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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,
)
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