Complete predict and score of kdb

Change new/delete to make_unique

src/Ensemble.cc

@@ -4,17 +4,22 @@ namespace bayesnet {
     using namespace std;
     using namespace torch;
 
-    Ensemble::Ensemble(BaseClassifier& model) : model(model), models(vector<BaseClassifier>()), m(0), n(0), metrics(Metrics()) {}
+    Ensemble::Ensemble() : m(0), n(0), n_models(0), metrics(Metrics()) {}
     Ensemble& Ensemble::build(vector<string>& features, string className, map<string, vector<int>>& states)
     {
-
         dataset = torch::cat({ X, y.view({y.size(0), 1}) }, 1);
         this->features = features;
         this->className = className;
         this->states = states;
         auto n_classes = states[className].size();
         metrics = Metrics(dataset, features, className, n_classes);
+        // Build models
         train();
+        // Train models
+        n_models = models.size();
+        for (auto i = 0; i < n_models; ++i) {
+            models[i].fit(X, y, features, className, states);
+        }
         return *this;
     }
     Ensemble& Ensemble::fit(Tensor& X, Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
@@ -37,16 +42,21 @@ namespace bayesnet {
     }
     Tensor Ensemble::predict(Tensor& X)
     {
-        auto m_ = X.size(0);
-        auto n_ = X.size(1);
-        vector<vector<int>> Xd(n_, vector<int>(m_, 0));
-        for (auto i = 0; i < n_; i++) {
-            auto temp = X.index({ "...", i });
-            Xd[i] = vector<int>(temp.data_ptr<int>(), temp.data_ptr<int>() + m_);
+        Tensor y_pred = torch::zeros({ X.size(0), n_models }, torch::kInt64);
+        for (auto i = 0; i < n_models; ++i) {
+            y_pred.index_put_({ "...", i }, models[i].predict(X));
         }
-        auto yp = model.predict(Xd);
-        auto ypred = torch::tensor(yp, torch::kInt64);
-        return ypred;
+        auto y_pred_ = y_pred.accessor<int64_t, 2>();
+        vector<int> y_pred_final;
+        for (int i = 0; i < y_pred.size(0); ++i) {
+            vector<float> votes(states[className].size(), 0);
+            for (int j = 0; j < y_pred.size(1); ++j) {
+                votes[y_pred_[i][j]] += 1;
+            }
+            auto indices = argsort(votes);
+            y_pred_final.push_back(indices[0]);
+        }
+        return torch::tensor(y_pred_final, torch::kInt64);
     }
     float Ensemble::score(Tensor& X, Tensor& y)
     {
@@ -55,7 +65,11 @@ namespace bayesnet {
     }
     vector<string> Ensemble::show()
     {
-        return model.show();
+        vector<string> result;
+        for (auto i = 0; i < n_models; ++i) {
+            auto res = models[i].show();
+            result.insert(result.end(), res.begin(), res.end());
+        }
+        return result;
     }
-
 }