FMRegressionModel

Companion object FMRegressionModel

class FMRegressionModel extends RegressionModel[Vector, FMRegressionModel] with FMRegressorParams with MLWritable

Model produced by FMRegressor.

Annotations: @Since( "3.0.0" )
Source: FMRegressor.scala

Linear Supertypes

MLWritable, FMRegressorParams, FactorizationMachinesParams, HasWeightCol, HasRegParam, HasFitIntercept, HasSeed, HasSolver, HasTol, HasStepSize, HasMaxIter, RegressionModel[Vector, FMRegressionModel], PredictionModel[Vector, FMRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[FMRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any

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FMRegressionModel
MLWritable
FMRegressorParams
FactorizationMachinesParams
HasWeightCol
HasRegParam
HasFitIntercept
HasSeed
HasSolver
HasTol
HasStepSize
HasMaxIter
RegressionModel
PredictionModel
PredictorParams
HasPredictionCol
HasFeaturesCol
HasLabelCol
Model
Transformer
PipelineStage
Logging
Params
Serializable
Serializable
Identifiable
AnyRef
Any

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Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def clear(param: Param[_]): FMRegressionModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
def copy(extra: ParamMap): FMRegressionModel
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
FMRegressionModel → Model → Transformer → PipelineStage → Params
Annotations
@Since( "3.0.0" )
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
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
def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

Attributes
protected
Definition Classes
PredictorParams
def extractInstances(dataset: Dataset[_]): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
PredictorParams
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with 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
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" )
val factors: Matrix

Annotations
@Since( "3.0.0" )
final val featuresCol: Param[String]
Param for features column name.
Param for features column name.

Definition Classes
HasFeaturesCol
def featuresDataType: DataType
Returns the SQL DataType corresponding to the FeaturesType type parameter.
Returns the SQL DataType corresponding to the FeaturesType type parameter.
This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.
The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

Attributes
protected
Definition Classes
PredictionModel
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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 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 getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
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 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 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 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
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
def hasParent: Boolean
Indicates whether this Model has a corresponding parent.
Indicates whether this Model has a corresponding parent.

Definition Classes
Model
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
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" )
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
val intercept: Double

Annotations
@Since( "3.0.0" )
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 isInstanceOf[T0]: Boolean

Definition Classes
Any
final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
final val labelCol: Param[String]
Param for label column name.
Param for label column name.

Definition Classes
HasLabelCol
val linear: Vector

Annotations
@Since( "3.0.0" )
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
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 def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
val numFeatures: Int
Returns the number of features the model was trained on.
Returns the number of features the model was trained on. If unknown, returns -1

Definition Classes
FMRegressionModel → PredictionModel
Annotations
@Since( "3.0.0" )
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.
var parent: Estimator[FMRegressionModel]
The parent estimator that produced this model.
The parent estimator that produced this model.

Definition Classes
Model
Note
For ensembles' component Models, this value can be null.
def predict(features: Vector): Double
Predict label for the given features.
Predict label for the given features. This method is used to implement transform() and output predictionCol.

Definition Classes
FMRegressionModel → PredictionModel
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
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( ... )
final val seed: LongParam
Param for random seed.
Param for random seed.

Definition Classes
HasSeed
final def set(paramPair: ParamPair[_]): FMRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): FMRegressionModel.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): FMRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): FMRegressionModel.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): FMRegressionModel.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value

Attributes
protected
Definition Classes
Params
def setFeaturesCol(value: String): FMRegressionModel

Definition Classes
PredictionModel
def setParent(parent: Estimator[FMRegressionModel]): FMRegressionModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).

Definition Classes
Model
def setPredictionCol(value: String): FMRegressionModel

Definition Classes
PredictionModel
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 def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
FMRegressionModel → Identifiable → AnyRef → Any
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
def transform(dataset: Dataset[_]): DataFrame
Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
dataset
input dataset
returns
transformed dataset with predictionCol of type Double

Definition Classes
PredictionModel → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
dataset
input dataset
paramMap
additional parameters, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
dataset
input dataset
firstParamPair
the first param pair, overwrite embedded params
otherParamPairs
other param pairs, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
def transformImpl(dataset: Dataset[_]): DataFrame

Attributes
protected
Definition Classes
PredictionModel
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 transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Definition Classes
PredictionModel → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
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
FMRegressionModel → Identifiable
Annotations
@Since( "3.0.0" )
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
Validates and transforms the input schema with the provided param map.
Validates and transforms the input schema with the provided param map.
schema
input schema
fitting
whether this is in fitting
featuresDataType
SQL DataType for FeaturesType. E.g., VectorUDT for vector features.
returns
output schema

Attributes
protected
Definition Classes
PredictorParams
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
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
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.

