GeneralizedLinearRegressionModel (Spark 2.2.0 JavaDoc)

Object
- org.apache.spark.ml.PipelineStage
- - org.apache.spark.ml.Transformer
  - - org.apache.spark.ml.Model<M>
    - - org.apache.spark.ml.PredictionModel<FeaturesType,M>
      - org.apache.spark.ml.regression.RegressionModel<Vector,GeneralizedLinearRegressionModel>
        
        org.apache.spark.ml.regression.GeneralizedLinearRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable, MLWritable
```
public class GeneralizedLinearRegressionModel
extends RegressionModel<Vector,GeneralizedLinearRegressionModel>
implements MLWritable
```
:: Experimental :: Model produced by GeneralizedLinearRegression.

See Also:

Serialized Form

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`static Params`	`clear(Param<?> param)`
`Vector`	`coefficients()`
`GeneralizedLinearRegressionModel`	`copy(ParamMap extra)` Creates a copy of this instance with the same UID and some extra params.
`GeneralizedLinearRegressionSummary`	`evaluate(Dataset<?> dataset)` Evaluate the model on the given dataset, returning a summary of the results.
`static String`	`explainParam(Param<?> param)`
`static String`	`explainParams()`
`static ParamMap`	`extractParamMap()`
`static ParamMap`	`extractParamMap(ParamMap extra)`
`static Param<String>`	`family()`
`Param<String>`	`family()` Param for the name of family which is a description of the error distribution to be used in the model.
`static Param<String>`	`featuresCol()`
`Param<String>`	`featuresCol()` Param for features column name.
`static BooleanParam`	`fitIntercept()`
`static <T> scala.Option<T>`	`get(Param<T> param)`
`static <T> scala.Option<T>`	`getDefault(Param<T> param)`
`static String`	`getFamily()`
`String`	`getFamily()`
`static String`	`getFeaturesCol()`
`String`	`getFeaturesCol()`
`static boolean`	`getFitIntercept()`
`static String`	`getLabelCol()`
`String`	`getLabelCol()`
`static String`	`getLink()`
`String`	`getLink()`
`static double`	`getLinkPower()`
`double`	`getLinkPower()`
`static String`	`getLinkPredictionCol()`
`String`	`getLinkPredictionCol()`
`static int`	`getMaxIter()`
`static <T> T`	`getOrDefault(Param<T> param)`
`static Param<Object>`	`getParam(String paramName)`
`static String`	`getPredictionCol()`
`String`	`getPredictionCol()`
`static double`	`getRegParam()`
`static String`	`getSolver()`
`static double`	`getTol()`
`static double`	`getVariancePower()`
`double`	`getVariancePower()`
`static String`	`getWeightCol()`
`static <T> boolean`	`hasDefault(Param<T> param)`
`boolean`	`hasLinkPredictionCol()` Checks whether we should output link prediction.
`static boolean`	`hasParam(String paramName)`
`static boolean`	`hasParent()`
`boolean`	`hasSummary()` Indicates if `summary` is available.
`double`	`intercept()`
`static boolean`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static Param<String>`	`labelCol()`
`Param<String>`	`labelCol()` Param for label column name.
`static Param<String>`	`link()`
`Param<String>`	`link()` Param for the name of link function which provides the relationship between the linear predictor and the mean of the distribution function.
`static DoubleParam`	`linkPower()`
`DoubleParam`	`linkPower()` Param for the index in the power link function.
`static Param<String>`	`linkPredictionCol()`
`Param<String>`	`linkPredictionCol()` Param for link prediction (linear predictor) column name.
`static GeneralizedLinearRegressionModel`	`load(String path)`
`static IntParam`	`maxIter()`
`int`	`numFeatures()` Returns the number of features the model was trained on.
`static Param<?>[]`	`params()`
`static void`	`parent_$eq(Estimator<M> x$1)`
`static Estimator<M>`	`parent()`
`static Param<String>`	`predictionCol()`
`Param<String>`	`predictionCol()` Param for prediction column name.
`static MLReader<GeneralizedLinearRegressionModel>`	`read()`
`static DoubleParam`	`regParam()`
`static void`	`save(String path)`
`static <T> Params`	`set(Param<T> param, T value)`
`static M`	`setFeaturesCol(String value)`
`GeneralizedLinearRegressionModel`	`setLinkPredictionCol(String value)` Sets the link prediction (linear predictor) column name.
`static M`	`setParent(Estimator<M> parent)`
`static M`	`setPredictionCol(String value)`
`static Param<String>`	`solver()`
`GeneralizedLinearRegressionTrainingSummary`	`summary()` Gets R-like summary of model on training set.
`static DoubleParam`	`tol()`
`static String`	`toString()`
`Dataset<Row>`	`transform(Dataset<?> dataset)` Transforms dataset by reading from `featuresCol`, calling `predict`, and storing the predictions as a new column `predictionCol`.
`static StructType`	`transformSchema(StructType schema)`
`String`	`uid()` An immutable unique ID for the object and its derivatives.
`static StructType`	`validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)`
`StructType`	`validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)`
`StructType`	`validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)` Validates and transforms the input schema with the provided param map.
`static DoubleParam`	`variancePower()`
`DoubleParam`	`variancePower()` Param for the power in the variance function of the Tweedie distribution which provides the relationship between the variance and mean of the distribution.
`static Param<String>`	`weightCol()`
`MLWriter`	`write()` Returns a `MLWriter` instance for this ML instance.

