public class LinearRegression extends Predictor<FeaturesType,Learner,M>
The learning objective is to minimize the squared error, with regularization. The specific squared error loss function used is: L = 1/2n ||A coefficients - y||^2^
This supports multiple types of regularization: - none (a.k.a. ordinary least squares) - L2 (ridge regression) - L1 (Lasso) - L2 + L1 (elastic net)
Constructor and Description |
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LinearRegression() |
LinearRegression(java.lang.String uid) |
Modifier and Type | Method and Description |
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protected static <T> T |
$(Param<T> param) |
static Params |
clear(Param<?> param) |
LinearRegression |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
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protected static <T extends Params> |
copyValues(T to,
ParamMap extra) |
protected static <T extends Params> |
copyValues$default$2() |
protected static <T extends Params> |
defaultCopy(ParamMap extra) |
static DoubleParam |
elasticNetParam() |
static java.lang.String |
explainParam(Param<?> param) |
static java.lang.String |
explainParams() |
protected static RDD<LabeledPoint> |
extractLabeledPoints(Dataset<?> dataset) |
static ParamMap |
extractParamMap() |
static ParamMap |
extractParamMap(ParamMap extra) |
static Param<java.lang.String> |
featuresCol() |
Param<java.lang.String> |
featuresCol()
Param for features column name.
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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 <T> scala.Option<T> |
getDefault(Param<T> param) |
static double |
getElasticNetParam() |
static java.lang.String |
getFeaturesCol() |
java.lang.String |
getFeaturesCol() |
static boolean |
getFitIntercept() |
static java.lang.String |
getLabelCol() |
java.lang.String |
getLabelCol() |
static int |
getMaxIter() |
static <T> T |
getOrDefault(Param<T> param) |
static Param<java.lang.Object> |
getParam(java.lang.String paramName) |
static java.lang.String |
getPredictionCol() |
java.lang.String |
getPredictionCol() |
static double |
getRegParam() |
static java.lang.String |
getSolver() |
static boolean |
getStandardization() |
static double |
getTol() |
static java.lang.String |
getWeightCol() |
static <T> boolean |
hasDefault(Param<T> param) |
static boolean |
hasParam(java.lang.String paramName) |
protected static void |
initializeLogIfNecessary(boolean isInterpreter) |
static boolean |
isDefined(Param<?> param) |
static boolean |
isSet(Param<?> param) |
protected static boolean |
isTraceEnabled() |
static Param<java.lang.String> |
labelCol() |
Param<java.lang.String> |
labelCol()
Param for label column name.
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static LinearRegression |
load(java.lang.String path) |
protected static org.slf4j.Logger |
log() |
protected static void |
logDebug(scala.Function0<java.lang.String> msg) |
protected static void |
logDebug(scala.Function0<java.lang.String> msg,
java.lang.Throwable throwable) |
protected static void |
logError(scala.Function0<java.lang.String> msg) |
protected static void |
logError(scala.Function0<java.lang.String> msg,
java.lang.Throwable throwable) |
protected static void |
logInfo(scala.Function0<java.lang.String> msg) |
protected static void |
logInfo(scala.Function0<java.lang.String> msg,
java.lang.Throwable throwable) |
protected static java.lang.String |
logName() |
protected static void |
logTrace(scala.Function0<java.lang.String> msg) |
protected static void |
logTrace(scala.Function0<java.lang.String> msg,
java.lang.Throwable throwable) |
protected static void |
logWarning(scala.Function0<java.lang.String> msg) |
protected static void |
logWarning(scala.Function0<java.lang.String> msg,
java.lang.Throwable throwable) |
static IntParam |
maxIter() |
static Param<?>[] |
params() |
static Param<java.lang.String> |
predictionCol() |
Param<java.lang.String> |
predictionCol()
Param for prediction column name.
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static DoubleParam |
regParam() |
static void |
save(java.lang.String path) |
static <T> Params |
set(Param<T> param,
T value) |
protected static Params |
set(ParamPair<?> paramPair) |
protected static Params |
set(java.lang.String param,
java.lang.Object value) |
protected static <T> Params |
setDefault(Param<T> param,
T value) |
protected static Params |
setDefault(scala.collection.Seq<ParamPair<?>> paramPairs) |
LinearRegression |
setElasticNetParam(double value)
Set the ElasticNet mixing parameter.
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static Learner |
setFeaturesCol(java.lang.String value) |
LinearRegression |
setFitIntercept(boolean value)
Set if we should fit the intercept
Default is true.
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static Learner |
setLabelCol(java.lang.String value) |
LinearRegression |
setMaxIter(int value)
Set the maximum number of iterations.
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static Learner |
setPredictionCol(java.lang.String value) |
LinearRegression |
setRegParam(double value)
Set the regularization parameter.
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LinearRegression |
setSolver(java.lang.String value)
Set the solver algorithm used for optimization.
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LinearRegression |
setStandardization(boolean value)
Whether to standardize the training features before fitting the model.
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LinearRegression |
setTol(double value)
Set the convergence tolerance of iterations.
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LinearRegression |
setWeightCol(java.lang.String value)
Whether to over-/under-sample training instances according to the given weights in weightCol.
