org.apache.spark.ml.classification

Class MultilayerPerceptronClassifier

java.lang.Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<M>

org.apache.spark.ml.Predictor<Vector,MultilayerPerceptronClassifier,MultilayerPerceptronClassificationModel>

org.apache.spark.ml.classification.MultilayerPerceptronClassifier

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

public class MultilayerPerceptronClassifier
extends Predictor<Vector,MultilayerPerceptronClassifier,MultilayerPerceptronClassificationModel>

:: Experimental :: 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.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
MultilayerPerceptronClassifier()
MultilayerPerceptronClassifier(java.lang.String uid)

Method Summary

Methods

Modifier and Type	Method and Description
IntParam	blockSize() Block size for stacking input data in matrices to speed up the computation.
MultilayerPerceptronClassifier	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<java.lang.String>	featuresCol() Param for features column name.
int	getBlockSize()
java.lang.String	getFeaturesCol()
java.lang.String	getLabelCol()
int[]	getLayers()
java.lang.String	getPredictionCol()
Param<java.lang.String>	labelCol() Param for label column name.
IntArrayParam	layers() Layer sizes including input size and output size.
Param<java.lang.String>	predictionCol() Param for prediction column name.
MultilayerPerceptronClassifier	setBlockSize(int value)
MultilayerPerceptronClassifier	setLayers(int[] value)
MultilayerPerceptronClassifier	setMaxIter(int value) Set the maximum number of iterations.
MultilayerPerceptronClassifier	setSeed(long value) Set the seed for weights initialization.
MultilayerPerceptronClassifier	setTol(double value) Set the convergence tolerance of iterations.
protected MultilayerPerceptronClassificationModel	train(DataFrame dataset) Train a model using the given dataset and parameters.
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.

Methods inherited from class org.apache.spark.ml.Predictor

extractLabeledPoints, fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

MultilayerPerceptronClassifier

public MultilayerPerceptronClassifier(java.lang.String uid)

MultilayerPerceptronClassifier

public MultilayerPerceptronClassifier()

Method Detail

uid

public java.lang.String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setLayers

public MultilayerPerceptronClassifier setLayers(int[] value)

setBlockSize

public MultilayerPerceptronClassifier setBlockSize(int value)

setMaxIter

public MultilayerPerceptronClassifier setMaxIter(int value)

Set the maximum number of iterations. Default is 100.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setTol

public MultilayerPerceptronClassifier setTol(double value)

Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-4.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSeed

public MultilayerPerceptronClassifier setSeed(long value)

Set the seed for weights initialization.

Parameters:

value - (undocumented)

Returns:

(undocumented)

copy

public MultilayerPerceptronClassifier copy(ParamMap extra)

Description copied from interface: 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.

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,MultilayerPerceptronClassifier,MultilayerPerceptronClassificationModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

train

protected MultilayerPerceptronClassificationModel train(DataFrame dataset)

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.

Specified by:

train in class Predictor<Vector,MultilayerPerceptronClassifier,MultilayerPerceptronClassificationModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

layers

public IntArrayParam layers()

Layer sizes including input size and output size. Default: Array(1, 1)

Returns:

(undocumented)

getLayers

public int[] getLayers()

blockSize

public IntParam blockSize()

Block size for stacking input data in matrices to speed up the computation. 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. Recommended size is between 10 and 1000. Default: 128

Returns:

(undocumented)

getBlockSize

public int getBlockSize()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

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

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema

labelCol

public Param<java.lang.String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public java.lang.String getLabelCol()

featuresCol

public Param<java.lang.String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public java.lang.String getFeaturesCol()

predictionCol

public Param<java.lang.String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public java.lang.String getPredictionCol()