public class GeneralizedLinearRegressionModel extends RegressionModel<Vector,GeneralizedLinearRegressionModel> implements MLWritable
GeneralizedLinearRegression
.Modifier and Type | Method and Description |
---|---|
protected static <T> T |
$(Param<T> param) |
static Params |
clear(Param<?> param) |
Vector |
coefficients() |
GeneralizedLinearRegressionModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
protected static <T extends Params> |
copyValues(T to,
ParamMap extra) |
protected static <T extends Params> |
copyValues$default$2() |
protected static <T extends Params> |
defaultCopy(ParamMap extra) |
GeneralizedLinearRegressionSummary |
evaluate(Dataset<?> dataset)
Evaluate the model on the given dataset, returning a summary of the results.
|
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> |
family() |
Param<java.lang.String> |
family()
Param for the name of family which is a description of the error distribution
to be used in the model.
|
org.apache.spark.ml.regression.GeneralizedLinearRegression.FamilyAndLink |
familyAndLink() |
org.apache.spark.ml.regression.GeneralizedLinearRegression.Family |
familyObj() |
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 java.lang.String |
getFamily() |
java.lang.String |
getFamily() |
static java.lang.String |
getFeaturesCol() |
java.lang.String |
getFeaturesCol() |
static boolean |
getFitIntercept() |
static java.lang.String |
getLabelCol() |
java.lang.String |
getLabelCol() |
static java.lang.String |
getLink() |
java.lang.String |
getLink() |
static java.lang.String |
getLinkPredictionCol() |
java.lang.String |
getLinkPredictionCol() |
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 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 if
summary is available. |
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 Param<java.lang.String> |
link() |
Param<java.lang.String> |
link()
Param for the name of link function which provides the relationship
between the linear predictor and the mean of the distribution function.
|
org.apache.spark.ml.regression.GeneralizedLinearRegression.Link |
linkObj() |
static Param<java.lang.String> |
linkPredictionCol() |
Param<java.lang.String> |
linkPredictionCol()
Param for link prediction (linear predictor) column name.
|
static GeneralizedLinearRegressionModel |
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 int |
numFeatures() |
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<GeneralizedLinearRegressionModel> |
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) |
GeneralizedLinearRegressionModel |
setLinkPredictionCol(java.lang.String value)
Sets the link prediction (linear predictor) column name.
|
static M |
setParent(Estimator<M> parent) |
static M |
setPredictionCol(java.lang.String value) |
static Param<java.lang.String> |
solver() |
GeneralizedLinearRegressionTrainingSummary |
summary()
Gets R-like summary of model on training set.
|
static DoubleParam |
tol() |
static java.lang.String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms dataset by reading from
featuresCol , calling predict() , and storing
the predictions as a new column predictionCol . |
protected 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.
|
static StructType |
validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType) |
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 an
MLWriter instance for this ML instance. |
featuresDataType, numFeatures, setFeaturesCol, setPredictionCol, 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<GeneralizedLinearRegressionModel> read()
public static GeneralizedLinearRegressionModel 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 $(Param<T> param)
public static final <T> scala.Option<T> getDefault(Param<T> param)
public static final <T> boolean hasDefault(Param<T> param)
public static final ParamMap extractParamMap()
protected static java.lang.String logName()
protected static org.slf4j.Logger log()
protected static void logInfo(scala.Function0<java.lang.String> msg)
protected static void logDebug(scala.Function0<java.lang.String> msg)
protected static void logTrace(scala.Function0<java.lang.String> msg)
protected static void logWarning(scala.Function0<java.lang.String> msg)
protected static void logError(scala.Function0<java.lang.String> msg)
protected static void logInfo(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logDebug(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logTrace(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logWarning(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void logError(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static boolean isTraceEnabled()
protected static void initializeLogIfNecessary(boolean isInterpreter)
protected static StructType transformSchema(StructType schema, boolean logging)
public static Estimator<M> parent()
public static void parent_$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()
public static M setFeaturesCol(java.lang.String value)
public static M setPredictionCol(java.lang.String value)
public static int numFeatures()
protected static DataType featuresDataType()
public static StructType transformSchema(StructType schema)
public static final BooleanParam fitIntercept()
public static final boolean getFitIntercept()
public static final IntParam maxIter()
public static final int getMaxIter()
public static final DoubleParam tol()
public static final double getTol()
public static final DoubleParam regParam()
public static final double getRegParam()
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 final Param<java.lang.String> family()
public static java.lang.String getFamily()
public static final Param<java.lang.String> link()
public static java.lang.String getLink()
public static final Param<java.lang.String> linkPredictionCol()
public static java.lang.String getLinkPredictionCol()
public static StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
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 GeneralizedLinearRegressionModel setLinkPredictionCol(java.lang.String value)
value
- (undocumented)public org.apache.spark.ml.regression.GeneralizedLinearRegression.Family familyObj()
public org.apache.spark.ml.regression.GeneralizedLinearRegression.Link linkObj()
public org.apache.spark.ml.regression.GeneralizedLinearRegression.FamilyAndLink familyAndLink()
protected double predict(Vector features)
PredictionModel
transform()
and output predictionCol
.predict
in class PredictionModel<Vector,GeneralizedLinearRegressionModel>
features
- (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
PredictionModel
featuresCol
, calling predict()
, and storing
the predictions as a new column predictionCol
.
transform
in class PredictionModel<Vector,GeneralizedLinearRegressionModel>
dataset
- input datasetpredictionCol
of type Double
protected Dataset<Row> transformImpl(Dataset<?> dataset)
transformImpl
in class PredictionModel<Vector,GeneralizedLinearRegressionModel>
public GeneralizedLinearRegressionTrainingSummary summary()
public boolean hasSummary()
summary
is available.public GeneralizedLinearRegressionSummary evaluate(Dataset<?> dataset)
dataset
- (undocumented)public GeneralizedLinearRegressionModel copy(ParamMap extra)
Params
copy
in interface Params
copy
in class Model<GeneralizedLinearRegressionModel>
extra
- (undocumented)defaultCopy()
public MLWriter write()
MLWritable
MLWriter
instance for this ML instance.write
in interface MLWritable
public Param<java.lang.String> family()
public java.lang.String getFamily()
public Param<java.lang.String> link()
public java.lang.String getLink()
public Param<java.lang.String> linkPredictionCol()
public java.lang.String getLinkPredictionCol()
public StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
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()