Fixed display stats

src/app.py

@@ -55,7 +55,7 @@ def heart() -> tuple[Dataset, MLAlgorithm, Any]:
     attributes_to_modify = ["Disease", "Sex", "ChestPainType"]
     ds.factorize(attributes_to_modify)
     ds.normalize(excepts=attributes_to_modify)
-    return (ds, LogisticRegression(ds, learning_rate=0.001), sklearn.linear_model.LogisticRegression())
+    return (ds, LogisticRegression(ds, learning_rate=0.01), sklearn.linear_model.LogisticRegression())
 
 # ********************
 # MultiLayerPerceptron

src/learning/data.py

@@ -47,7 +47,9 @@ class Dataset:
         return self
 
     def normalize(self, excepts:list[str]=[]) -> Self:
-        excepts.append(self.target)
+        if excepts is None: excepts = []
+        else: excepts.append(self.target)
+
         for col in self.data:
             if col not in excepts:
                 index = self.data.columns.get_loc(col)
@@ -134,6 +136,32 @@ class ConfusionMatrix:
         tn = total - (tp + fp + fn)
         return tn / (tn + fp)
 
+    def accuracy(self) -> float:
+        tp = np.diag(self.matrix).sum()
+        total = self.matrix.sum()
+        return tp / total
+
+    def precision(self) -> float:
+        precision_per_class = self.precision_per_class()
+        support = np.sum(self.matrix, axis=1)
+        return np.average(precision_per_class, weights=support)
+
+    def recall(self) -> float:
+        recall_per_class = self.recall_per_class()
+        support = np.sum(self.matrix, axis=1)
+        return np.average(recall_per_class, weights=support)
+
+    def f1_score(self) -> float:
+        f1_per_class = self.f1_score_per_class()
+        support = np.sum(self.matrix, axis=1)
+        return np.average(f1_per_class, weights=support)
+
+    def specificity(self) -> float:
+        specificity_per_class = self.specificity_per_class()
+        support = np.sum(self.matrix, axis=1)
+        return np.average(specificity_per_class, weights=support)
+
+
 if __name__ == "__main__":
     ds = Dataset("datasets\\classification\\frogs.csv", "Species", TargetType.MultiClassification)
     ds.remove(["Family", "Genus", "RecordID"])

src/learning/ml.py

@@ -78,11 +78,11 @@ class MLAlgorithm(ABC):
             print(f"R^2        : {self.test_r_squared():0.5f}")
         else:
             conf = self.test_confusion_matrix()
-            print(f"Accuracy   : {conf.accuracy_per_class()}")
+            print(f"Accuracy   : {conf.accuracy():0.5f} - classes {conf.accuracy_per_class()}")
-            print(f"Precision  : {conf.precision_per_class()}")
+            print(f"Precision  : {conf.precision():0.5f} - classes {conf.precision_per_class()}")
-            print(f"Recall     : {conf.recall_per_class()}")
+            print(f"Recall     : {conf.recall():0.5f} - classes {conf.recall_per_class()}")
-            print(f"F1 score   : {conf.f1_score_per_class()}")
+            print(f"F1 score   : {conf.f1_score():0.5f} - classes {conf.f1_score_per_class()}")
-            print(f"Specificity: {conf.specificity_per_class()}")
+            print(f"Specificity: {conf.specificity():0.5f} - classes {conf.specificity_per_class()}")
 
     def test_confusion_matrix(self) -> ConfusionMatrix:
         if self._target_type != TargetType.Classification\