Unit tests

.github/workflows/publish.yml

@@ -0,0 +1,33 @@
+name: Publish to PyPI
+
+on:
+    release:
+      types: [created]
+
+jobs:
+  build-n-publish:
+    name: Build and publish to PyPI
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    - name: Install Poetry
+      run: |
+        python -m pip install --upgrade pip
+        pip install poetry
+        poetry config virtualenvs.in-project true
+    - name: Install dependencies
+      run: |
+        poetry install
+    - name: Build and publish
+      env:
+        POETRY_PYPI_TOKEN_PYPI: ${{ secrets.POETRY_PYPI_TOKEN_PYPI }}
+      run: |
+        poetry version $(git describe --tags --abbrev=0)
+        poetry add $(cat requirements.txt)
+        poetry build
+        poetry publish

.github/workflows/ruff.yml

@@ -0,0 +1,8 @@
+name: Ruff
+on: [push, pull_request]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: chartboost/ruff-action@v1

.github/workflows/tests.yml

@@ -0,0 +1,43 @@
+name: Tests
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+permissions:
+  contents: read
+
+jobs:
+  tests:
+
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install Poetry
+      run: |
+        python -m pip install --upgrade pip
+        pip install poetry
+        poetry config virtualenvs.in-project true
+    - name: Install dependencies
+      run: |
+        poetry install
+    - name: Run pytest and generate coverage report
+      run: |
+        .venv/bin/pytest --cov-report=xml
+    - name: Upload coverage report to Codecov
+      uses: codecov/codecov-action@v3
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: ./coverage.xml
+        flags: unittests
+        verbose: true

.gitignore

@@ -19,6 +19,7 @@ venv*/
 rollouts
 profile
 dist
+.coverage
 
 # Sphinx documentation
 docs/_build/

.pre-commit-config.yaml

@@ -21,17 +21,9 @@ repos:
       - id: trailing-whitespace
       - id: end-of-file-fixer
       - id: requirements-txt-fixer
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-        args: [ --profile, black ]
-  - repo: https://github.com/ambv/black
-    rev: 23.3.0
-    hooks:
-      - id: black
-        args: ['--config=./pyproject.toml']
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: 'v0.0.265'
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: 'v0.1.8'
     hooks:
       - id: ruff
+        args: [ --fix ]
+      - id: ruff-format

