| """Plot the per-condition eMCR results (docs/condition_matrix.json, produced by |
| build_condition_matrix.py) as: |
| |
| 1. fig_condition_dotplot - the 4 representative models from Table 4 |
| (BM25 / Q3E8 / Q3R8 / VLR8) across all 15 atomic |
| conditions, grouped by dimension (A/B/C), with |
| Wilson 95% CI error bars. Main-text candidate. |
| 2. fig_condition_heatmap - the full 16-model x 15-condition matrix, |
| paradigm-grouped rows, dimension-grouped columns. |
| Appendix candidate. |
| |
| Usage: |
| python scripts/plot_condition_matrix.py |
| """ |
|
|
| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| MATRIX_PATH = ROOT / "docs" / "condition_matrix.json" |
| OUT_DIR = ROOT / "docs" / "figures" |
| OUT_DIR.mkdir(parents=True, exist_ok=True) |
|
|
| |
| |
| |
| ATOM_GROUPS = [ |
| ("A. Expression variation", ["paraphrase", "expand", "restructure", "correction"]), |
| ("B. Intent type", ["content_intent", "sku_intent", "knowledge", "general_sem.", |
| "attribute_scene", "implicit_intent"]), |
| ("C. Constraint signal", ["brand", "style", "negative_intent", "price_query", "image_clue"]), |
| ] |
| ATOMS = [a for _, atoms in ATOM_GROUPS for a in atoms] |
|
|
| MODEL_GROUPS = [ |
| ("Sparse", ["BM25"]), |
| ("Text dense", ["BGE-M3", "GritLM-7B", "E5-Mistral-7B", "Qwen3-Emb-4B", "Qwen3-Emb-8B"]), |
| ("Text rerank", ["BGE-Reranker-v2-m3", "Qwen3-Reranker-4B", "Qwen3-Reranker-8B"]), |
| ("MM dense", ["MM-Embed", "VLM2Vec-V2", "Qwen3-VL-Emb-2B", "Qwen3-VL-Emb-8B"]), |
| ("MM rerank", ["Jina-Reranker-m0", "Qwen3-VL-Reranker-2B", "Qwen3-VL-Reranker-8B"]), |
| ] |
| MODELS = [m for _, models in MODEL_GROUPS for m in models] |
|
|
| REPRESENTATIVE = [ |
| ("BM25", "BM25", "#7f7f7f", "o"), |
| ("Qwen3-Emb-8B", "Q3E8 (text dense)", "#1f77b4", "s"), |
| ("Qwen3-Reranker-8B", "Q3R8 (text rerank)", "#2ca02c", "^"), |
| ("Qwen3-VL-Reranker-8B", "VLR8 (mm rerank)", "#d62728", "D"), |
| ] |
|
|
|
|
| def load_matrix(): |
| with open(MATRIX_PATH, encoding="utf-8") as f: |
| return json.load(f)["models"] |
|
|
|
|
| def plot_dotplot(data): |
| fig, ax = plt.subplots(figsize=(11, 4.2)) |
| x = np.arange(len(ATOMS)) |
|
|
| for model_key, label, color, marker in REPRESENTATIVE: |
| ys, lo, hi = [], [], [] |
| for atom in ATOMS: |
| cell = data[model_key]["atoms"][atom] |
| ys.append(cell["p1"]) |
| lo.append(cell["p1"] - cell["ci95"][0]) |
| hi.append(cell["ci95"][1] - cell["p1"]) |
| ax.errorbar( |
| x, ys, yerr=[lo, hi], label=label, color=color, marker=marker, |
| markersize=5, linewidth=1.4, capsize=2, elinewidth=0.8, alpha=0.9, |
| ) |
|
|
| |
| offset = 0 |
| band_colors = ["#f7f7f7", "#ffffff", "#f0f0f0"] |
| for i, (gname, atoms) in enumerate(ATOM_GROUPS): |
| n = len(atoms) |
| ax.axvspan(offset - 0.5, offset + n - 0.5, color=band_colors[i % 3], zorder=0) |
| ax.text(offset + n / 2 - 0.5, 103, gname, ha="center", va="bottom", |
| fontsize=9, fontweight="bold", color="#444444") |
| if offset > 0: |
