModel

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    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  76. def setPredictionCol(value: String): KMeansModel.this.type

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    Annotations
    @Since( "2.0.0" )
  77. def summary: KMeansSummary

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    Gets summary of model on training set.

    Gets summary of model on training set. An exception is thrown if trainingSummary == None.

    Annotations
    @Since( "2.0.0" )
  78. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  79. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  80. final val tol: DoubleParam

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    Param for the convergence tolerance for iterative algorithms (>= 0).

    Param for the convergence tolerance for iterative algorithms (>= 0).

    Definition Classes
    HasTol
  81. def transform(dataset: Dataset[_]): DataFrame

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    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes
    KMeansModelTransformer
    Annotations
    @Since( "2.0.0" )
  82. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    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" )
  83. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    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()
  84. def transformSchema(schema: StructType): StructType

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    :: DeveloperApi ::

    :: DeveloperApi ::

    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.

    Definition Classes
    KMeansModelPipelineStage
    Annotations
    @Since( "1.5.0" )
  85. def transformSchema(schema: StructType, logging: Boolean): StructType

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    :: 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()
  86. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    KMeansModelIdentifiable
    Annotations
    @Since( "1.5.0" )
  87. def validateAndTransformSchema(schema: StructType): StructType

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    Validates and transforms the input schema.

    Validates and transforms the input schema.

    schema

    input schema

    returns

    output schema

    Attributes
    protected
    Definition Classes
    KMeansParams
  88. final def wait(): Unit

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    @throws( ... )
  89. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  90. final def wait(arg0: Long): Unit

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    Annotations
    @throws( ... )
  91. def write: MLWriter

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    Returns a org.apache.spark.ml.util.MLWriter instance for this ML instance.

    Returns a org.apache.spark.ml.util.MLWriter 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
    KMeansModelMLWritable
    Annotations
    @Since( "1.6.0" )

Inherited from MLWritable

Inherited from KMeansParams

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 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.

(expert-only) Parameter getters