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
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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
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Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  2. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  3. 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" )
  4. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  5. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  6. 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
  7. 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
  8. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  9. 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
  10. 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
  11. 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

Members

  1. 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
  2. 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
    MultilayerPerceptronClassifierPredictorEstimatorPipelineStageParams
    Annotations
    @Since( "1.5.0" )
  3. 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
  4. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  6. 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
  7. def fit(dataset: Dataset[_]): MultilayerPerceptronClassificationModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    PredictorEstimator
  8. 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" )
  9. 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" )
  10. 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()
  11. 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
  12. 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
  13. 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
  14. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  15. 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
  16. 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
  17. 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
  18. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  19. 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.

  20. 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( ... )
  21. 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
  22. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  23. 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
    PredictorPipelineStage
  24. 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
    MultilayerPerceptronClassifierIdentifiable
    Annotations
    @Since( "1.5.0" )
  25. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Parameter setters

  1. def setFeaturesCol(value: String): MultilayerPerceptronClassifier

    Definition Classes
    Predictor
  2. def setLabelCol(value: String): MultilayerPerceptronClassifier

    Definition Classes
    Predictor
  3. 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" )
  4. 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" )
  5. def setPredictionCol(value: String): MultilayerPerceptronClassifier

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

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

    Definition Classes
    Classifier
  8. 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" )
  9. 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" )
  10. def setThresholds(value: Array[Double]): MultilayerPerceptronClassifier

    Definition Classes
    ProbabilisticClassifier
  11. 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" )

Parameter getters

  1. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  2. final def getLabelCol: String

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

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

    Definition Classes
    HasMaxIter
  5. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  6. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  7. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  8. final def getSeed: Long

    Definition Classes
    HasSeed
  9. final def getSolver: String

    Definition Classes
    HasSolver
  10. final def getStepSize: Double

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

    Definition Classes
    HasThresholds
  12. final def getTol: Double

    Definition Classes
    HasTol

(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.

  1. 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
  2. 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" )
  3. 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" )

(expert-only) Parameter setters

  1. 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" )
  2. def setInitialWeights(value: Vector): MultilayerPerceptronClassifier.this.type

    Sets the value of param initialWeights.

    Sets the value of param initialWeights.

    Annotations
    @Since( "2.0.0" )
  3. 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" )

(expert-only) Parameter getters

  1. final def getBlockSize: Int

    Definition Classes
    HasBlockSize
  2. final def getInitialWeights: Vector

    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "2.0.0" )