Add UPU46 benchmark

ml_peg/analysis/conformers/UpU46/analyse_UpU46.py

@@ -0,0 +1,151 @@
+"""
+Analyse the UpU46 benchmark dataset for RNA backbone conformations.
+
+Journal of Chemical Theory and Computation,
+2015 11 (10), 4972-4991.
+DOI: 10.1021/acs.jctc.5b00515.
+"""
+
+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)
+
+
+EV_TO_KCAL = units.mol / units.kcal
+CALC_PATH = CALCS_ROOT / "conformers" / "UpU46" / "outputs"
+OUT_PATH = APP_ROOT / "data" / "conformers" / "UpU46"
+
+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.
+    """
+    labels_list = []
+    for model_name in MODELS:
+        for system_path in sorted((CALC_PATH / model_name).glob("*")):
+            labels_list.append(system_path.stem)
+        break  # only need the first model to list available systems
+    return labels_list
+
+
+@pytest.fixture
+@plot_parity(
+    filename=OUT_PATH / "figure_upu46.json",
+    title="Energies",
+    x_label="Predicted energy / kcal/mol",
+    y_label="Reference energy / kcal/mol",
+    hoverdata={
+        "Labels": labels(),
+    },
+)
+def conformer_energies() -> dict[str, list]:
+    """
+    Get conformer energies for all systems.
+
+    Returns
+    -------
+    dict[str, list]
+        Dictionary of all reference and predicted barrier heights.
+    """
+    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")
+
+            results[model_name].append(atoms.info["model_rel_energy"] * EV_TO_KCAL)
+            if not ref_stored:
+                results["ref"].append(atoms.info["ref_energy"] * EV_TO_KCAL)
+
+            # 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(conformer_energies) -> dict[str, float]:
+    """
+    Get mean absolute error for conformer energies.
+
+    Parameters
+    ----------
+    conformer_energies
+        Dictionary of reference and predicted conformer energies.
+
+    Returns
+    -------
+    dict[str, float]
+        Dictionary of predicted conformer energies errors for all models.
+    """
+    results = {}
+    for model_name in MODELS:
+        results[model_name] = mae(
+            conformer_energies["ref"], conformer_energies[model_name]
+        )
+    return results
+
+
+@pytest.fixture
+@build_table(
+    filename=OUT_PATH / "upu46_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_upu46(metrics: dict[str, dict]) -> None:
+    """
+    Run UpU46 test.
+
+    Parameters
+    ----------
+    metrics
+        All new benchmark metric names and dictionary of values for each model.
+    """
+    return

ml_peg/analysis/conformers/UpU46/metrics.yml

@@ -0,0 +1,7 @@
+metrics:
+  MAE:
+    good: 0.0
+    bad: 20.0
+    unit: kcal/mol
+    tooltip: Mean Absolute Error for all systems
+    level_of_theory: CCSD(T)

ml_peg/app/conformers/UpU46/app_UpU46.py

@@ -0,0 +1,88 @@
+"""Run UpU46 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 = "UpU46"
+DOCS_URL = "https://ddmms.github.io/ml-peg/user_guide/benchmarks/molecular.html#upu46"
+DATA_PATH = APP_ROOT / "data" / "conformers" / "UpU46"
+
+
+class UpU46App(BaseApp):
+    """UpU46 benchmark app layout and callbacks."""
+
+    def register_callbacks(self) -> None:
+        """Register callbacks to app."""
+        scatter = read_plot(
+            DATA_PATH / "figure_upu46.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/conformers/UpU46/{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() -> UpU46App:
+    """
+    Get UpU46 benchmark app layout and callback registration.
+
+    Returns
+    -------
+    UpU46App
+        Benchmark layout and callback registration.
+    """
+    return UpU46App(
+        name=BENCHMARK_NAME,
+        description=(
+            "Performance in predicting RNA backbone conformer energies for the "
+            "UpU46 dataset comprising 46 uracil dinucleotides (UpU), representing "
+            "all known 46 RNA backbone conformational families. Reference data "
+            "from CCSD(T) calculations."
+        ),
+        docs_url=DOCS_URL,
+        table_path=DATA_PATH / "upu46_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=8067, debug=True)

