public class IsotonicRegression extends Estimator<IsotonicRegressionModel> implements IsotonicRegressionBase, DefaultParamsWritable
Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.
Uses IsotonicRegression
.
Constructor and Description |
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IsotonicRegression() |
IsotonicRegression(String uid) |
Modifier and Type | Method and Description |
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IsotonicRegression |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
IsotonicRegressionModel |
fit(Dataset<?> dataset)
Fits a model to the input data.
|
static IsotonicRegression |
load(String path) |
static MLReader<T> |
read() |
IsotonicRegression |
setFeatureIndex(int value) |
IsotonicRegression |
setFeaturesCol(String value) |
IsotonicRegression |
setIsotonic(boolean value) |
IsotonicRegression |
setLabelCol(String value) |
IsotonicRegression |
setPredictionCol(String value) |
IsotonicRegression |
setWeightCol(String value) |
StructType |
transformSchema(StructType schema)
:: DeveloperApi ::
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
extractWeightedLabeledPoints, featureIndex, getFeatureIndex, getIsotonic, hasWeightCol, isotonic, validateAndTransformSchema
featuresCol, getFeaturesCol
getLabelCol, labelCol
getPredictionCol, predictionCol
getWeightCol, weightCol
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
toString
initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning
write
save
public IsotonicRegression(String uid)
public IsotonicRegression()
public static IsotonicRegression load(String path)
public static MLReader<T> read()
public String uid()
Identifiable
uid
in interface Identifiable
public IsotonicRegression setLabelCol(String value)
public IsotonicRegression setFeaturesCol(String value)
public IsotonicRegression setPredictionCol(String value)
public IsotonicRegression setIsotonic(boolean value)
public IsotonicRegression setWeightCol(String value)
public IsotonicRegression setFeatureIndex(int value)
public IsotonicRegression copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Estimator<IsotonicRegressionModel>
extra
- (undocumented)public IsotonicRegressionModel fit(Dataset<?> dataset)
Estimator
fit
in class Estimator<IsotonicRegressionModel>
dataset
- (undocumented)public StructType transformSchema(StructType schema)
PipelineStage
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during transformSchema
and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate()
.
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema
in class PipelineStage
schema
- (undocumented)