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from pathlib import Path
import numpy as np
import pandas as pd
import streamlit as st
from mlip_arena.models import REGISTRY as MODELS
valid_models = [
model
for model, metadata in MODELS.items()
if Path(__file__).stem in metadata.get("gpu-tasks", [])
]
DATA_DIR = Path("mlip_arena/tasks/diatomics")
dfs = [
pd.read_json(DATA_DIR / MODELS[model].get("family") / "homonuclear-diatomics.json")
for model in valid_models
]
df = pd.concat(dfs, ignore_index=True)
table = pd.DataFrame()
for model in valid_models:
rows = df[df["method"] == model]
metadata = MODELS.get(model, {})
new_row = {
"Model": model,
"Conservation deviation [eV/Å]": rows["conservation-deviation"].mean(),
"Spearman's coeff. (Energy - repulsion)": rows[
"spearman-repulsion-energy"
].mean(),
"Spearman's coeff. (Force - descending)": rows[
"spearman-descending-force"
].mean(),
"Tortuosity": rows["tortuosity"].mean(),
"Energy jump [eV]": rows["energy-jump"].mean(),
"Force flips": rows["force-flip-times"].mean(),
"Spearman's coeff. (Energy - attraction)": rows[
"spearman-attraction-energy"
].mean(),
"Spearman's coeff. (Force - ascending)": rows[
"spearman-ascending-force"
].mean(),
}
table = pd.concat([table, pd.DataFrame([new_row])], ignore_index=True)
table.set_index("Model", inplace=True)
table.sort_values("Conservation deviation [eV/Å]", ascending=True, inplace=True)
table["Rank"] = np.argsort(table["Conservation deviation [eV/Å]"].to_numpy())
table.sort_values(
"Spearman's coeff. (Energy - repulsion)", ascending=True, inplace=True
)
table["Rank"] += np.argsort(table["Spearman's coeff. (Energy - repulsion)"].to_numpy())
table.sort_values(
"Spearman's coeff. (Force - descending)", ascending=True, inplace=True
)
table["Rank"] += np.argsort(table["Spearman's coeff. (Force - descending)"].to_numpy())
table.sort_values("Tortuosity", ascending=True, inplace=True)
table["Rank"] += np.argsort(table["Tortuosity"].to_numpy())
table.sort_values("Energy jump [eV]", ascending=True, inplace=True)
table["Rank"] += np.argsort(table["Energy jump [eV]"].to_numpy())
table.sort_values("Force flips", ascending=True, inplace=True)
table["Rank"] += np.argsort(table["Force flips"].to_numpy())
table["Rank"] += 1
table.sort_values(["Rank", "Conservation deviation [eV/Å]"], ascending=True, inplace=True)
table["Rank aggr."] = table["Rank"]
table["Rank"] = table["Rank aggr."].rank(method='min').astype(int)
table = table.reindex(
columns=[
"Rank",
"Rank aggr.",
"Conservation deviation [eV/Å]",
"Spearman's coeff. (Energy - repulsion)",
"Spearman's coeff. (Force - descending)",
"Tortuosity",
"Energy jump [eV]",
"Force flips",
"Spearman's coeff. (Energy - attraction)",
"Spearman's coeff. (Force - ascending)",
]
)
s = (
table.style.background_gradient(
cmap="viridis_r",
subset=["Conservation deviation [eV/Å]"],
gmap=np.log(table["Conservation deviation [eV/Å]"].to_numpy()),
)
.background_gradient(
cmap="Reds",
subset=[
"Spearman's coeff. (Energy - repulsion)",
"Spearman's coeff. (Force - descending)",
],
# vmin=-1, vmax=-0.5
)
.background_gradient(
cmap="RdPu",
subset=["Tortuosity", "Energy jump [eV]", "Force flips"],
)
.background_gradient(
cmap="Blues",
subset=["Rank", "Rank aggr."],
)
.format(
"{:.4f}",
subset=[
"Conservation deviation [eV/Å]",
"Spearman's coeff. (Energy - repulsion)",
"Spearman's coeff. (Force - descending)",
"Tortuosity",
"Energy jump [eV]",
"Force flips",
"Spearman's coeff. (Energy - attraction)",
"Spearman's coeff. (Force - ascending)",
]
)
)
def render():
st.dataframe(
s,
use_container_width=True,
)
with st.expander("Explanation", icon=":material/info:"):
st.caption(
"""
- **Conservation deviation**: The average deviation of force from negative energy gradient along the diatomic curves.
$$
\\text{Conservation deviation} = \\left\\langle\\left| \\mathbf{F}(\\mathbf{r})\\cdot\\frac{\\mathbf{r}}{\\|\\mathbf{r}\\|} + \\nabla_rE\\right|\\right\\rangle_{r = \\|\\mathbf{r}\\|}
$$
- **Spearman's coeff. (Energy - repulsion)**: Spearman's correlation coefficient of energy prediction within equilibrium distance $r \\in (r_{min}, r_o = \\argmin_{r} E(r))$.
- **Spearman's coeff. (Force - descending)**: Spearman's correlation coefficient of force prediction within equilibrium distance $r \\in (r_{min}, r_o = \\argmin_{r} E(r))$.
- **Tortuosity**: The ratio between total variation in energy and sum of absolute energy differences between $r_{min}$, $r_o$, and $r_{max}$.
- **Energy jump**: The sum of energy discontinuity.
- **Force flips**: The number of sign changes.
"""
)
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