/usr/include/BALL/QSAR/classificationValidation.h is in libball1.4-dev 1.4.3~beta1-3.
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// vi: set ts=2:
//
//
#ifndef CLASVALIDATION
#define CLASVALIDATION
#ifndef QSARDATA
#include <BALL/QSAR/QSARData.h>
#endif
#ifndef VALIDATION
#include <BALL/QSAR/validation.h>
#endif
#include <iterator>
namespace BALL
{
namespace QSAR
{
class ClassificationModel;
/** class for validation of QSAR regression models */
class BALL_EXPORT ClassificationValidation : public Validation
{
public:
/** @name Constructors and Destructors
*/
//@{
/** constructor
@param m pointer to the regression model, which the object of this class should test */
ClassificationValidation(ClassificationModel* m);
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
//@}
/** @name Accessors
*/
//@{
void crossValidation(int k, bool restore=1);
double getCVRes();
double getFitRes();
void setCVRes(double d);
void testInputData(bool transform=0);
/** return pointer to the matrix containing the number of TP, FP, TN, FN in one column for each class */
const Eigen::MatrixXd* getConfusionMatrix();
/** returns a RowVector holding the one value contituting the validation result for each class if "average accuracy" or "average MCC" is chosen (see selectStat()). */
const Eigen::VectorXd* getClassResults();
/** starts bootstrapping with k samples \n
@param k no of bootstrap samples */
void bootstrap(int k, bool restore=1);
/** Y randomization test \n
Randomizes all columns of model.Y, trains the model, runs crossValidation and testInputData and saves the resulting accuracy_input_test and accuracy_cv value to a vector, where Eigen::MatrixXd(i,0)=accuracy_input_test, Eigen::MatrixXd(i,1)=accuracy_cv \n
@param runs this is repeated as often as specified by 'runs' */
const Eigen::MatrixXd& yRandomizationTest(int runs, int k);
/** get average accuracy value as determined after cross validation */
double getAccuracyCV();
/** get average accuracy value as determined after testing of input data(); */
double getAccuracyInputTest();
void selectStat(int s);
void saveToFile(string filename) const;
void saveToFile(string filename, const double& quality_input_test, const double& predictive_quality) const;
void readFromFile(string filename);
//@}
private:
/** @name Accessors
*/
//@{
/** Tests the current model with all substances in the (unchanged) test data set */
void testAllSubstances(bool transform);
/** calculate average accuracy with the current values of TP, FP, FN, TN in matrix ClassificationValidation.predictions. */
void calculateAverageSensitivity();
/** calculate weighted average accuracy of all classes. Weighted by the number of training compounds within each class */
void calculateWeightedSensitivity();
/** calculate accuracy for all classes at once */
void calculateOverallAccuracy();
/** calculate one MCC for each class and use the average */
void calculateAverageMCC();
/** calculate MCC for all classes at once */
void calculateOverallMCC();
/** calculate the True Discovery Rate (only applicable to binary classification validation results). */
void calculateTDR();
//@}
/** @name Attributes
*/
//@{
/** matrix containing the number of TP, FP, FN, TN in one column for each class */
Eigen::MatrixXd confusion_matrix_;
/** RowVector holding the one value contituting the validation result for each class if "average sensitivity" or "average MCC" is chosen (see selectStat()). */
Eigen::VectorXd class_results_;
double quality_;
double quality_input_test_;
double quality_cv_;
/** pointer to the regression model, which the object of this class should test */
ClassificationModel* clas_model;
void (ClassificationValidation::* qualCalculation)();
//@}
};
}
}
#endif // REGVALIDATION
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