Upload utils.py

utils.py

@@ -207,6 +207,7 @@ def evaluate_model(model, test_loader, device):
 
 # Visualization
 import matplotlib.cm as cm
+
 def plot_metrics(test_f1_scores, input_types, n_train=None, flag=0):
     """
     Plots the F1-score over epochs or number of training samples.
@@ -222,11 +223,14 @@ def plot_metrics(test_f1_scores, input_types, n_train=None, flag=0):
     markers = ['o', 's', 'D', '^', 'v', 'P', '*', 'X', 'h']  # Different markers for curves
     
     for r in range(test_f1_scores.shape[0]):
-        color = colors(r / (test_f1_scores.shape[0] - 1))  # Normalize color index
+        color = colors(0.5 if test_f1_scores.shape[0] == 1 else r / (test_f1_scores.shape[0] - 1))  # Normalize color index
         marker = markers[r % len(markers)]  # Cycle through markers
         if flag == 0:
-            plt.plot(test_f1_scores[r], linewidth=2, marker=marker, markersize=5, markeredgewidth=1.5, 
-                     markeredgecolor=color, color=color, label=f"{input_types[r]}")
+            if test_f1_scores.shape[0] == 1:
+                plt.plot(test_f1_scores[r], linewidth=2, color=color, label=f"{input_types[r]}")
+            else:
+                plt.plot(test_f1_scores[r], linewidth=2, marker=marker, markersize=5, markeredgewidth=1.5, 
+                         markeredgecolor=color, color=color, label=f"{input_types[r]}")
         else:
             plt.plot(n_train, test_f1_scores[r], linewidth=2, marker=marker, markersize=6, markeredgewidth=1.5, 
                      markeredgecolor=color, markerfacecolor='none', color=color, label=f"{input_types[r]}")