diff --git a/.gitignore b/.gitignore index 041b225d..a8a6c9fc 100644 --- a/.gitignore +++ b/.gitignore @@ -158,4 +158,6 @@ md_config.yaml # data *.extxyz *.traj -*.npz \ No newline at end of file +*.npz + +/models diff --git a/apax/nodes/__init__.py b/apax/nodes/__init__.py index ebc5d418..8dc74615 100644 --- a/apax/nodes/__init__.py +++ b/apax/nodes/__init__.py @@ -1,7 +1,8 @@ from .md import ApaxJaxMD from .model import Apax, ApaxCalibrate, ApaxEnsemble, ApaxImport +from .utils import AddData -__all__ = ["Apax", "ApaxEnsemble", "ApaxJaxMD", "ApaxImport", "ApaxCalibrate"] +__all__ = ["Apax", "ApaxEnsemble", "ApaxJaxMD", "ApaxImport", "ApaxCalibrate", "AddData"] try: from .analysis import ApaxBatchPrediction # noqa: F401 diff --git a/apax/nodes/analysis.py b/apax/nodes/analysis.py index d3e86da9..89b93c8a 100644 --- a/apax/nodes/analysis.py +++ b/apax/nodes/analysis.py @@ -1,14 +1,17 @@ import logging +import pathlib -import zntrack.utils -from ipsuite import base +import ase +import h5py +import znh5md +import zntrack -from .model import Apax +from apax.nodes.model import Apax log = logging.getLogger(__name__) -class ApaxBatchPrediction(base.ProcessAtoms): +class ApaxBatchPrediction(zntrack.Node): """Create and Save the predictions from model on atoms. Attributes @@ -24,13 +27,19 @@ class ApaxBatchPrediction(base.ProcessAtoms): predictions: list[Atoms] the atoms that have the predicted properties from model """ - _module_ = "apax.nodes" + data: list[ase.Atoms] = zntrack.deps() model: Apax = zntrack.deps() batch_size: int = zntrack.params(1) + frames_path: pathlib.Path = zntrack.outs_path(zntrack.nwd / "frames.h5") def run(self): - self.atoms = [] calc = self.model.get_calculator() - data = self.get_data() - self.atoms = calc.batch_eval(data, self.batch_size) + frames = calc.batch_eval(self.data, self.batch_size) + znh5md.write(self.frames_path, frames) + + @property + def frames(self) -> list[ase.Atoms]: + with self.state.fs.open(self.frames_path, "rb") as f: + with h5py.File(f, "r") as h5: + return znh5md.IO(file_handle=h5)[:] diff --git a/apax/nodes/md.py b/apax/nodes/md.py index 4a90270b..d1713b37 100644 --- a/apax/nodes/md.py +++ b/apax/nodes/md.py @@ -1,4 +1,3 @@ -import functools import logging import pathlib import typing @@ -10,9 +9,8 @@ import zntrack.utils from apax.md.simulate import run_md - -from .model import ApaxBase -from .utils import check_duplicate_keys +from apax.nodes.model import ApaxBase +from apax.nodes.utils import check_duplicate_keys log = logging.getLogger(__name__) @@ -28,7 +26,7 @@ class ApaxJaxMD(zntrack.Node): index of the configuration from the data list to use model: ApaxModel model to use for the simulation - repeat: float + repeat: None|int|tuple[int, int, int] number of repeats config: str path to the MD simulation parameter file @@ -38,7 +36,7 @@ class ApaxJaxMD(zntrack.Node): data_id: int = zntrack.params(-1) model: ApaxBase = zntrack.deps() - repeat = zntrack.params(None) + repeat: None | int | tuple[int, int, int] = zntrack.params(None) config: str = zntrack.params_path(None) @@ -47,35 +45,35 @@ class ApaxJaxMD(zntrack.Node): zntrack.nwd / "initial_structure.extxyz" ) - _parameter: dict = None - - def _post_load_(self) -> None: - self._handle_parameter_file() - - def _handle_parameter_file(self): - with self.state.use_tmp_path(): - self._parameter = yaml.safe_load(pathlib.Path(self.config).read_text()) + @property + def parameter(self) -> dict: + with self.state.fs.open(self.config, "r") as f: + parameter = yaml.safe_load(f) custom_parameters = { "sim_dir": self.sim_dir.as_posix(), "initial_structure": self.init_struc_dir.as_posix(), } - check_duplicate_keys(custom_parameters, self._parameter, log) - self._parameter.update(custom_parameters) + check_duplicate_keys(custom_parameters, parameter, log) + parameter.update(custom_parameters) + + return parameter + + def _write_initial_structure(self): + atoms = self.data[self.data_id] + if self.repeat is not None: + atoms = atoms.repeat(self.repeat) + ase.io.write(self.init_struc_dir.as_posix(), atoms) def run(self): """Primary method to run which executes all steps of the model training""" - if not self.state.restarted: - atoms = self.data[self.data_id] - if self.repeat is not None: - atoms = atoms.repeat(self.repeat) - ase.io.write(self.init_struc_dir.as_posix(), atoms) + self._write_initial_structure() - run_md(self.model._parameter, self._parameter, log_level="info") + run_md(self.model.parameter, self.parameter, log_level="info") - @functools.cached_property - def atoms(self) -> typing.List[ase.Atoms]: + @property + def frames(self) -> typing.List[ase.Atoms]: with self.state.fs.open(self.sim_dir / "md.h5", "rb") as f: with h5py.File(f) as file: return znh5md.IO(file_handle=file)[:] diff --git a/apax/nodes/model.py b/apax/nodes/model.py index 76dede9b..90d6f49f 100644 --- a/apax/nodes/model.py +++ b/apax/nodes/model.py @@ -20,7 +20,8 @@ class ApaxBase(zntrack.Node): - pass + def get_calculator(self, **kwargs): + raise NotImplementedError class Apax(ApaxBase): @@ -40,13 +41,13 @@ class Apax(ApaxBase): verbosity of logging during training """ - data: list = zntrack.deps() + data: list[ase.Atoms] = zntrack.deps() config: str = zntrack.params_path() - validation_data = zntrack.deps() - model: t.Optional[t.Any] = zntrack.deps(None) + validation_data: list[ase.Atoms] = zntrack.deps() + model: t.Optional[ApaxBase] = zntrack.deps(None) nl_skin: float = zntrack.params(0.5) transformations: t.Optional[list[dict[str, dict]]] = zntrack.params(None) - log_level: str = zntrack.meta.Text("info") + log_level: str = zntrack.params("info") model_directory: pathlib.Path = zntrack.outs_path(zntrack.nwd / "apax_model") @@ -55,38 +56,34 @@ class Apax(ApaxBase): zntrack.nwd / "val_atoms.extxyz" ) - metrics = zntrack.metrics() + metrics: dict = zntrack.metrics() - _parameter: dict = None + @property + def parameter(self) -> dict: + parameter = yaml.safe_load(self.state.fs.read_text(self.config)) - def _post_load_(self) -> None: - self._handle_parameter_file() + custom_parameters = { + "directory": self.model_directory.as_posix(), + "experiment": "", + "train_data_path": self.train_data_file.as_posix(), + "val_data_path": self.validation_data_file.as_posix(), + } - def _handle_parameter_file(self): - self._parameter = yaml.safe_load(self.state.fs.read_text(self.config)) + if self.model is not None: + param_files = self.model.parameter["data"]["directory"] + base_path = {"base_model_checkpoint": param_files} + try: + parameter["checkpoints"].update(base_path) + except KeyError: + parameter["checkpoints"] = base_path - with self.state.use_tmp_path(): - custom_parameters = { - "directory": self.model_directory.as_posix(), - "experiment": "", - "train_data_path": self.train_data_file.as_posix(), - "val_data_path": self.validation_data_file.as_posix(), - } - - if self.model is not None: - param_files = self.model._parameter["data"]["directory"] - base_path = {"base_model_checkpoint": param_files} - try: - self._parameter["checkpoints"].update(base_path) - except KeyError: - self._parameter["checkpoints"] = base_path - - check_duplicate_keys(custom_parameters, self._parameter["data"], log) - self._parameter["data"].update(custom_parameters) + check_duplicate_keys(custom_parameters, parameter["data"], log) + parameter["data"].update(custom_parameters) + return parameter def train_model(self): """Train the model using `apax.train.run`""" - apax_run(self._parameter, log_level=self.log_level) + apax_run(self.parameter, log_level=self.log_level) def get_metrics(self): """In addition to the plots write a model metric""" @@ -104,7 +101,7 @@ def run(self): if self.state.restarted and csv_path.is_file(): metrics_df = pd.read_csv(self.model_directory / "log.csv") - if metrics_df["epoch"].iloc[-1] >= self._parameter["n_epochs"] - 1: + if metrics_df["epoch"].iloc[-1] >= self.parameter["n_epochs"] - 1: return self.train_model() @@ -156,8 +153,7 @@ def get_calculator(self, **kwargs) -> ase.calculators.calculator.Calculator: calc: ase calculator object """ - - param_files = [m._parameter["data"]["directory"] for m in self.models] + param_files = [m.parameter["data"]["directory"] for m in self.models] transformations = [] if self.transformations: @@ -192,14 +188,9 @@ class ApaxImport(zntrack.Node): nl_skin: float = zntrack.params(0.5) transformations: t.Optional[list[dict[str, dict]]] = zntrack.params(None) - _parameter: dict = None - - def _post_load_(self) -> None: - self._handle_parameter_file() - - def _handle_parameter_file(self): - with self.state.use_tmp_path(): - self._parameter = yaml.safe_load(pathlib.Path(self.config).read_text()) + @property + def parameter(self) -> dict: + return yaml.safe_load(self.state.fs.read_text(self.config)) def get_calculator(self, **kwargs) -> ase.calculators.calculator.Calculator: """Property to return a model specific ase calculator object. @@ -210,8 +201,8 @@ def get_calculator(self, **kwargs) -> ase.calculators.calculator.Calculator: ase calculator object """ - directory = self._parameter["data"]["directory"] - exp = self._parameter["data"]["experiment"] + directory = self.parameter["data"]["directory"] + exp = self.parameter["data"]["experiment"] model_dir = directory + "/" + exp transformations = [] @@ -251,7 +242,7 @@ class ApaxCalibrate(ApaxBase): See the apax documentation for available methods. """ - model: t.Any = zntrack.deps() + model: ApaxBase = zntrack.deps() validation_data: list[Atoms] = zntrack.deps() batch_size: int = zntrack.params(32) criterion: str = zntrack.params("ma_cal") @@ -262,7 +253,7 @@ class ApaxCalibrate(ApaxBase): nl_skin: float = zntrack.params(0.5) - metrics = zntrack.metrics() + metrics: dict = zntrack.metrics() def run(self): """Primary method to run which executes all steps of the model training""" @@ -294,7 +285,7 @@ def get_calculator(self, **kwargs) -> ase.calculators.calculator.Calculator: e_factor = self.metrics["e_factor"] f_factor = self.metrics["f_factor"] - config_file = self.model._parameter["data"]["directory"] + config_file = self.model.parameter["data"]["directory"] calibration = GlobalCalibration( energy_factor=e_factor, diff --git a/apax/nodes/selection.py b/apax/nodes/selection.py index 374b7081..7e40cc8b 100644 --- a/apax/nodes/selection.py +++ b/apax/nodes/selection.py @@ -1,11 +1,10 @@ import logging import typing +from pathlib import Path import ase.io import numpy as np import zntrack.utils -from ipsuite.analysis.ensemble import plot_with_uncertainty -from ipsuite.configuration_selection.base import BatchConfigurationSelection from matplotlib import pyplot as plt try: @@ -19,7 +18,47 @@ log = logging.getLogger(__name__) -class BatchKernelSelection(BatchConfigurationSelection): +def plot_with_uncertainty(value, ylabel: str, xlabel: str, x=None, **kwargs) -> tuple: + """Parameters + ---------- + value: data