| 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): |
| |
| 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 |
|
|
| |
| 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}") |
