scripts/plot_condition_matrix.py

"""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)

# ---------------------------------------------------------------------------
# Fixed presentation order (mirrors Table 2 / Table 4 in the paper).
# ---------------------------------------------------------------------------
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,
        )

    # dimension-group shading + separators
    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)

    # paradigm-group separators (rows) + labels
    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

    # dimension-group separators (cols)
    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)