draw/tools/data_source_distribution.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import json
from matplotlib import font_manager as fm
from matplotlib.font_manager import FontProperties

font_path = "/usr/share/fonts/truetype/msttcorefonts/Arial.ttf"
font_bold_path = "/usr/share/fonts/truetype/msttcorefonts/Arial_Bold.ttf"
fm.fontManager.addfont(font_path)
fm.fontManager.addfont(font_bold_path)
plt.rcParams["font.family"] = "Arial"
plt.rcParams["font.weight"] = "bold"
plt.rcParams["axes.labelweight"] = "bold"
plt.rcParams["axes.titleweight"] = "bold"
plt.rcParams["font.size"] = 12
plt.rcParams["xtick.labelsize"] = 12
plt.rcParams["ytick.labelsize"] = 12
bold_font = FontProperties(fname=font_bold_path, weight="bold")


'''
数据获取和统计
'''
# with open("/cpfs/user/chenziyang/LongBenchmark/eval/dataset/longbench_pro_final.json", "r") as f:
#     data = json.load(f)
# print(len(data))
# en, zh = 0, 0
# full, partial = 0, 0
# k8, k16, k32, k64, k128, k256 = 0, 0, 0, 0, 0, 0
# extreme, hard, moderate, easy = 0, 0, 0, 0
# for d in data:
#     if d["language"] == "English":
#         en += 1
#     elif d["language"] == "Chinese":
#         zh += 1
#     if d["contextual_requirement"] == "Full":
#         full += 1
#     elif d["contextual_requirement"] == "Partial":
#         partial += 1
#     if d["token_length"] == "8k":
#         k8 += 1
#     elif d["token_length"] == "16k":
#         k16 += 1
#     elif d["token_length"] == "32k":
#         k32 += 1
#     elif d["token_length"] == "64k":
#         k64 += 1
#     elif d["token_length"] == "128k":
#         k128 += 1
#     elif d["token_length"] == "256k":
#         k256 += 1
#     if d["difficulty"] == "Extreme":
#         extreme += 1
#     elif d["difficulty"] == "Hard":
#         hard += 1
#     elif d["difficulty"] == "Moderate":
#         moderate += 1
#     elif d["difficulty"] == "Easy":
#         easy += 1
# print(f"English: {en}, Chinese: {zh}")
# print(f"Full: {full}, Partial: {partial}")
# print(f"k8: {k8}, k16: {k16}, k32: {k32}, k64: {k64}, k128: {k128}, k256: {k256}")
# print(f"extreme: {extreme}, hard: {hard}, moderate: {moderate}, easy: {easy}")

# from collections import Counter
# with open("/cpfs/user/chenziyang/LongBenchmark/eval/dataset/doc_type/doc_type_stat_final_patch.json", "r") as f:
#     data = json.load(f)
# print(len(data))
# text_type_conter = Counter()
# for item in data:
#     text_type_conter[item["doc_type"]] += 1
# print(text_type_conter)


# ---------------------------
# Example data (replace with your real values)
# ---------------------------
data = {
    "Difficulty": {"Extreme": 440, "Hard": 289, "Moderate": 289, "Easy": 482},
    "Length": {"8k": 250, "16k": 250, "32k": 250, "64k": 250, "128k": 250, "256k": 250},
    "Context\nRequirement": {"Full": 840, "Partial": 660},
    "Language": {"English": 750, "Chinese": 750},
    "Text Type": {
        "Literature (Novel)": 320,
        "Literature (Other)": 179,
        "Law": 176,
        "Social": 121,
        "Finance": 115,
        "Technology": 113,
        "Science": 107,
        "History": 103,
        "News": 69,
        "Policy": 55,
        "Education": 51,
        "Structured": 41,
        "Medicine": 27,
        "Other": 23,
    },
}

# ---------------------------
# Convert to dataframe and compute percentages
# ---------------------------
all_categories = []
for dimension_data in data.values():
 for category in dimension_data.keys():
        if category not in all_categories:
            all_categories.append(category)

df = pd.DataFrame([{c: d.get(c, 0) for c in all_categories} for d in data.values()],
                  index=list(data.keys()), columns=all_categories)
difficulty_categories = set(data["Difficulty"].keys())

row_sums = df.sum(axis=1)

# ---------------------------
# Plot
# ---------------------------
fig, ax = plt.subplots(figsize=(16, 4))  # compact wide layout

y_spacing = 0.8  # reduce gap between bars
y_pos = np.arange(len(df)) * y_spacing
left = np.zeros(len(df))

# Use a discrete colormap
category_colors = {
    # Difficulty
    "Extreme": "#DA7B89",
    "Hard": "#E6C875",
    "Moderate": "#C5DB80",
    "Easy": "#92C56A",
    # Length
    "8k": "#d2e3f3",
    "16k": "#aacee4",
    "32k": "#68abd5",
    "64k": "#3788bf",
    "128k": "#0f5ba6",
    "256k": "#09336f",
    # Context Requirement
    "Full": "#495481",
    "Partial": "#e3882f",
    # Language
    "English": "#80a492",
    "Chinese": "#d23918",
    # Text Type
    "Literature (Novel)": "#393B79",
    "Law": "#5254A3",
    "Literature (Other)": "#6B6ECF",
    "Social": "#9C9EDE",
    "Technology": "#637939",
    "Finance": "#8CA252",
    "Science": "#B5CF6B",
    "History": "#CEDB9C",
    "News": "#8C6D31",
    "Policy": "#BD9E39",
    "Education": "#E7BA52",
    "Structured": "#E7CB94",
    "Medicine": "#843C39",
    "Other": "#AD494A",
}

for i, cat in enumerate(all_categories):
    values = df[cat].values
    bar_color = category_colors[cat]
    ax.barh(y_pos, values, left=left, color=bar_color, edgecolor="white", height=0.45)

    # put category name INSIDE bar if segment > threshold
    for j, val in enumerate(values):
        if val >= 52:  # threshold to avoid clutter
            percentage = (val / row_sums[j]) * 100 if row_sums[j] > 0 else 0
            text_x = left[j] + val / 2
            text_y = y_pos[j]
            text_color = "white"
            ax.text(text_x, text_y + 0.08, cat,
                    va="center", ha="center", fontsize=10, color=text_color,
                    fontproperties=bold_font)
            ax.text(text_x, text_y - 0.08, f"{percentage:.1f}%",
                    va="center", ha="center", fontsize=8, color=text_color,
                    fontproperties=bold_font)

    left += values

# ---------------------------
# Formatting
# ---------------------------
ax.set_yticks(y_pos)
ax.set_yticklabels(df.index, fontsize=12, fontweight="bold")
for label in ax.get_yticklabels():
    label.set_horizontalalignment("center")
    label.set_fontproperties(bold_font)
ax.tick_params(axis='y', pad=40, labelsize=12)
ax.set_xlim(0, max(row_sums))
xlabel = ax.set_xlabel("Percentage of Samples (Total Samples = 1,500)", fontsize=12, fontweight="bold")
xlabel.set_fontproperties(bold_font)
ax.set_xticks([])
ax.tick_params(axis='x', bottom=False, labelbottom=False, labelsize=12)

# No legend
ax.legend().remove()

plt.tight_layout()
plt.show()

save_path = "/cpfs/user/chenziyang/LongBenchmark/draw/images/data_source_distribution.png"
plt.savefig(save_path, dpi=600, bbox_inches='tight', facecolor='white', edgecolor='none')
print(f"Figure saved to: {save_path}")