serve/ranks/eos_bulk.py

from pathlib import Path

import pandas as pd
import streamlit as st

DATA_DIR = Path("benchmarks/eos_bulk")


table = pd.read_csv(DATA_DIR / "summary.csv")


table = table.rename(
    columns={
        "model": "Model",
        "rank": "Rank",
        "rank-aggregation": "Rank aggr.",
        "energy-diff-flip-times": "Derivative flips",
        "tortuosity": "Tortuosity",
        "spearman-compression-energy": "Spearman's coeff. (compression)",
        "spearman-tension-energy": "Spearman's coeff. (tension)",
        "spearman-compression-derivative": "Spearman's coeff. (compression derivative)",
        "missing": "Missing",
    },
)

table.set_index("Model", inplace=True)


@st.cache_data
def get_table():
    return table


def render():
    s = (
        get_table()
        .style.background_gradient(
            cmap="Blues",
            subset=["Rank", "Rank aggr."],
        )
        .background_gradient(
            cmap="Reds",
            subset=[
                "Spearman's coeff. (compression)",
            ],
        )
        .background_gradient(
            cmap="Reds_r",
            subset=[
                "Spearman's coeff. (tension)",
                "Spearman's coeff. (compression derivative)",
            ],
        )
        .background_gradient(
            cmap="RdPu",
            subset=["Tortuosity", "Derivative flips"],
        )
        .format(
            "{:.5f}",
            subset=[
                "Spearman's coeff. (compression)",
                "Spearman's coeff. (tension)",
                "Spearman's coeff. (compression derivative)",
                "Tortuosity",
                "Derivative flips",
            ],
        )
    )
    st.dataframe(
        s,
        use_container_width=True,
    )