org.apache.spark.ml.regression

Class LinearRegressionModel

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,LinearRegressionModel>

org.apache.spark.ml.regression.LinearRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, GeneralMLWritable, Identifiable, MLWritable

public class LinearRegressionModel
extends RegressionModel<Vector,LinearRegressionModel>
implements GeneralMLWritable

Model produced by LinearRegression.

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
static IntParam	aggregationDepth()
static Params	clear(Param<?> param)
Vector	coefficients()
LinearRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static DoubleParam	elasticNetParam()
static DoubleParam	epsilon()
DoubleParam	epsilon() The shape parameter to control the amount of robustness.
LinearRegressionSummary	evaluate(Dataset<?> dataset) Evaluates the model on a test dataset.
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static Param<String>	featuresCol()
static BooleanParam	fitIntercept()
static <T> scala.Option<T>	get(Param<T> param)
static int	getAggregationDepth()
static <T> scala.Option<T>	getDefault(Param<T> param)
static double	getElasticNetParam()
static double	getEpsilon()
double	getEpsilon()
static String	getFeaturesCol()
static boolean	getFitIntercept()
static String	getLabelCol()
static String	getLoss()
static int	getMaxIter()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
static double	getRegParam()
static String	getSolver()
static boolean	getStandardization()
static double	getTol()
static String	getWeightCol()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	hasParent()
boolean	hasSummary() Indicates whether a training summary exists for this model instance.
double	intercept()
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Param<String>	labelCol()
static LinearRegressionModel	load(String path)
static Param<String>	loss()
Param<String>	loss() The loss function to be optimized.
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()
double	predict(Vector features) Predict label for the given features.
static Param<String>	predictionCol()
static MLReader<LinearRegressionModel>	read()
static DoubleParam	regParam()
static void	save(String path)
double	scale()
static <T> Params	set(Param<T> param, T value)
static M	setFeaturesCol(String value)
static M	setParent(Estimator<M> parent)
static M	setPredictionCol(String value)
static Param<String>	solver()
Param<String>	solver() The solver algorithm for optimization.
static BooleanParam	standardization()
LinearRegressionTrainingSummary	summary() Gets summary (e.g.
static DoubleParam	tol()
static String	toString()
static Dataset<Row>	transform(Dataset<?> dataset)
static Dataset<Row>	transform(Dataset<?> dataset, ParamMap paramMap)
static Dataset<Row>	transform(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
static Dataset<Row>	transform(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
static StructType	transformSchema(StructType schema)
String	uid() An immutable unique ID for the object and its derivatives.
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 Param<String>	weightCol()
GeneralMLWriter	write() Returns a GeneralMLWriter instance for this ML instance.

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

setFeaturesCol, setPredictionCol, transform, 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.param.shared.HasRegParam

getRegParam, regParam

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

elasticNetParam, getElasticNetParam

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.HasFitIntercept

fitIntercept, getFitIntercept

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

getStandardization, standardization

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.ml.param.shared.HasAggregationDepth

aggregationDepth, getAggregationDepth

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

getLoss

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.util.MLWritable

save

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.internal.Logging

initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

read

public static MLReader<LinearRegressionModel> read()

load

public static LinearRegressionModel 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()

transform

public static Dataset<Row> transform(Dataset<?> dataset,
                                     ParamPair<?> firstParamPair,
                                     scala.collection.Seq<ParamPair<?>> otherParamPairs)

transform

public static Dataset<Row> transform(Dataset<?> dataset,
                                     ParamMap paramMap)

transform

public static Dataset<Row> transform(Dataset<?> dataset,
                                     ParamPair<?> firstParamPair,
                                     ParamPair<?>... otherParamPairs)

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)

transform

public static Dataset<Row> transform(Dataset<?> dataset)

regParam

public static final DoubleParam regParam()

getRegParam

public static final double getRegParam()

elasticNetParam

public static final DoubleParam elasticNetParam()

getElasticNetParam

public static final double getElasticNetParam()

maxIter

public static final IntParam maxIter()

getMaxIter

public static final int getMaxIter()

tol

public static final DoubleParam tol()

getTol

public static final double getTol()

fitIntercept

public static final BooleanParam fitIntercept()

getFitIntercept

public static final boolean getFitIntercept()

standardization

public static final BooleanParam standardization()

getStandardization

public static final boolean getStandardization()

weightCol

public static final Param<String> weightCol()

getWeightCol

public static final String getWeightCol()

getSolver

public static final String getSolver()

aggregationDepth

public static final IntParam aggregationDepth()

getAggregationDepth

public static final int getAggregationDepth()

getLoss

public static final String getLoss()

solver

public static final Param<String> solver()

loss

public static final Param<String> loss()

epsilon

public static final DoubleParam epsilon()

getEpsilon

public static double getEpsilon()

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()

scale

public double scale()

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,LinearRegressionModel>

summary

public LinearRegressionTrainingSummary summary()

Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is thrown if trainingSummary == None.

Returns:

(undocumented)

hasSummary

public boolean hasSummary()

Indicates whether a training summary exists for this model instance.

evaluate

public LinearRegressionSummary evaluate(Dataset<?> dataset)

Evaluates the model on a test dataset.

Parameters:

dataset - Test dataset to evaluate model on.

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,LinearRegressionModel>

Parameters:

features - (undocumented)

Returns:

(undocumented)

copy

public LinearRegressionModel 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<LinearRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public GeneralMLWriter write()

Returns a GeneralMLWriter instance for this ML instance.

For LinearRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

This also does not save the parent currently.

Specified by:

write in interface GeneralMLWritable

Specified by:

write in interface MLWritable

Returns:

(undocumented)

epsilon

public DoubleParam epsilon()

The shape parameter to control the amount of robustness. Must be &gt; 1.0. At larger values of epsilon, the huber criterion becomes more similar to least squares regression; for small values of epsilon, the criterion is more similar to L1 regression. Default is 1.35 to get as much robustness as possible while retaining 95% statistical efficiency for normally distributed data. It matches sklearn HuberRegressor and is "M" from A robust hybrid of lasso and ridge regression. Only valid when "loss" is "huber".

Returns:

(undocumented)

getEpsilon

public double getEpsilon()

loss

public Param<String> loss()

The loss function to be optimized. Supported options: "squaredError" and "huber". Default: "squaredError"

Specified by:

loss in interface HasLoss

Returns:

(undocumented)

solver

public Param<String> solver()

The solver algorithm for optimization. Supported options: "l-bfgs", "normal" and "auto". Default: "auto"

Specified by:

solver in interface HasSolver

Returns:

(undocumented)

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