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| import logging | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import pandas as pd | |
| from pathlib import Path | |
| from typing import Dict | |
| logger = logging.getLogger(__name__) | |
| class ResultVisualizer: | |
| """Visualizes evaluation metrics across Base, LoRA, RAG, and LLM categories.""" | |
| def __init__(self, output_dir: str = "output/plots"): | |
| self.output_dir = Path(output_dir) | |
| self.output_dir.mkdir(parents=True, exist_ok=True) | |
| sns.set_theme(style="whitegrid", palette="muted") | |
| def plot_comparison(self, eval_results: Dict[str, Dict[str, float]], save_name: str = "model_comparison.png", bertscore_model_label: str = None): | |
| # Flatten dictionary for seaborn | |
| data = [] | |
| for model_name, metrics in eval_results.items(): | |
| for metric_name, value in metrics.items(): | |
| data.append({"Model": model_name, "Metric": metric_name, "Score": value}) | |
| df = pd.DataFrame(data) | |
| if df.empty: | |
| logger.warning("No data to plot.") | |
| return | |
| plt.figure(figsize=(14, 8)) | |
| sns.barplot(x="Metric", y="Score", hue="Model", data=df) | |
| plt.title("Comprehensive Model Comparison: Base vs LoRA vs RAG vs LLM", fontsize=16, fontweight='bold') | |
| if bertscore_model_label: | |
| plt.suptitle(f"BERTScore backbone: {bertscore_model_label}", fontsize=9, y=0.93, color="gray") | |
| plt.ylabel("Score", fontsize=12) | |
| plt.xlabel("Evaluation Metric", fontsize=12) | |
| plt.xticks(rotation=45, ha='right') | |
| plt.legend(title="Model Category", bbox_to_anchor=(1.05, 1), loc='upper left') | |
| plt.tight_layout() | |
| save_path = self.output_dir / save_name | |
| plt.savefig(save_path, dpi=300) | |
| plt.close() | |
| logger.info(f"✅ Saved comparison plot to {save_path}") | |
| def plot_radar_chart(self, eval_results: Dict[str, Dict[str, float]], save_name: str = "radar_comparison.png"): | |
| """Generates a radar chart for a holistic view of model capabilities.""" | |
| import numpy as np | |
| categories = list(next(iter(eval_results.values())).keys()) | |
| N = len(categories) | |
| if N == 0: return | |
| # Compute the angle for each axis | |
| angles = [n / float(N) * 2 * 3.14159 for n in range(N)] | |
| angles += angles[:1] | |
| fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True)) | |
| for model_name, metrics in eval_results.items(): | |
| values = list(metrics.values()) | |
| values += values[:1] | |
| ax.plot(angles, values, linewidth=2, label=model_name) | |
| ax.fill(angles, values, alpha=0.15) | |
| ax.set_xticks(angles[:-1]) | |
| ax.set_xticklabels(categories, fontsize=10) | |
| plt.title("Model Capability Radar", size=15, y=1.1, fontweight='bold') | |
| plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1)) | |
| save_path = self.output_dir / save_name | |
| plt.savefig(save_path, dpi=300, bbox_inches='tight') | |
| plt.close() | |
| logger.info(f"✅ Saved radar chart to {save_path}") |