Negative Wilcoxon-Mann-Whitney statistic. More...
#include <shark/ObjectiveFunctions/NegativeAUC.h>
Inheritance diagram for shark::NegativeWilcoxonMannWhitneyStatistic< LabelType, OutputType >:
Public Member Functions | |
| NegativeWilcoxonMannWhitneyStatistic (bool invert=false) | |
| std::string | name () const |
| From INameable: return the class name. | |
| double | eval (Data< LabelType > const &target, Data< OutputType > const &prediction, unsigned int column) const |
| Computes Wilcoxon-Mann-Whitney statistic. | |
| double | eval (Data< LabelType > const &target, Data< OutputType > const &prediction) const |
| Public Member Functions inherited from shark::AbstractCost< LabelT, OutputT > | |
| virtual | ~AbstractCost () |
| const Features & | features () const |
| virtual void | updateFeatures () |
| bool | hasFirstDerivative () const |
| returns true when the first parameter derivative is implemented | |
| bool | isLossFunction () const |
| returns true when the cost function is in fact a loss function | |
| double | operator() (Data< LabelType > const &targets, Data< OutputType > const &predictions) const |
| Public Member Functions inherited from shark::INameable | |
| virtual | ~INameable () |
Additional Inherited Members | |
| Public Types inherited from shark::AbstractCost< LabelT, OutputT > | |
| enum | Feature { HAS_FIRST_DERIVATIVE = 1 , HAS_SECOND_DERIVATIVE = 2 , IS_LOSS_FUNCTION = 4 } |
| list of features a cost function can have More... | |
| typedef OutputT | OutputType |
| typedef LabelT | LabelType |
| typedef Batch< OutputType >::type | BatchOutputType |
| typedef Batch< LabelType >::type | BatchLabelType |
| typedef TypedFlags< Feature > | Features |
| typedef TypedFeatureNotAvailableException< Feature > | FeatureNotAvailableException |
| Protected Attributes inherited from shark::AbstractCost< LabelT, OutputT > | |
| Features | m_features |
Detailed Description
class shark::NegativeWilcoxonMannWhitneyStatistic< LabelType, OutputType >
Negative Wilcoxon-Mann-Whitney statistic.
This class computes the Wilcoxon-Mann-Whitney statistic, which is an unbiased estimate of the area under the ROC curve.
See, for example: Corinna Cortes, Mehryar Mohri. Confidence Intervals for the Area under the ROC Curve. NIPS, 2004
The area is negated so that optimizing the AUC corresponds to a minimization task.
Definition at line 164 of file NegativeAUC.h.
Constructor & Destructor Documentation
◆ NegativeWilcoxonMannWhitneyStatistic()
|
inline |
Constructor.
- Parameters
-
invert if set to true, the role of positive and negative class are switched
Definition at line 169 of file NegativeAUC.h.
Member Function Documentation
◆ eval() [1/2]
|
inlinevirtual |
Evaluates the cost of predictions, given targets.
- Parameters
-
targets target values predictions predictions, typically made by a model
Implements shark::AbstractCost< LabelT, OutputT >.
Definition at line 230 of file NegativeAUC.h.
References shark::dataDimension(), shark::NegativeWilcoxonMannWhitneyStatistic< LabelType, OutputType >::eval(), shark::Data< Type >::numberOfElements(), and SHARK_RUNTIME_CHECK.
◆ eval() [2/2]
|
inline |
Computes Wilcoxon-Mann-Whitney statistic.
- Parameters
-
target interpreted as binary class label prediction interpreted as binary class label column indicates the column of the prediction vector interpreted as probability of positive class
Definition at line 181 of file NegativeAUC.h.
References shark::Data< Type >::element(), and SHARK_RUNTIME_CHECK.
Referenced by shark::NegativeWilcoxonMannWhitneyStatistic< LabelType, OutputType >::eval().
◆ name()
|
inlinevirtual |
From INameable: return the class name.
Reimplemented from shark::INameable.
Definition at line 174 of file NegativeAUC.h.
The documentation for this class was generated from the following file:
- include/shark/ObjectiveFunctions/NegativeAUC.h