GeneralizedLinearRegression

Companion object GeneralizedLinearRegression

class GeneralizedLinearRegression extends Regressor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with DefaultParamsWritable with Logging

Fit a Generalized Linear Model (see Generalized linear model (Wikipedia)) specified by giving a symbolic description of the linear predictor (link function) and a description of the error distribution (family). It supports "gaussian", "binomial", "poisson", "gamma" and "tweedie" as family. Valid link functions for each family is listed below. The first link function of each family is the default one.

"gaussian" : "identity", "log", "inverse"
"binomial" : "logit", "probit", "cloglog"
"poisson" : "log", "identity", "sqrt"
"gamma" : "inverse", "identity", "log"
"tweedie" : power link function specified through "linkPower". The default link power in the tweedie family is 1 - variancePower.

Annotations: @Since("2.0.0")
Source: GeneralizedLinearRegression.scala

Linear Supertypes

DefaultParamsWritable, MLWritable, GeneralizedLinearRegressionBase, HasAggregationDepth, HasSolver, HasWeightCol, HasRegParam, HasTol, HasMaxIter, HasFitIntercept, Regressor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel], Predictor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[GeneralizedLinearRegressionModel], PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any

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Inherited

GeneralizedLinearRegression
DefaultParamsWritable
MLWritable
GeneralizedLinearRegressionBase
HasAggregationDepth
HasSolver
HasWeightCol
HasRegParam
HasTol
HasMaxIter
HasFitIntercept
Regressor
Predictor
PredictorParams
HasPredictionCol
HasFeaturesCol
HasLabelCol
Estimator
PipelineStage
Logging
Params
Serializable
Identifiable
AnyRef
Any

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Visibility

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Protected

Instance Constructors

new GeneralizedLinearRegression()
Annotations
@Since("2.0.0")
new GeneralizedLinearRegression(uid: String)
Annotations
@Since("2.0.0")