Methods inherited from class org.apache.spark.ml.PredictionModel
setFeaturesCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Model
hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer
transform, transform, transform

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.internal.Logging
initializeLogging, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

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

Methods inherited from interface org.apache.spark.ml.param.Params
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable
toString

- Method Detail
  - read
```
public static MLReader<GeneralizedLinearRegressionModel> read()
```
  - load
```
public static GeneralizedLinearRegressionModel load(String path)
```
  - toString
```
public static String toString()
```
  - params
```
public static Param<?>[] params()
```
  - explainParam
```
public static String explainParam(Param<?> param)
```
  - explainParams
```
public static String explainParams()
```
  - isSet
```
public static final boolean isSet(Param<?> param)
```
  - isDefined
```
public static final boolean isDefined(Param<?> param)
```
  - hasParam
```
public static boolean hasParam(String paramName)
```
  - getParam
```
public static Param<Object> getParam(String paramName)
```
  - set
```
public static final <T> Params set(Param<T> param,
                                   T value)
```
  - get
```
public static final <T> scala.Option<T> get(Param<T> param)
```
  - clear
```
public static final Params clear(Param<?> param)
```
  - getOrDefault
```
public static final <T> T getOrDefault(Param<T> param)
```
  - getDefault
```
public static final <T> scala.Option<T> getDefault(Param<T> param)
```
  - hasDefault
```
public static final <T> boolean hasDefault(Param<T> param)
```
  - extractParamMap
```
public static final ParamMap extractParamMap(ParamMap extra)
```
  - extractParamMap
```
public static final ParamMap extractParamMap()
```
  - parent
```
public static Estimator<M> parent()
```
  - parent_$eq
```
public static void parent_$eq(Estimator<M> x$1)
```
  - setParent
```
public static M setParent(Estimator<M> parent)
```
  - hasParent
```
public static boolean hasParent()
```
  - labelCol
```
public static final Param<String> labelCol()
```
  - getLabelCol
```
public static final String getLabelCol()
```
  - featuresCol
```
public static final Param<String> featuresCol()
```
  - getFeaturesCol
```
public static final String getFeaturesCol()
```
  - predictionCol
```
public static final Param<String> predictionCol()
```
  - getPredictionCol
```
public static final String getPredictionCol()
```
  - setFeaturesCol
```
public static M setFeaturesCol(String value)
```
  - setPredictionCol
```
public static M setPredictionCol(String value)
```
  - transformSchema
```
public static StructType transformSchema(StructType schema)
```
  - fitIntercept
```
public static final BooleanParam fitIntercept()
```
  - getFitIntercept
```
public static final boolean getFitIntercept()
```
  - maxIter
```
public static final IntParam maxIter()
```
  - getMaxIter
```
public static final int getMaxIter()
```
  - tol
```
public static final DoubleParam tol()
```
  - getTol
```
public static final double getTol()
```
  - regParam
```
public static final DoubleParam regParam()
```
  - getRegParam
```
public static final double getRegParam()
```
  - weightCol
```
public static final Param<String> weightCol()
```
  - getWeightCol
```
public static final String getWeightCol()
```
  - solver
```
public static final Param<String> solver()
```
  - getSolver
```
public static final String getSolver()
```
  - family
```
public static final Param<String> family()
```
  - getFamily
```
public static String getFamily()
```
  - variancePower
```
public static final DoubleParam variancePower()
```
  - getVariancePower
```
public static double getVariancePower()
```
  - link
```
public static final Param<String> link()
```
  - getLink
```
public static String getLink()
```
  - linkPower
```
public static final DoubleParam linkPower()
```
  - getLinkPower
```
public static double getLinkPower()
```
  - linkPredictionCol
```
public static final Param<String> linkPredictionCol()
```
  - getLinkPredictionCol
```
public static String getLinkPredictionCol()
```
  - validateAndTransformSchema
```
public static StructType validateAndTransformSchema(StructType schema,
                                                    boolean fitting,
                                                    DataType featuresDataType)
```
  - save
```
public static void save(String path)
                 throws java.io.IOException
```
    Throws:
    
    java.io.IOException
  - uid
```
public String uid()
```
    Description copied from interface: Identifiable
    
    An immutable unique ID for the object and its derivatives.
    
    Specified by:
    
    uid in interface Identifiable
    
    Returns:
    
    (undocumented)
  - coefficients
```
public Vector coefficients()
```
  - intercept
```
public double intercept()
```
  - setLinkPredictionCol
```
public GeneralizedLinearRegressionModel setLinkPredictionCol(String value)
```
    Sets the link prediction (linear predictor) column name.
    
