Fix Xspode

bayesnet/classifiers/Classifier.cc

@@ -10,7 +10,6 @@
 
 namespace bayesnet {
     Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
-    const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
     Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing)
     {
         this->features = features;
@@ -22,11 +21,8 @@ namespace bayesnet {
         auto n_classes = states.at(className).size();
         metrics = Metrics(dataset, features, className, n_classes);
         model.initialize();
-        std::cout << "Ahora buildmodel"<< std::endl;
         buildModel(weights);
-        std::cout << "Ahora trainmodel"<< std::endl;
         trainModel(weights, smoothing);
-        std::cout << "Después de trainmodel"<< std::endl;
         fitted = true;
         return *this;
     }

bayesnet/classifiers/Classifier.h

@@ -50,6 +50,7 @@ namespace bayesnet {
         virtual void buildModel(const torch::Tensor& weights) = 0;
         void trainModel(const torch::Tensor& weights, const Smoothing_t smoothing) override;
         void buildDataset(torch::Tensor& y);
+        const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
     private:
         Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights, const Smoothing_t smoothing);
     };

bayesnet/classifiers/XSPODE.cc

@@ -110,7 +110,6 @@ namespace bayesnet {
             instance[nFeatures_] = dataset[-1][i].item<int>();
             addSample(instance, weights[i].item<double>());
         }
-
         switch (smoothing) {
             case bayesnet::Smoothing_t::ORIGINAL:
                 alpha_ = 1.0 / m;
@@ -414,9 +413,6 @@ namespace bayesnet {
     }
     float XSpode::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
     {
-        if (!fitted) {
-            throw std::logic_error(CLASSIFIER_NOT_FITTED);
-        }
         auto y_pred = this->predict(X);
         int correct = 0;
         for (int i = 0; i < y_pred.size(); ++i) {

bayesnet/classifiers/XSPODE.h

@@ -51,8 +51,6 @@ namespace bayesnet {
         int statesClass_;
         std::vector<int> states_;          // [states_feat0, ..., states_feat(N-1)] (class not included in this array)
 
-        const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
-
         // Class counts
         std::vector<double> classCounts_;  // [c], accumulative
         std::vector<double> classPriors_;  // [c], after normalization

tests/TestBayesModels.cc

@@ -58,8 +58,10 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
             auto raw = RawDatasets(file_name, discretize);
             if (name == "XSPODE") {
                 std::cout << "Fitting XSPODE" << std::endl;
+            } else {
+                std::cout << "Fitting something else [" << name << "]" << std::endl;
             }
-            clf->fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
+            clf->fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
             auto score = clf->score(raw.Xt, raw.yt);
             std::cout << "Classifier: " << name << " File: " << file_name << " Score: " << score << " expected = " << scores[{file_name, name}] << std::endl;
             INFO("Classifier: " << name << " File: " << file_name);