Experimental

org.apache.spark.ml.regression

Class GeneralizedLinearRegressionModel

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, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasMaxIter, HasPredictionCol, HasRegParam, HasSolver, HasTol, HasWeightCol, PredictorParams, GeneralizedLinearRegressionBase, Identifiable, MLWritable

public class GeneralizedLinearRegressionModel
extends RegressionModel<Vector,GeneralizedLinearRegressionModel>
implements GeneralizedLinearRegressionBase, MLWritable

Model produced by GeneralizedLinearRegression.

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
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.
boolean	hasSummary() Indicates if summary is available.
double	intercept()
static GeneralizedLinearRegressionModel	load(String path)
int	numFeatures() Returns the number of features the model was trained on.
double	predict(Vector features) Predict label for the given features.
static MLReader<GeneralizedLinearRegressionModel>	read()
GeneralizedLinearRegressionModel	setLinkPredictionCol(String value) Sets the link prediction (linear predictor) column name.
GeneralizedLinearRegressionTrainingSummary	summary() Gets R-like summary of model on training set.
Dataset<Row>	transform(Dataset<?> dataset) Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
String	uid() An immutable unique ID for the object and its derivatives.
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.ml.regression.GeneralizedLinearRegressionBase

family, getFamily, getLink, getLinkPower, getLinkPredictionCol, getOffsetCol, getVariancePower, hasLinkPredictionCol, hasOffsetCol, hasWeightCol, link, linkPower, linkPredictionCol, offsetCol, solver, validateAndTransformSchema, variancePower

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol, labelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

featuresCol, getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol, predictionCol

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

Methods inherited from interface org.apache.spark.ml.param.shared.HasFitIntercept

fitIntercept, getFitIntercept

Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter

getMaxIter, maxIter

Methods inherited from interface org.apache.spark.ml.param.shared.HasTol

getTol, tol

Methods inherited from interface org.apache.spark.ml.param.shared.HasRegParam

getRegParam, regParam

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol, weightCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasSolver

getSolver

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

initializeLogging, initializeLogIfNecessary, 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

Method Detail

read

public static MLReader<GeneralizedLinearRegressionModel> read()

load

public static GeneralizedLinearRegressionModel load(String path)

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)

predict

public double predict(Vector features)

Description copied from class: PredictionModel

Predict label for the given features. This method is used to implement transform() and output predictionCol.

Specified by:

predict in class PredictionModel<Vector,GeneralizedLinearRegressionModel>

Parameters:

features - (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>