    Parameters:
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
  - transform
```
public Dataset<Row> transform(Dataset<?> dataset)
```
    Description copied from class: PredictionModel
    
    Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
    
    Overrides:
    
    transform in class PredictionModel<Vector,GeneralizedLinearRegressionModel>
    
    Parameters:
    
    dataset - input dataset
    
    Returns:
    
    transformed dataset with predictionCol of type Double
  - summary
```
public GeneralizedLinearRegressionTrainingSummary summary()
```
    Gets R-like summary of model on training set. An exception is thrown if there is no summary available.
    
    Returns:
    
    (undocumented)
  - hasSummary
```
public boolean hasSummary()
```
    Indicates if summary is available.
    
    Returns:
    
    (undocumented)
  - evaluate
```
public GeneralizedLinearRegressionSummary evaluate(Dataset<?> dataset)
```
    Evaluate the model on the given dataset, returning a summary of the results.
    
    Parameters:
    
    dataset - (undocumented)
    
    Returns:
    
    (undocumented)
  - copy
```
public GeneralizedLinearRegressionModel copy(ParamMap extra)
```
    Description copied from interface: Params
    
    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().
    
    Specified by:
    
    copy in interface Params
    
    Specified by:
    
    copy in class Model<GeneralizedLinearRegressionModel>
    
    Parameters:
    
    extra - (undocumented)
    
    Returns:
    
    (undocumented)
  - write
```
public MLWriter write()
```
    Returns a MLWriter instance for this ML instance.
    For GeneralizedLinearRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.
    
    Specified by:
    
    write in interface MLWritable
    
    Returns:
    
    (undocumented)
  - numFeatures
```
public int numFeatures()
```
    Description copied from class: PredictionModel
    
    Returns the number of features the model was trained on. If unknown, returns -1
    
    Overrides:
    
    numFeatures in class PredictionModel<Vector,GeneralizedLinearRegressionModel>
  - family
```
public Param<String> family()
```
    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".
    
    Returns:
    
    (undocumented)
  - getFamily
```
public String getFamily()
```
  - variancePower
```
public DoubleParam variancePower()
```
    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.
    
    Returns:
    
    (undocumented)
  - getVariancePower
```
public double getVariancePower()
```
  - link
```
public Param<String> link()
```
    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.
    
    Returns:
    
    (undocumented)
  - getLink
```
public String getLink()
```
  - linkPower
```
public DoubleParam linkPower()
```
    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.
    
    Returns:
    
    (undocumented)
  - getLinkPower
```
public double getLinkPower()
```
  - linkPredictionCol
```
public Param<String> linkPredictionCol()
```
    Param for link prediction (linear predictor) column name. Default is not set, which means we do not output link prediction.
    
    Returns:
    
    (undocumented)
  - getLinkPredictionCol
```
public String getLinkPredictionCol()
```
  - hasLinkPredictionCol
```
public boolean hasLinkPredictionCol()
```
    Checks whether we should output link prediction.
  - validateAndTransformSchema
```
public StructType validateAndTransformSchema(StructType schema,
                                             boolean fitting,
                                             DataType featuresDataType)
```
  - validateAndTransformSchema
```
public StructType validateAndTransformSchema(StructType schema,
                                             boolean fitting,
                                             DataType featuresDataType)
```
    Validates and transforms the input schema with the provided param map.
    
    Parameters:
    
    schema - input schema
    
    fitting - whether this is in fitting
    
    featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.
    
    Returns:
    
    output schema
  - labelCol
```
public Param<String> labelCol()
```
    Param for label column name.
    
    Returns:
    
    (undocumented)
  - getLabelCol
```
public String getLabelCol()
```
  - featuresCol
```
public Param<String> featuresCol()
```
    Param for features column name.
    
    Returns:
    
    (undocumented)
  - getFeaturesCol
```
public String getFeaturesCol()
```
  - predictionCol
```
public Param<String> predictionCol()
```
    Param for prediction column name.
    
    Returns:
    
    (undocumented)
  - getPredictionCol
```
public String getPredictionCol()
```

Class GeneralizedLinearRegressionModel

Method Summary

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

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

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

Methods inherited from class Object

Methods inherited from interface org.apache.spark.internal.Logging

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

Methods inherited from interface org.apache.spark.ml.param.Params

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

Method Detail

read

load

toString

params

explainParam

explainParams

isSet

isDefined

hasParam

getParam

set

get

clear

getOrDefault

getDefault

hasDefault

extractParamMap

extractParamMap

parent

parent_$eq

setParent

hasParent

labelCol

getLabelCol

featuresCol

getFeaturesCol

predictionCol

getPredictionCol

setFeaturesCol

setPredictionCol

transformSchema

fitIntercept

getFitIntercept

maxIter

getMaxIter

tol

getTol

regParam

getRegParam

weightCol

getWeightCol

solver

getSolver

family

getFamily

variancePower

getVariancePower

link

getLink

linkPower

getLinkPower

linkPredictionCol

getLinkPredictionCol

validateAndTransformSchema

save

uid

coefficients

intercept

setLinkPredictionCol

transform

summary

hasSummary

evaluate

copy

write

numFeatures

family

getFamily

variancePower