LinearRegression (Spark 2.4.0 JavaDoc)

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, DefaultParamsWritable, Identifiable, MLWritable
```
public class LinearRegression
extends Predictor<FeaturesType,Learner,M>
implements DefaultParamsWritable, Logging
```
Linear regression.
The learning objective is to minimize the specified loss function, with regularization. This supports two kinds of loss: - squaredError (a.k.a squared loss) - huber (a hybrid of squared error for relatively small errors and absolute error for relatively large ones, and we estimate the scale parameter from training data)
This supports multiple types of regularization: - none (a.k.a. ordinary least squares) - L2 (ridge regression) - L1 (Lasso) - L2 + L1 (elastic net)
The squared error objective function is:

$$ \begin{align} \min_{w}\frac{1}{2n}{\sum_{i=1}^n(X_{i}w - y_{i})^{2} + \lambda\left[\frac{1-\alpha}{2}{||w||_{2}}^{2} + \alpha{||w||_{1}}\right]} \end{align} $$

The huber objective function is:

$$ \begin{align} \min_{w, \sigma}\frac{1}{2n}{\sum_{i=1}^n\left(\sigma + H_m\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \frac{1}{2}\lambda {||w||_2}^2} \end{align} $$

where

$$ \begin{align} H_m(z) = \begin{cases} z^2, & \text {if } |z| < \epsilon, \\ 2\epsilon|z| - \epsilon^2, & \text{otherwise} \end{cases} \end{align} $$

Note: Fitting with huber loss only supports none and L2 regularization.

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

LinearRegression()

LinearRegression(String uid)

Constructors
Constructor and Description
`LinearRegression()`
`LinearRegression(String uid)`

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`static IntParam`	`aggregationDepth()`
`static Params`	`clear(Param<?> param)`
`LinearRegression`	`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.
`static String`	`explainParam(Param<?> param)`
`static String`	`explainParams()`
`static ParamMap`	`extractParamMap()`
`static ParamMap`	`extractParamMap(ParamMap extra)`
`static Param<String>`	`featuresCol()`
`static M`	`fit(Dataset<?> dataset)`
`static M`	`fit(Dataset<?> dataset, ParamMap paramMap)`
`static scala.collection.Seq<M>`	`fit(Dataset<?> dataset, ParamMap[] paramMaps)`
`static M`	`fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)`
`static M`	`fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)`
`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`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static Param<String>`	`labelCol()`
`static LinearRegression`	`load(String path)`
`static Param<String>`	`loss()`
`Param<String>`	`loss()` The loss function to be optimized.
`static int`	`MAX_FEATURES_FOR_NORMAL_SOLVER()` When using `LinearRegression.solver` == "normal", the solver must limit the number of features to at most this number.
`static IntParam`	`maxIter()`
`static Param<?>[]`	`params()`
`static Param<String>`	`predictionCol()`
`static DoubleParam`	`regParam()`
`static void`	`save(String path)`
`static <T> Params`	`set(Param<T> param, T value)`
`LinearRegression`	`setAggregationDepth(int value)` Suggested depth for treeAggregate (greater than or equal to 2).
`LinearRegression`	`setElasticNetParam(double value)` Set the ElasticNet mixing parameter.
`LinearRegression`	`setEpsilon(double value)` Sets the value of param `epsilon`.
`static Learner`	`setFeaturesCol(String value)`
`LinearRegression`	`setFitIntercept(boolean value)` Set if we should fit the intercept.
`static Learner`	`setLabelCol(String value)`
`LinearRegression`	`setLoss(String value)` Sets the value of param `loss`.
`LinearRegression`	`setMaxIter(int value)` Set the maximum number of iterations.
`static Learner`	`setPredictionCol(String value)`
`LinearRegression`	`setRegParam(double value)` Set the regularization parameter.
`LinearRegression`	`setSolver(String value)` Set the solver algorithm used for optimization.
`LinearRegression`	`setStandardization(boolean value)` Whether to standardize the training features before fitting the model.
`LinearRegression`	`setTol(double value)` Set the convergence tolerance of iterations.
`LinearRegression`	`setWeightCol(String value)` Whether to over-/under-sample training instances according to the given weights in weightCol.
`static Param<String>`	`solver()`
`Param<String>`	`solver()` The solver algorithm for optimization.
`static BooleanParam`	`standardization()`
`static DoubleParam`	`tol()`
`static String`	`toString()`
`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()`
`static MLWriter`	`write()`

Methods inherited from class org.apache.spark.ml.Predictor
fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

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

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.DefaultParamsWritable
write

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

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.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

- Constructor Detail
  - LinearRegression
```
public LinearRegression(String uid)
```
  - LinearRegression
```
public LinearRegression()
```
- Method Detail
  - load
```
public static LinearRegression load(String path)
```
  - MAX_FEATURES_FOR_NORMAL_SOLVER
```
public static int MAX_FEATURES_FOR_NORMAL_SOLVER()
```
    When using LinearRegression.solver == "normal", the solver must limit the number of features to at most this number. The entire covariance matrix X^T^X will be collected to the driver. This limit helps prevent memory overflow errors.
    