ml_peg/calcs/conformers/UpU46/calc_UpU46.py

@@ -0,0 +1,147 @@
+"""
+Calculate the UpU46 benchmark dataset for RNA backbone conformations.
+
+Journal of Chemical Theory and Computation,
+2015 11 (10), 4972-4991.
+DOI: 10.1021/acs.jctc.5b00515.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from ase import Atoms, units
+from ase.io import read, write
+import mlipx
+from mlipx.abc import NodeWithCalculator
+from tqdm import tqdm
+import zntrack
+
+from ml_peg.calcs.utils.utils import chdir, download_s3_data
+from ml_peg.models.get_models import load_models
+from ml_peg.models.models import current_models
+
+MODELS = load_models(current_models)
+
+KCAL_TO_EV = units.kcal / units.mol
+
+OUT_PATH = Path(__file__).parent / "outputs"
+
+
+class UpU46Benchmark(zntrack.Node):
+    """Compute the benchmark."""
+
+    model: NodeWithCalculator = zntrack.deps()
+    model_name: str = zntrack.params()
+
+    @staticmethod
+    def get_atoms(atoms_path: Path) -> Atoms:
+        """
+        Read atoms object and add charge and spin.
+
+        Parameters
+        ----------
+        atoms_path
+            Path to atoms object.
+
+        Returns
+        -------
+        atoms
+            ASE atoms object.
+        """
+        atoms = read(atoms_path)
+        atoms.info["charge"] = -1
+        atoms.info["spin"] = 1
+        if atoms.calc is not None:
+            if "energy" in atoms.calc.results:
+                del atoms.calc.results["energy"]
+        return atoms
+
+    def get_ref_energies(self, data_path: Path) -> None:
+        """
+        Get reference conformer energies.
+
+        Parameters
+        ----------
+        data_path
+            Path to the structure.
+        """
+        self.ref_energies = {}
+        with open(data_path / "references") as lines:
+            for i, line in enumerate(lines):
+                # Skip the comment lines
+                if i < 5:
+                    continue
+                items = line.strip().split()
+                label = items[2]
+                self.ref_energies[label] = float(items[7]) * KCAL_TO_EV
+
+    def run(self):
+        """Run new benchmark."""
+        data_path = (
+            download_s3_data(
+                filename="UPU46.zip",
+                key="inputs/conformers/UpU46/UpU46.zip",
+            )
+            / "UPU46"
+        )
+        zero_conf_label = "2p"
+        self.get_ref_energies(data_path)
+        # Read in data and attach calculator
+        calc = self.model.get_calculator()
+        # Add D3 calculator for this test
+        calc = self.model.add_d3_calculator(calc)
+
+        conf_lowest = self.get_atoms(data_path / f"{zero_conf_label}.xyz")
+        conf_lowest.calc = calc
+        e_conf_lowest_model = conf_lowest.get_potential_energy()
+
+        for label, e_ref in tqdm(self.ref_energies.items()):
+            # Skip the reference conformer for
+            # which the error is automatically zero
+            if label == zero_conf_label:
+                continue
+
+            atoms = self.get_atoms(data_path / f"{label}.xyz")
+            atoms.calc = calc
+            atoms.info["model_rel_energy"] = (
+                atoms.get_potential_energy() - e_conf_lowest_model
+            )
+            atoms.info["ref_energy"] = e_ref
+            atoms.calc = None
+
+            write_dir = OUT_PATH / self.model_name
+            write_dir.mkdir(parents=True, exist_ok=True)
+            write(write_dir / f"{label}.xyz", atoms)
+
+
+def build_project(repro: bool = False) -> None:
+    """
+    Build mlipx project.
+
+    Parameters
+    ----------
+    repro
+        Whether to call dvc repro -f after building.
+    """
+    project = mlipx.Project()
+    benchmark_node_dict = {}
+
+    for model_name, model in MODELS.items():
+        with project.group(model_name):
+            benchmark = UpU46Benchmark(
+                model=model,
+                model_name=model_name,
+            )
+            benchmark_node_dict[model_name] = benchmark
+
+    if repro:
+        with chdir(Path(__file__).parent):
+            project.repro(build=True, force=True)
+    else:
+        project.build()
+
+
+def test_upu46():
+    """Run UpU46 benchmark via pytest."""
+    build_project(repro=True)