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"""Analyse ncia_sh250x10 benchmark."""

from __future__ import annotations

from pathlib import Path

from ase import units
from ase.io import read, write
import pytest

from ml_peg.analysis.utils.decorators import build_table, plot_parity
from ml_peg.analysis.utils.utils import build_d3_name_map, load_metrics_config, mae
from ml_peg.app import APP_ROOT
from ml_peg.calcs import CALCS_ROOT
from ml_peg.models.get_models import load_models
from ml_peg.models.models import current_models

MODELS = load_models(current_models)
D3_MODEL_NAMES = build_d3_name_map(MODELS)

KCAL_TO_EV = units.kcal / units.mol
EV_TO_KCAL = 1 / KCAL_TO_EV
CALC_PATH = CALCS_ROOT / "non_covalent_interactions" / "ncia_sh250x10" / "outputs"
OUT_PATH = APP_ROOT / "data" / "non_covalent_interactions" / "ncia_sh250x10"

METRICS_CONFIG_PATH = Path(__file__).with_name("metrics.yml")
DEFAULT_THRESHOLDS, DEFAULT_TOOLTIPS, DEFAULT_WEIGHTS = load_metrics_config(
METRICS_CONFIG_PATH
)


def labels() -> list:
"""
Get list of system names.

Returns
-------
list
List of all system names.
"""
for model in MODELS:
labels_list = sorted([path.stem for path in (CALC_PATH / model).glob("*.xyz")])
break
return labels_list


@pytest.fixture
@plot_parity(
filename=OUT_PATH / "figure_ncia_sh250x10.json",
title="Interaction energies",
x_label="Predicted energy / eV",
y_label="Reference energy / eV",
hoverdata={
"Labels": labels(),
},
)
def interaction_energies() -> dict[str, list]:
"""
Get interaction energies for all systems.

Returns
-------
dict[str, list]
Dictionary of all reference and predicted interaction energies.
"""
results = {"ref": []} | {mlip: [] for mlip in MODELS}

ref_stored = False

for model_name in MODELS:
for label in labels():
atoms = read(CALC_PATH / model_name / f"{label}.xyz")
if not ref_stored:
results["ref"].append(atoms.info["ref_int_energy"])

results[model_name].append(atoms.info["model_int_energy"])

# Write structures for app
structs_dir = OUT_PATH / model_name
structs_dir.mkdir(parents=True, exist_ok=True)
write(structs_dir / f"{label}.xyz", atoms)

ref_stored = True
return results


@pytest.fixture
def get_mae(interaction_energies) -> dict[str, float]:
"""
Get mean absolute error for energies.

Parameters
----------
interaction_energies
Dictionary of reference and predicted energies.

Returns
-------
dict[str, float]
Dictionary of predicted energy errors for all models.
"""
results = {}
for model_name in MODELS:
results[model_name] = mae(
interaction_energies["ref"], interaction_energies[model_name]
)
return results


@pytest.fixture
@build_table(
filename=OUT_PATH / "ncia_sh250x10_metrics_table.json",
metric_tooltips=DEFAULT_TOOLTIPS,
thresholds=DEFAULT_THRESHOLDS,
mlip_name_map=D3_MODEL_NAMES,
)
def metrics(get_mae: dict[str, float]) -> dict[str, dict]:
"""
Get all metrics.

Parameters
----------
get_mae
Mean absolute errors for all models.

Returns
-------
dict[str, dict]
Metric names and values for all models.
"""
return {
"MAE": get_mae,
}


def test_ncia_sh250x10(metrics: dict[str, dict]) -> None:
"""
Run ncia_sh250x10 test.

Parameters
----------
metrics
All new benchmark metric names and dictionary of values for each model.
"""
return
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metrics:
MAE:
good: 0.0
bad: 0.5
unit: eV
tooltip: Mean Absolute Error for all systems
level_of_theory: CCSD(T)
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"""Run NCIA_SH250x10 app."""

from __future__ import annotations

from dash import Dash
from dash.html import Div

from ml_peg.app import APP_ROOT
from ml_peg.app.base_app import BaseApp
from ml_peg.app.utils.build_callbacks import (
plot_from_table_column,
struct_from_scatter,
)
from ml_peg.app.utils.load import read_plot
from ml_peg.models.get_models import get_model_names
from ml_peg.models.models import current_models

MODELS = get_model_names(current_models)
BENCHMARK_NAME = "NCIA_SH250x10"
DOCS_URL = (
"https://ddmms.github.io/ml-peg/user_guide/benchmarks/"
"non_covalent_interactions.html#ncia-sh250x10"
)
DATA_PATH = APP_ROOT / "data" / "non_covalent_interactions" / "ncia_sh250x10"


class NCIASH250x10App(BaseApp):
"""NCIA_SH250x10 benchmark app layout and callbacks."""

def register_callbacks(self) -> None:
"""Register callbacks to app."""
scatter = read_plot(
DATA_PATH / "figure_ncia_sh250x10.json",
id=f"{BENCHMARK_NAME}-figure",
)

model_dir = DATA_PATH / MODELS[0]
if model_dir.exists():
labels = sorted([f.stem for f in model_dir.glob("*.xyz")])
structs = [
f"assets/non_covalent_interactions/ncia_sh250x10/{MODELS[0]}/{label}.xyz"
for label in labels
]
else:
structs = []

plot_from_table_column(
table_id=self.table_id,
plot_id=f"{BENCHMARK_NAME}-figure-placeholder",
column_to_plot={"MAE": scatter},
)

struct_from_scatter(
scatter_id=f"{BENCHMARK_NAME}-figure",
struct_id=f"{BENCHMARK_NAME}-struct-placeholder",
structs=structs,
mode="struct",
)


def get_app() -> NCIASH250x10App:
"""
Get NCIA_SH250x10 benchmark app layout and callback registration.

Returns
-------
NCIASH250x10App
Benchmark layout and callback registration.
"""
return NCIASH250x10App(
name=BENCHMARK_NAME,
description=(
"Performance in predicting sigma-hole interaction energies "
"for the NCIA SH250x10 dataset (halogen-bonded dimers). "
"Reference data from CCSD(T) calculations."
),
docs_url=DOCS_URL,
table_path=DATA_PATH / "ncia_sh250x10_metrics_table.json",
extra_components=[
Div(id=f"{BENCHMARK_NAME}-figure-placeholder"),
Div(id=f"{BENCHMARK_NAME}-struct-placeholder"),
],
)


if __name__ == "__main__":
full_app = Dash(__name__, assets_folder=DATA_PATH.parent.parent)
benchmark_app = get_app()
full_app.layout = benchmark_app.layout
benchmark_app.register_callbacks()
full_app.run(port=8061, debug=True)
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/config.local
/tmp
/cache
Empty file.
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# Add patterns of files dvc should ignore, which could improve
# the performance. Learn more at
# https://dvc.org/doc/user-guide/dvcignore
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