| ax.axvline(offset - 0.5, color="#bbbbbb", linewidth=0.8, zorder=0) |
| offset += n |
|
|
| ax.set_xticks(x) |
| ax.set_xticklabels([a.replace("_", "\n") for a in ATOMS], fontsize=8, rotation=0) |
| ax.set_ylabel("Precision@1 (%)", fontsize=10) |
| ax.set_ylim(0, 108) |
| ax.set_yticks(range(0, 101, 20)) |
| ax.legend(loc="lower center", bbox_to_anchor=(0.5, 1.08), ncol=4, frameon=False, fontsize=9) |
| ax.spines["top"].set_visible(False) |
| ax.spines["right"].set_visible(False) |
| ax.grid(axis="y", linestyle=":", linewidth=0.5, color="#cccccc", zorder=0) |
| fig.tight_layout() |
|
|
| for ext in ("pdf", "png"): |
| fig.savefig(OUT_DIR / f"fig_condition_dotplot.{ext}", dpi=200, bbox_inches="tight") |
| plt.close(fig) |
| print(f"wrote {OUT_DIR / 'fig_condition_dotplot.pdf'} (+ .png)") |
|
|
|
|
| def plot_heatmap(data): |
| matrix = np.full((len(MODELS), len(ATOMS)), np.nan) |
| for i, model in enumerate(MODELS): |
| for j, atom in enumerate(ATOMS): |
| cell = data[model]["atoms"].get(atom) |
| if cell is not None: |
| matrix[i, j] = cell["p1"] |
|
|
| fig, ax = plt.subplots(figsize=(10.5, 8.5)) |
| im = ax.imshow(matrix, cmap="YlGnBu", vmin=0, vmax=100, aspect="auto") |
|
|
| ax.set_xticks(np.arange(len(ATOMS))) |
| ax.set_xticklabels(ATOMS, rotation=45, ha="right", fontsize=8) |
| ax.set_yticks(np.arange(len(MODELS))) |
| ax.set_yticklabels(MODELS, fontsize=8) |
|
|
| for i in range(len(MODELS)): |
| for j in range(len(ATOMS)): |
| v = matrix[i, j] |
| if not np.isnan(v): |
| txt_color = "white" if v > 60 else "black" |
| ax.text(j, i, f"{v:.0f}", ha="center", va="center", fontsize=6.2, color=txt_color) |
|
|
| |
| row_off = 0 |
| for gname, models in MODEL_GROUPS: |
| n = len(models) |
| if row_off > 0: |
| ax.axhline(row_off - 0.5, color="black", linewidth=1.0) |
| ax.text(-0.7, row_off + n / 2 - 0.5, gname, ha="right", va="center", |
| fontsize=8, fontweight="bold", rotation=0, |
| transform=ax.transData) |
| row_off += n |
|
|
| |
| col_off = 0 |
| for gname, atoms in ATOM_GROUPS: |
| n = len(atoms) |
| if col_off > 0: |
| ax.axvline(col_off - 0.5, color="black", linewidth=1.0) |
| col_off += n |
|
|
| ax.set_xticks(np.arange(-0.5, len(ATOMS), 1), minor=True) |
| ax.set_yticks(np.arange(-0.5, len(MODELS), 1), minor=True) |
| ax.grid(which="minor", color="white", linewidth=0.6) |
| ax.tick_params(which="minor", length=0) |
|
|
| cbar = fig.colorbar(im, ax=ax, fraction=0.03, pad=0.02) |
| cbar.set_label("Precision@1 (%)", fontsize=9) |
|
|
| ax.set_title("P@1 by model x atomic condition (16 models x 15 conditions)", fontsize=11, pad=14) |
| fig.tight_layout() |
|
|
| for ext in ("pdf", "png"): |
| fig.savefig(OUT_DIR / f"fig_condition_heatmap.{ext}", dpi=200, bbox_inches="tight") |
| plt.close(fig) |
| print(f"wrote {OUT_DIR / 'fig_condition_heatmap.pdf'} (+ .png)") |
|
|
|
|
| if __name__ == "__main__": |
| data = load_matrix() |
| plot_dotplot(data) |
| plot_heatmap(data) |
|
|
