Add _y_hat_prev for adaptive tradeoff scheme

examples/analytical-function/example_analytical_function_testSequential.py

@@ -238,6 +238,7 @@ if __name__ == "__main__":
         - exploration = 'Voronoi' creates mismatching numbers of candidates and weights
         - exploration = 'loocv' needs MetaModel.create_ModelError, which does not exist
         - exploration = 'dual annealing' restricted in what it allows as exploitation methods
+        - tradeoff = 'adaptive' perhaps not possible in the first AL iteration?
         
     The following combinations are running through:
         - BayesActDesign quite fast
@@ -253,7 +254,7 @@ if __name__ == "__main__":
     tradeoff_schemes = [None, 'equal', 'epsilon-decreasing', 'adaptive']
     #sampling_method = ['random', 'latin-hypercube', 'sobol', 'halton', 
     #                   'hammersley', 'chebyshev', 'grid']
-    exploration_schemes = ['Voronoi', 'random', 'latin-hypercube', 'LOOCV', 
+    exploration_schemes = ['Voronoi', 'global_mc', 'random', 'latin-hypercube', 'LOOCV', 
                            'dual annealing']
     exploration_schemes = ['random', 'dual annealing']
     exploitation_schemes = ['BayesOptDesign', 'BayesActDesign', 'VarOptDesign', 

src/bayesvalidrox/surrogate_models/engine.py

@@ -488,6 +488,7 @@ class Engine:
                     # self.MetaModel.ExpDesignFlag = 'sequential'
                     # TODO: this is only a fix, remove as soon as possible to 
                     #       avoid circular dependencies!
+                    self.SeqDesign._y_hat_prev = self._y_hat_prev
                     if self.ExpDesign.exploit_method.lower() == 'bayesoptdesign':
                         self.SeqDesign.engine = self
                     Xnew, updatedPrior = self.SeqDesign.choose_next_sample(TotalSigma2,

src/bayesvalidrox/surrogate_models/sequential_design.py

@@ -597,6 +597,11 @@ class SequentialDesign:
                 mseError = mean_squared_error(pce_y, y)
 
                 # Mean squared CV - error for last design point
+                # TODO: _y_hat_prev is only calculated after the first sequential
+                #       iteration scheme! Either work around this, or calculate 
+                #       anyway.
+                if self._y_hat_prev is None:
+                    raise AttributeError('Adaptive tradeoff scheme cannot be used in the first sequential iteration!')
                 pce_y_prev = np.array(list(self._y_hat_prev.values()))[:, 0]
                 mseCVError = mean_squared_error(pce_y_prev, y)