Packages

class MultilayerPerceptronClassifier extends ProbabilisticClassifier[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel] with MultilayerPerceptronParams with DefaultParamsWritable

Classifier trainer based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax. Number of inputs has to be equal to the size of feature vectors. Number of outputs has to be equal to the total number of labels.

Annotations
@Since( "1.5.0" )
Source
MultilayerPerceptronClassifier.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, MultilayerPerceptronParams, HasBlockSize, HasSolver, HasStepSize, HasTol, HasMaxIter, HasSeed, ProbabilisticClassifier[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[MultilayerPerceptronClassificationModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. MultilayerPerceptronClassifier
  2. DefaultParamsWritable
  3. MLWritable
  4. MultilayerPerceptronParams
  5. HasBlockSize
  6. HasSolver
  7. HasStepSize
  8. HasTol
  9. HasMaxIter
  10. HasSeed
  11. ProbabilisticClassifier
  12. ProbabilisticClassifierParams
  13. HasThresholds
  14. HasProbabilityCol
  15. Classifier
  16. ClassifierParams
  17. HasRawPredictionCol
  18. Predictor
  19. PredictorParams
  20. HasPredictionCol
  21. HasFeaturesCol
  22. HasLabelCol
  23. Estimator
  24. PipelineStage
  25. Logging
  26. Params
  27. Serializable
  28. Serializable
  29. Identifiable
  30. AnyRef
  31. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new MultilayerPerceptronClassifier()
    Annotations
    @Since( "1.5.0" )
  2. new MultilayerPerceptronClassifier(uid: String)
    Annotations
    @Since( "1.5.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final val blockSize: IntParam

    Param for block size for stacking input data in matrices.

    Param for block size for stacking input data in matrices. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data..

    Definition Classes
    HasBlockSize
  7. final def clear(param: Param[_]): MultilayerPerceptronClassifier.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  9. def copy(extra: ParamMap): MultilayerPerceptronClassifier

    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
    MultilayerPerceptronClassifier → Predictor → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.5.0" )
  10. 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 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
  11. 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
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. 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
  15. def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes
    Params
  16. def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    ClassifierParams
  17. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes
    protected
    Definition Classes
    PredictorParams
  18. def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    PredictorParams
  19. def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

    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).

    numClasses

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

    Attributes
    protected
    Definition Classes
    Classifier
    Note

    Throws SparkException if any label is a non-integer or is negative

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

    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
  21. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  22. 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
  23. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  24. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. def fit(dataset: Dataset[_]): MultilayerPerceptronClassificationModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    Predictor → Estimator
  26. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[MultilayerPerceptronClassificationModel]

    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" )
  27. def fit(dataset: Dataset[_], paramMap: ParamMap): MultilayerPerceptronClassificationModel

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

    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()
  29. 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
  30. final def getBlockSize: Int

    Definition Classes
    HasBlockSize
  31. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  32. 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
  33. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  34. final def getInitialWeights: Vector

    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "2.0.0" )
  35. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  36. final def getLayers: Array[Int]

    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "1.5.0" )
  37. final def getMaxIter: Int

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

    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

  39. 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
  40. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  41. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  42. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  43. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  44. final def getSeed: Long

    Definition Classes
    HasSeed
  45. final def getSolver: String

    Definition Classes
    HasSolver
  46. final def getStepSize: Double

    Definition Classes
    HasStepSize
  47. def getThresholds: Array[Double]

    Definition Classes
    HasThresholds
  48. final def getTol: Double

    Definition Classes
    HasTol
  49. 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
  50. 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
  51. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  52. final val initialWeights: Param[Vector]

    The initial weights of the model.

    The initial weights of the model.

    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "2.0.0" )
  53. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  54. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. 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
  56. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  57. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  58. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  59. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  60. final val layers: IntArrayParam

    Layer sizes including input size and output size.

    Layer sizes including input size and output size.

    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "1.5.0" )
  61. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  62. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  69. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  74. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  75. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  76. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  77. 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.

  78. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  79. 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
  80. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

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

    Definition Classes
    HasRawPredictionCol
  81. 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( ... )
  82. final val seed: LongParam

    Param for random seed.

    Param for random seed.

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  84. final def set(param: String, value: Any): MultilayerPerceptronClassifier.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
  85. final def set[T](param: Param[T], value: T): MultilayerPerceptronClassifier.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  86. def setBlockSize(value: Int): MultilayerPerceptronClassifier.this.type

    Sets the value of param blockSize.