Definition Classes
FMRegressionModel → MLWritable
Annotations
@Since( "3.0.0" )

Inherited from MLWritable

Value Members

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( ... )

Inherited from FactorizationMachinesParams

Value Members

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 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 def getFactorSize: Int

Definition Classes
FactorizationMachinesParams
Annotations
@Since( "3.0.0" )
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 getMiniBatchFraction: Double

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 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 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" )

Inherited from HasWeightCol

Value Members

final def getWeightCol: String

Definition Classes
HasWeightCol
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

Inherited from HasRegParam

Value Members

final def getRegParam: Double

Definition Classes
HasRegParam
final val regParam: DoubleParam
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).

Definition Classes
HasRegParam

Inherited from HasFitIntercept

Value Members

final val fitIntercept: BooleanParam
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.

Definition Classes
HasFitIntercept
final def getFitIntercept: Boolean

Definition Classes
HasFitIntercept

Inherited from HasSeed

Value Members

final def getSeed: Long

Definition Classes
HasSeed
final val seed: LongParam
Param for random seed.
Param for random seed.

Definition Classes
HasSeed

Inherited from HasSolver

Value Members

final def getSolver: String

Definition Classes
HasSolver

Inherited from HasTol

Value Members

final def getTol: Double

Definition Classes
HasTol
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

Inherited from HasStepSize

Value Members

final def getStepSize: Double

Definition Classes
HasStepSize
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

Inherited from HasMaxIter

Value Members

final def getMaxIter: Int

Definition Classes
HasMaxIter
final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).

Definition Classes
HasMaxIter

Inherited from PredictionModel[Vector, FMRegressionModel]

Value Members

def featuresDataType: DataType
Returns the SQL DataType corresponding to the FeaturesType type parameter.
Returns the SQL DataType corresponding to the FeaturesType type parameter.
This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.
The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

Attributes
protected
Definition Classes
PredictionModel
def setFeaturesCol(value: String): FMRegressionModel

Definition Classes
PredictionModel
def setPredictionCol(value: String): FMRegressionModel

Definition Classes
PredictionModel
def transform(dataset: Dataset[_]): DataFrame
Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
dataset
input dataset
returns
transformed dataset with predictionCol of type Double

Definition Classes
PredictionModel → Transformer
def transformImpl(dataset: Dataset[_]): DataFrame

Attributes
protected
Definition Classes
PredictionModel
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 transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Definition Classes
PredictionModel → PipelineStage

Inherited from PredictorParams

Value Members

def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

Attributes
protected
Definition Classes
PredictorParams
def extractInstances(dataset: Dataset[_]): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
PredictorParams
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
Validates and transforms the input schema with the provided param map.
Validates and transforms the input schema with the provided param map.
schema
input schema
fitting
whether this is in fitting
featuresDataType
SQL DataType for FeaturesType. E.g., VectorUDT for vector features.
returns
output schema

Attributes
protected
Definition Classes
PredictorParams

Inherited from HasPredictionCol

Value Members

final def getPredictionCol: String

Definition Classes
HasPredictionCol
final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.

Definition Classes
HasPredictionCol

Inherited from HasFeaturesCol

Value Members

final val featuresCol: Param[String]
Param for features column name.
Param for features column name.

Definition Classes
HasFeaturesCol
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol

Inherited from HasLabelCol

Value Members

final def getLabelCol: String

Definition Classes
HasLabelCol
final val labelCol: Param[String]
Param for label column name.
Param for label column name.

Definition Classes
HasLabelCol

Inherited from Model[FMRegressionModel]

Value Members

def hasParent: Boolean
Indicates whether this Model has a corresponding parent.
Indicates whether this Model has a corresponding parent.

Definition Classes
Model
var parent: Estimator[FMRegressionModel]
The parent estimator that produced this model.
The parent estimator that produced this model.

Definition Classes
Model
Note
For ensembles' component Models, this value can be null.
def setParent(parent: Estimator[FMRegressionModel]): FMRegressionModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).

Definition Classes
Model

Inherited from Transformer

Value Members

def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
dataset
input dataset
paramMap
additional parameters, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
dataset
input dataset
firstParamPair
the first param pair, overwrite embedded params
otherParamPairs
other param pairs, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()

Inherited from PipelineStage

Value Members

def transformSchema(schema: StructType, logging: Boolean): StructType
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()

Inherited from Logging

Value Members

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging

Inherited from Params

Value Members

final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def clear(param: Param[_]): FMRegressionModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes
protected
Definition Classes
Params
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
extractParamMap with no extra values.
extractParamMap with 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
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.
final def set(paramPair: ParamPair[_]): FMRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): FMRegressionModel.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): FMRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): FMRegressionModel.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): FMRegressionModel.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value

Attributes
protected
Definition Classes
Params

Inherited from AnyRef

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()

Inherited from Any

Value Members

final def asInstanceOf[T0]: T0

Definition Classes
Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any

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

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T
An alias for getOrDefault().
An alias for getOrDefault().