Type Members

implicit class LogStringContext extends AnyRef
Definition Classes
Logging

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 val aggregationDepth: IntParam
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).
Definition Classes
HasAggregationDepth
final def asInstanceOf[T0]: T0
Definition Classes
Any
final def clear(param: Param[_]): GeneralizedLinearRegression.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(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
def copy(extra: ParamMap): GeneralizedLinearRegression
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
GeneralizedLinearRegression → Predictor → Estimator → PipelineStage → Params
Annotations
@Since("2.0.0")
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: AnyRef): 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
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 family: Param[String]
Param for the name of family which is a description of the error distribution to be used in the model.
Param for the name of family which is a description of the error distribution to be used in the model. Supported options: "gaussian", "binomial", "poisson", "gamma" and "tweedie". Default is "gaussian".
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final val featuresCol: Param[String]
Param for features column name.
Param for features column name.
Definition Classes
HasFeaturesCol
def fit(dataset: Dataset[_]): GeneralizedLinearRegressionModel
Fits a model to the input data.
Fits a model to the input data.
Definition Classes
Predictor → Estimator
def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GeneralizedLinearRegressionModel]
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): GeneralizedLinearRegressionModel
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[_]*): GeneralizedLinearRegressionModel
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 val fitIntercept: BooleanParam
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.
Definition Classes
HasFitIntercept
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 getAggregationDepth: Int
Definition Classes
HasAggregationDepth
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @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
def getFamily: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final def getFeaturesCol: String
Definition Classes
HasFeaturesCol
final def getFitIntercept: Boolean
Definition Classes
HasFitIntercept
final def getLabelCol: String
Definition Classes
HasLabelCol
def getLink: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
def getLinkPower: Double
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
def getLinkPredictionCol: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final def getMaxIter: Int
Definition Classes
HasMaxIter
def getOffsetCol: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.3.0")
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 getSolver: String
Definition Classes
HasSolver
final def getTol: Double
Definition Classes
HasTol
def getVariancePower: Double
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
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 hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit
Attributes
protected
Definition Classes
Logging
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
final val link: Param[String]
Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function.
Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function. Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and "sqrt". This is used only when family is not "tweedie". The link function for the "tweedie" family must be specified through linkPower.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final val linkPower: DoubleParam
Param for the index in the power link function.
Param for the index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R "statmod" package.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
final val linkPredictionCol: Param[String]
Param for link prediction (linear predictor) column name.
Param for link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
def log: Logger
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry): 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 def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final val offsetCol: Param[String]
Param for offset column name.
Param for offset column name. If this is not set or empty, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.3.0")
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.
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("If the input path already exists but overwrite is not enabled.")
final def set(paramPair: ParamPair[_]): GeneralizedLinearRegression.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): GeneralizedLinearRegression.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): GeneralizedLinearRegression.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Definition Classes
Params
def setAggregationDepth(value: Int): GeneralizedLinearRegression.this.type
Annotations
@Since("3.0.0")
final def setDefault(paramPairs: ParamPair[_]*): GeneralizedLinearRegression.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): GeneralizedLinearRegression.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[ml]
Definition Classes
Params
def setFamily(value: String): GeneralizedLinearRegression.this.type
Sets the value of param family.
Sets the value of param family. Default is "gaussian".
Annotations
@Since("2.0.0")
def setFeaturesCol(value: String): GeneralizedLinearRegression
Definition Classes
Predictor
def setFitIntercept(value: Boolean): GeneralizedLinearRegression.this.type
Sets if we should fit the intercept.
Sets if we should fit the intercept. Default is true.
Annotations
@Since("2.0.0")
def setLabelCol(value: String): GeneralizedLinearRegression
Definition Classes
Predictor
def setLink(value: String): GeneralizedLinearRegression.this.type
Sets the value of param link.
Sets the value of param link. Used only when family is not "tweedie".
Annotations
@Since("2.0.0")
def setLinkPower(value: Double): GeneralizedLinearRegression.this.type
Sets the value of param linkPower.
Sets the value of param linkPower. Used only when family is "tweedie".
Annotations
@Since("2.2.0")
def setLinkPredictionCol(value: String): GeneralizedLinearRegression.this.type
Sets the link prediction (linear predictor) column name.
Sets the link prediction (linear predictor) column name.
Annotations
@Since("2.0.0")
def setMaxIter(value: Int): GeneralizedLinearRegression.this.type
Sets the maximum number of iterations (applicable for solver "irls").
Sets the maximum number of iterations (applicable for solver "irls"). Default is 25.
Annotations
@Since("2.0.0")
def setOffsetCol(value: String): GeneralizedLinearRegression.this.type
Sets the value of param offsetCol.
Sets the value of param offsetCol. If this is not set or empty, we treat all instance offsets as 0.0. Default is not set, so all instances have offset 0.0.
Annotations
@Since("2.3.0")
def setPredictionCol(value: String): GeneralizedLinearRegression
Definition Classes
Predictor
def setRegParam(value: Double): GeneralizedLinearRegression.this.type
Sets the regularization parameter for L2 regularization.
Sets the regularization parameter for L2 regularization. The regularization term is
$$ 0.5 * regParam * L2norm(coefficients)^2 $$
Default is 0.0.
Annotations
@Since("2.0.0")
def setSolver(value: String): GeneralizedLinearRegression.this.type
Sets the solver algorithm used for optimization.
Sets the solver algorithm used for optimization. Currently only supports "irls" which is also the default solver.
Annotations
@Since("2.0.0")
def setTol(value: Double): GeneralizedLinearRegression.this.type
Sets the convergence tolerance of iterations.
Sets the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.
Annotations
@Since("2.0.0")
def setVariancePower(value: Double): GeneralizedLinearRegression.this.type
Sets the value of param variancePower.
Sets the value of param variancePower. Used only when family is "tweedie". Default is 0.0, which corresponds to the "gaussian" family.
Annotations
@Since("2.2.0")
def setWeightCol(value: String): GeneralizedLinearRegression.this.type
Sets the value of param weightCol.
Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one. In the Binomial family, weights correspond to number of trials and should be integer. Non-integer weights are rounded to integer in AIC calculation.
Annotations
@Since("2.0.0")
final val solver: Param[String]
The solver algorithm for optimization.
The solver algorithm for optimization. Supported options: "irls" (iteratively reweighted least squares). Default: "irls"
Definition Classes
GeneralizedLinearRegressionBase → HasSolver
Annotations
@Since("2.0.0")
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
def toString(): String
Definition Classes
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 train(dataset: Dataset[_]): GeneralizedLinearRegressionModel
Train a model using the given dataset and parameters.
Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.
dataset
Training dataset
returns
Fitted model
Attributes
protected
Definition Classes
GeneralizedLinearRegression → Predictor
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
Predictor → 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
GeneralizedLinearRegression → Identifiable
Annotations
@Since("2.0.0")
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
Definition Classes
GeneralizedLinearRegressionBase → PredictorParams
Annotations
@Since("2.0.0")
final val variancePower: DoubleParam
Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution.
Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
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 withLogContext(context: HashMap[String, String])(body: => Unit): Unit
Attributes
protected
Definition Classes
Logging
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
Definition Classes
DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Value Members

def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
Definition Classes
DefaultParamsWritable → MLWritable

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("If the input path already exists but overwrite is not enabled.")

Inherited from GeneralizedLinearRegressionBase

Value Members

final val family: Param[String]
Param for the name of family which is a description of the error distribution to be used in the model.
Param for the name of family which is a description of the error distribution to be used in the model. Supported options: "gaussian", "binomial", "poisson", "gamma" and "tweedie". Default is "gaussian".
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
def getFamily: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
def getLink: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
def getLinkPower: Double
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
def getLinkPredictionCol: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
def getOffsetCol: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.3.0")
def getVariancePower: Double
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
final val link: Param[String]
Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function.
Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function. Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and "sqrt". This is used only when family is not "tweedie". The link function for the "tweedie" family must be specified through linkPower.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final val linkPower: DoubleParam
Param for the index in the power link function.
Param for the index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R "statmod" package.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
final val linkPredictionCol: Param[String]
Param for link prediction (linear predictor) column name.
Param for link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final val offsetCol: Param[String]
Param for offset column name.
Param for offset column name. If this is not set or empty, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.3.0")
final val solver: Param[String]
The solver algorithm for optimization.
The solver algorithm for optimization. Supported options: "irls" (iteratively reweighted least squares). Default: "irls"
Definition Classes
GeneralizedLinearRegressionBase → HasSolver
Annotations
@Since("2.0.0")
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
Definition Classes
GeneralizedLinearRegressionBase → PredictorParams
Annotations
@Since("2.0.0")
final val variancePower: DoubleParam
Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution.
Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")

Inherited from HasAggregationDepth

Value Members

final val aggregationDepth: IntParam
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).
Definition Classes
HasAggregationDepth
final def getAggregationDepth: Int
Definition Classes
HasAggregationDepth

Inherited from HasSolver

Value Members

final def getSolver: String
Definition Classes
HasSolver

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 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 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 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 Predictor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel]

Value Members

def fit(dataset: Dataset[_]): GeneralizedLinearRegressionModel
Fits a model to the input data.
Fits a model to the input data.
Definition Classes
Predictor → Estimator
def setFeaturesCol(value: String): GeneralizedLinearRegression
Definition Classes
Predictor
def setLabelCol(value: String): GeneralizedLinearRegression
Definition Classes
Predictor
def setPredictionCol(value: String): GeneralizedLinearRegression
Definition Classes
Predictor
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
Predictor → PipelineStage

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 Estimator[GeneralizedLinearRegressionModel]

Value Members

def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GeneralizedLinearRegressionModel]
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): GeneralizedLinearRegressionModel
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[_]*): GeneralizedLinearRegressionModel
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()

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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(msg: => String): Unit
Attributes
protected
Definition Classes
Logging
def withLogContext(context: HashMap[String, String])(body: => Unit): 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[_]): GeneralizedLinearRegression.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[_]): GeneralizedLinearRegression.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): GeneralizedLinearRegression.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): GeneralizedLinearRegression.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[_]*): GeneralizedLinearRegression.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): GeneralizedLinearRegression.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[ml]
Definition Classes
Params

Inherited from Identifiable

Value Members

def toString(): String
Definition Classes
Identifiable → AnyRef → Any

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(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(arg0: AnyRef): Boolean
Definition Classes
AnyRef → Any
final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

