Negative area under the curve. More...
#include <shark/ObjectiveFunctions/NegativeAUC.h>
Inheritance diagram for shark::NegativeAUC< LabelType, OutputType >:
Public Types | |
| typedef KeyValuePair< double, LabelType > | AUCPair |
| Public Types inherited from shark::AbstractCost< LabelType, OutputType > | |
| enum | Feature |
| list of features a cost function can have More... | |
| typedef OutputType | OutputType |
| typedef LabelType | LabelType |
| typedef Batch< OutputType >::type | BatchOutputType |
| typedef Batch< LabelType >::type | BatchLabelType |
| typedef TypedFlags< Feature > | Features |
| typedef TypedFeatureNotAvailableException< Feature > | FeatureNotAvailableException |
Public Member Functions | |
| NegativeAUC (bool invert=false) | |
| std::string | name () const |
| From INameable: return the class name. More... | |
| double | eval (Data< LabelType > const &target, Data< OutputType > const &prediction, unsigned int column) const |
| Computes area under the curve. More... | |
| double | eval (Data< LabelType > const &target, Data< OutputType > const &prediction) const |
| Computes area under the curve. If the prediction vector is 1-dimensional, the "positive" class is mapped to larger values. If the prediction vector is 2-dimensional, the second dimension is viewed as the "positive" class. For higher dimensional vectors, an exception is thrown. In such a case, the column has to be explicitly specified as an additional parameter. More... | |
| Public Member Functions inherited from shark::AbstractCost< LabelType, OutputType > | |
| virtual | ~AbstractCost () |
| const Features & | features () const |
| virtual void | updateFeatures () |
| bool | hasFirstDerivative () const |
| returns true when the first parameter derivative is implemented More... | |
| bool | isLossFunction () const |
| returns true when the cost function is in fact a loss function More... | |
| double | operator() (Data< LabelType > const &targets, Data< OutputType > const &predictions) const |
| Public Member Functions inherited from shark::INameable | |
| virtual | ~INameable () |
Protected Member Functions | |
| double | trapArea (double x1, double x2, double y1, double y2) const |
Protected Attributes | |
| bool | m_invert |
| Protected Attributes inherited from shark::AbstractCost< LabelType, OutputType > | |
| Features | m_features |
Detailed Description
template<class LabelType = unsigned int, class OutputType = RealVector>
class shark::NegativeAUC< LabelType, OutputType >
Negative area under the curve.
This class computes the area under the ROC (receiver operating characteristic) curve. It implements the algorithm described in: Tom Fawcett. ROC Graphs: Notes and Practical Considerations for Researchers. 2004
The area is negated so that optimizing the AUC corresponds to a minimization task.
Definition at line 52 of file NegativeAUC.h.
Member Typedef Documentation
◆ AUCPair
| typedef KeyValuePair< double, LabelType > shark::NegativeAUC< LabelType, OutputType >::AUCPair |
Definition at line 55 of file NegativeAUC.h.
Constructor & Destructor Documentation
◆ NegativeAUC()
|
inline |
Constructor.
- Parameters
-
invert if set to true, the role of positive and negative class are switched
Definition at line 59 of file NegativeAUC.h.
References shark::NegativeAUC< LabelType, OutputType >::m_invert.
Member Function Documentation
◆ eval() [1/2]
|
inline |
Computes area under the curve.
- Parameters
-
target class label, 0 or 1 prediction prediction by classifier, OutputType-valued vector column indicates the column of the prediction vector interpreted as probability of positive class
Definition at line 71 of file NegativeAUC.h.
Referenced by shark::NegativeAUC< LabelType, OutputType >::eval(), and shark::NegativeWilcoxonMannWhitneyStatistic< LabelType, OutputType >::eval().
◆ eval() [2/2]
|
inlinevirtual |
Computes area under the curve. If the prediction vector is 1-dimensional, the "positive" class is mapped to larger values. If the prediction vector is 2-dimensional, the second dimension is viewed as the "positive" class. For higher dimensional vectors, an exception is thrown. In such a case, the column has to be explicitly specified as an additional parameter.
- Parameters
-
target class label, 0 or 1 prediction prediction by classifier, OutputType-valued vector
Implements shark::AbstractCost< LabelType, OutputType >.
Definition at line 130 of file NegativeAUC.h.
References shark::dataDimension(), shark::NegativeAUC< LabelType, OutputType >::eval(), shark::Data< Type >::numberOfElements(), and SHARK_RUNTIME_CHECK.
◆ name()
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inlinevirtual |
From INameable: return the class name.
Reimplemented from shark::INameable.
Definition at line 64 of file NegativeAUC.h.
◆ trapArea()
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inlineprotected |
Definition at line 143 of file NegativeAUC.h.
Member Data Documentation
◆ m_invert
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protected |
Definition at line 149 of file NegativeAUC.h.
Referenced by shark::NegativeWilcoxonMannWhitneyStatistic< LabelType, OutputType >::eval(), shark::NegativeAUC< LabelType, OutputType >::NegativeAUC(), and shark::NegativeWilcoxonMannWhitneyStatistic< LabelType, OutputType >::NegativeWilcoxonMannWhitneyStatistic().
The documentation for this class was generated from the following file:
- include/shark/ObjectiveFunctions/NegativeAUC.h