of shape (n, m) where n is the number of ensembles. + x: optional x values of shape (m,) + + Returns + ------- + + """ + if isinstance(value, dict): + data = value + else: + data = { + "mean": np.mean(value, axis=0), + "std": np.std(value, axis=0), + "max": np.max(value, axis=0), + "min": np.min(value, axis=0), + } + + fig, ax = plt.subplots(**kwargs) + if x is None: + x = np.arange(len(data["mean"])) + ax.fill_between( + x, + data["mean"] + data["std"], + data["mean"] - data["std"], + facecolor="lightblue", + ) + if "max" in data: + ax.plot(x, data["max"], linestyle="--", color="darkcyan") + if "min" in data: + ax.plot(x, data["min"], linestyle="--", color="darkcyan") + ax.plot(x, data["mean"], color="black") + ax.set_ylabel(ylabel) + ax.set_xlabel(xlabel) + + return fig, ax, data + + +class BatchKernelSelection(zntrack.Node): """Interface to the batch active learning methods implemented in apax. Check the apax documentation for a list and explanation of implemented properties. @@ -48,19 +87,47 @@ class BatchKernelSelection(BatchConfigurationSelection): Does not affect the result, just the speed of computing features. """ - _module_ = "apax.nodes" + data: list[ase.Atoms] = zntrack.deps() + train_data: list[ase.Atoms] = zntrack.deps() + + selected_ids: list[int] = zntrack.outs(independent=True) models: typing.List[ApaxBase] = zntrack.deps() - base_feature_map: dict = zntrack.params({"name": "ll_grad", "layer_name": "dense_2"}) + base_feature_map: dict = zntrack.params( + default_factory=lambda: {"name": "ll_grad", "layer_name": "dense_2"} + ) selection_method: str = zntrack.params("max_dist") n_configurations: int = zntrack.params() - min_distance_threshold: str = zntrack.params(0.0) - processing_batch_size: str = zntrack.meta.Text(64) + min_distance_threshold: int = zntrack.params(0.0) + processing_batch_size: int = zntrack.params(64) rank_all: bool = zntrack.params(True) - img_selection = zntrack.outs_path(zntrack.nwd / "selection.png") - img_distances = zntrack.outs_path(zntrack.nwd / "distances.png") - img_features = zntrack.outs_path(zntrack.nwd / "features.png") + img_selection: Path = zntrack.outs_path(zntrack.nwd / "selection.png") + img_distances: Path = zntrack.outs_path(zntrack.nwd / "distances.png") + img_features: Path = zntrack.outs_path(zntrack.nwd / "features.png") + + def get_data(self) -> list[ase.Atoms]: + """Get the atoms data to process.""" + if self.data is not None: + return self.data + else: + raise ValueError("No data given.") + + def run(self): + """ZnTrack Node Run method.""" + + log.debug(f"Selecting from {len(self.data)} configurations.") + self.selected_ids = self.select_atoms(self.data) + + @property + def frames(self) -> list[ase.Atoms]: + """Get a list of the selected atoms objects.""" + return [atoms for i, atoms in enumerate(self.data) if i in self.selected_ids] + + @property + def excluded_frames(self) -> list[ase.Atoms]: + """Get a list of the atoms objects that were not selected.""" + return [atoms for i, atoms in enumerate(self.data) if i not in self.selected_ids] def select_atoms(self, atoms_lst: typing.List[ase.Atoms]) -> typing.List[int]: if isinstance(self.models, list): diff --git a/apax/nodes/utils.py b/apax/nodes/utils.py index 8efef777..71210d2d 100644 --- a/apax/nodes/utils.py +++ b/apax/nodes/utils.py @@ -1,4 +1,3 @@ -import functools import logging import ase.io @@ -7,17 +6,17 @@ class AddData(zntrack.Node): file: str = zntrack.deps_path() + start: int = zntrack.params(0) + stop: int = zntrack.params(None) + step: int = zntrack.params(1) def run(self): pass - @functools.cached_property - def