README.md

@@ -39,18 +39,18 @@ pip install --upgrade jax[cuda12_pip]==0.4.20 -f https://storage.googleapis.com/
 ```
 
 ### MacOS
-Currently, only the CPU installation works. You will need to change a few small things to get it going: 
+Currently, only the CPU installation works. You will need to change a few small things to get it going:
 - Clone installation: in `pyproject.toml` change the torch version from `2.1.0+cpu` to `2.1.0`. Then, remove the `poetry.lock` file and run `poetry install --only main`.
 - Configs: You will need to set `f64: False` and `num_workers: 0` in the `configs/` files.
 
 Although the current [`jax-metal==0.0.5` library](https://pypi.org/project/jax-metal/) supports jax in general, there seems to be a missing feature used by `jax-md` related to padding -> see [this issue](https://github.com/google/jax/issues/16366#issuecomment-1591085071).
 
 ## Usage
 ### Standalone benchmark library
-A general tutorial is provided in the example notebook "Training GNS on the 2D Taylor Green Vortex" under `./notebooks/tutorial.ipynb` on the [LagrangeBench repository](https://github.com/tumaer/lagrangebench). The notebook covers the basics of LagrangeBench, such as loading a dataset, setting up a case, training a model from scratch and evaluating it's performance.
+A general tutorial is provided in the example notebook "Training GNS on the 2D Taylor Green Vortex" under `./notebooks/tutorial.ipynb` on the [LagrangeBench repository](https://github.com/tumaer/lagrangebench). The notebook covers the basics of LagrangeBench, such as loading a dataset, setting up a case, training a model from scratch and evaluating its performance.
 
 ### Running in a local clone (`main.py`)
-Alternatively, experiments can also be set up with `main.py`, based around extensive YAML config files and cli arguments (check [`configs/`](configs/)). By default, the arguments have priority as: 1) passed cli arguments, 2) YAML config and 3) [`defaults.py`](lagrangebench/defaults.py) (`lagrangebench` defaults).
+Alternatively, experiments can also be set up with `main.py`, based on extensive YAML config files and cli arguments (check [`configs/`](configs/)). By default, the arguments have priority as: 1) passed cli arguments, 2) YAML config and 3) [`defaults.py`](lagrangebench/defaults.py) (`lagrangebench` defaults).
 
 When loading a saved model with `--model_dir` the config from the checkpoint is automatically loaded and training is restarted. For more details check the [`experiments/`](experiments/) directory and the [`run.py`](experiments/run.py) file.
 
@@ -94,8 +94,8 @@ The datasets are hosted on Zenodo under the DOI: [10.5281/zenodo.10021925](https
 
 
 ### Notebooks
-Whe provide three notebooks that show LagrangeBench functionalities, namely:
-- [`tutorial.ipynb`](notebooks/tutorial.ipynb) with a general overview of LagrangeBench library, with trainin and evaluation of a simple GNS model,
+We provide three notebooks that show LagrangeBench functionalities, namely:
+- [`tutorial.ipynb`](notebooks/tutorial.ipynb) with a general overview of LagrangeBench library, with training and evaluation of a simple GNS model,
 - [`datasets.ipynb`](notebooks/datasets.ipynb) with more details and visualizations on the datasets, and
 - [`gns_data.ipynb`](notebooks/gns_data.ipynb) showing how to train models within LagrangeBench on the datasets from the paper [Learning to Simulate Complex Physics with Graph Networks](https://arxiv.org/abs/2002.09405).
 

configs/defaults.yaml

@@ -114,3 +114,5 @@ metrics_infer:
 metrics_stride_infer: 1
 out_type_infer: pkl
 eval_n_trajs_infer: -1
+# batch size for validation/testing
+batch_size_infer: 2

docs/requirements.txt

@@ -2,7 +2,7 @@
 
 cloudpickle
 dm_haiku>=0.0.10
-e3nn_jax>=0.20.0
+e3nn_jax==0.20.3
 h5py
 jax[cpu]==0.4.20
 jax_md>=0.2.8
@@ -15,6 +15,6 @@ pyvista
 PyYAML
 sphinx==7.2.6
 sphinx-rtd-theme==1.3.0
-torch>=2.1.0+cpu
+torch==2.1.0+cpu
 wandb
 wget

experiments/run.py

@@ -8,9 +8,9 @@
 import jax.numpy as jnp
 import jmp
 import numpy as np
+import wandb
 import yaml
 
-import wandb
 from experiments.utils import setup_data, setup_model
 from lagrangebench import Trainer, infer
 from lagrangebench.case_setup import case_builder
@@ -123,6 +123,7 @@ def train_or_infer(args: Namespace):
             eval_steps=args.config.eval_steps,
             metrics_stride=args.config.metrics_stride,
             num_workers=args.config.num_workers,
+            batch_size_infer=args.config.batch_size_infer,
         )
         _, _, _ = trainer(
             step_max=args.config.step_max,
@@ -150,7 +151,7 @@ def train_or_infer(args: Namespace):
         metrics = infer(
             model,
             case,
-            data_test,
+            data_test if args.config.test else data_valid,
             load_checkpoint=args.config.model_dir,
             metrics=args.config.metrics_infer,
             rollout_dir=args.config.rollout_dir,
@@ -160,6 +161,7 @@ def train_or_infer(args: Namespace):
             n_extrap_steps=args.config.n_extrap_steps,
             seed=args.config.seed,
             metrics_stride=args.config.metrics_stride_infer,
+            batch_size=args.config.batch_size_infer,
         )
 
         split = "test" if args.config.test else "valid"

lagrangebench/case_setup/case.py

@@ -3,7 +3,7 @@
 from typing import Callable, Dict, Optional, Tuple, Union
 
 import jax.numpy as jnp
-from jax import jit, lax, random, vmap
+from jax import Array, jit, lax, vmap
 from jax_md import space
 from jax_md.dataclasses import dataclass, static_field
 from jax_md.partition import NeighborList, NeighborListFormat
@@ -15,16 +15,14 @@
 from .features import FeatureDict, TargetDict, physical_feature_builder
 from .partition import neighbor_list
 
-TrainCaseOut = Tuple[random.KeyArray, FeatureDict, TargetDict, NeighborList]
+TrainCaseOut = Tuple[Array, FeatureDict, TargetDict, NeighborList]
 EvalCaseOut = Tuple[FeatureDict, NeighborList]
 SampleIn = Tuple[jnp.ndarray, jnp.ndarray]
 
-AllocateFn = Callable[[random.KeyArray, SampleIn, float, int], TrainCaseOut]
+AllocateFn = Callable[[Array, SampleIn, float, int], TrainCaseOut]
 AllocateEvalFn = Callable[[SampleIn], EvalCaseOut]
 
-PreprocessFn = Callable[
-    [random.KeyArray, SampleIn, float, NeighborList, int], TrainCaseOut
-]
+PreprocessFn = Callable[[Array, SampleIn, float, NeighborList, int], TrainCaseOut]
 PreprocessEvalFn = Callable[[SampleIn, NeighborList], EvalCaseOut]
 
 IntegrateFn = Callable[[jnp.ndarray, jnp.ndarray], jnp.ndarray]

lagrangebench/case_setup/partition.py

@@ -300,7 +300,7 @@ def scan_body(carry, input):
                 if not is_sparse(format):
                     capacity_limit = N - 1 if mask_self else N
                 elif format is NeighborListFormat.Sparse:
-                    capacity_limit = N * (N - 1) if mask_self else N ** 2
+                    capacity_limit = N * (N - 1) if mask_self else N**2
                 else:
                     capacity_limit = N * (N - 1) // 2
                 if max_occupancy > capacity_limit:

lagrangebench/data/data.py

@@ -242,7 +242,7 @@ def get_window(self, idx: int):
     def __getitem__(self, idx: int):
         """
         Get a sequence of positions (of size windows) from the dataset at index idx.
-        
+
         Returns:
             Array of shape (num_particles_max, input_seq_length + 1, dim). Along axis=1
                 the position sequence (length input_seq_length) and the last position to

lagrangebench/defaults.py

@@ -59,6 +59,7 @@ class defaults:
     out_type: str = "none"  # type of output. None means no rollout is stored
     n_extrap_steps: int = 0  # number of extrapolation steps
     metrics_stride: int = 10  # stride for e_kin and sinkhorn
+    batch_size_infer: int = 2  # batch size for validation/testing
 
     # logging
     log_steps: int = 1000  # number of steps between logs

lagrangebench/evaluate/metrics.py

@@ -45,7 +45,7 @@ def __init__(
             metadata: Metadata of the dataset.
             loss_ranges: List of horizon lengths to compute the loss for.
             input_seq_length: Length of the input sequence.
-            stride: Rollout subsample frequency for Sinkhorn.
+            stride: Rollout subsample frequency for e_kin and sinkhorn.
             ot_backend: Backend for sinkhorn computation. "ott" or "pot".
         """
         if active_metrics is None: