org.apache.spark.ml.classification

Class MultilayerPerceptronClassifier

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, Params, DefaultParamsWritable, Identifiable, MLWritable

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

Constructor Summary

Constructors

Constructor and Description
MultilayerPerceptronClassifier()
MultilayerPerceptronClassifier(String uid)

Method Summary

Methods

Modifier and Type	Method and Description
static IntParam	blockSize()
IntParam	blockSize() Block size for stacking input data in matrices to speed up the computation.
static Params	clear(Param<?> param)
MultilayerPerceptronClassifier	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static Param<String>	featuresCol()
Param<String>	featuresCol() Param for features column name.
static M	fit(Dataset<?> dataset)
static M	fit(Dataset<?> dataset, ParamMap paramMap)
static scala.collection.Seq<M>	fit(Dataset<?> dataset, ParamMap[] paramMaps)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
static <T> scala.Option<T>	get(Param<T> param)
static int	getBlockSize()
int	getBlockSize()
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getFeaturesCol()
String	getFeaturesCol()
static Vector	getInitialWeights()
Vector	getInitialWeights()
static String	getLabelCol()
String	getLabelCol()
static int[]	getLayers()
int[]	getLayers()
static int	getMaxIter()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
String	getPredictionCol()
static long	getSeed()
static String	getSolver()
String	getSolver()
static double	getStepSize()
static double	getTol()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static Param<Vector>	initialWeights()
Param<Vector>	initialWeights() The initial weights of the model.
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Param<String>	labelCol()
Param<String>	labelCol() Param for label column name.
static IntArrayParam	layers()
IntArrayParam	layers() Layer sizes including input size and output size.
static MultilayerPerceptronClassifier	load(String path)
static IntParam	maxIter()
static Param<?>[]	params()
static Param<String>	predictionCol()
Param<String>	predictionCol() Param for prediction column name.
static void	save(String path)
static LongParam	seed()
static <T> Params	set(Param<T> param, T value)
MultilayerPerceptronClassifier	setBlockSize(int value) Sets the value of param blockSize.
static Learner	setFeaturesCol(String value)
MultilayerPerceptronClassifier	setInitialWeights(Vector value) Sets the value of param initialWeights.
static Learner	setLabelCol(String value)
MultilayerPerceptronClassifier	setLayers(int[] value) Sets the value of param layers.
MultilayerPerceptronClassifier	setMaxIter(int value) Set the maximum number of iterations.
static Learner	setPredictionCol(String value)
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.
static Param<String>	solver()
Param<String>	solver() The solver algorithm for optimization.
static DoubleParam	stepSize()
static DoubleParam	tol()
static String	toString()
static StructType	transformSchema(StructType schema)
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.
static MLWriter	write()

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

fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

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

fit, fit, fit, fit

Methods inherited from class 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.util.MLWritable

save

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

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

toString

Constructor Detail

MultilayerPerceptronClassifier

public MultilayerPerceptronClassifier(String uid)

MultilayerPerceptronClassifier

public MultilayerPerceptronClassifier()

Method Detail

load

public static MultilayerPerceptronClassifier load(String path)

toString

public static String toString()

params

public static Param<?>[] params()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
             T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    scala.collection.Seq<ParamPair<?>> otherParamPairs)

fit

public static M fit(Dataset<?> dataset,
    ParamMap paramMap)

fit

public static scala.collection.Seq<M> fit(Dataset<?> dataset,
                          ParamMap[] paramMaps)

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    ParamPair<?>... otherParamPairs)

labelCol

public static final Param<String> labelCol()

getLabelCol

public static final String getLabelCol()

featuresCol

public static final Param<String> featuresCol()

getFeaturesCol

public static final String getFeaturesCol()

predictionCol

public static final Param<String> predictionCol()

getPredictionCol

public static final String getPredictionCol()

setLabelCol

public static Learner setLabelCol(String value)

setFeaturesCol

public static Learner setFeaturesCol(String value)

setPredictionCol

public static Learner setPredictionCol(String value)

fit

public static M fit(Dataset<?> dataset)

transformSchema

public static StructType transformSchema(StructType schema)

seed

public static final LongParam seed()

getSeed

public static final long getSeed()

maxIter

public static final IntParam maxIter()

getMaxIter

public static final int getMaxIter()

tol

public static final DoubleParam tol()

getTol

public static final double getTol()

stepSize

public static final DoubleParam stepSize()

getStepSize

public static final double getStepSize()

layers

public static final IntArrayParam layers()

getLayers

public static final int[] getLayers()

blockSize

public static final IntParam blockSize()

getBlockSize

public static final int getBlockSize()

solver

public static final Param<String> solver()

getSolver

public static final String getSolver()

initialWeights

public static final Param<Vector> initialWeights()

getInitialWeights

public static final Vector getInitialWeights()

save

public static void save(String path)
                 throws java.io.IOException

Throws:

java.io.IOException

write

public static MLWriter write()

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)

layers

public IntArrayParam layers()

Layer sizes including input size and output size.

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()

solver

public Param<String> solver()

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

Returns:

(undocumented)

getSolver

public String getSolver()

initialWeights

public Param<Vector> initialWeights()

The initial weights of the model.

Returns:

(undocumented)

getInitialWeights

public Vector getInitialWeights()

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<String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public String getLabelCol()

featuresCol

public Param<String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public String getFeaturesCol()

predictionCol

public Param<String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public String getPredictionCol()