org.apache.spark.ml.regression

Class IsotonicRegressionModel

java.lang.Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<IsotonicRegressionModel>

org.apache.spark.ml.regression.IsotonicRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable, MLWritable

public class IsotonicRegressionModel
extends Model<IsotonicRegressionModel>
implements MLWritable

:: Experimental :: Model fitted by IsotonicRegression. Predicts using a piecewise linear function.

For detailed rules see org.apache.spark.mllib.regression.IsotonicRegressionModel.predict().

param: oldModel A IsotonicRegressionModel model trained by IsotonicRegression.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
Vector	boundaries() Boundaries in increasing order for which predictions are known.
IsotonicRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
RDD<scala.Tuple3<java.lang.Object,java.lang.Object,java.lang.Object>>	extractWeightedLabeledPoints(DataFrame 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).
static IsotonicRegressionModel	load(java.lang.String path)
Vector	predictions() Predictions associated with the boundaries at the same index, monotone because of isotonic regression.
static MLReader<IsotonicRegressionModel>	read()
IsotonicRegressionModel	setFeatureIndex(int value)
IsotonicRegressionModel	setFeaturesCol(java.lang.String value)
IsotonicRegressionModel	setPredictionCol(java.lang.String value)
DataFrame	transform(DataFrame dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema, boolean fitting)
MLWriter	write() Returns an MLWriter instance for this ML instance.

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

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

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

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

toString

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

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

save

Method Detail

read

public static MLReader<IsotonicRegressionModel> read()

load

public static IsotonicRegressionModel load(java.lang.String path)

uid

public java.lang.String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setFeaturesCol

public IsotonicRegressionModel setFeaturesCol(java.lang.String value)

setPredictionCol

public IsotonicRegressionModel setPredictionCol(java.lang.String value)

setFeatureIndex

public IsotonicRegressionModel setFeatureIndex(int value)

boundaries

public Vector boundaries()

Boundaries in increasing order for which predictions are known.

predictions

public Vector predictions()

Predictions associated with the boundaries at the same index, monotone because of isotonic regression.

Returns:

(undocumented)

copy

public IsotonicRegressionModel copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly.

Specified by:

copy in interface Params

Specified by:

copy in class Model<IsotonicRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

transform

public DataFrame transform(DataFrame dataset)

Description copied from class: Transformer

Transforms the input dataset.

Specified by:

transform in class Transformer

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

Derives the output schema from the input schema.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

isotonic

public BooleanParam isotonic()

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

Returns:

(undocumented)

getIsotonic

public boolean getIsotonic()

featureIndex

public IntParam featureIndex()

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

Returns:

(undocumented)

getFeatureIndex

public int getFeatureIndex()

hasWeightCol

public boolean hasWeightCol()

Checks whether the input has weight column.

extractWeightedLabeledPoints

public RDD<scala.Tuple3<java.lang.Object,java.lang.Object,java.lang.Object>> extractWeightedLabeledPoints(DataFrame dataset)

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

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting)