Update visualization.py

visualization.py

@@ -149,8 +149,18 @@ def plot_combined_mse(df, mse_embeddings, mse_posture, mse_voice, title, anomaly
             segments.append(current_segment)
         return segments
 
+    # Scale all MSE values to the same range (0 to 1)
+    def scale_mse(mse_values):
+        min_val = np.min(mse_values)
+        max_val = np.max(mse_values)
+        return (mse_values - min_val) / (max_val - min_val)
+
+    mse_embeddings_scaled = scale_mse(mse_embeddings)
+    mse_posture_scaled = scale_mse(mse_posture)
+    mse_voice_scaled = scale_mse(mse_voice)
+
     # Plot each data series
-    for mse_values, color, label in zip([mse_embeddings, mse_posture, mse_voice], 
+    for mse_values, color, label in zip([mse_embeddings_scaled, mse_posture_scaled, mse_voice_scaled], 
                                         ['navy', 'purple', 'green'], 
                                         ['Facial Features', 'Body Posture', 'Voice']):
         segments = get_continuous_segments(df['Seconds'], mse_values)
@@ -176,6 +186,10 @@ def plot_combined_mse(df, mse_embeddings, mse_posture, mse_voice, title, anomaly
         threshold = np.mean(mse_values) + anomaly_threshold * np.std(mse_values)
         ax.axhline(y=threshold, color=color, linestyle=':', alpha=0.5, label=f'{label} Threshold')
 
+        # Plot anomalies in red
+        anomalies = mse_values > threshold
+        ax.scatter(df['Seconds'][anomalies], mse_values[anomalies], color='red', s=20, zorder=5)
+
     max_seconds = df['Seconds'].max()
     num_ticks = 100
     tick_locations = np.linspace(0, max_seconds, num_ticks)
@@ -185,7 +199,7 @@ def plot_combined_mse(df, mse_embeddings, mse_posture, mse_voice, title, anomaly
     ax.set_xticklabels(tick_labels, rotation=90, ha='center', fontsize=6)
 
     ax.set_xlabel('Timecode')
-    ax.set_ylabel('Mean Squared Error')
+    ax.set_ylabel('Scaled Mean Squared Error')
     ax.set_title(title)
 
     ax.grid(True, linestyle='--', alpha=0.7)
@@ -194,7 +208,6 @@ def plot_combined_mse(df, mse_embeddings, mse_posture, mse_voice, title, anomaly
     plt.close()
     return fig
 
-
 def plot_mse_histogram(mse_values, title, anomaly_threshold, color='blue'):
     plt.figure(figsize=(16, 3), dpi=300)
     fig, ax = plt.subplots(figsize=(16, 3))