Fix epsilont early stopping in BoostAODE

src/BayesNet/BoostAODE.cc

@@ -121,6 +121,7 @@ namespace bayesnet {
     }
     void BoostAODE::trainModel(const torch::Tensor& weights)
     {
+        fitted = true;
         // Algorithm based on the adaboost algorithm for classification
         // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
         std::unordered_set<int> featuresUsed;
@@ -161,7 +162,6 @@ namespace bayesnet {
                     continue;
                 }
             }
-            featuresUsed.insert(feature);
             model = std::make_unique<SPODE>(feature);
             model->fit(dataset, features, className, states, weights_);
             auto ypred = model->predict(X_train);
@@ -170,6 +170,12 @@ namespace bayesnet {
             auto mask_right = ypred == y_train;
             auto masked_weights = weights_ * mask_wrong.to(weights_.dtype());
             double epsilon_t = masked_weights.sum().item<double>();
+            if (epsilon_t > 0.5) {
+                // Inverse the weights policy (plot ln(wt))
+                // "In each round of AdaBoost, there is a sanity check to ensure that the current base 
+                // learner is better than random guess" (Zhi-Hua Zhou, 2012)
+                break;
+            }
             double wt = (1 - epsilon_t) / epsilon_t;
             double alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
             // Step 3.2: Update weights for next classifier
@@ -181,6 +187,7 @@ namespace bayesnet {
             double totalWeights = torch::sum(weights_).item<double>();
             weights_ = weights_ / totalWeights;
             // Step 3.4: Store classifier and its accuracy to weigh its future vote
+            featuresUsed.insert(feature);
             models.push_back(std::move(model));
             significanceModels.push_back(alpha_t);
             n_models++;
@@ -197,15 +204,13 @@ namespace bayesnet {
                 }
                 priorAccuracy = accuracy;
             }
-            // epsilon_t > 0.5 => inverse the weights policy (plot ln(wt))
-            exitCondition = n_models >= maxModels && repeatSparent || epsilon_t > 0.5 || count > tolerance;
+            exitCondition = n_models >= maxModels && repeatSparent || count > tolerance;
         }
         if (featuresUsed.size() != features.size()) {
             notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()));
             status = WARNING;
         }
         notes.push_back("Number of models: " + std::to_string(n_models));
-        fitted = true;
     }
     std::vector<std::string> BoostAODE::graph(const std::string& title) const
     {