data/processed/RQ2/plot_llm_share_heatmap.py

#!/usr/bin/env python3
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.ticker import FuncFormatter

# Use Times New Roman globally for the figure
plt.rcParams["font.family"] = "Times New Roman"


def plot_llm_share_heatmap():
    base_dir = Path(__file__).parent
    csv_file = base_dir / "llm_share_summary.csv"

    df = pd.read_csv(csv_file, index_col=0)

    series_info = [
        ("Email Responder", 2),
        ("Recruitment", 3),
        ("Markdown Val.", 2),
        ("Game Builder", 2),
        ("SQL Asst.", 3),
        ("Landing Pg.", 3),
        ("Book Writer", 3),
        ("Social M. M.", 3),
    ]

    # Reorder columns within each series as CrewAI, MCP, A2A, A2A Mix
    desired_suffix_order = ["CrewAI", "MCP", "A2A", "A2A_mix"]
    suffix_display = {
        "CrewAI": "CrewAI",
        "MCP": "MCP",
        "A2A": "A2A",
        "A2A_mix": "H-A2A",
    }

    new_columns = []
    suffixes = []
    for series_name, _ in series_info:
        for suf in desired_suffix_order:
            col_name = f"{series_name} ({suf})"
            if col_name in df.columns:
                new_columns.append(col_name)
                suffixes.append(suffix_display[suf])

    if new_columns:
        df = df[new_columns]

    fig, ax = plt.subplots(figsize=(18, 5.2))

    colors = ["#d73027", "#fee08b", "#d9ef8b", "#66bd63", "#1a9850"]
    n_bins = 100
    cmap = LinearSegmentedColormap.from_list("custom", colors, N=n_bins)

    im = ax.imshow(df.values, cmap=cmap, aspect="auto", vmin=0.85, vmax=1.0)

    ax.set_xticks(np.arange(len(df.columns)))
    ax.set_yticks(np.arange(len(df.index)))
    ax.set_xticklabels(
        suffixes, rotation=0, ha="center", fontsize=12, fontweight="bold"
    )

    y_labels = []
    for label in df.index:
        if "Gemini-2.5-flash-nothinking" in label:
            y_labels.append("Gemini-2.5\n-flash\n-nothinking")
        elif "Gemini-2.5-flash" in label:
            y_labels.append("Gemini-2.5\n-flash")
        elif "DeepSeek-V3" in label:
            y_labels.append("DeepSeek\n-V3")
        elif "DeepSeek-R1" in label:
            y_labels.append("DeepSeek\n-R1")
        elif "GPT-4o-mini" in label:
            y_labels.append("GPT-4o\n-mini")
        elif "Qwen3-235b" in label:
            y_labels.append("Qwen3\n-235b")
        elif "Overall" in label:
            y_labels.append("Overall\nAverage")
        else:
            y_labels.append(label)
    ax.set_yticklabels(y_labels, fontsize=12, fontweight="bold")

    for i in range(len(df.index)):
        for j in range(len(df.columns)):
            value = df.iloc[i, j]
            if pd.notna(value):
                text_color = "white" if value < 0.92 else "black"
                display_pct = round(value * 100, 2)
                if display_pct >= 100.0:
                    display_pct = 99.99
                text = ax.text(
                    j,
                    i,
                    f"{display_pct:.2f}",
                    ha="center",
                    va="center",
                    color=text_color,
                    fontsize=15,
                    fontweight="bold",
                )

    cbar = fig.colorbar(im, ax=ax, fraction=0.03, pad=0.01)
    cbar.ax.tick_params(labelsize=13)
    for label in cbar.ax.get_yticklabels():
        label.set_fontweight("bold")
    cbar.ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f"{x * 100:.2f}%"))
    cbar.set_label("")

    ax.spines["top"].set_visible(False)
    ax.spines["right"].set_visible(False)
    ax.spines["bottom"].set_visible(False)
    ax.spines["left"].set_visible(False)

    ax.set_xticks(np.arange(len(df.columns) + 1) - 0.5, minor=True)
    ax.set_yticks(np.arange(len(df.index) + 1) - 0.5, minor=True)
    ax.grid(which="minor", color="gray", linestyle="-", linewidth=0.5)
    ax.tick_params(which="minor", size=0)

    # Series names as a common base label under each group
    cum_pos = 0
    for series_name, count in series_info:
        center_pos = cum_pos + (count - 1) / 2
        ax.text(
            center_pos,
            len(df.index) + 0.18,
            series_name,
            ha="center",
            va="top",
            fontsize=12,
            fontweight="bold",
        )

        if cum_pos > 0:
            ax.axvline(x=cum_pos - 0.5, color="black", linewidth=2, linestyle="-")

        cum_pos += count

    # Compact margins so bottom labels are close to suffixes and legend is tight
    plt.subplots_adjust(bottom=0.1, top=0.97, left=0.08, right=0.96)

    output_file = base_dir / "llm_share_heatmap.pdf"
    plt.savefig(output_file, dpi=300, bbox_inches="tight")
    print(f"Heatmap saved to: {output_file}")

    plt.close()


if __name__ == "__main__":
    plot_llm_share_heatmap()