Package org.apache.spark.ml.classification

Class FMClassificationModel

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,FMClassificationModel>

org.apache.spark.ml.classification.FMClassificationModel

All Implemented Interfaces:

Serializable, org.apache.spark.internal.Logging, ClassifierParams, FMClassifierParams, ProbabilisticClassifierParams, Params, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasMaxIter, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasRegParam, HasSeed, HasSolver, HasStepSize, HasThresholds, HasTol, HasWeightCol, PredictorParams, FactorizationMachinesParams, HasTrainingSummary<FMClassificationTrainingSummary>, Identifiable, MLWritable

public class FMClassificationModel
extends ProbabilisticClassificationModel<Vector,FMClassificationModel>
implements FMClassifierParams, MLWritable, HasTrainingSummary<FMClassificationTrainingSummary>

Model produced by FMClassifier

See Also:

• Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	FMClassificationModel.Data$

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

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

Method Summary

Modifier and Type	Method	Description
FMClassificationModel	copy(ParamMap extra)	Creates a copy of this instance with the same UID and some extra params.
long	estimatedSize()
FMClassificationSummary	evaluate(Dataset<?> dataset)	Evaluates the model on a test dataset.
Matrix	factors()
final IntParam	factorSize()	Param for dimensionality of the factors (&gt;= 0)
final BooleanParam	fitIntercept()	Param for whether to fit an intercept term.
final BooleanParam	fitLinear()	Param for whether to fit linear term (aka 1-way term)
final DoubleParam	initStd()	Param for standard deviation of initial coefficients
double	intercept()
Vector	linear()
static FMClassificationModel	load(String path)
final IntParam	maxIter()	Param for maximum number of iterations (&gt;= 0).
final DoubleParam	miniBatchFraction()	Param for mini-batch fraction, must be in range (0, 1]
int	numClasses()	Number of classes (values which the label can take).
int	numFeatures()	Returns the number of features the model was trained on.
Vector	predictRaw(Vector features)	Raw prediction for each possible label.
static MLReader<FMClassificationModel>	read()
final DoubleParam	regParam()	Param for regularization parameter (&gt;= 0).
final LongParam	seed()	Param for random seed.
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).
FMClassificationTrainingSummary	summary()	Gets summary of model on training set.
final 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.
final Param<String>	weightCol()	Param for weight column name.
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

predict, 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 java.lang.Object

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

Methods inherited from interface org.apache.spark.ml.regression.FactorizationMachinesParams

getFactorSize, getFitLinear, getInitStd, getMiniBatchFraction

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

featuresCol, getFeaturesCol

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

getFitIntercept

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.HasRegParam

getRegParam

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.HasTrainingSummary

hasSummary, setSummary

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

getWeightCol

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.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

Method Details

read

public static MLReader<FMClassificationModel> read()

load

public static FMClassificationModel load(String path)

factorSize

public final IntParam factorSize()

Description copied from interface: FactorizationMachinesParams

Param for dimensionality of the factors (&gt;= 0)

Specified by:

factorSize in interface FactorizationMachinesParams

Returns:

(undocumented)

fitLinear

public final BooleanParam fitLinear()

Description copied from interface: FactorizationMachinesParams

Param for whether to fit linear term (aka 1-way term)

Specified by:

fitLinear in interface FactorizationMachinesParams

Returns:

(undocumented)

miniBatchFraction

public final DoubleParam miniBatchFraction()

Description copied from interface: FactorizationMachinesParams

Param for mini-batch fraction, must be in range (0, 1]

Specified by:

miniBatchFraction in interface FactorizationMachinesParams

Returns:

(undocumented)

initStd

public final DoubleParam initStd()

Description copied from interface: FactorizationMachinesParams

Param for standard deviation of initial coefficients

Specified by:

initStd in interface FactorizationMachinesParams

Returns:

(undocumented)

solver

public final Param<String> solver()

Description copied from interface: FactorizationMachinesParams

The solver algorithm for optimization. Supported options: "gd", "adamW". Default: "adamW"

Specified by:

solver in interface FactorizationMachinesParams

Specified by:

solver in interface HasSolver

Returns:

(undocumented)

weightCol

public final Param<String> weightCol()

Description copied from interface: HasWeightCol

Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

Specified by:

weightCol in interface HasWeightCol

Returns:

(undocumented)

regParam

public final DoubleParam regParam()

Description copied from interface: HasRegParam

Param for regularization parameter (&gt;= 0).

Specified by:

regParam in interface HasRegParam

Returns:

(undocumented)

fitIntercept

public final BooleanParam fitIntercept()

Description copied from interface: HasFitIntercept

Param for whether to fit an intercept term.

Specified by:

fitIntercept in interface HasFitIntercept

Returns:

(undocumented)

seed

public final LongParam seed()

Description copied from interface: HasSeed

Param for random seed.

Specified by:

seed in interface HasSeed

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)

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)

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)

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)

intercept

public double intercept()

linear

public Vector linear()

factors

public Matrix factors()

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,FMClassificationModel>

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,FMClassificationModel>

summary

public FMClassificationTrainingSummary summary()

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

Specified by:

summary in interface HasTrainingSummary<FMClassificationTrainingSummary>

Returns:

(undocumented)

evaluate

public FMClassificationSummary evaluate(Dataset<?> dataset)

Evaluates the model on a test dataset.

Parameters:

dataset - Test dataset to evaluate model on.

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 ClassificationModel.rawPredictionCol().

Specified by:

predictRaw in class ClassificationModel<Vector,FMClassificationModel>

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.

copy

public FMClassificationModel 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<FMClassificationModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

estimatedSize

public long estimatedSize()

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)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object