diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index c333db9..0646198 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -15,12 +15,13 @@ jobs: with: python-version: "3.11" - run: pip install ruff - - run: ruff check src/ tests/ - - run: ruff format --check src/ tests/ + - run: ruff check src/ tests/ examples/ reproduce/ + - run: ruff format --check src/ tests/ examples/ reproduce/ test: runs-on: ubuntu-latest strategy: + fail-fast: false matrix: python-version: ["3.9", "3.10", "3.11", "3.12"] steps: @@ -30,8 +31,8 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | + python -m pip install --upgrade pip pip install -e ".[dev]" - pip install rdkit-pypi - name: Run tests run: pytest -m "not slow" --tb=short -q --cov=three_dbench --cov-report=xml - name: Upload coverage @@ -39,6 +40,6 @@ jobs: uses: codecov/codecov-action@v4 with: files: coverage.xml - fail_ci_if_not_required: false + fail_ci_if_error: false env: CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} diff --git a/.gitignore b/.gitignore index c9532ee..97d7fb9 100644 --- a/.gitignore +++ b/.gitignore @@ -49,3 +49,6 @@ examples/.demo_cache/ # Claude Code local settings .claude/ + +# Reproduction script outputs +reproduce/*/out/ diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..57427b1 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,42 @@ +cff-version: 1.2.0 +message: "If you use 3DCS, please cite the ICLR 2026 paper." +title: "3DCS: Datasets and Benchmark for Evaluating Conformational Sensitivity in Molecular Representations" +type: software +license: MIT +repository-code: "https://github.com/ComDec/3DCS" +url: "https://openreview.net/forum?id=JAb0y8lkqL" +authors: + - family-names: Wang + given-names: Xi + - family-names: Zhang + given-names: Yang + - family-names: Zhang + given-names: Yingjia + - family-names: Cai + given-names: Yejia + - family-names: Wang + given-names: Shengjie +keywords: + - molecular representation learning + - conformers + - chirality + - benchmark +preferred-citation: + type: conference-paper + title: "3DCS: Datasets and Benchmark for Evaluating Conformational Sensitivity in Molecular Representations" + authors: + - family-names: Wang + given-names: Xi + - family-names: Zhang + given-names: Yang + - family-names: Zhang + given-names: Yingjia + - family-names: Cai + given-names: Yejia + - family-names: Wang + given-names: Shengjie + collection-title: "The Fourteenth International Conference on Learning Representations (ICLR 2026)" + conference: + name: "International Conference on Learning Representations (ICLR)" + year: 2026 + url: "https://openreview.net/forum?id=JAb0y8lkqL" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 85e6158..ce7314b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -14,12 +14,10 @@ cd 3DCS python -m venv .venv source .venv/bin/activate -# Install in editable mode with dev dependencies +# Install in editable mode with dev dependencies (RDKit comes from the `rdkit` package on PyPI; +# do not install `rdkit-pypi`) pip install -e ".[dev]" -# Install RDKit (required for molecule parsing) -pip install rdkit-pypi - # Install pre-commit hooks pre-commit install ``` @@ -37,19 +35,21 @@ pytest --cov=three_dbench pytest -m "not slow" ``` +CI runs `pytest -m "not slow"` on Python 3.9, 3.10, 3.11 and 3.12. + ## Code Style This project uses [ruff](https://docs.astral.sh/ruff/) for linting and formatting. ```bash -# Check for issues -ruff check src/ tests/ +# Check for issues (the same paths as CI and `make lint`) +ruff check src/ tests/ examples/ reproduce/ # Auto-fix issues -ruff check --fix src/ tests/ +ruff check --fix src/ tests/ examples/ reproduce/ # Format code -ruff format src/ tests/ +ruff format src/ tests/ examples/ reproduce/ ``` Key style rules: @@ -65,6 +65,8 @@ Key style rules: 4. Add a benchmark entry point in `src/three_dbench/benchmarks/my_task.py` 5. Register the task in `src/three_dbench/__main__.py` 6. Add tests under `tests/` +7. Document the metric definitions under `docs/metrics/` and, if the task reproduces a paper table, + add `reproduce//` with `run.sh` and `expected.csv` (see `reproduce/README.md`) ## Submitting Changes diff --git a/Makefile b/Makefile index f4a5007..6535576 100644 --- a/Makefile +++ b/Makefile @@ -5,19 +5,20 @@ install: dev: pip install -e ".