Implement block update algorithm fix in BoostAODE

bayesnet/ensembles/BoostAODE.cc

@@ -129,22 +129,67 @@ namespace bayesnet {
     }
     std::tuple<torch::Tensor&, double, bool> BoostAODE::update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights)
     {
+        /* Update Block algorithm
+            k = # of models in block
+            n_models = # of models in ensemble to make predictions
+            n_models_bak = # models saved
+            models = vector of models to make predictions
+            models_bak = models not used to make predictions
+            significances_bak = backup of significances vector
+
+            Case list
+            A) k = 1, n_models = 1		=> n = 0 , n_models = n + k
+            B) k = 1, n_models = n + 1	=> n_models = n + k
+            C) k > 1, n_models = k + 1 	=> n= 1, n_models = n + k
+            D) k > 1, n_models = k		=> n = 0, n_models = n + k
+            E) k > 1, n_models = k + n	=> n_models = n + k
+
+            A, D) n=0, k > 0, n_models == k
+            1. n_models_bak <- n_models
+            2. significances_bak <- significances
+            3. significances = vector(k, 1)
+            4. Don’t move any classifiers out of models
+            5. n_models <- k
+            6. Make prediction, compute alpha, update weights
+            7. Don’t restore any classifiers to models
+            8. significances <- significances_bak
+            9. Update last k significances
+            10. n_models <- n_models_bak
+
+            B, C, E) n > 0, k > 0, n_models == n + k
+            1. n_models_bak <- n_models
+            2. significances_bak <- significances
+            3. significances = vector(k, 1)
+            4. Move first n classifiers to models_bak
+            5. n_models <- k
+            6. Make prediction, compute alpha, update weights
+            7. Insert classifiers in models_bak to be the first n models
+            8. significances <- significances_bak
+            9. Update last k significances
+            10. n_models <- n_models_bak
+        */
         //
         // Make predict with only the last k models
         //
         std::unique_ptr<Classifier> model;
         std::vector<std::unique_ptr<Classifier>> models_bak;
+        // 1. n_models_bak <- n_models 2. significances_bak <- significances
         auto significance_bak = significanceModels;
         auto n_models_bak = n_models;
-        // Remove the first n_models - k models
+        // 3. significances = vector(k, 1)
+        significanceModels = std::vector<double>(k, 1.0);
+        // 4. Move first n classifiers to models_bak
+        // backup the first n_models - k models (if n_models == k, don't backup any)
+        VLOG_SCOPE_F(1, "upd_weights_block n_models=%d k=%d", n_models, k);
         for (int i = 0; i < n_models - k; ++i) {
             model = std::move(models[0]);
             models.erase(models.begin());
             models_bak.push_back(std::move(model));
         }
         assert(models.size() == k);
-        significanceModels = std::vector<double>(k, 1.0);
+        // 5. n_models <- k
         n_models = k;
+        // 6. Make prediction, compute alpha, update weights
         auto ypred = predict(X_train);
         //
         // Update weights
@@ -155,20 +200,28 @@ namespace bayesnet {
         //
         // Restore the models if needed
         //
+        // 7. Insert classifiers in models_bak to be the first n models
+        // if n_models_bak == k, don't restore any, because none of them were moved
         if (k != n_models_bak) {
-            for (int i = k - 1; i >= 0; --i) {
+            // Insert in the same order as they were extracted
-                model = std::move(models_bak[i]);
+            int bak_size = models_bak.size();
+            for (int i = 0; i < bak_size; ++i) {
+                model = std::move(models_bak[bak_size - 1 - i]);
+                models_bak.erase(models_bak.end() - 1);
                 models.insert(models.begin(), std::move(model));
             }
         }
+        // 8. significances <- significances_bak
         significanceModels = significance_bak;
-        n_models = n_models_bak;
         //
         // Update the significance of the last k models
         //
+        // 9. Update last k significances
         for (int i = 0; i < k; ++i) {
-            significanceModels[n_models - k + i] = alpha_t;
+            significanceModels[n_models_bak - k + i] = alpha_t;
         }
+        // 10. n_models <- n_models_bak
+        n_models = n_models_bak;
         return { weights, alpha_t, terminate };
     }
     std::vector<int> BoostAODE::initializeModels()
@@ -265,9 +318,8 @@ namespace bayesnet {
             );
             int k = pow(2, tolerance);
             int counter = 0; // The model counter of the current pack
-            VLOG_SCOPE_F(1, "k=%d featureSelection.size: %zu", k, featureSelection.size());
+            VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
             while (counter++ < k && featureSelection.size() > 0) {
-                VLOG_SCOPE_F(2, "counter: %d numItemsPack: %d", counter, numItemsPack);
                 auto feature = featureSelection[0];
                 featureSelection.erase(featureSelection.begin());
                 std::unique_ptr<Classifier> model;

tests/TestBoostAODE.cc

@@ -157,13 +157,18 @@ TEST_CASE("Bisection", "[BoostAODE]")
 TEST_CASE("Block Update", "[BoostAODE]")
 {
     auto clf = bayesnet::BoostAODE();
-    auto raw = RawDatasets("mfeat-factors", true);
+    // auto raw = RawDatasets("mfeat-factors", true);
+    auto raw = RawDatasets("glass", true);
     clf.setHyperparameters({
         {"bisection", true},
         {"block_update", true},
         {"maxTolerance", 3},
         {"convergence", true},
         });
+    // clf.setHyperparameters({
+    //     {"block_update", true},
+    //     });
+
     clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
     REQUIRE(clf.getNumberOfNodes() == 217);
     REQUIRE(clf.getNumberOfEdges() == 431);