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8c10cf2 c4e7970 8c10cf2 c4e7970 8c10cf2 c4e7970 8c10cf2 c4e7970 8c10cf2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | #!/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()
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