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https://github.com/ION606/COGMOD-HWI.git
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63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
import glob
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import json
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import pandas as pd
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import matplotlib.pyplot as plt
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# training diagnostics
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histories = []
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for csv in glob.glob("analytics/history_*.csv"):
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df = pd.read_csv(csv) # epochs × metrics
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tag = "_".join(csv.split("_")[1:4]) # like sgd_none_42.csv
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df['condition'] = tag.replace(".csv", "")
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histories.append(df)
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logs = pd.concat(histories, ignore_index=True)
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# average across seeds, optimiser, augmentation for clarity
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mean_log = logs.groupby('epoch').mean(numeric_only=True)
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plt.figure()
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plt.plot(mean_log.index, mean_log['train_acc'], label='train')
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plt.plot(mean_log.index, mean_log['test_acc'], label='validation')
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plt.xlabel("epoch")
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plt.ylabel("accuracy")
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plt.legend()
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plt.tight_layout()
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plt.savefig("train_val_accuracy.png")
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plt.figure()
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plt.plot(mean_log.index, mean_log['train_loss'], label='train')
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plt.plot(mean_log.index, mean_log['test_loss'], label='validation')
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plt.xlabel("epoch")
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plt.ylabel("cross‑entropy loss")
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plt.legend()
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plt.tight_layout()
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plt.savefig("train_val_loss.png")
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# robustness curves
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with open("results.json") as f:
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res = json.load(f)
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df = pd.DataFrame(res)
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records = [] # one row per sigma
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for _, row in df.iterrows():
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for sigma, acc in row['robustness'].items():
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records.append({
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'optimizer': row['optimizer'],
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'augmentation': row['augmentation'],
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'sigma': float(sigma),
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'acc': acc,
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})
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rob_df = pd.DataFrame(records)
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pivot = rob_df.groupby(['optimizer', 'sigma']).acc.mean().unstack(0)
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# sigma on x‑axis, one line per optimiser
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pivot.plot(marker='o')
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plt.xlabel("Gaussian noise sigma")
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plt.ylabel("accuracy")
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plt.title("Noise robustness")
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plt.tight_layout()
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plt.savefig("robustness_curve.png")
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print("saved robustness_curve.png")
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