public abstract class ProbabilisticClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>> extends ClassificationModel<FeaturesType,M>
Model produced by a ProbabilisticClassifier
.
Classes are indexed {0, 1, ..., numClasses - 1}.
Constructor and Description |
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ProbabilisticClassificationModel() |
Modifier and Type | Method and Description |
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Param<String> |
featuresCol()
Param for features column name.
|
String |
getFeaturesCol() |
String |
getLabelCol() |
String |
getPredictionCol() |
String |
getRawPredictionCol() |
Param<String> |
labelCol()
Param for label column name.
|
static void |
normalizeToProbabilitiesInPlace(DenseVector v)
Normalize a vector of raw predictions to be a multinomial probability vector, in place.
|
Param<String> |
predictionCol()
Param for prediction column name.
|
Param<String> |
rawPredictionCol()
Param for raw prediction (a.k.a.
|
M |
setProbabilityCol(String value) |
M |
setThresholds(double[] value) |
DataFrame |
transform(DataFrame 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 |
validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType) |
StructType |
validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType)
Validates and transforms the input schema with the provided param map.
|
numClasses, setRawPredictionCol
numFeatures, setFeaturesCol, setPredictionCol, transformSchema
transform, transform, transform
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
clear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParams
toString, uid
initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning
public static void normalizeToProbabilitiesInPlace(DenseVector v)
The input raw predictions should be >= 0. The output vector sums to 1, unless the input vector is all-0 (in which case the output is all-0 too).
NOTE: This is NOT applicable to all models, only ones which effectively use class instance counts for raw predictions.
v
- (undocumented)public M setProbabilityCol(String value)
public M setThresholds(double[] value)
public DataFrame transform(DataFrame 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 StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
public Param<String> rawPredictionCol()
public String getRawPredictionCol()
public StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
schema
- input schemafitting
- whether this is in fittingfeaturesDataType
- SQL DataType for FeaturesType.
E.g., VectorUDT
for vector features.public Param<String> labelCol()
public String getLabelCol()
public Param<String> featuresCol()
public String getFeaturesCol()
public Param<String> predictionCol()
public String getPredictionCol()