class FMRegressor extends Regressor[Vector, FMRegressor, FMRegressionModel] with FactorizationMachines with FMRegressorParams with DefaultParamsWritable with Logging
Factorization Machines learning algorithm for regression. It supports normal gradient descent and AdamW solver.
The implementation is based on: 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.
- Annotations
- @Since("3.0.0")
- Source
- FMRegressor.scala
- Grouped
- Alphabetic
- By Inheritance
- FMRegressor
- DefaultParamsWritable
- MLWritable
- FMRegressorParams
- FactorizationMachines
- FactorizationMachinesParams
- HasWeightCol
- HasRegParam
- HasFitIntercept
- HasSeed
- HasSolver
- HasTol
- HasStepSize
- HasMaxIter
- Regressor
- Predictor
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Identifiable
- AnyRef
- Any
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- Public
- Protected
Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
- final val factorSize: IntParam
Param for dimensionality of the factors (>= 0)
Param for dimensionality of the factors (>= 0)
- Definition Classes
- FactorizationMachinesParams
- Annotations
- @Since("3.0.0")
- final val featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
- final val fitIntercept: BooleanParam
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.
- Definition Classes
- HasFitIntercept
- final val fitLinear: BooleanParam
Param for whether to fit linear term (aka 1-way term)
Param for whether to fit linear term (aka 1-way term)
- Definition Classes
- FactorizationMachinesParams
- Annotations
- @Since("3.0.0")
- final val initStd: DoubleParam
Param for standard deviation of initial coefficients
Param for standard deviation of initial coefficients
- Definition Classes
- FactorizationMachinesParams
- Annotations
- @Since("3.0.0")
- final val labelCol: Param[String]
Param for label column name.
Param for label column name.
- Definition Classes
- HasLabelCol
- final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
- Definition Classes
- HasMaxIter
- final val miniBatchFraction: DoubleParam
Param for mini-batch fraction, must be in range (0, 1]
Param for mini-batch fraction, must be in range (0, 1]
- Definition Classes
- FactorizationMachinesParams
- Annotations
- @Since("3.0.0")
- final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
- final val regParam: DoubleParam
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).
- Definition Classes
- HasRegParam
- final val seed: LongParam
Param for random seed.
Param for random seed.
- Definition Classes
- HasSeed
- final val solver: Param[String]
The solver algorithm for optimization.
The solver algorithm for optimization. Supported options: "gd", "adamW". Default: "adamW"
- Definition Classes
- FactorizationMachinesParams → HasSolver
- Annotations
- @Since("3.0.0")
- val stepSize: DoubleParam
Param for Step size to be used for each iteration of optimization (> 0).
Param for Step size to be used for each iteration of optimization (> 0).
- Definition Classes
- HasStepSize
- final val tol: DoubleParam
Param for the convergence tolerance for iterative algorithms (>= 0).
Param for the convergence tolerance for iterative algorithms (>= 0).
- Definition Classes
- HasTol
- final val weightCol: Param[String]
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
- Definition Classes
- HasWeightCol
Members
- implicit class LogStringContext extends AnyRef
- Definition Classes
- Logging
- final def clear(param: Param[_]): FMRegressor.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
- def copy(extra: ParamMap): FMRegressor
Creates a copy of this instance with the same UID and some extra 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().- Definition Classes
- FMRegressor → Predictor → Estimator → PipelineStage → Params
- Annotations
- @Since("3.0.0")
- def explainParam(param: Param[_]): String
Explains a param.
Explains a param.
- param
input param, must belong to this instance.
- returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
- Definition Classes
- Params
- def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam().- Definition Classes
- Params
- final def extractParamMap(): ParamMap
extractParamMapwith no extra values.extractParamMapwith no extra values.- Definition Classes
- Params
- final def extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
- Definition Classes
- Params
- def fit(dataset: Dataset[_]): FMRegressionModel
Fits a model to the input data.
- def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[FMRegressionModel]
Fits multiple models to the input data with multiple sets of parameters.
Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.
- dataset
input dataset
- paramMaps
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted models, matching the input parameter maps
- Definition Classes
- Estimator
- Annotations
- @Since("2.0.0")
- def fit(dataset: Dataset[_], paramMap: ParamMap): FMRegressionModel
Fits a single model to the input data with provided parameter map.
Fits a single model to the input data with provided parameter map.
- dataset
input dataset
- paramMap
Parameter map. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since("2.0.0")
- def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): FMRegressionModel
Fits a single model to the input data with optional parameters.
Fits a single model to the input data with optional parameters.
