Package org.apache.spark.ml.regression

Interface IsotonicRegressionBase

All Superinterfaces:

HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol, Identifiable, org.apache.spark.internal.Logging, Params, Serializable

All Known Implementing Classes:

IsotonicRegression, IsotonicRegressionModel

public interface IsotonicRegressionBase
extends Params, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol, org.apache.spark.internal.Logging

Params for isotonic regression.

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

Method Summary

Modifier and Type	Method	Description
RDD<scala.Tuple3<Object,Object,Object>>	extractWeightedLabeledPoints(Dataset<?> dataset)	Extracts (label, feature, weight) from input dataset.
IntParam	featureIndex()	Param for the index of the feature if featuresCol is a vector column (default: 0), no effect otherwise.
int	getFeatureIndex()
boolean	getIsotonic()
boolean	hasWeightCol()	Checks whether the input has weight column.
BooleanParam	isotonic()	Param for whether the output sequence should be isotonic/increasing (true) or antitonic/decreasing (false).
StructType	validateAndTransformSchema(StructType schema, boolean fitting)	Validates and transforms input schema.

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

featuresCol, getFeaturesCol

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

getLabelCol, labelCol

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

getPredictionCol, predictionCol

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

getWeightCol, weightCol

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

toString, uid

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

initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

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

clear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Method Details

extractWeightedLabeledPoints

RDD<scala.Tuple3<Object,Object,Object>> extractWeightedLabeledPoints(Dataset<?> dataset)

Extracts (label, feature, weight) from input dataset.

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

featureIndex

IntParam featureIndex()

Param for the index of the feature if featuresCol is a vector column (default: 0), no effect otherwise.

Returns:

(undocumented)

getFeatureIndex

int getFeatureIndex()

getIsotonic

boolean getIsotonic()

hasWeightCol

boolean hasWeightCol()

Checks whether the input has weight column.

isotonic

BooleanParam isotonic()

Param for whether the output sequence should be isotonic/increasing (true) or antitonic/decreasing (false). Default: true

Returns:

(undocumented)

validateAndTransformSchema

StructType validateAndTransformSchema(StructType schema,
 boolean fitting)

Validates and transforms input schema.

Parameters:

schema - input schema

fitting - whether this is in fitting or prediction

Returns:

output schema