added base project

Project/plot_diagnostics.py

@@ -0,0 +1,62 @@
+import glob
+import json
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# training diagnostics
+histories = []
+for csv in glob.glob("analytics/history_*.csv"):
+    df = pd.read_csv(csv)                       # epochs × metrics
+    tag = "_".join(csv.split("_")[1:4])         # like sgd_none_42.csv
+    df['condition'] = tag.replace(".csv", "")
+    histories.append(df)
+logs = pd.concat(histories, ignore_index=True)
+
+# average across seeds, optimiser, augmentation for clarity
+mean_log = logs.groupby('epoch').mean(numeric_only=True)
+
+plt.figure()
+plt.plot(mean_log.index, mean_log['train_acc'], label='train')
+plt.plot(mean_log.index, mean_log['test_acc'], label='validation')
+plt.xlabel("epoch")
+plt.ylabel("accuracy")
+plt.legend()
+plt.tight_layout()
+plt.savefig("train_val_accuracy.png")
+
+plt.figure()
+plt.plot(mean_log.index, mean_log['train_loss'], label='train')
+plt.plot(mean_log.index, mean_log['test_loss'], label='validation')
+plt.xlabel("epoch")
+plt.ylabel("cross‑entropy loss")
+plt.legend()
+plt.tight_layout()
+plt.savefig("train_val_loss.png")
+
+# robustness curves
+with open("results.json") as f:
+    res = json.load(f)
+
+df = pd.DataFrame(res)
+records = [] # one row per sigma
+
+for _, row in df.iterrows():
+    for sigma, acc in row['robustness'].items():
+        records.append({
+            'optimizer': row['optimizer'],
+            'augmentation': row['augmentation'],
+            'sigma': float(sigma),
+            'acc': acc,
+        })
+
+rob_df = pd.DataFrame(records)
+pivot = rob_df.groupby(['optimizer', 'sigma']).acc.mean().unstack(0)
+
+# sigma on x‑axis, one line per optimiser
+pivot.plot(marker='o')
+plt.xlabel("Gaussian noise sigma")
+plt.ylabel("accuracy")
+plt.title("Noise robustness")
+plt.tight_layout()
+plt.savefig("robustness_curve.png")
+print("saved robustness_curve.png")