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## Developer: inkbytefo
## Modified: 2025-11-22
"""
Benchmark Comparison: Turkish vs English
Analyzes training curves and tests the hypothesis.
"""
import json
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
def load_metrics(lang="english"):
"""Load training metrics from JSON"""
filename = f"metrics_{lang}.json" if lang == "turkish" else "metrics_english.json"
try:
with open(filename, 'r') as f:
return json.load(f)
except FileNotFoundError:
print(f"Warning: {filename} not found")
return None
def plot_comparison():
"""Plot BPC curves for Turkish vs English"""
en = load_metrics("english")
tr = load_metrics("turkish")
if not en or not tr:
print("Missing metrics files. Run both training scripts first.")
return
plt.figure(figsize=(12, 6))
# Subplot 1: Training BPC
plt.subplot(1, 2, 1)
plt.plot(en["steps"], en["train_bpc"], label="English (enwik8)", alpha=0.7)
plt.plot(tr["steps"], tr["train_bpc"], label="Turkish (trwiki)", alpha=0.7)
plt.xlabel("Training Steps")
plt.ylabel("BPC (Bits Per Character)")
plt.title("Training BPC: Turkish vs English")
plt.legend()
plt.grid(True, alpha=0.3)
# Subplot 2: Validation BPC
plt.subplot(1, 2, 2)
# Val BPC is logged every 200 steps
val_steps_en = [i * 200 for i in range(len(en["val_bpc"]))]
val_steps_tr = [i * 200 for i in range(len(tr["val_bpc"]))]
plt.plot(val_steps_en, en["val_bpc"], label="English (enwik8)", marker='o', alpha=0.7)
plt.plot(val_steps_tr, tr["val_bpc"], label="Turkish (trwiki)", marker='s', alpha=0.7)
plt.xlabel("Training Steps")
plt.ylabel("Validation BPC")
plt.title("Validation BPC: Turkish vs English")
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig("comparison_turkish_vs_english.png", dpi=300)
print("Saved comparison plot to comparison_turkish_vs_english.png")
plt.close()
def statistical_test():
"""Perform statistical significance test"""
en = load_metrics("english")
tr = load_metrics("turkish")
if not en or not tr:
return
# Final BPC values
final_bpc_en = en["val_bpc"][-1]
final_bpc_tr = tr["val_bpc"][-1]
print("\n" + "=" * 60)
print("STATISTICAL COMPARISON")
print("=" * 60)
print(f"\nFinal Validation BPC:")
print(f" English (enwik8): {final_bpc_en:.4f}")
print(f" Turkish (trwiki): {final_bpc_tr:.4f}")
print(f" Difference: {final_bpc_en - final_bpc_tr:.4f}")
# Convergence speed (steps to reach 2.5 BPC)
threshold = 2.5
steps_to_threshold_en = next((s for s, bpc in zip(en["steps"], en["train_bpc"]) if bpc < threshold), None)
steps_to_threshold_tr = next((s for s, bpc in zip(tr["steps"], tr["train_bpc"]) if bpc < threshold), None)
print(f"\nSteps to reach BPC < {threshold}:")
print(f" English: {steps_to_threshold_en if steps_to_threshold_en else 'Not reached'}")
print(f" Turkish: {steps_to_threshold_tr if steps_to_threshold_tr else 'Not reached'}")
# Hypothesis test
print("\n" + "-" * 60)
print("HYPOTHESIS TEST")
print("-" * 60)
if final_bpc_tr < final_bpc_en:
print("✅ HYPOTHESIS CONFIRMED")
print("Turkish model achieved lower BPC than English model.")
print("This supports the claim that byte-level models are more")
print("efficient for agglutinative languages.")
improvement = ((final_bpc_en - final_bpc_tr) / final_bpc_en) * 100
print(f"Improvement: {improvement:.2f}%")
else:
print("❌ HYPOTHESIS REJECTED")
print("English model achieved lower or equal BPC.")
print("=" * 60)
def generate_report():
"""Generate markdown report"""
en = load_metrics("english")
tr = load_metrics("turkish")
if not en or not tr:
return
report = f"""# Kaşgarlı Testi - Benchmark Results
## Hypothesis
**H1:** Byte-level models learn agglutinative languages (Turkish) more efficiently than analytic languages (English).
## Experimental Setup
- **Model:** AGIFORMER (identical architecture, 50M parameters)
- **Hyperparameters:** Same for both (d_model=512, n_layers=6, thinking_steps=3)
- **Training:** 5000 steps, batch_size=4, lr=3e-4
- **English Dataset:** enwik8 (100MB Wikipedia)
- **Turkish Dataset:** trwiki (Turkish Wikipedia)
## Results
### Final BPC (Lower is Better)
| Language | Validation BPC |
|----------|----------------|
| English | {en["val_bpc"][-1]:.4f} |
| Turkish | {tr["val_bpc"][-1]:.4f} |
**Difference:** {abs(en["val_bpc"][-1] - tr["val_bpc"][-1]):.4f} BPC
### Convergence Speed
Steps to reach BPC < 2.5:
- English: {next((s for s, bpc in zip(en["steps"], en["train_bpc"]) if bpc < 2.5), "Not reached")}
- Turkish: {next((s for s, bpc in zip(tr["steps"], tr["train_bpc"]) if bpc < 2.5), "Not reached")}
## Conclusion
{"Turkish model outperformed English, confirming the hypothesis." if tr["val_bpc"][-1] < en["val_bpc"][-1] else "Hypothesis not confirmed in this experiment."}
## Visualization
---
**Generated:** 2025-11-22
**Experimenter:** inkbytefo
"""
with open("benchmark_report.md", "w") as f:
f.write(report)
print("\nGenerated benchmark_report.md")
if __name__ == "__main__":
plot_comparison()
statistical_test()
generate_report()
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