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 family: Param[String]
Param for the name of family which is a description of the error distribution to be used in the model.
Param for the name of family which is a description of the error distribution to be used in the model. Supported options: "gaussian", "binomial", "poisson", "gamma" and "tweedie". Default is "gaussian".
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
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 labelCol: Param[String]
Param for label column name.
Param for label column name.
Definition Classes
HasLabelCol
final val link: Param[String]
Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function.
Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function. Supported options: "identity", "log", "inverse", "logit", "probit", "cloglog" and "sqrt". This is used only when family is not "tweedie". The link function for the "tweedie" family must be specified through linkPower.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final val linkPower: DoubleParam
Param for the index in the power link function.
Param for the index in the power link function. Only applicable to the Tweedie family. Note that link power 0, 1, -1 or 0.5 corresponds to the Log, Identity, Inverse or Sqrt link, respectively. When not set, this value defaults to 1 - variancePower, which matches the R "statmod" package.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
final val linkPredictionCol: Param[String]
Param for link prediction (linear predictor) column name.
Param for link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final val maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
Definition Classes
HasMaxIter
final val offsetCol: Param[String]
Param for offset column name.
Param for offset column name. If this is not set or empty, we treat all instance offsets as 0.0. The feature specified as offset has a constant coefficient of 1.0.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.3.0")
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 solver: Param[String]
The solver algorithm for optimization.
The solver algorithm for optimization. Supported options: "irls" (iteratively reweighted least squares). Default: "irls"
Definition Classes
GeneralizedLinearRegressionBase → HasSolver
Annotations
@Since("2.0.0")
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 variancePower: DoubleParam
Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution.
Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution. Only applicable to the Tweedie family. (see Tweedie Distribution (Wikipedia)) Supported values: 0 and [1, Inf). Note that variance power 0, 1, or 2 corresponds to the Gaussian, Poisson or Gamma family, respectively.
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
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 !=(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[_]): GeneralizedLinearRegression.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(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
def copy(extra: ParamMap): GeneralizedLinearRegression
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
GeneralizedLinearRegression → Predictor → Estimator → PipelineStage → Params
Annotations
@Since("2.0.0")
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: AnyRef): 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
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
def fit(dataset: Dataset[_]): GeneralizedLinearRegressionModel
Fits a model to the input data.
Fits a model to the input data.
Definition Classes
Predictor → Estimator
def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GeneralizedLinearRegressionModel]
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): GeneralizedLinearRegressionModel
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[_]*): GeneralizedLinearRegressionModel
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 getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @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 hashCode(): Int
Definition Classes
AnyRef → Any
Annotations
@IntrinsicCandidate() @native()
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit
Attributes
protected
Definition Classes
Logging
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
def log: Logger
Attributes
protected
Definition Classes
Logging
def logDebug(msg: => String, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logDebug(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logError(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logInfo(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logTrace(entry: LogEntry): 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(entry: LogEntry, throwable: Throwable): Unit
Attributes
protected
Definition Classes
Logging
def logWarning(entry: LogEntry): 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
@IntrinsicCandidate() @native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@IntrinsicCandidate() @native()
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(paramPair: ParamPair[_]): GeneralizedLinearRegression.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): GeneralizedLinearRegression.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): GeneralizedLinearRegression.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[_]*): GeneralizedLinearRegression.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): GeneralizedLinearRegression.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[ml]
Definition Classes
Params
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
def toString(): String
Definition Classes
Identifiable → AnyRef → Any
def train(dataset: Dataset[_]): GeneralizedLinearRegressionModel
Train a model using the given dataset and parameters.
Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.
dataset
Training dataset
returns
Fitted model
Attributes
protected
Definition Classes
GeneralizedLinearRegression → Predictor
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
Predictor → 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
GeneralizedLinearRegression → Identifiable
Annotations
@Since("2.0.0")
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
Definition Classes
GeneralizedLinearRegressionBase → PredictorParams
Annotations
@Since("2.0.0")
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
Attributes
protected
Definition Classes
Logging
def write: MLWriter
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
Definition Classes
DefaultParamsWritable → MLWritable
def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable]) @Deprecated
Deprecated
(Since version 9)