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def clear(param: Param[_]): FMRegressionModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
def copy(extra: ParamMap): FMRegressionModel
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
FMRegressionModel → Model → Transformer → PipelineStage → Params
Annotations
@Since( "3.0.0" )
def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.
to
the target instance, which should work with the same set of default Params as this source instance
extra
extra params to be copied to the target's paramMap
returns
the target instance with param values copied

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
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
def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

Attributes
protected
Definition Classes
PredictorParams
def extractInstances(dataset: Dataset[_]): RDD[Instance]
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

Attributes
protected
Definition Classes
PredictorParams
final def extractParamMap(): ParamMap
extractParamMap with no extra values.
extractParamMap with 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
val factors: Matrix

Annotations
@Since( "3.0.0" )
def featuresDataType: DataType
Returns the SQL DataType corresponding to the FeaturesType type parameter.
Returns the SQL DataType corresponding to the FeaturesType type parameter.
This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.
The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

Attributes
protected
Definition Classes
PredictionModel
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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 getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
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
def hasParent: Boolean
Indicates whether this Model has a corresponding parent.
Indicates whether this Model has a corresponding parent.

Definition Classes
Model
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
val intercept: Double

Annotations
@Since( "3.0.0" )
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 isInstanceOf[T0]: Boolean

Definition Classes
Any
final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
val linear: Vector

Annotations
@Since( "3.0.0" )
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
val numFeatures: Int
Returns the number of features the model was trained on.
Returns the number of features the model was trained on. If unknown, returns -1

Definition Classes
FMRegressionModel → PredictionModel
Annotations
@Since( "3.0.0" )
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.
var parent: Estimator[FMRegressionModel]
The parent estimator that produced this model.
The parent estimator that produced this model.

Definition Classes
Model
Note
For ensembles' component Models, this value can be null.
def predict(features: Vector): Double
Predict label for the given features.
Predict label for the given features. This method is used to implement transform() and output predictionCol.

Definition Classes
FMRegressionModel → PredictionModel
Annotations
@Since( "3.0.0" )
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( ... )
final def set(paramPair: ParamPair[_]): FMRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): FMRegressionModel.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): FMRegressionModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): FMRegressionModel.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.
paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): FMRegressionModel.this.type
Sets a default value for a param.
Sets a default value for a param.
param
param to set the default value. Make sure that this param is initialized before this method gets called.
value
the default value

Attributes
protected
Definition Classes
Params
def setParent(parent: Estimator[FMRegressionModel]): FMRegressionModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).

Definition Classes
Model
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
FMRegressionModel → Identifiable → AnyRef → Any
def transform(dataset: Dataset[_]): DataFrame
Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
dataset
input dataset
returns
transformed dataset with predictionCol of type Double

Definition Classes
PredictionModel → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
dataset
input dataset
paramMap
additional parameters, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
dataset
input dataset
firstParamPair
the first param pair, overwrite embedded params
otherParamPairs
other param pairs, overwrite embedded params
returns
transformed dataset

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
def transformImpl(dataset: Dataset[_]): DataFrame

Attributes
protected
Definition Classes
PredictionModel
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 transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Definition Classes
PredictionModel → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
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
FMRegressionModel → Identifiable
Annotations
@Since( "3.0.0" )
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
Validates and transforms the input schema with the provided param map.
Validates and transforms the input schema with the provided param map.
schema
input schema
fitting
whether this is in fitting
featuresDataType
SQL DataType for FeaturesType. E.g., VectorUDT for vector features.
returns
output schema

Attributes
protected
Definition Classes
PredictorParams
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.

Definition Classes
FMRegressionModel → MLWritable
Annotations
@Since( "3.0.0" )

Parameter setters

def setFeaturesCol(value: String): FMRegressionModel

Definition Classes
PredictionModel
def setPredictionCol(value: String): FMRegressionModel

Definition Classes
PredictionModel

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

Packages

FMRegressionModel 

Companion object FMRegressionModel

class FMRegressionModel extends RegressionModel[Vector, FMRegressionModel] with FMRegressorParams with MLWritable

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Inherited from MLWritable

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Inherited from FactorizationMachinesParams

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Inherited from HasWeightCol

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Inherited from HasRegParam

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Inherited from HasFitIntercept

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Inherited from HasSeed

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Inherited from HasSolver

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Inherited from HasTol

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Inherited from HasStepSize

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Inherited from HasMaxIter

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Inherited from PredictionModel[Vector, FMRegressionModel]

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Inherited from PredictorParams

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Inherited from HasPredictionCol

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Inherited from HasFeaturesCol

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Inherited from HasLabelCol

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Inherited from Model[FMRegressionModel]

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Inherited from Transformer

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Inherited from PipelineStage

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Inherited from Logging

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Inherited from Params

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Inherited from AnyRef

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Inherited from Any

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Parameters

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Parameter setters

Parameter getters

FMRegressionModel