    Returns:
    
    (undocumented)
  - 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()
```
  - fit
```
public static M fit(Dataset<?> dataset,
                    ParamPair<?> firstParamPair,
                    scala.collection.Seq<ParamPair<?>> otherParamPairs)
```
  - fit
```
public static M fit(Dataset<?> dataset,
                    ParamMap paramMap)
```
  - fit
```
public static scala.collection.Seq<M> fit(Dataset<?> dataset,
                                          ParamMap[] paramMaps)
```
  - fit
```
public static M fit(Dataset<?> dataset,
                    ParamPair<?> firstParamPair,
                    ParamPair<?>... otherParamPairs)
```
  - 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()
```
  - setLabelCol
```
public static Learner setLabelCol(String value)
```
  - setFeaturesCol
```
public static Learner setFeaturesCol(String value)
```
  - setPredictionCol
```
public static Learner setPredictionCol(String value)
```
  - fit
```
public static M fit(Dataset<?> dataset)
```
  - transformSchema
```
public static StructType transformSchema(StructType schema)
```
  - 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
  - write
```
public static MLWriter write()
```
  - 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)
  - 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. Default is true.
    
    Parameters:
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Note:
    
    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.
  - 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 alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.
    Note: Fitting with huber loss only supports None and L2 regularization, so throws exception if this param is non-zero value.
    
    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)
  - setWeightCol
```
public LinearRegression setWeightCol(String value)
```
    Whether to over-/under-sample training instances according to the given weights in weightCol. If not set or empty, all instances are treated equally (weight 1.0). Default is not set, so all instances have weight one.
    
    Parameters:
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
  - setSolver
```
public LinearRegression setSolver(String value)
```
    Set the solver algorithm used for optimization. In case of linear regression, this can be "l-bfgs", "normal" and "auto". - "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton optimization method. - "normal" denotes using Normal Equation as an analytical solution to the linear regression problem. This solver is limited to LinearRegression.MAX_FEATURES_FOR_NORMAL_SOLVER. - "auto" (default) means that the solver algorithm is selected automatically. The Normal Equations solver will be used when possible, but this will automatically fall back to iterative optimization methods when needed.
    Note: Fitting with huber loss doesn't support normal solver, so throws exception if this param was set with "normal".
    
    Parameters:
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
  - setAggregationDepth
```
public LinearRegression setAggregationDepth(int value)
```
    Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.
    
    Parameters:
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
  - setLoss
```
public LinearRegression setLoss(String value)
```
    Sets the value of param loss. Default is "squaredError".
    
    Parameters:
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
  - setEpsilon
```
public LinearRegression setEpsilon(double value)
```
    Sets the value of param epsilon. Default is 1.35.
    
    Parameters:
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
  - 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. See defaultCopy().
    
    Specified by:
    
    copy in interface Params
    
    Specified by:
    
    copy in class Predictor<Vector,LinearRegression,LinearRegressionModel>
    
    Parameters:
    
    extra - (undocumented)
    
    Returns:
    
    (undocumented)
  - epsilon
```
public DoubleParam epsilon()
```
    The shape parameter to control the amount of robustness. Must be > 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

Class LinearRegression

Constructor Summary

Method Summary

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

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

Methods inherited from class Object

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Constructor Detail

LinearRegression

LinearRegression

Method Detail

load

MAX_FEATURES_FOR_NORMAL_SOLVER

toString

params

explainParam

explainParams

isSet

isDefined

hasParam

getParam

set

get

clear

getOrDefault

getDefault

hasDefault

extractParamMap

extractParamMap

fit

fit

fit

fit

labelCol

getLabelCol

featuresCol

getFeaturesCol

predictionCol

getPredictionCol

setLabelCol

setFeaturesCol

setPredictionCol

fit

transformSchema

regParam

getRegParam

elasticNetParam

getElasticNetParam

maxIter

getMaxIter

tol

getTol

fitIntercept

getFitIntercept

standardization

getStandardization

weightCol

getWeightCol

getSolver

aggregationDepth

getAggregationDepth

getLoss

solver