org.apache.spark.ml.regression

Class LinearRegression

java.lang.Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<M>

org.apache.spark.ml.Predictor<FeaturesType,Learner,M>

org.apache.spark.ml.regression.LinearRegression

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

public class LinearRegression
extends Predictor<FeaturesType,Learner,M>
implements Logging

:: Experimental :: Linear regression.

The learning objective is to minimize the squared error, with regularization. The specific squared error loss function used is: L = 1/2n ||A weights - y||^2^

This support multiple types of regularization: - none (a.k.a. ordinary least squares) - L2 (ridge regression) - L1 (Lasso) - L2 + L1 (elastic net)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LinearRegression()
LinearRegression(java.lang.String uid)

Method Summary

Methods

Modifier and Type	Method and Description
LinearRegression	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
LinearRegression	setElasticNetParam(double value) Set the ElasticNet mixing parameter.
LinearRegression	setFitIntercept(boolean value) Set if we should fit the intercept Default is true.
LinearRegression	setMaxIter(int value) Set the maximum number of iterations.
LinearRegression	setRegParam(double value) Set the regularization parameter.
LinearRegression	setStandardization(boolean value) Whether to standardize the training features before fitting the model.
LinearRegression	setTol(double value) Set the convergence tolerance of iterations.
protected LinearRegressionModel	train(DataFrame dataset) Train a model using the given dataset and parameters.
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.

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

extractLabeledPoints, fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

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

fit, fit, fit, fit

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

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

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

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, validateParams

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

toString

Constructor Detail

LinearRegression

public LinearRegression(java.lang.String uid)

LinearRegression

public LinearRegression()

Method Detail

uid

public java.lang.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)

setRegParam

public LinearRegression setRegParam(double value)

Set the regularization parameter. Default is 0.0.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setFitIntercept

public LinearRegression setFitIntercept(boolean value)

Set if we should fit the intercept Default is true.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setStandardization

public LinearRegression setStandardization(boolean value)

Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Note that with/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well. Default is true.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setElasticNetParam

public LinearRegression setElasticNetParam(double value)

Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For 0 < alpha < 1, the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMaxIter

public LinearRegression setMaxIter(int value)

Set the maximum number of iterations. Default is 100.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setTol

public LinearRegression setTol(double value)

Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

Parameters:

value - (undocumented)

Returns:

(undocumented)

train

protected LinearRegressionModel train(DataFrame dataset)

Description copied from class: Predictor

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.

Specified by:

train in class Predictor<Vector,LinearRegression,LinearRegressionModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

copy

public LinearRegression 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.

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,LinearRegression,LinearRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

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