Class

org.apache.spark.ml.classification

ProbabilisticClassifier

Related Doc: package classification

Permalink

abstract class ProbabilisticClassifier[FeaturesType, E <: ProbabilisticClassifier[FeaturesType, E, M], M <: ProbabilisticClassificationModel[FeaturesType, M]] extends Classifier[FeaturesType, E, M] with ProbabilisticClassifierParams

:: DeveloperApi ::

Single-label binary or multiclass classifier which can output class conditional probabilities.

FeaturesType

Type of input features. E.g., Vector

E

Concrete Estimator type

M

Concrete Model type

Annotations
@DeveloperApi()
Source
ProbabilisticClassifier.scala
Linear Supertypes
ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[FeaturesType, E, M], ClassifierParams, HasRawPredictionCol, Predictor[FeaturesType, E, M], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[M], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. ProbabilisticClassifier
  2. ProbabilisticClassifierParams
  3. HasThresholds
  4. HasProbabilityCol
  5. Classifier
  6. ClassifierParams
  7. HasRawPredictionCol
  8. Predictor
  9. PredictorParams
  10. HasPredictionCol
  11. HasFeaturesCol
  12. HasLabelCol
  13. Estimator
  14. PipelineStage
  15. Logging
  16. Params
  17. Serializable
  18. Serializable
  19. Identifiable
  20. AnyRef
  21. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new ProbabilisticClassifier()

    Permalink

Abstract Value Members

  1. abstract def copy(extra: ParamMap): E

    Permalink

    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.

    Definition Classes
    PredictorEstimatorPipelineStageParams
    See also

    defaultCopy()

  2. abstract def train(dataset: Dataset[_]): M

    Permalink

    Train a model using the given dataset and parameters.

    Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes
    protected
    Definition Classes
    Predictor
  3. abstract val uid: String

    Permalink

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    Identifiable

Concrete Value Members

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

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    Permalink

    An alias for getOrDefault().

    An alias for getOrDefault().

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

    Permalink
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0

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

    Permalink

    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

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Permalink

    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
  9. final def defaultCopy[T <: Params](extra: ParamMap): T

    Permalink

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

    Permalink
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  12. def explainParam(param: Param[_]): String

    Permalink

    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
  13. def explainParams(): String

    Permalink

    Explains all params of this instance.

    Explains all params of this instance.

    Definition Classes
    Params
    See also

    explainParam()

  14. def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

    Permalink

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    dataset

    DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector). Labels are cast to DoubleType.

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    Classifier
    Exceptions thrown

    SparkException if any label is not an integer >= 0

  15. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    Permalink

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    Predictor
  16. final def extractParamMap(): ParamMap

    Permalink

    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

    Permalink

    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 < user-supplied values < 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 < user-supplied values < extra.

    Definition Classes
    Params
  18. final val featuresCol: Param[String]

    Permalink

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  19. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  20. def fit(dataset: Dataset[_]): M

    Permalink

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    PredictorEstimator
  21. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[M]

    Permalink

    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" )
  22. def fit(dataset: Dataset[_], paramMap: ParamMap): M

    Permalink

    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" )
  23. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): M

    Permalink

    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()
  24. final def get[T](param: Param[T]): Option[T]

    Permalink

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

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

    Permalink
    Definition Classes
    AnyRef → Any
  26. final def getDefault[T](param: Param[T]): Option[T]

    Permalink

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  27. final def getFeaturesCol: String

    Permalink

    Definition Classes
    HasFeaturesCol
  28. final def getLabelCol: String

    Permalink

    Definition Classes
    HasLabelCol
  29. def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int

    Permalink

    Get the number of classes.

    Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.

    Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().

    dataset

    Dataset which contains a column labelCol

    maxNumClasses

    Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.

    returns

    number of classes

    Attributes
    protected
    Definition Classes
    Classifier
    Exceptions thrown

    IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses

  30. final def getOrDefault[T](param: Param[T]): T

    Permalink

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

    Permalink

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  32. final def getPredictionCol: String

    Permalink

    Definition Classes
    HasPredictionCol
  33. final def getProbabilityCol: String

    Permalink

    Definition Classes
    HasProbabilityCol
  34. final def getRawPredictionCol: String

    Permalink

    Definition Classes
    HasRawPredictionCol
  35. def getThresholds: Array[Double]

    Permalink

    Definition Classes
    HasThresholds
  36. final def hasDefault[T](param: Param[T]): Boolean

    Permalink

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

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

    Permalink

    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
  38. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  39. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  40. final def isDefined(param: Param[_]): Boolean

    Permalink

    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
  41. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  42. final def isSet(param: Param[_]): Boolean

    Permalink

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  43. def isTraceEnabled(): Boolean

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  44. final val labelCol: Param[String]

    Permalink

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  45. def log: Logger

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  46. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  47. def logDebug(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  48. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  49. def logError(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  50. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  51. def logInfo(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  52. def logName: String

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  53. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  54. def logTrace(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  55. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  56. def logWarning(msg: ⇒ String): Unit

    Permalink
    Attributes
    protected
    Definition Classes
    Logging
  57. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  58. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  59. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  60. lazy val params: Array[Param[_]]

    Permalink

    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.

    Note: Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

    Definition Classes
    Params
  61. final val predictionCol: Param[String]

    Permalink

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  62. final val probabilityCol: Param[String]

    Permalink

    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
  63. final val rawPredictionCol: Param[String]

    Permalink

    Param for raw prediction (a.k.a.

    Param for raw prediction (a.k.a. confidence) column name.

    Definition Classes
    HasRawPredictionCol
  64. final def set(paramPair: ParamPair[_]): ProbabilisticClassifier.this.type

    Permalink

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    Permalink

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

    Permalink

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    Permalink

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

    Permalink

    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
  69. def setFeaturesCol(value: String): E

    Permalink

    Definition Classes
    Predictor
  70. def setLabelCol(value: String): E

    Permalink

    Definition Classes
    Predictor
  71. def setPredictionCol(value: String): E

    Permalink

    Definition Classes
    Predictor
  72. def setProbabilityCol(value: String): E

    Permalink

  73. def setRawPredictionCol(value: String): E

    Permalink

    Definition Classes
    Classifier
  74. def setThresholds(value: Array[Double]): E

    Permalink

  75. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  76. final val thresholds: DoubleArrayParam

    Permalink

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.

    Definition Classes
    HasThresholds
  77. def toString(): String

    Permalink
    Definition Classes
    Identifiable → AnyRef → Any
  78. def transformSchema(schema: StructType): StructType

    Permalink

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema.

    Definition Classes
    PredictorPipelineStage
  79. def transformSchema(schema: StructType, logging: Boolean): StructType

    Permalink

    :: 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()
  80. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Permalink

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    fitting

    whether this is in fitting

    featuresDataType

    SQL DataType for FeaturesType. E.g., org.apache.spark.mllib.linalg.VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  81. final def wait(): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  82. final def wait(arg0: Long, arg1: Int): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  83. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def validateParams(): Unit

    Permalink

    Validates parameter values stored internally.

    Validates parameter values stored internally. Raise an exception if any parameter value is invalid.

    This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.

    Definition Classes
    Params
    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) Will be removed in 2.1.0. Checks should be merged into transformSchema.

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from Classifier[FeaturesType, E, M]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[FeaturesType, E, M]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[M]

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