public class FMRegressor extends Regressor<Vector,FMRegressor,FMRegressionModel> implements FactorizationMachines, FMRegressorParams, DefaultParamsWritable, org.apache.spark.internal.Logging
The implementation is based upon: S. Rendle. "Factorization machines" 2010.
FM is able to estimate interactions even in problems with huge sparsity (like advertising and recommendation system). FM formula is:
$$ \begin{align} y = w_0 + \sum\limits^n_{i-1} w_i x_i + \sum\limits^n_{i=1} \sum\limits^n_{j=i+1} \langle v_i, v_j \rangle x_i x_j \end{align} $$First two terms denote global bias and linear term (as same as linear regression), and last term denotes pairwise interactions term. v_i describes the i-th variable with k factors.
FM regression model uses MSE loss which can be solved by gradient descent method, and regularization terms like L2 are usually added to the loss function to prevent overfitting.
| Constructor and Description |
|---|
FMRegressor() |
FMRegressor(String uid) |
| Modifier and Type | Method and Description |
|---|---|
FMRegressor |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
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
|
static FMRegressor |
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]
|
static MLReader<T> |
read() |
DoubleParam |
regParam()
Param for regularization parameter (>= 0).
|
LongParam |
seed()
Param for random seed.
|
FMRegressor |
setFactorSize(int value)
Set the dimensionality of the factors.
|
FMRegressor |
setFitIntercept(boolean value)
Set whether to fit intercept term.
|
FMRegressor |
setFitLinear(boolean value)
Set whether to fit linear term.
|
FMRegressor |
setInitStd(double value)
Set the standard deviation of initial coefficients.
|
FMRegressor |
setMaxIter(int value)
Set the maximum number of iterations.
|
FMRegressor |
setMiniBatchFraction(double value)
Set the mini-batch fraction parameter.
|
FMRegressor |
setRegParam(double value)
Set the L2 regularization parameter.
|
FMRegressor |
setSeed(long value)
Set the random seed for weight initialization.
|
FMRegressor |
setSolver(String value)
Set the solver algorithm used for optimization.
|
FMRegressor |
setStepSize(double value)
Set the initial step size for the first step (like learning rate).
|
FMRegressor |
setTol(double value)
Set the convergence tolerance of iterations.
|
Param<String> |
solver()
The solver algorithm for optimization.
|
DoubleParam |
stepSize()
Param for Step size to be used for each iteration of optimization (> 0).
|
DoubleParam |
tol()
Param for the convergence tolerance for iterative algorithms (>= 0).
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
Param<String> |
weightCol()
Param for weight column name.
|
featuresCol, fit, labelCol, predictionCol, setFeaturesCol, setLabelCol, setPredictionCol, transformSchemaparamsequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitinitCoefficients, trainImplgetFactorSize, getFitLinear, getInitStd, getMiniBatchFractionvalidateAndTransformSchemagetLabelCol, labelColfeaturesCol, getFeaturesColgetPredictionCol, predictionColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringgetMaxItergetStepSizegetFitInterceptgetRegParamgetWeightColwritesave$init$, 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_, uninitializepublic static FMRegressor load(String path)
public static MLReader<T> read()
public final IntParam factorSize()
FactorizationMachinesParamsfactorSize in interface FactorizationMachinesParamspublic final BooleanParam fitLinear()
FactorizationMachinesParamsfitLinear in interface FactorizationMachinesParamspublic final DoubleParam miniBatchFraction()
FactorizationMachinesParamsminiBatchFraction in interface FactorizationMachinesParamspublic final DoubleParam initStd()
FactorizationMachinesParamsinitStd in interface FactorizationMachinesParamspublic final Param<String> solver()
FactorizationMachinesParamssolver in interface HasSolversolver in interface FactorizationMachinesParamspublic final Param<String> weightCol()
HasWeightColweightCol in interface HasWeightColpublic final DoubleParam regParam()
HasRegParamregParam in interface HasRegParampublic final BooleanParam fitIntercept()
HasFitInterceptfitIntercept in interface HasFitInterceptpublic final LongParam seed()
HasSeedpublic final DoubleParam tol()
HasTolpublic DoubleParam stepSize()
HasStepSizestepSize in interface HasStepSizepublic final IntParam maxIter()
HasMaxItermaxIter in interface HasMaxIterpublic String uid()
Identifiableuid in interface Identifiablepublic FMRegressor setFactorSize(int value)
value - (undocumented)public FMRegressor setFitIntercept(boolean value)
value - (undocumented)public FMRegressor setFitLinear(boolean value)
value - (undocumented)public FMRegressor setRegParam(double value)
value - (undocumented)public FMRegressor setMiniBatchFraction(double value)
value - (undocumented)public FMRegressor setInitStd(double value)
value - (undocumented)public FMRegressor setMaxIter(int value)
value - (undocumented)public FMRegressor setStepSize(double value)
value - (undocumented)public FMRegressor setTol(double value)
value - (undocumented)public FMRegressor setSolver(String value)
value - (undocumented)public FMRegressor setSeed(long value)
value - (undocumented)public FMRegressor copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Predictor<Vector,FMRegressor,FMRegressionModel>extra - (undocumented)