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, transform
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
save
public static MLReader<IsotonicRegressionModel> read()
public static IsotonicRegressionModel load(String path)
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 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)public MLWriter write()
MLWritable
MLWriter
instance for this ML instance.write
in interface MLWritable