public class LinearRegressionModel extends RegressionModel<Vector,LinearRegressionModel> implements MLWritable
LinearRegression
.Modifier and Type | Method and Description |
---|---|
protected static <T> T |
$(Param<T> param) |
static Params |
clear(Param<?> param) |
Vector |
coefficients() |
LinearRegressionModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
protected static <T extends Params> |
copyValues(T to,
ParamMap extra) |
protected static <T extends Params> |
copyValues$default$2() |
protected static <T extends Params> |
defaultCopy(ParamMap extra) |
static DoubleParam |
elasticNetParam() |
LinearRegressionSummary |
evaluate(Dataset<?> dataset)
Evaluates the model on a test dataset.
|
static java.lang.String |
explainParam(Param<?> param) |
static java.lang.String |
explainParams() |
static ParamMap |
extractParamMap() |
static ParamMap |
extractParamMap(ParamMap extra) |
static Param<java.lang.String> |
featuresCol() |
Param<java.lang.String> |
featuresCol()
Param for features column name.
|
protected static DataType |
featuresDataType() |
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) |
static boolean |
hasParent() |
boolean |
hasSummary()
Indicates whether a training summary exists for this model instance.
|
protected static void |
initializeLogIfNecessary(boolean isInterpreter) |
double |
intercept() |
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.
|
static LinearRegressionModel |
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() |
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() |
protected double |
predict(Vector features)
Predict label for the given features.
|
static Param<java.lang.String> |
predictionCol() |
Param<java.lang.String> |
predictionCol()
Param for prediction column name.
|
static MLReader<LinearRegressionModel> |
read() |
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) |
static M |
setFeaturesCol(java.lang.String value) |
static M |
setParent(Estimator<M> parent) |
static M |
setPredictionCol(java.lang.String value) |
static Param<java.lang.String> |
solver() |
static BooleanParam |
standardization() |
LinearRegressionTrainingSummary |
summary()
Gets summary (e.g.
|
static DoubleParam |
tol() |
static java.lang.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) |
protected static Dataset<Row> |
transformImpl(Dataset<?> dataset) |
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.
|
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.
|
static void |
validateParams() |
static Param<java.lang.String> |
weightCol() |
MLWriter |
write()
Returns a
MLWriter instance for this ML instance. |
featuresDataType, setFeaturesCol, setPredictionCol, transform, transformImpl, transformSchema
transform, transform, transform
transformSchema
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
save
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 static MLReader<LinearRegressionModel> read()
public static LinearRegressionModel 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 $(Param<T> param)
public static final <T> scala.Option<T> getDefault(Param<T> param)
public static final <T> boolean hasDefault(Param<T> param)
public static final ParamMap extractParamMap()
protected static java.lang.String logName()
protected static org.slf4j.Logger log()
protected static void logInfo(scala.Function0<java.lang.String> msg)
protected static void logDebug(scala.Function0<java.lang.String> msg)
protected static void logTrace(scala.Function0<java.lang.String> msg)
protected static void logWarning(scala.Function0<java.lang.String> msg)
protected static void logError(scala.Function0<java.lang.String> msg)
protected static void logInfo(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logDebug(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logTrace(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logWarning(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logError(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static boolean isTraceEnabled()
protected static void initializeLogIfNecessary(boolean isInterpreter)
protected static StructType transformSchema(StructType schema, boolean logging)
public static Dataset<Row> transform(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
public static Dataset<Row> transform(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
public static Estimator<M> parent()
public static void parent_$eq(Estimator<M> x$1)
public static M setParent(Estimator<M> parent)
public static boolean hasParent()
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 M setFeaturesCol(java.lang.String value)
public static M setPredictionCol(java.lang.String value)
protected static DataType featuresDataType()
public static StructType transformSchema(StructType schema)
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 java.lang.String uid()
Identifiable
uid
in interface Identifiable
public Vector coefficients()
public double intercept()
public int numFeatures()
PredictionModel
numFeatures
in class PredictionModel<Vector,LinearRegressionModel>
public LinearRegressionTrainingSummary summary()
trainingSummary == None
.public boolean hasSummary()
public LinearRegressionSummary evaluate(Dataset<?> dataset)
dataset
- Test dataset to evaluate model on.protected double predict(Vector features)
PredictionModel
transform()
and output predictionCol
.predict
in class PredictionModel<Vector,LinearRegressionModel>
features
- (undocumented)public LinearRegressionModel copy(ParamMap extra)
Params
copy
in interface Params
copy
in class Model<LinearRegressionModel>
extra
- (undocumented)defaultCopy()
public MLWriter write()
MLWriter
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.
write
in interface MLWritable
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()