[dev]" - pip install rdkit-pypi pre-commit install test: pytest +LINT_PATHS = src/ tests/ examples/ reproduce/ + lint: - ruff check src/ tests/ - ruff format --check src/ tests/ + ruff check $(LINT_PATHS) + ruff format --check $(LINT_PATHS) format: - ruff check --fix src/ tests/ - ruff format src/ tests/ + ruff check --fix $(LINT_PATHS) + ruff format $(LINT_PATHS) clean: rm -rf build/ dist/ *.egg-info .pytest_cache .mypy_cache .ruff_cache htmlcov/ diff --git a/README.md b/README.md index 7ee8025..6d41264 100644 --- a/README.md +++ b/README.md @@ -5,56 +5,50 @@ [![Python 3.9+](https://img.shields.io/badge/python-3.9%2B-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) -3DCS is an evaluation toolkit for 3D molecular embeddings. It provides benchmarks for -chirality separation, trajectory alignment, and rotation invariance. The repository uses -Hugging Face datasets as the standard input format for all benchmarks, making it easy to -upload and reuse datasets. +Evaluation toolkit for **3DCS: Datasets and Benchmark for Evaluating Conformational Sensitivity in +Molecular Representations** (ICLR 2026, [OpenReview](https://openreview.net/forum?id=JAb0y8lkqL)). -## Highlights +3DCS tests whether the representations of different conformers of the same molecule preserve +geometric variation (rotation / relaxed-scan dataset), separate stereoisomers (chirality dataset) +and reflect the energy landscape (rMD17 trajectories). The package name is `3dcs`; the Python module +is `three_dbench`. -- Hugging Face dataset integration for reproducible evaluation -- CLI and Python API for converting datasets and evaluating embeddings -- Three benchmarks: chirality, trajectory, and rotation -- Bundled example embeddings for instant demo -- Consistent outputs (per-sample JSON/CSV and aggregated summaries) +| Resource | Location | +|---|---| +| Datasets | [`EscheWang/3dcs`](https://huggingface.co/datasets/EscheWang/3dcs), configs `chirality`, `rotation`, `traj_frames`, `traj_energies` (license CC BY-SA 4.0) | +| Baseline embeddings and original metric outputs | [`EscheWang/3dcs-embeddings`](https://huggingface.co/datasets/EscheWang/3dcs-embeddings) (see [docs/EMBEDDINGS.md](docs/EMBEDDINGS.md)) | +| Metric definitions (`paper` and `v2`) | [docs/METRICS.md](docs/METRICS.md) | +| Per-table reproduction scripts | [reproduce/](reproduce/README.md) | +| rMD17 splits | [splits/rmd17/](splits/rmd17/README.md) | -## Quick Start - -Package name: **3DCS** (`pip install` name). Python module: `three_dbench`. +## Quick start ### Install ```bash -# Basic install -pip install -e . - -# With RDKit (required for molecule parsing) -pip install rdkit-pypi - -# Development install (includes pytest, ruff, pre-commit) -pip install -e ".[dev]" +git clone https://github.com/ComDec/3DCS.git +cd 3DCS +pip install -e . # RDKit is installed from PyPI as the `rdkit` package +pip install -e ".[dev]" # optional: pytest, ruff, pre-commit ``` -### Try the demo +Python 3.9–3.12 are supported. Do not install the old `rdkit-pypi` package; it has no wheels for +Python 3.12 and fails to import with NumPy 2. -The repository includes small GemNet embedding fixtures so you can run an evaluation -immediately without generating your own embeddings: +### Download data ```bash -# Run chirality demo (downloads HF dataset on first run) -python examples/demo.py chirality - -# Run trajectory demo -python examples/demo.py trajectory - -# Run both -python examples/demo.py all +# Datasets (save_to_disk copies under data/hf/) +python -m three_dbench download dataset --task chirality # data/hf/chirality (~0.1 GB) +python -m three_dbench download dataset --task traj # data/hf/traj/{energies,frames} +python -m three_dbench download dataset --task rotation # data/hf/rotation (~7.5 GB download) + +# Published baseline embeddings (verified against the SHA-256 in manifest.csv) +python -m three_dbench download embeddings --task chirality --models gemnet unimol --out data/embeddings +python -m three_dbench download embeddings --task rotation --models gemnet --out data/embeddings ``` -### Download datasets from Hugging Face - -The datasets are hosted at [`EscheWang/3dcs`](https://huggingface.co/datasets/EscheWang/3dcs) -with configs: `chirality`, `traj_energies`, `traj_frames`, `rotation`. +The