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re-ran the tests

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ION606
2025-04-21 22:34:51 -04:00
parent 23899c703f
commit ebc64d766f
95 changed files with 2446 additions and 74 deletions
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Project/plot_diagnostics.py
+31 -13
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@@ -3,42 +3,58 @@ import json
import pandas as pd
import matplotlib.pyplot as plt
BASE_DIR = "./on_GPU"
# training diagnostics
histories = []
for csv in glob.glob("analytics/history_*.csv"):
df = pd.read_csv(csv) # epochs × metrics
for csv in glob.glob(f"{BASE_DIR}/analytics/history_*.csv"):
df = pd.read_csv(csv) # epochs x 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.plot(mean_log.index, mean_log['train_acc'],
color='tab:blue', label='train')
plt.plot(mean_log.index, mean_log['test_acc'],
color='tab:orange', label='validation')
plt.xlabel("epoch")
plt.ylabel("accuracy")
plt.legend()
plt.grid(True)
ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig("train_val_accuracy.png")
plt.savefig(f"{BASE_DIR}/train_val_accuracy.png")
# Plot train vs validation loss
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.plot(mean_log.index, mean_log['train_loss'],
color='tab:blue', label='train')
plt.plot(mean_log.index, mean_log['test_loss'],
color='tab:orange', label='validation')
plt.xlabel("epoch")
plt.ylabel("cross‑entropy loss")
plt.legend()
plt.grid(True)
ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig("train_val_loss.png")
plt.savefig(f"{BASE_DIR}/train_val_loss.png")
# robustness curves
with open("results.json") as f:
with open(f"{BASE_DIR}/results.json") as f:
res = json.load(f)
df = pd.DataFrame(res)
records = [] # one row per sigma
records = [] # one row per sigma
for _, row in df.iterrows():
for sigma, acc in row['robustness'].items():
@@ -52,11 +68,13 @@ for _, row in df.iterrows():
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')
ax = pivot.plot(marker='o', linestyle='-', color=['#0072B2', '#D55E00'])
plt.xlabel("Gaussian noise sigma")
plt.ylabel("accuracy")
plt.title("Noise robustness")
plt.grid(True)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig("robustness_curve.png")
plt.savefig(f"{BASE_DIR}/robustness_curve.png")
print("saved robustness_curve.png")