public class IsotonicRegressionModel extends Model<IsotonicRegressionModel> implements IsotonicRegressionBase, MLWritable
For detailed rules see org.apache.spark.mllib.regression.IsotonicRegressionModel.predict()
.
param: oldModel A IsotonicRegressionModel
model trained by IsotonicRegression
.
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.
|
IntParam |
featureIndex()
Param for the index of the feature if
featuresCol is a vector column (default: 0 ), no
effect otherwise. |
Param<String> |
featuresCol()
Param for features column name.
|
BooleanParam |
isotonic()
Param for whether the output sequence should be isotonic/increasing (true) or
antitonic/decreasing (false).
|
Param<String> |
labelCol()
Param for label column name.
|
static IsotonicRegressionModel |
load(String path) |
int |
numFeatures() |
double |
predict(double value) |
Param<String> |
predictionCol()
Param for prediction column name.
|
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(String value) |
IsotonicRegressionModel |
setPredictionCol(String value) |
String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
Param<String> |
weightCol()
Param for weight column name.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transform
params
extractWeightedLabeledPoints, getFeatureIndex, getIsotonic, hasWeightCol, validateAndTransformSchema
getFeaturesCol
getLabelCol
getPredictionCol
getWeightCol
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize
save
public static MLReader<IsotonicRegressionModel> read()
public static IsotonicRegressionModel load(String path)
public final BooleanParam isotonic()
IsotonicRegressionBase
isotonic
in interface IsotonicRegressionBase
public final IntParam featureIndex()
IsotonicRegressionBase
featuresCol
is a vector column (default: 0
), no
effect otherwise.featureIndex
in interface IsotonicRegressionBase
public final Param<String> weightCol()
HasWeightCol
weightCol
in interface HasWeightCol
public final Param<String> predictionCol()
HasPredictionCol
predictionCol
in interface HasPredictionCol
public final Param<String> labelCol()
HasLabelCol
labelCol
in interface HasLabelCol
public final Param<String> featuresCol()
HasFeaturesCol
featuresCol
in interface HasFeaturesCol
public String uid()
Identifiable
uid
in interface Identifiable
public IsotonicRegressionModel setFeaturesCol(String value)
public IsotonicRegressionModel setPredictionCol(String value)
public IsotonicRegressionModel setFeatureIndex(int value)
public Vector boundaries()
public Vector predictions()
public IsotonicRegressionModel copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Model<IsotonicRegressionModel>
extra
- (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
Transformer
transform
in class Transformer
dataset
- (undocumented)public double predict(double value)
public StructType transformSchema(StructType schema)
PipelineStage
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)public MLWriter write()
MLWritable
MLWriter
instance for this ML instance.write
in interface MLWritable
public int numFeatures()
public String toString()
toString
in interface Identifiable
toString
in class Object