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.
|
static IsotonicRegressionModel |
load(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(String value) |
IsotonicRegressionModel |
setPredictionCol(String value) |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
StructType |
transformSchema(StructType schema)
:: DeveloperApi ::
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transformequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitextractWeightedLabeledPoints, featureIndex, getFeatureIndex, getIsotonic, hasWeightCol, isotonic, validateAndTransformSchemafeaturesCol, getFeaturesColgetLabelCol, labelColgetPredictionCol, predictionColgetWeightCol, weightColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringinitializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarningsavepublic static MLReader<IsotonicRegressionModel> read()
public static IsotonicRegressionModel load(String path)
public String uid()
Identifiableuid in interface Identifiablepublic 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)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<IsotonicRegressionModel>extra - (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
Transformertransform in class Transformerdataset - (undocumented)public StructType transformSchema(StructType schema)
PipelineStageCheck 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 PipelineStageschema - (undocumented)public MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritable