public class IsotonicRegressionModel extends Model<IsotonicRegressionModel> implements 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.
|
static Params |
clear(Param<?> param) |
IsotonicRegressionModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
static String |
explainParam(Param<?> param) |
static String |
explainParams() |
static ParamMap |
extractParamMap() |
static ParamMap |
extractParamMap(ParamMap extra) |
RDD<scala.Tuple3<Object,Object,Object>> |
extractWeightedLabeledPoints(Dataset<?> dataset)
Extracts (label, feature, weight) from input dataset.
|
static IntParam |
featureIndex() |
IntParam |
featureIndex()
Param for the index of the feature if
featuresCol is a vector column (default: 0 ), no
effect otherwise. |
static Param<String> |
featuresCol() |
static <T> scala.Option<T> |
get(Param<T> param) |
static <T> scala.Option<T> |
getDefault(Param<T> param) |
static int |
getFeatureIndex() |
int |
getFeatureIndex() |
static String |
getFeaturesCol() |
static boolean |
getIsotonic() |
boolean |
getIsotonic() |
static String |
getLabelCol() |
static <T> T |
getOrDefault(Param<T> param) |
static Param<Object> |
getParam(String paramName) |
static String |
getPredictionCol() |
static String |
getWeightCol() |
static <T> boolean |
hasDefault(Param<T> param) |
static boolean |
hasParam(String paramName) |
static boolean |
hasParent() |
boolean |
hasWeightCol()
Checks whether the input has weight column.
|
static boolean |
isDefined(Param<?> param) |
static BooleanParam |
isotonic() |
BooleanParam |
isotonic()
Param for whether the output sequence should be isotonic/increasing (true) or
antitonic/decreasing (false).
|
static boolean |
isSet(Param<?> param) |
static Param<String> |
labelCol() |
static IsotonicRegressionModel |
load(String path) |
static Param<?>[] |
params() |
static void |
parent_$eq(Estimator<M> x$1) |
static Estimator<M> |
parent() |
static Param<String> |
predictionCol() |
Vector |
predictions()
Predictions associated with the boundaries at the same index, monotone because of isotonic
regression.
|
static MLReader<IsotonicRegressionModel> |
read() |
static void |
save(String path) |
static <T> Params |
set(Param<T> param,
T value) |
IsotonicRegressionModel |
setFeatureIndex(int value) |
IsotonicRegressionModel |
setFeaturesCol(String value) |
static M |
setParent(Estimator<M> parent) |
IsotonicRegressionModel |
setPredictionCol(String value) |
static String |
toString() |
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.
|
StructType |
validateAndTransformSchema(StructType schema,
boolean fitting)
Validates and transforms input schema.
|
static Param<String> |
weightCol() |
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transform
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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 static String toString()
public static Param<?>[] params()
public static String explainParam(Param<?> param)
public static String explainParams()
public static final boolean isSet(Param<?> param)
public static final boolean isDefined(Param<?> param)
public static boolean hasParam(String paramName)
public static Param<Object> getParam(String paramName)
public static final <T> scala.Option<T> get(Param<T> param)
public static final <T> T getOrDefault(Param<T> param)
public static final <T> scala.Option<T> getDefault(Param<T> param)
public static final <T> boolean hasDefault(Param<T> param)
public static final ParamMap extractParamMap()
public static Estimator<M> parent()
public static void parent_$eq(Estimator<M> x$1)
public static M setParent(Estimator<M> parent)
public static boolean hasParent()
public static final Param<String> featuresCol()
public static final String getFeaturesCol()
public static final Param<String> labelCol()
public static final String getLabelCol()
public static final Param<String> predictionCol()
public static final String getPredictionCol()
public static final Param<String> weightCol()
public static final String getWeightCol()
public static final BooleanParam isotonic()
public static final boolean getIsotonic()
public static final IntParam featureIndex()
public static final int getFeatureIndex()
public static void save(String path) throws java.io.IOException
java.io.IOException
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
public RDD<scala.Tuple3<Object,Object,Object>> extractWeightedLabeledPoints(Dataset<?> dataset)
dataset
- (undocumented)public IntParam featureIndex()
featuresCol
is a vector column (default: 0
), no
effect otherwise.public int getFeatureIndex()
public boolean getIsotonic()
public boolean hasWeightCol()
public BooleanParam isotonic()
public StructType validateAndTransformSchema(StructType schema, boolean fitting)
schema
- input schemafitting
- whether this is in fitting or prediction