org.apache.spark.ml.classification

Class MultilayerPerceptronClassificationModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.PredictionModel<FeaturesType,M>

org.apache.spark.ml.classification.ClassificationModel<FeaturesType,M>

org.apache.spark.ml.classification.ProbabilisticClassificationModel<Vector,MultilayerPerceptronClassificationModel>

org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, ClassifierParams, MultilayerPerceptronParams, ProbabilisticClassifierParams, Params, HasBlockSize, HasFeaturesCol, HasLabelCol, HasMaxIter, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasSeed, HasSolver, HasStepSize, HasThresholds, HasTol, PredictorParams, HasTrainingSummary<MultilayerPerceptronClassificationTrainingSummary>, Identifiable, MLWritable

public class MultilayerPerceptronClassificationModel
extends ProbabilisticClassificationModel<Vector,MultilayerPerceptronClassificationModel>
implements MultilayerPerceptronParams, scala.Serializable, MLWritable, HasTrainingSummary<MultilayerPerceptronClassificationTrainingSummary>

Classification model based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax.

param: uid uid param: weights the weights of layers

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
IntParam	blockSize() Param for block size for stacking input data in matrices.
MultilayerPerceptronClassificationModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
MultilayerPerceptronClassificationSummary	evaluate(Dataset<?> dataset) Evaluates the model on a test dataset.
Param<Vector>	initialWeights() The initial weights of the model.
IntArrayParam	layers() Layer sizes including input size and output size.
static MultilayerPerceptronClassificationModel	load(String path)
IntParam	maxIter() Param for maximum number of iterations (&gt;= 0).
int	numClasses() Number of classes (values which the label can take).
int	numFeatures() Returns the number of features the model was trained on.
double	predict(Vector features) Predict label for the given features.
Vector	predictRaw(Vector features) Raw prediction for each possible label.
static MLReader<MultilayerPerceptronClassificationModel>	read()
LongParam	seed() Param for random seed.
Param<String>	solver() The solver algorithm for optimization.
DoubleParam	stepSize() Param for Step size to be used for each iteration of optimization (&gt; 0).
MultilayerPerceptronClassificationTrainingSummary	summary() Gets summary of model on training set.
DoubleParam	tol() Param for the convergence tolerance for iterative algorithms (&gt;= 0).
String	toString()
String	uid() An immutable unique ID for the object and its derivatives.
Vector	weights()
MLWriter	write() Returns an MLWriter instance for this ML instance.

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

normalizeToProbabilitiesInPlace, predictProbability, probabilityCol, setProbabilityCol, setThresholds, thresholds, transform, transformSchema

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

rawPredictionCol, setRawPredictionCol, transformImpl

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

featuresCol, labelCol, predictionCol, setFeaturesCol, setPredictionCol

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

hasParent, parent, setParent

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

transform, transform, transform

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

params

Methods inherited from class Object

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

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

getInitialWeights, getLayers

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

validateAndTransformSchema

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

extractInstances

Methods inherited from interface org.apache.spark.ml.PredictorParams

extractInstances, extractInstances

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

getLabelCol, labelCol

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

featuresCol, getFeaturesCol

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

getPredictionCol, predictionCol

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.param.shared.HasRawPredictionCol

getRawPredictionCol, rawPredictionCol

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

getProbabilityCol, probabilityCol

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

getThresholds, thresholds

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

getSeed

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

getMaxIter

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

getTol

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

getStepSize

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

getSolver

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

getBlockSize

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

save

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

hasSummary, setSummary

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

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Method Detail

read

public static MLReader<MultilayerPerceptronClassificationModel> read()

load

public static MultilayerPerceptronClassificationModel load(String path)

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 MultilayerPerceptronParams

Specified by:

solver in interface HasSolver

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)

weights

public Vector weights()

numFeatures

public int numFeatures()

Description copied from class: PredictionModel

Returns the number of features the model was trained on. If unknown, returns -1

Overrides:

numFeatures in class PredictionModel<Vector,MultilayerPerceptronClassificationModel>

summary

public MultilayerPerceptronClassificationTrainingSummary summary()

Gets summary of model on training set. An exception is thrown if hasSummary is false.

Specified by:

summary in interface HasTrainingSummary<MultilayerPerceptronClassificationTrainingSummary>

Returns:

(undocumented)

evaluate

public MultilayerPerceptronClassificationSummary evaluate(Dataset<?> dataset)

Evaluates the model on a test dataset.

Parameters:

dataset - Test dataset to evaluate model on.

Returns:

(undocumented)

predict

public double predict(Vector features)

Predict label for the given features. This internal method is used to implement transform() and output predictionCol.

Overrides:

predict in class ClassificationModel<Vector,MultilayerPerceptronClassificationModel>

Parameters:

features - (undocumented)

Returns:

(undocumented)

copy

public MultilayerPerceptronClassificationModel 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 Model<MultilayerPerceptronClassificationModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

predictRaw

public Vector predictRaw(Vector features)

Description copied from class: ClassificationModel

Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

Specified by:

predictRaw in class ClassificationModel<Vector,MultilayerPerceptronClassificationModel>

Parameters:

features - (undocumented)

Returns:

vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

numClasses

public int numClasses()

Description copied from class: ClassificationModel

Number of classes (values which the label can take).

Specified by:

numClasses in class ClassificationModel<Vector,MultilayerPerceptronClassificationModel>

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object