Package org.apache.spark.ml.classification

Class MultilayerPerceptronClassifier

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.Predictor<FeaturesType,E,M>

org.apache.spark.ml.classification.Classifier<FeaturesType,E,M>

org.apache.spark.ml.classification.ProbabilisticClassifier<Vector,MultilayerPerceptronClassifier,MultilayerPerceptronClassificationModel>

org.apache.spark.ml.classification.MultilayerPerceptronClassifier

All Implemented Interfaces:

Serializable, org.apache.spark.internal.Logging, ClassifierParams, MultilayerPerceptronParams, ProbabilisticClassifierParams, Params, HasBlockSize, HasFeaturesCol, HasLabelCol, HasMaxIter, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasSeed, HasSolver, HasStepSize, HasThresholds, HasTol, PredictorParams, DefaultParamsWritable, Identifiable, MLWritable

public class MultilayerPerceptronClassifier
extends ProbabilisticClassifier<Vector,MultilayerPerceptronClassifier,MultilayerPerceptronClassificationModel>
implements MultilayerPerceptronParams, 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.

See Also:

• Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor	Description
MultilayerPerceptronClassifier()
MultilayerPerceptronClassifier(String uid)

Method Summary

Modifier and Type	Method	Description
final IntParam	blockSize()	Param for block size for stacking input data in matrices.
MultilayerPerceptronClassifier	copy(ParamMap extra)	Creates a copy of this instance with the same UID and some extra params.
final Param<Vector>	initialWeights()	The initial weights of the model.
final IntArrayParam	layers()	Layer sizes including input size and output size.
static MultilayerPerceptronClassifier	load(String path)
final IntParam	maxIter()	Param for maximum number of iterations (&gt;= 0).
static MLReader<T>	read()
final LongParam	seed()	Param for random seed.
MultilayerPerceptronClassifier	setBlockSize(int value)	Sets the value of param blockSize().
MultilayerPerceptronClassifier	setInitialWeights(Vector value)	Sets the value of param initialWeights().
MultilayerPerceptronClassifier	setLayers(int[] value)	Sets the value of param layers().
MultilayerPerceptronClassifier	setMaxIter(int value)	Set the maximum number of iterations.
MultilayerPerceptronClassifier	setSeed(long value)	Set the seed for weights initialization if weights are not set
MultilayerPerceptronClassifier	setSolver(String value)	Sets the value of param solver().
MultilayerPerceptronClassifier	setStepSize(double value)	Sets the value of param stepSize() (applicable only for solver "gd").
MultilayerPerceptronClassifier	setTol(double value)	Set the convergence tolerance of iterations.
final Param<String>	solver()	The solver algorithm for optimization.
DoubleParam	stepSize()	Param for Step size to be used for each iteration of optimization (&gt; 0).
final DoubleParam	tol()	Param for the convergence tolerance for iterative algorithms (&gt;= 0).
String	uid()	An immutable unique ID for the object and its derivatives.

Methods inherited from class org.apache.spark.ml.classification.ProbabilisticClassifier

probabilityCol, setProbabilityCol, setThresholds, thresholds

Methods inherited from class org.apache.spark.ml.classification.Classifier

rawPredictionCol, setRawPredictionCol

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

featuresCol, fit, labelCol, predictionCol, 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

params

Methods inherited from class java.lang.Object

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

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

write

Methods inherited from interface org.apache.spark.ml.param.shared.HasBlockSize

getBlockSize

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

featuresCol, getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol, labelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter

getMaxIter

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol, predictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasProbabilityCol

getProbabilityCol, probabilityCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasRawPredictionCol

getRawPredictionCol, rawPredictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasSeed

getSeed

Methods inherited from interface org.apache.spark.ml.param.shared.HasSolver

getSolver

Methods inherited from interface org.apache.spark.ml.param.shared.HasStepSize

getStepSize

Methods inherited from interface org.apache.spark.ml.param.shared.HasThresholds

getThresholds, thresholds

Methods inherited from interface org.apache.spark.ml.param.shared.HasTol

getTol

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

toString

Methods inherited from interface org.apache.spark.internal.Logging

initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logBasedOnLevel, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, MDC, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

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

save

Methods inherited from interface org.apache.spark.ml.classification.MultilayerPerceptronParams

getInitialWeights, getLayers

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

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

Methods inherited from interface org.apache.spark.ml.classification.ProbabilisticClassifierParams

validateAndTransformSchema

Constructor Details

MultilayerPerceptronClassifier

public MultilayerPerceptronClassifier(String uid)

MultilayerPerceptronClassifier

public MultilayerPerceptronClassifier()

Method Details

load

public static MultilayerPerceptronClassifier load(String path)

read

public static MLReader<T> read()

layers

public final IntArrayParam layers()

Description copied from interface: MultilayerPerceptronParams

Layer sizes including input size and output size.

Specified by:

layers in interface MultilayerPerceptronParams

Returns:

(undocumented)

solver

public final Param<String> solver()

Description copied from interface: MultilayerPerceptronParams

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

Specified by:

solver in interface HasSolver

Specified by:

solver in interface MultilayerPerceptronParams

Returns:

(undocumented)

initialWeights

public final Param<Vector> initialWeights()

Description copied from interface: MultilayerPerceptronParams

The initial weights of the model.

Specified by:

initialWeights in interface MultilayerPerceptronParams

Returns:

(undocumented)

blockSize

public final IntParam blockSize()

Description copied from interface: HasBlockSize

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..

Specified by:

blockSize in interface HasBlockSize

Returns:

(undocumented)

stepSize

public DoubleParam stepSize()

Description copied from interface: HasStepSize

Param for Step size to be used for each iteration of optimization (&gt; 0).

Specified by:

stepSize in interface HasStepSize

Returns:

(undocumented)

tol

public final DoubleParam tol()

Description copied from interface: HasTol

Param for the convergence tolerance for iterative algorithms (&gt;= 0).

Specified by:

tol in interface HasTol

Returns:

(undocumented)

maxIter

public final IntParam maxIter()

Description copied from interface: HasMaxIter

Param for maximum number of iterations (&gt;= 0).

Specified by:

maxIter in interface HasMaxIter

Returns:

(undocumented)

seed

public final LongParam seed()

Description copied from interface: HasSeed

Param for random seed.

Specified by:

seed in interface HasSeed

Returns:

(undocumented)

uid

public 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)

Sets the value of param layers().

Parameters:

value - (undocumented)

Returns:

(undocumented)

setBlockSize

public MultilayerPerceptronClassifier setBlockSize(int value)

Sets the value of param blockSize(). Default is 128.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSolver

public MultilayerPerceptronClassifier setSolver(String value)

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

Parameters:

value - (undocumented)

Returns:

(undocumented)

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-6.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSeed

public MultilayerPerceptronClassifier setSeed(long value)

Set the seed for weights initialization if weights are not set

Parameters:

value - (undocumented)

Returns:

(undocumented)

setInitialWeights

public MultilayerPerceptronClassifier setInitialWeights(Vector value)

Sets the value of param initialWeights().

Parameters:

value - (undocumented)

Returns:

(undocumented)

setStepSize

public MultilayerPerceptronClassifier setStepSize(double value)

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

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. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,MultilayerPerceptronClassifier,MultilayerPerceptronClassificationModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)