org.apache.spark.ml.regression

Class FMRegressionModel

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.regression.RegressionModel<Vector,FMRegressionModel>

org.apache.spark.ml.regression.FMRegressionModel

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, Params, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasMaxIter, HasPredictionCol, HasRegParam, HasSeed, HasSolver, HasStepSize, HasTol, PredictorParams, FactorizationMachinesParams, FMRegressorParams, Identifiable, MLWritable

public class FMRegressionModel
extends RegressionModel<Vector,FMRegressionModel>
implements FMRegressorParams, MLWritable

Model produced by FMRegressor.

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
FMRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Matrix	factors()
IntParam	factorSize() Param for dimensionality of the factors (>= 0)
BooleanParam	fitIntercept() Param for whether to fit an intercept term.
BooleanParam	fitLinear() Param for whether to fit linear term (aka 1-way term)
DoubleParam	initStd() Param for standard deviation of initial coefficients
double	intercept()
Vector	linear()
static FMRegressionModel	load(String path)
IntParam	maxIter() Param for maximum number of iterations (>= 0).
DoubleParam	miniBatchFraction() Param for mini-batch fraction, must be in range (0, 1]
int	numFeatures() Returns the number of features the model was trained on.
double	predict(Vector features) Predict label for the given features.
static MLReader<FMRegressionModel>	read()
DoubleParam	regParam() Param for regularization parameter (&gt;= 0).
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).
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.
MLWriter	write() Returns an MLWriter instance for this ML instance.

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

featuresCol, labelCol, predictionCol, setFeaturesCol, setPredictionCol, transform, transformSchema

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.regression.FactorizationMachinesParams

getFactorSize, getFitLinear, getInitStd, getMiniBatchFraction

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

extractInstances, extractInstances, validateAndTransformSchema

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.HasMaxIter

getMaxIter

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

getStepSize

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

getTol

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

getSolver

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

getSeed

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

getFitIntercept

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

getRegParam

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

save

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<FMRegressionModel> read()

load

public static FMRegressionModel 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 HasSolver

Specified by:

solver in interface FactorizationMachinesParams

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()

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,FMRegressionModel>

predict

public double predict(Vector features)

Description copied from class: PredictionModel

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

Specified by:

predict in class PredictionModel<Vector,FMRegressionModel>

Parameters:

features - (undocumented)

Returns:

(undocumented)

copy

public FMRegressionModel 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<FMRegressionModel>

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)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object