export SVM::trainAuto to python #7224 (#8373)

* export SVM::trainAuto to python #7224

* workaround for ABI compatibility of SVM::trainAuto

* add parameter comments to new SVM::trainAuto function

* Export ParamGrid member variables

modules/ml/include/opencv2/ml.hpp

@@ -104,7 +104,7 @@ enum SampleTypes
 It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate
 being computed by cross-validation.
  */
-class CV_EXPORTS ParamGrid
+class CV_EXPORTS_W ParamGrid
 {
 public:
     /** @brief Default constructor */
@@ -112,8 +112,8 @@ public:
     /** @brief Constructor with parameters */
     ParamGrid(double _minVal, double _maxVal, double _logStep);
 
-    double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
+    CV_PROP_RW double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
-    double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
+    CV_PROP_RW double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
     /** @brief Logarithmic step for iterating the statmodel parameter.
 
     The grid determines the following iteration sequence of the statmodel parameter values:
@@ -122,7 +122,15 @@ public:
     \f[\texttt{minVal} * \texttt{logStep} ^n <  \texttt{maxVal}\f]
     The grid is logarithmic, so logStep must always be greater then 1. Default value is 1.
     */
-    double logStep;
+    CV_PROP_RW double logStep;
+
+    /** @brief Creates a ParamGrid Ptr that can be given to the %SVM::trainAuto method
+
+    @param minVal minimum value of the parameter grid
+    @param maxVal maximum value of the parameter grid
+    @param logstep Logarithmic step for iterating the statmodel parameter
+    */
+    CV_WRAP static Ptr<ParamGrid> create(double minVal=0., double maxVal=0., double logstep=1.);
 };
 
 /** @brief Class encapsulating training data.
@@ -691,6 +699,46 @@ public:
                     ParamGrid degreeGrid = getDefaultGrid(DEGREE),
                     bool balanced=false) = 0;
 
+    /** @brief Trains an %SVM with optimal parameters
+
+    @param samples training samples
+    @param layout See ml::SampleTypes.
+    @param responses vector of responses associated with the training samples.
+    @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
+        subset is used to test the model, the others form the train set. So, the %SVM algorithm is
+    @param Cgrid grid for C
+    @param gammaGrid grid for gamma
+    @param pGrid grid for p
+    @param nuGrid grid for nu
+    @param coeffGrid grid for coeff
+    @param degreeGrid grid for degree
+    @param balanced If true and the problem is 2-class classification then the method creates more
+        balanced cross-validation subsets that is proportions between classes in subsets are close
+        to such proportion in the whole train dataset.
+
+    The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
+    nu, coef0, degree. Parameters are considered optimal when the cross-validation
+    estimate of the test set error is minimal.
+
+    This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
+    offers rudimentary parameter options.
+
+    This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
+    regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
+    the usual %SVM with parameters specified in params is executed.
+    */
+    CV_WRAP bool trainAuto(InputArray samples,
+            int layout,
+            InputArray responses,
+            int kFold = 10,
+            Ptr<ParamGrid> Cgrid = SVM::getDefaultGridPtr(SVM::C),
+            Ptr<ParamGrid> gammaGrid  = SVM::getDefaultGridPtr(SVM::GAMMA),
+            Ptr<ParamGrid> pGrid      = SVM::getDefaultGridPtr(SVM::P),
+            Ptr<ParamGrid> nuGrid     = SVM::getDefaultGridPtr(SVM::NU),
+            Ptr<ParamGrid> coeffGrid  = SVM::getDefaultGridPtr(SVM::COEF),
+            Ptr<ParamGrid> degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE),
+            bool balanced=false);
+
     /** @brief Retrieves all the support vectors
 
     The method returns all the support vectors as a floating-point matrix, where support vectors are
@@ -733,6 +781,16 @@ public:
      */
     static ParamGrid getDefaultGrid( int param_id );
 
+    /** @brief Generates a grid for %SVM parameters.
+
+    @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
+    generated for the parameter with this ID.
+
+    The function generates a grid pointer for the specified parameter of the %SVM algorithm.
+    The grid may be passed to the function SVM::trainAuto.
+     */
+    CV_WRAP static Ptr<ParamGrid> getDefaultGridPtr( int param_id );
+
     /** Creates empty model.
     Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
     find the best parameters for your problem, it can be done with SVM::trainAuto. */

modules/ml/src/inner_functions.cpp

@@ -50,6 +50,10 @@ ParamGrid::ParamGrid(double _minVal, double _maxVal, double _logStep)
     logStep = std::max(_logStep, 1.);
 }
 
+Ptr<ParamGrid> ParamGrid::create(double minval, double maxval, double logstep) {
+  return makePtr<ParamGrid>(minval, maxval, logstep);
+}
+
 bool StatModel::empty() const { return !isTrained(); }
 
 int StatModel::getVarCount() const { return 0; }

modules/ml/src/svm.cpp

@@ -362,6 +362,12 @@ static void sortSamplesByClasses( const Mat& _samples, const Mat& _responses,
 
 //////////////////////// SVM implementation //////////////////////////////
 
+Ptr<ParamGrid> SVM::getDefaultGridPtr( int param_id)
+{
+  ParamGrid grid = getDefaultGrid(param_id); // this is not a nice solution..
+  return makePtr<ParamGrid>(grid.minVal, grid.maxVal, grid.logStep);
+}
+
 ParamGrid SVM::getDefaultGrid( int param_id )
 {
     ParamGrid grid;
@@ -1920,6 +1926,24 @@ public:
         bool returnDFVal;
     };
 
+    bool trainAuto_(InputArray samples, int layout,
+            InputArray responses, int kfold, Ptr<ParamGrid> Cgrid,
+            Ptr<ParamGrid> gammaGrid, Ptr<ParamGrid> pGrid, Ptr<ParamGrid> nuGrid,
+            Ptr<ParamGrid> coeffGrid, Ptr<ParamGrid> degreeGrid, bool balanced)
+    {
+        Ptr<TrainData> data = TrainData::create(samples, layout, responses);
+        return this->trainAuto(
+                data, kfold,
+                *Cgrid.get(),
+                *gammaGrid.get(),
+                *pGrid.get(),
+                *nuGrid.get(),
+                *coeffGrid.get(),
+                *degreeGrid.get(),
+                balanced);
+    }
+
+
     float predict( InputArray _samples, OutputArray _results, int flags ) const
     {
         float result = 0;
@@ -2281,6 +2305,19 @@ Mat SVM::getUncompressedSupportVectors() const
     return this_->getUncompressedSupportVectors_();
 }
 
+bool SVM::trainAuto(InputArray samples, int layout,
+            InputArray responses, int kfold, Ptr<ParamGrid> Cgrid,
+            Ptr<ParamGrid> gammaGrid, Ptr<ParamGrid> pGrid, Ptr<ParamGrid> nuGrid,
+            Ptr<ParamGrid> coeffGrid, Ptr<ParamGrid> degreeGrid, bool balanced)
+{
+  SVMImpl* this_ = dynamic_cast<SVMImpl*>(this);
+  if (!this_) {
+    CV_Error(Error::StsNotImplemented, "the class is not SVMImpl");
+  }
+  return this_->trainAuto_(samples, layout, responses,
+    kfold, Cgrid, gammaGrid, pGrid, nuGrid, coeffGrid, degreeGrid, balanced);
+}
+
 }
 }