- dataset
input dataset
- firstParamPair
the first param pair, overrides embedded params
- otherParamPairs
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since("2.0.0") @varargs()
- final def get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
- Definition Classes
- Params
- final def getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
- final def getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
- Definition Classes
- Params
- def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
- final def hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
- Definition Classes
- Params
- def hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
- Definition Classes
- Params
- final def isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
- Definition Classes
- Params
- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
- lazy val params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
- Definition Classes
- Params
- Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
- def save(path: String): Unit
Saves this ML instance to the input path, a shortcut of
write.save(path).Saves this ML instance to the input path, a shortcut of
write.save(path).- Definition Classes
- MLWritable
- Annotations
- @Since("1.6.0") @throws("If the input path already exists but overwrite is not enabled.")
- final def set[T](param: Param[T], value: T): FMRegressor.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
- def toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
- def transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during
transformSchemaand raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate().Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- Predictor → PipelineStage
- val uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
- Definition Classes
- FMRegressor → Identifiable
- Annotations
- @Since("3.0.0")
- def write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Parameter setters
- def setFactorSize(value: Int): FMRegressor.this.type
Set the dimensionality of the factors.
Set the dimensionality of the factors. Default is 8.
- Annotations
- @Since("3.0.0")
- def setFeaturesCol(value: String): FMRegressor
- Definition Classes
- Predictor
- def setFitIntercept(value: Boolean): FMRegressor.this.type
Set whether to fit intercept term.
Set whether to fit intercept term. Default is true.
- Annotations
- @Since("3.0.0")
- def setFitLinear(value: Boolean): FMRegressor.this.type
Set whether to fit linear term.
Set whether to fit linear term. Default is true.
- Annotations
- @Since("3.0.0")
- def setInitStd(value: Double): FMRegressor.this.type
Set the standard deviation of initial coefficients.
Set the standard deviation of initial coefficients. Default is 0.01.
- Annotations
- @Since("3.0.0")
- def setLabelCol(value: String): FMRegressor
- Definition Classes
- Predictor
- def setMaxIter(value: Int): FMRegressor.this.type
Set the maximum number of iterations.
Set the maximum number of iterations. Default is 100.
- Annotations
- @Since("3.0.0")
- def setMiniBatchFraction(value: Double): FMRegressor.this.type
Set the mini-batch fraction parameter.
Set the mini-batch fraction parameter. Default is 1.0.
- Annotations
- @Since("3.0.0")
- def setPredictionCol(value: String): FMRegressor
- Definition Classes
- Predictor
- def setRegParam(value: Double): FMRegressor.this.type
Set the L2 regularization parameter.
Set the L2 regularization parameter. Default is 0.0.
- Annotations
- @Since("3.0.0")
- def setSeed(value: Long): FMRegressor.this.type
Set the random seed for weight initialization.
Set the random seed for weight initialization.
- Annotations
- @Since("3.0.0")
- def setSolver(value: String): FMRegressor.this.type
Set the solver algorithm used for optimization.
Set the solver algorithm used for optimization. Supported options: "gd", "adamW". Default: "adamW"
- Annotations
- @Since("3.0.0")
- def setStepSize(value: Double): FMRegressor.this.type
Set the initial step size for the first step (like learning rate).
Set the initial step size for the first step (like learning rate). Default is 1.0.
- Annotations
- @Since("3.0.0")
- def setTol(value: Double): FMRegressor.this.type
Set the convergence tolerance of iterations.
Set the convergence tolerance of iterations. Default is 1E-6.
- Annotations
- @Since("3.0.0")
Parameter getters
- final def getFactorSize: Int
- Definition Classes
- FactorizationMachinesParams
- Annotations
- @Since("3.0.0")
- final def getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
- final def getFitIntercept: Boolean
- Definition Classes
- HasFitIntercept
- final def getFitLinear: Boolean
- Definition Classes
- FactorizationMachinesParams
- Annotations
- @Since("3.0.0")
- final def getInitStd: Double
- Definition Classes
- FactorizationMachinesParams
- Annotations
- @Since("3.0.0")
- final def getLabelCol: String
- Definition Classes
- HasLabelCol
- final def getMaxIter: Int
- Definition Classes
- HasMaxIter
- final def getMiniBatchFraction: Double
- Definition Classes
- FactorizationMachinesParams
- Annotations
- @Since("3.0.0")
- final def getPredictionCol: String
- Definition Classes
- HasPredictionCol
- final def getRegParam: Double
- Definition Classes
- HasRegParam
- final def getSeed: Long
- Definition Classes
- HasSeed
- final def getSolver: String
- Definition Classes
- HasSolver
- final def getStepSize: Double
- Definition Classes
- HasStepSize
- final def getTol: Double
- Definition Classes
- HasTol
- final def getWeightCol: String
- Definition Classes
- HasWeightCol