atoms(self) -> list[ase.Atoms]: - data = [] - for atoms in ase.io.iread(self.file): - data.append(atoms) - if len(data) == 50: - return data + @property + def frames(self) -> list[ase.Atoms]: + with self.state.fs.open(self.file, "r") as f: + return ase.io.read(f, index=slice(self.start, self.stop, self.step)) def check_duplicate_keys(dict_a: dict, dict_b: dict, log: logging.Logger) -> None: diff --git a/poetry.lock b/poetry.lock index 58d8f357..2282a646 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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{file = "varname-0.13.5-py3-none-any.whl", hash = "sha256:55bc967573215ff93cbf77d6fe979c353b199080eac660adfb3b35b303f6eda2"}, - {file = "varname-0.13.5.tar.gz", hash = "sha256:82076a5c08e1b8b4fd0c9b5b1bf4a211a86d5367f97aa7f91d8eccb84017b3bf"}, -] - -[package.dependencies] -executing = ">=2.1,<3.0" - -[package.extras] -all = ["asttokens (==2.*)", "pure_eval (==0.*)"] - [[package]] name = "vesin" version = "0.2.0" @@ -7236,23 +7242,33 @@ enabler = ["pytest-enabler (>=2.2)"] test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] type = ["pytest-mypy"] +[[package]] +name = "znfields" +version = "0.1.2" +description = "Lazy evaluation of dataclass fields" +optional = true +python-versions = "<4.0,>=3.10" +files = [ + {file = "znfields-0.1.2-py3-none-any.whl", hash = "sha256:621c4746276a55637bb251494251e09671f4f4e74fbdaad7158d8a225e0d94e8"}, + {file = "znfields-0.1.2.tar.gz", hash = "sha256:5d9e1894f1a766dd5675db1b1b05cfc6b5c9d78da84a5b01d0baffd5ca49a065"}, +] + [[package]] name = "znflow" -version = "0.1.14" +version = "0.2.2" description = "A general purpose framework for building and running computational graphs." optional = true -python-versions = ">=3.8,<4.0" +python-versions = "<4.0,>=3.9" files = [ - {file = "znflow-0.1.14-py3-none-any.whl", hash = "sha256:f94f21cdaece949754e6dd5beaedfb078b9331ca49e32d9a2dfaa4ac1d8f8324"}, - {file = "znflow-0.1.14.tar.gz", hash = "sha256:bf85dbb4c816a3c1ae98ed62f75feb10a22032e8bccf8d016ee6d406873c9c03"}, + {file = "znflow-0.2.2-py3-none-any.whl", hash = "sha256:0fd83ba42235a1448a347aff1cba83d618a693199d592054d6c540ac18546c28"}, + {file = "znflow-0.2.2.tar.gz", hash = "sha256:04cea6d52983824888ca6b441aabd686dd88900be69dcb372045e6ae401ac21b"}, ] [package.dependencies] -matplotlib = ">=3.6.3,<4.0.0" -networkx = ">=3.0,<4.0" +networkx = ">=3,<4" [package.extras] -dask = ["bokeh (>=2.4.2,<3.0.0)", "dask (>=2022.12.1,<2023.0.0)", "dask-jobqueue (>=0.8.1,<0.9.0)", "distributed (>=2022.12.1,<2023.0.0)"] +dask = ["dask (>=2024,<2025)", "distributed (>=2024,<2025)"] [[package]] name = "znh5md" @@ -7271,20 +7287,6 @@ h5py = ">=3,<4" tqdm = ">=4,<5" typer = {version = ">=0,<1", extras = ["all"]} -[[package]] -name = "zninit" -version = "0.1.11" -description = "Descriptor based dataclass implementation" -optional = true -python-versions = ">=3.8,<4.0" -files = [ - {file = "zninit-0.1.11-py3-none-any.whl", hash = "sha256:78e5c7e1d0d50c131cf8efab58aad4a213446a1c38f38360731bf11b778e29e1"}, - {file = "zninit-0.1.11.tar.gz", hash = "sha256:fc14a55bd85a38f8f1411bd319cac541c2e6e16ab402f3f11a94e182b0681965"}, -] - -[package.extras] -typeguard = ["typeguard (>=2.13.3,<3.0.0)"] - [[package]] name = "znjson" version = "0.2.6" @@ -7298,26 +7300,23 @@ files = [ [[package]] name = "zntrack" -version = "0.7.3" +version = "0.8.0" description = "Create, Run and Benchmark DVC Pipelines in Python" optional = true -python-versions = "<4.0.0,>=3.9" +python-versions = "<4,>=3.10" files = [ - {file = "zntrack-0.7.3-py3-none-any.whl", hash = "sha256:93b307ff9c94fb23184d759e5cd2fe68a7189ce443502e09f13ef937f8596f3b"}, - {file = "zntrack-0.7.3.tar.gz", hash = "sha256:b02952974d5cf37b4d3463e0885beb069aa9be8c4e3a84c675261706a7eadcd3"}, + {file = "zntrack-0.8.0-py3-none-any.whl", hash = "sha256:8b1d3e98b026ad3312bddf7ef8cef6ba56455c542be0163629daf1214500a205"}, + {file = "zntrack-0.8.0.tar.gz", hash = "sha256:5ad0dd4ec517880cb49ac794e0ff990b7dc2ba8395f1b3d9326998ecb69ebe97"}, ] [package.dependencies] -dot4dict = ">=0.1,<0.2" -dvc = ">=3.3,<4.0" +dvc = ">=3.50,<4.0" pandas = ">=2,<3" pyyaml = ">=6,<7" -pyzmq = ">=25,<26" tqdm = ">=4,<5" -typer = ">=0.7,<0.8" -varname = ">=0.13,<0.14" -znflow = ">=0.1,<0.2" -zninit = ">=0.1,<0.2" +typer = ">=0.13,<0.14" +znfields = ">=0.1.2,<0.2.0" +znflow = ">=0.2.2,<0.3.0" znjson = ">=0.2,<0.3" [extras] @@ -7327,4 +7326,4 @@ zntrack = ["zntrack"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<4.0" -content-hash = "c53436fa04d98c9215dcbba0947c9f6a22e4989ac3d13c0193d0b0a3453decfc" +content-hash = "386e7c5d747be8b854927d1e316be0c2f681a5d0db1980538fd8cb70d6759d39" diff --git a/pyproject.toml b/pyproject.toml index 555b1a91..7994da99 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,7 @@ tensorflow = "^2.12.0" optax = "^0.1.3" einops = "^0.6.0" clu = "^0.0.7" -typer = "^0.7" +typer = "^0.13" lazy-loader = "^0.2" znh5md = "^0.3" pydantic = "^2.3.0" @@ -28,7 +28,7 @@ flax = "0.8.4" uncertainty-toolbox = "^0.1.1" e3x = "^1.0.2" vesin = "^0.2.0" -zntrack = {version = "^0.7.3", optional = true} +zntrack = {version = "^0.8", optional = true} mlflow = {version = "^2", optional = true} [tool.poetry.extras] diff --git a/tests/nodes/example.yaml b/tests/nodes/example.yaml index a644cde9..b2a281d6 100644 --- a/tests/nodes/example.yaml +++ b/tests/nodes/example.yaml @@ -20,13 +20,13 @@ model: name: gmnn basis: name: gaussian - r_max: 5.5 + r_max: 5.0 r_min: 0.7 n_basis: 7 n_radial: 5 nn: - - 512 - - 512 + - 16 + - 16 b_init: zeros emb_init: uniform diff --git a/tests/nodes/test_n_md.py b/tests/nodes/test_n_md.py index 033ea147..67ed5f0c 100644 --- a/tests/nodes/test_n_md.py +++ b/tests/nodes/test_n_md.py @@ -1,6 +1,7 @@ import os import pathlib import shutil +import subprocess import zntrack @@ -14,15 +15,17 @@ def test_n_jax_md(tmp_path, get_md22_stachyose): os.chdir(tmp_path) + subprocess.run(["git", "init"], check=True) + subprocess.run(["dvc", "init"], check=True) shutil.copy(CONFIG_PATH, tmp_path / "example.yaml") shutil.copy(MD_CONFIG_PATH, tmp_path / "md.yaml") proj = zntrack.Project() with proj: - data = AddData(file=get_md22_stachyose) - model = Apax(data=data.atoms, validation_data=data.atoms, config="example.yaml") - md = ApaxJaxMD(model=model, config="md.yaml", data=data.atoms) + data = AddData(file=get_md22_stachyose, stop=100) + model = Apax(data=data.frames, validation_data=data.frames, config="example.yaml") + md = ApaxJaxMD(model=model, config="md.yaml", data=data.frames) - proj.run() + proj.repro() md = md.from_rev() - assert len(md.atoms) == 50 + assert len(md.frames) == 50 diff --git a/tests/nodes/test_n_model.py b/tests/nodes/test_n_model.py index 02fdcc22..e5e01853 100644 --- a/tests/nodes/test_n_model.py +++ b/tests/nodes/test_n_model.py @@ -1,5 +1,6 @@ import os import pathlib +import subprocess import sys try: @@ -32,18 +33,20 @@ def save_config_with_seed(path: str, seed: int = 1) -> None: def test_n_train_model(tmp_path, get_md22_stachyose): os.chdir(tmp_path) + subprocess.run(["git", "init"], check=True) + subprocess.run(["dvc", "init"], check=True) save_config_with_seed(tmp_path / "example.yaml") proj = zntrack.Project() with proj: - data = AddData(file=get_md22_stachyose) - model = Apax(data=data.atoms, validation_data=data.atoms, config="example.yaml") + data = AddData(file=get_md22_stachyose, stop=100) + model = Apax(data=data.frames, validation_data=data.frames, config="example.yaml") - proj.run() + proj.repro() model = model.from_rev() data = data.from_rev() - atoms = data.atoms[0] + atoms = data.frames[0] atoms.calc = model.get_calculator() assert atoms.get_potential_energy() < 0 @@ -52,6 +55,8 @@ def test_n_train_model(tmp_path, get_md22_stachyose): @pytest.mark.skipif("ipsuite" not in sys.modules, reason="requires new ipsuite release") def test_n_train_2_model(tmp_path, get_md22_stachyose): os.chdir(tmp_path) + subprocess.run(["git", "init"], check=True) + subprocess.run(["dvc", "init"], check=True) save_config_with_seed(tmp_path / "example.yaml") save_config_with_seed(tmp_path / "example2.yaml", seed=2) @@ -61,12 +66,16 @@ def test_n_train_2_model(tmp_path, get_md22_stachyose): time_step=1.0, temperature=100.0, friction=0.01 ) with proj: - data = AddData(file=get_md22_stachyose) - model1 = Apax(data=data.atoms, validation_data=data.atoms, config="example.yaml") - model2 = Apax(data=data.atoms, validation_data=data.atoms, config="example2.yaml") + data = AddData(file=get_md22_stachyose, stop=100) + model1 = Apax( + data=data.frames, validation_data=data.frames, config="example.yaml" + ) + model2 = Apax( + data=data.frames, validation_data=data.frames, config="example2.yaml" + ) ensemble = ApaxEnsemble(models=[model1, model2]) md = ips.calculators.ASEMD( - data=data.atoms, + data=data.frames, model=ensemble, thermostat=thermostat, steps=20, @@ -80,32 +89,28 @@ def test_n_train_2_model(tmp_path, get_md22_stachyose): selection_batch_size = 3 kernel_selection = apax.nodes.BatchKernelSelection( data=md.atoms, - train_data=data.atoms, + train_data=data.frames, models=[model1, model2], n_configurations=selection_batch_size, processing_batch_size=4, ) - prediction = ips.analysis.Prediction(data=kernel_selection.atoms, model=ensemble) + prediction = ips.analysis.Prediction(data=kernel_selection.frames, model=ensemble) analysis = ips.analysis.PredictionMetrics( - x=kernel_selection.atoms, y=prediction.atoms + x=kernel_selection.frames, y=prediction.atoms ) - proj.run() + proj.repro() model = ensemble.from_rev() data = data.from_rev() - atoms = data.atoms[0] + atoms = data.frames[0] atoms.calc = model.get_calculator() assert atoms.get_potential_energy() < 0 - uncertainty_selection.load() - kernel_selection.load() - md.load() - uncertainties = [x.calc.results["energy_uncertainty"] for x in md.atoms] assert [md.atoms[np.argmax(uncertainties)]] == uncertainty_selection.atoms - assert len(kernel_selection.atoms) == selection_batch_size + assert len(kernel_selection.frames) == selection_batch_size diff --git a/tests/nodes/test_n_utils.py b/tests/nodes/test_n_utils.py index 6f6690c8..c05b7e4b 100644 --- a/tests/nodes/test_n_utils.py +++ b/tests/nodes/test_n_utils.py @@ -1,4 +1,5 @@ import os +import subprocess import ase.io import zntrack @@ -8,12 +9,14 @@ def test_add_data(tmp_path, get_md22_stachyose): os.chdir(tmp_path) + subprocess.run(["git", "init"], check=True) + subprocess.run(["dvc", "init"], check=True) proj = zntrack.Project() with proj: - data = AddData(file=get_md22_stachyose) + data = AddData(file=get_md22_stachyose, stop=50) - proj.run() + proj.repro() data = data.from_rev() - assert isinstance(data.atoms, list) - assert len(data.atoms) == 50 - assert all(isinstance(atoms, ase.Atoms) for atoms in data.atoms) + assert isinstance(data.frames, list) + assert len(data.frames) == 50 + assert all(isinstance(atoms, ase.Atoms) for atoms in data.frames)