FeaturesType - Type of input features. E.g., VectorM - Concrete Model typepublic abstract class ProbabilisticClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>> extends ClassificationModel<FeaturesType,M> implements ProbabilisticClassifierParams
ProbabilisticClassifier.
Classes are indexed {0, 1, ..., numClasses - 1}.
| Constructor and Description |
|---|
ProbabilisticClassificationModel() |
| Modifier and Type | Method and Description |
|---|---|
static void |
normalizeToProbabilitiesInPlace(DenseVector v)
Normalize a vector of raw predictions to be a multinomial probability vector, in place.
|
Vector |
predictProbability(FeaturesType features)
Predict the probability of each class given the features.
|
Param<String> |
probabilityCol()
Param for Column name for predicted class conditional probabilities.
|
M |
setProbabilityCol(String value) |
M |
setThresholds(double[] value) |
DoubleArrayParam |
thresholds()
Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
|
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms dataset by reading from
featuresCol, and appending new columns as specified by
parameters:
- predicted labels as predictionCol of type Double
- raw predictions (confidences) as rawPredictionCol of type Vector
- probability of each class as probabilityCol of type Vector. |
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
numClasses, predict, predictRaw, rawPredictionCol, setRawPredictionCol, transformImplfeaturesCol, labelCol, numFeatures, predictionCol, setFeaturesCol, setPredictionColtransform, transform, transformparamsequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitvalidateAndTransformSchemagetLabelCol, labelColfeaturesCol, getFeaturesColgetPredictionCol, predictionColclear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoString, uidgetRawPredictionCol, rawPredictionColgetProbabilityColgetThresholds$init$, 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_, uninitializepublic static void normalizeToProbabilitiesInPlace(DenseVector v)
The input raw predictions should be nonnegative. The output vector sums to 1.
NOTE: This is NOT applicable to all models, only ones which effectively use class instance counts for raw predictions.
v - (undocumented)IllegalArgumentException - if the input vector is all-0 or including negative valuespublic DoubleArrayParam thresholds()
HasThresholdsthresholds in interface HasThresholdspublic final Param<String> probabilityCol()
HasProbabilityColprobabilityCol in interface HasProbabilityColpublic M setProbabilityCol(String value)
public M setThresholds(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 ClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>>schema - (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
featuresCol, and appending new columns as specified by
parameters:
- predicted labels as predictionCol of type Double
- raw predictions (confidences) as rawPredictionCol of type Vector
- probability of each class as probabilityCol of type Vector.
transform in class ClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>>dataset - input datasetpublic Vector predictProbability(FeaturesType features)
This internal method is used to implement transform() and output probabilityCol.
features - (undocumented)