    Sets the value of param blockSize. Default is 128.

    Annotations
    @Since( "1.5.0" )
  87. final def setDefault(paramPairs: ParamPair[_]*): MultilayerPerceptronClassifier.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
  88. final def setDefault[T](param: Param[T], value: T): MultilayerPerceptronClassifier.this.type

    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
  89. def setFeaturesCol(value: String): MultilayerPerceptronClassifier

    Definition Classes
    Predictor
  90. def setInitialWeights(value: Vector): MultilayerPerceptronClassifier.this.type

    Sets the value of param initialWeights.

    Sets the value of param initialWeights.

    Annotations
    @Since( "2.0.0" )
  91. def setLabelCol(value: String): MultilayerPerceptronClassifier

    Definition Classes
    Predictor
  92. def setLayers(value: Array[Int]): MultilayerPerceptronClassifier.this.type

    Sets the value of param layers.

    Sets the value of param layers.

    Annotations
    @Since( "1.5.0" )
  93. def setMaxIter(value: Int): MultilayerPerceptronClassifier.this.type

    Set the maximum number of iterations.

    Set the maximum number of iterations. Default is 100.

    Annotations
    @Since( "1.5.0" )
  94. def setPredictionCol(value: String): MultilayerPerceptronClassifier

    Definition Classes
    Predictor
  95. def setProbabilityCol(value: String): MultilayerPerceptronClassifier

    Definition Classes
    ProbabilisticClassifier
  96. def setRawPredictionCol(value: String): MultilayerPerceptronClassifier

    Definition Classes
    Classifier
  97. def setSeed(value: Long): MultilayerPerceptronClassifier.this.type

    Set the seed for weights initialization if weights are not set

    Set the seed for weights initialization if weights are not set

    Annotations
    @Since( "1.5.0" )
  98. def setSolver(value: String): MultilayerPerceptronClassifier.this.type

    Sets the value of param solver.

    Sets the value of param solver. Default is "l-bfgs".

    Annotations
    @Since( "2.0.0" )
  99. def setStepSize(value: Double): MultilayerPerceptronClassifier.this.type

    Sets the value of param stepSize (applicable only for solver "gd").

    Sets the value of param stepSize (applicable only for solver "gd"). Default is 0.03.

    Annotations
    @Since( "2.0.0" )
  100. def setThresholds(value: Array[Double]): MultilayerPerceptronClassifier

    Definition Classes
    ProbabilisticClassifier
  101. def setTol(value: Double): MultilayerPerceptronClassifier.this.type

    Set the convergence tolerance of iterations.

    Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

    Annotations
    @Since( "1.5.0" )
  102. final val solver: Param[String]

    The solver algorithm for optimization.

    The solver algorithm for optimization. Supported options: "gd" (minibatch gradient descent) or "l-bfgs". Default: "l-bfgs"

    Definition Classes
    MultilayerPerceptronParams → HasSolver
    Annotations
    @Since( "2.0.0" )
  103. val stepSize: DoubleParam

    Param for Step size to be used for each iteration of optimization (> 0).

    Param for Step size to be used for each iteration of optimization (> 0).

    Definition Classes
    HasStepSize
  104. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  105. val thresholds: DoubleArrayParam

    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 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

    Definition Classes
    HasThresholds
  106. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  107. 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
  108. def train(dataset: Dataset[_]): MultilayerPerceptronClassificationModel

    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
    MultilayerPerceptronClassifier → Predictor
  109. 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 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
    Predictor → PipelineStage
  110. 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()
  111. 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
    MultilayerPerceptronClassifier → Identifiable
    Annotations
    @Since( "1.5.0" )
  112. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    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., VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  113. def validateLabel(label: Double, numClasses: Int): Unit

    Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Validates the label on the classifier is a valid integer in the range [0, numClasses).

    label

    The label to validate.

    numClasses

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

    Attributes
    protected
    Definition Classes
    Classifier
  114. def validateNumClasses(numClasses: Int): Unit

    Validates that number of classes is greater than zero.

    Validates that number of classes is greater than zero.

    numClasses

    Number of classes label can take.

    Attributes
    protected
    Definition Classes
    Classifier
  115. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  116. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  117. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  118. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from MultilayerPerceptronParams

Inherited from HasBlockSize

Inherited from HasSolver

Inherited from HasStepSize

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasSeed

Inherited from ProbabilisticClassifier[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from Classifier[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[MultilayerPerceptronClassificationModel]

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 setters

(expert-only) Parameter getters