public class MultilabelClassificationEvaluator extends Evaluator implements HasPredictionCol, HasLabelCol, DefaultParamsWritable
| Constructor and Description |
|---|
MultilabelClassificationEvaluator() |
MultilabelClassificationEvaluator(String uid) |
| Modifier and Type | Method and Description |
|---|---|
MultilabelClassificationEvaluator |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
double |
evaluate(Dataset<?> dataset)
Evaluates model output and returns a scalar metric.
|
double |
getMetricLabel() |
String |
getMetricName() |
MultilabelMetrics |
getMetrics(Dataset<?> dataset)
Get a MultilabelMetrics, which can be used to get multilabel classification
metrics such as accuracy, precision, precisionByLabel, etc.
|
boolean |
isLargerBetter()
Indicates whether the metric returned by
evaluate should be maximized (true, default)
or minimized (false). |
Param<String> |
labelCol()
Param for label column name.
|
static MultilabelClassificationEvaluator |
load(String path) |
DoubleParam |
metricLabel()
param for the class whose metric will be computed in
"precisionByLabel", "recallByLabel",
"f1MeasureByLabel". |
Param<String> |
metricName()
param for metric name in evaluation (supports
"f1Measure" (default), "subsetAccuracy",
"accuracy", "hammingLoss", "precision", "recall", "precisionByLabel",
"recallByLabel", "f1MeasureByLabel", "microPrecision", "microRecall",
"microF1Measure") |
Param<String> |
predictionCol()
Param for prediction column name.
|
static MLReader<T> |
read() |
MultilabelClassificationEvaluator |
setLabelCol(String value) |
MultilabelClassificationEvaluator |
setMetricLabel(double value) |
MultilabelClassificationEvaluator |
setMetricName(String value) |
MultilabelClassificationEvaluator |
setPredictionCol(String value) |
String |
toString() |
String |
uid()
An immutable unique ID for the object and its derivatives.
|
getPredictionColgetLabelColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnwritesavepublic MultilabelClassificationEvaluator(String uid)
public MultilabelClassificationEvaluator()
public static MultilabelClassificationEvaluator load(String path)
public static MLReader<T> read()
public final Param<String> labelCol()
HasLabelCollabelCol in interface HasLabelColpublic final Param<String> predictionCol()
HasPredictionColpredictionCol in interface HasPredictionColpublic String uid()
Identifiableuid in interface Identifiablepublic final Param<String> metricName()
"f1Measure" (default), "subsetAccuracy",
"accuracy", "hammingLoss", "precision", "recall", "precisionByLabel",
"recallByLabel", "f1MeasureByLabel", "microPrecision", "microRecall",
"microF1Measure")public String getMetricName()
public MultilabelClassificationEvaluator setMetricName(String value)
public final DoubleParam metricLabel()
"precisionByLabel", "recallByLabel",
"f1MeasureByLabel".public double getMetricLabel()
public MultilabelClassificationEvaluator setMetricLabel(double value)
public MultilabelClassificationEvaluator setPredictionCol(String value)
public MultilabelClassificationEvaluator setLabelCol(String value)
public double evaluate(Dataset<?> dataset)
EvaluatorisLargerBetter specifies whether larger values are better.
public MultilabelMetrics getMetrics(Dataset<?> dataset)
dataset - a dataset that contains labels/observations and predictions.public boolean isLargerBetter()
Evaluatorevaluate should be maximized (true, default)
or minimized (false).
A given evaluator may support multiple metrics which may be maximized or minimized.isLargerBetter in class Evaluatorpublic MultilabelClassificationEvaluator copy(ParamMap extra)
ParamsdefaultCopy().public String toString()
toString in interface IdentifiabletoString in class Object