Parameter setters

def setFamily(value: String): GeneralizedLinearRegression.this.type
Sets the value of param family.
Sets the value of param family. Default is "gaussian".
Annotations
@Since("2.0.0")
def setFeaturesCol(value: String): GeneralizedLinearRegression
Definition Classes
Predictor
def setFitIntercept(value: Boolean): GeneralizedLinearRegression.this.type
Sets if we should fit the intercept.
Sets if we should fit the intercept. Default is true.
Annotations
@Since("2.0.0")
def setLabelCol(value: String): GeneralizedLinearRegression
Definition Classes
Predictor
def setLink(value: String): GeneralizedLinearRegression.this.type
Sets the value of param link.
Sets the value of param link. Used only when family is not "tweedie".
Annotations
@Since("2.0.0")
def setLinkPower(value: Double): GeneralizedLinearRegression.this.type
Sets the value of param linkPower.
Sets the value of param linkPower. Used only when family is "tweedie".
Annotations
@Since("2.2.0")
def setLinkPredictionCol(value: String): GeneralizedLinearRegression.this.type
Sets the link prediction (linear predictor) column name.
Sets the link prediction (linear predictor) column name.
Annotations
@Since("2.0.0")
def setMaxIter(value: Int): GeneralizedLinearRegression.this.type
Sets the maximum number of iterations (applicable for solver "irls").
Sets the maximum number of iterations (applicable for solver "irls"). Default is 25.
Annotations
@Since("2.0.0")
def setOffsetCol(value: String): GeneralizedLinearRegression.this.type
Sets the value of param offsetCol.
Sets the value of param offsetCol. If this is not set or empty, we treat all instance offsets as 0.0. Default is not set, so all instances have offset 0.0.
Annotations
@Since("2.3.0")
def setPredictionCol(value: String): GeneralizedLinearRegression
Definition Classes
Predictor
def setRegParam(value: Double): GeneralizedLinearRegression.this.type
Sets the regularization parameter for L2 regularization.
Sets the regularization parameter for L2 regularization. The regularization term is
$$ 0.5 * regParam * L2norm(coefficients)^2 $$
Default is 0.0.
Annotations
@Since("2.0.0")
def setSolver(value: String): GeneralizedLinearRegression.this.type
Sets the solver algorithm used for optimization.
Sets the solver algorithm used for optimization. Currently only supports "irls" which is also the default solver.
Annotations
@Since("2.0.0")
def setTol(value: Double): GeneralizedLinearRegression.this.type
Sets the convergence tolerance of iterations.
Sets the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.
Annotations
@Since("2.0.0")
def setVariancePower(value: Double): GeneralizedLinearRegression.this.type
Sets the value of param variancePower.
Sets the value of param variancePower. Used only when family is "tweedie". Default is 0.0, which corresponds to the "gaussian" family.
Annotations
@Since("2.2.0")
def setWeightCol(value: String): GeneralizedLinearRegression.this.type
Sets the value of param weightCol.
Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one. In the Binomial family, weights correspond to number of trials and should be integer. Non-integer weights are rounded to integer in AIC calculation.
Annotations
@Since("2.0.0")

Parameter getters

def getFamily: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final def getFeaturesCol: String
Definition Classes
HasFeaturesCol
final def getFitIntercept: Boolean
Definition Classes
HasFitIntercept
final def getLabelCol: String
Definition Classes
HasLabelCol
def getLink: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
def getLinkPower: Double
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
def getLinkPredictionCol: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.0.0")
final def getMaxIter: Int
Definition Classes
HasMaxIter
def getOffsetCol: String
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.3.0")
final def getPredictionCol: String
Definition Classes
HasPredictionCol
final def getRegParam: Double
Definition Classes
HasRegParam
final def getSolver: String
Definition Classes
HasSolver
final def getTol: Double
Definition Classes
HasTol
def getVariancePower: Double
Definition Classes
GeneralizedLinearRegressionBase
Annotations
@Since("2.2.0")
final def getWeightCol: String
Definition Classes
HasWeightCol

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

final val aggregationDepth: IntParam
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).
Definition Classes
HasAggregationDepth

(expert-only) Parameter setters

def setAggregationDepth(value: Int): GeneralizedLinearRegression.this.type
Annotations
@Since("3.0.0")

(expert-only) Parameter getters

final def getAggregationDepth: Int
Definition Classes
HasAggregationDepth

Packages

GeneralizedLinearRegression

Companion object GeneralizedLinearRegression

class GeneralizedLinearRegression extends Regressor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel] with GeneralizedLinearRegressionBase with DefaultParamsWritable with Logging

Instance Constructors

Type Members

Value Members

Deprecated Value Members

Inherited from DefaultParamsWritable

Value Members

Inherited from MLWritable

Value Members

Inherited from GeneralizedLinearRegressionBase

Value Members

Inherited from HasAggregationDepth

Value Members

Inherited from HasSolver

Value Members

Inherited from HasWeightCol

Value Members

Inherited from HasRegParam

Value Members

Inherited from HasTol

Value Members

Inherited from HasMaxIter

Value Members

Inherited from HasFitIntercept

Value Members

Inherited from Predictor[Vector, GeneralizedLinearRegression, GeneralizedLinearRegressionModel]

Value Members

Inherited from HasPredictionCol

Value Members

Inherited from HasFeaturesCol

Value Members

Inherited from HasLabelCol

Value Members

Inherited from Estimator[GeneralizedLinearRegressionModel]

Value Members

Inherited from PipelineStage

Value Members

Inherited from Logging

Value Members

Inherited from Params

Value Members

Inherited from Identifiable

Value Members

Inherited from AnyRef

Value Members

Inherited from Any

Value Members

Parameters

Members

Parameter setters

Parameter getters

(expert-only) Parameters

(expert-only) Parameter setters

(expert-only) Parameter getters

GeneralizedLinearRegression