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static Param<java.lang.String> |
solver() |
static BooleanParam |
standardization() |
static DoubleParam |
tol() |
static java.lang.String |
toString() |
protected LinearRegressionModel |
train(Dataset<?> dataset)
Train a model using the given dataset and parameters.
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static StructType |
transformSchema(StructType schema) |
protected static StructType |
transformSchema(StructType schema,
boolean logging) |
java.lang.String |
uid()
An immutable unique ID for the object and its derivatives.
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protected static 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.
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static void |
validateParams() |
static Param<java.lang.String> |
weightCol() |
static MLWriter |
write() |
extractLabeledPoints, fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema
transformSchema
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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
toString
public LinearRegression(java.lang.String uid)
public LinearRegression()
public static LinearRegression load(java.lang.String path)
public static java.lang.String toString()
public static Param<?>[] params()
public static void validateParams()
public static java.lang.String explainParam(Param<?> param)
public static java.lang.String explainParams()
public static final boolean isSet(Param<?> param)
public static final boolean isDefined(Param<?> param)
public static boolean hasParam(java.lang.String paramName)
public static Param<java.lang.Object> getParam(java.lang.String paramName)
protected static final Params set(java.lang.String param, java.lang.Object value)
public static final <T> scala.Option<T> get(Param<T> param)
public static final <T> T getOrDefault(Param<T> param)
protected static final <T> T $(Param<T> param)
public static final <T> scala.Option<T> getDefault(Param<T> param)
public static final <T> boolean hasDefault(Param<T> param)
public static final ParamMap extractParamMap()
protected static java.lang.String logName()
protected static org.slf4j.Logger log()
protected static void logInfo(scala.Function0<java.lang.String> msg)
protected static void logDebug(scala.Function0<java.lang.String> msg)
protected static void logTrace(scala.Function0<java.lang.String> msg)
protected static void logWarning(scala.Function0<java.lang.String> msg)
protected static void logError(scala.Function0<java.lang.String> msg)
protected static void logInfo(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logDebug(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logTrace(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logWarning(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logError(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static boolean isTraceEnabled()
protected static void initializeLogIfNecessary(boolean isInterpreter)
protected static StructType transformSchema(StructType schema, boolean logging)
public static M fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
public static M fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
public static final Param<java.lang.String> labelCol()
public static final java.lang.String getLabelCol()
public static final Param<java.lang.String> featuresCol()
public static final java.lang.String getFeaturesCol()
public static final Param<java.lang.String> predictionCol()
public static final java.lang.String getPredictionCol()
protected static StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
public static Learner setLabelCol(java.lang.String value)
public static Learner setFeaturesCol(java.lang.String value)
public static Learner setPredictionCol(java.lang.String value)
public static M fit(Dataset<?> dataset)
public static StructType transformSchema(StructType schema)
protected static RDD<LabeledPoint> extractLabeledPoints(Dataset<?> dataset)
public static final DoubleParam regParam()
public static final double getRegParam()
public static final DoubleParam elasticNetParam()
public static final double getElasticNetParam()
public static final IntParam maxIter()
public static final int getMaxIter()
public static final DoubleParam tol()
public static final double getTol()
public static final BooleanParam fitIntercept()
public static final boolean getFitIntercept()
public static final BooleanParam standardization()
public static final boolean getStandardization()
public static final Param<java.lang.String> weightCol()
public static final java.lang.String getWeightCol()
public static final Param<java.lang.String> solver()
public static final java.lang.String getSolver()
public static void save(java.lang.String path) throws java.io.IOException
java.io.IOException
public static MLWriter write()
public java.lang.String uid()
Identifiable
uid
in interface Identifiable
public LinearRegression setRegParam(double value)
value
- (undocumented)public LinearRegression setFitIntercept(boolean value)
value
- (undocumented)public LinearRegression setStandardization(boolean value)
value
- (undocumented)public LinearRegression setElasticNetParam(double value)
value
- (undocumented)public LinearRegression setMaxIter(int value)
value
- (undocumented)public LinearRegression setTol(double value)
value
- (undocumented)public LinearRegression setWeightCol(java.lang.String value)
value
- (undocumented)public LinearRegression setSolver(java.lang.String value)
value
- (undocumented)protected LinearRegressionModel train(Dataset<?> dataset)
Predictor
fit()
to avoid dealing with schema validation
and copying parameters into the model.
train
in class Predictor<Vector,LinearRegression,LinearRegressionModel>
dataset
- Training datasetpublic LinearRegression copy(ParamMap extra)
Params
copy
in interface Params
copy
in class Predictor<Vector,LinearRegression,LinearRegressionModel>
extra
- (undocumented)defaultCopy()
public StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
schema
- input schemafitting
- whether this is in fittingfeaturesDataType
- SQL DataType for FeaturesType.
E.g., VectorUDT
for vector features.public Param<java.lang.String> labelCol()
public java.lang.String getLabelCol()
public Param<java.lang.String> featuresCol()
public java.lang.String getFeaturesCol()
public Param<java.lang.String> predictionCol()
public java.lang.String getPredictionCol()