datasets can also be loaded directly with Hugging Face `datasets`: ```python from datasets import load_dataset @@ -63,79 +57,131 @@ ds = load_dataset("EscheWang/3dcs", name="chirality", split="train") ds.save_to_disk("data/hf/chirality") ``` -### Evaluate your embeddings +### Evaluate embeddings ```bash -# Chirality +# Chirality: one embedding per conformer, in dataset row order python -m three_dbench evaluate chirality \ --dataset-dir data/hf/chirality \ - --embeddings your_embeddings.npz \ - --embedding-key arr_0 \ - --model-name your_model + --embeddings data/embeddings/chirality/gemnet/sampled_feature.npz --embedding-key gemnet \ + --model-name gemnet -# Trajectory +# Trajectory (rMD17): a directory with one rmd17_.npz per molecule python -m three_dbench evaluate traj \ --dataset-dir data/hf/traj/energies \ - --embeddings your_traj_embeddings_dir/ \ - --embedding-key arr_0 \ - --model-name your_model + --embeddings data/embeddings/traj/gemnet --embedding-key gemnet \ + --model-name gemnet --n-jobs 16 -# Rotation +# Rotation: one file per shard (rows aligned with the per-shard `offset`) python -m three_dbench evaluate rotation \ --dataset-dir data/hf/rotation \ - --embeddings your_rotation_embeddings.npz \ - --embedding-key arr_0 \ - --model-name your_model + --embeddings data/embeddings/rotation/gemnet --layout by-shard \ + --model-name gemnet --metrics cosine --shards 0 --n-jobs 16 ``` -### Convert raw data to Hugging Face format +Every evaluation writes a summary (`summary.csv`), per-molecule results and the configuration to +`--output-dir` (default `results//`, relative to the working directory or +`$THREE_DBENCH_HOME`). `--metric-version paper` (default) uses the definitions behind the published +numbers; `--metric-version v2` uses the corrected definitions described in +[docs/METRICS.md](docs/METRICS.md). See [docs/USAGE.md](docs/USAGE.md) for all options, embedding +layouts and the Python API. -If you have the original raw data (pickle, LMDB), you can convert it: +A small demo with bundled GemNet fixtures: ```bash -python -m three_dbench convert chirality \ - --input-pkl data/chirality/chirality_bench_conformers_noised_only.pkl \ - --output-dir data/hf/chirality - -python -m three_dbench convert traj \ - --mol-pkl-dir data/traj/mol_pkl \ - --energy-dir data/traj/npz_data \ - --output-dir data/hf/traj - -python -m three_dbench convert rotation \ - --lmdb-root data/rotation/results \ - --output-dir data/hf/rotation +python examples/demo.py all ``` -## Dataset Structure - -| Config | Fields | -|--------|--------| -| `chirality` | key, mol_id, en_id, n_conformers, offset, mol_blocks | -| `rotation` | key, shard, n_conformers, offset, mol_blocks, torsion_deg | -| `traj_frames` | mol_type, frame_idx, mol_block | -| `traj_energies` | mol_type, n_frames, energies | - -The `mol_blocks` field stores MolBlock strings with 3D coordinates. Rebuild RDKit -objects with `three_dbench.datasets.serialization.mol_from_block`. +## Reproducing the paper + +Each table has a directory under [`reproduce/`](reproduce/README.md) with a `run.sh` (download, +evaluation, `results.csv`) and an `expected.csv` (printed value, recomputed value with 6 decimals, +tolerance and notes). `reproduce/compare.py` prints PASS/FAIL per cell. + +| Table | Script | What it recomputes | Runtime (24 workers) | +|---|---|---|---| +| Table 1 (geometry) | `bash reproduce/table1_geometry/run.sh` | GemNet from the published rotation embeddings | ~40–50 min for two metric versions (estimated from single-shard runs); `QUICK=1` (one shard): ~4 min | +| Table 2 (chirality, zero-shot) | `bash reproduce/table2_chirality/run.sh` | all 7 models from the published embeddings | ~40–60 min for all variants | +| Tables 3, 6, 7 (energy, zero-shot) | `bash reproduce/energy_tables_3_6_7/run.sh` | all 7 models from the published embeddings, float64 energies | ~50 min per metric version | + +Download sizes: rotation dataset ~7.5 GB, rotation GemNet embeddings ~5.2 GB, trajectory embeddings +~7 GB, chirality embeddings ~0.4 GB. + +### Status + +"Reproducible" means that the published value is recomputed from released data, embeddings and code +within the tolerance in `expected.csv`; the notes in each `expected.csv` list the known exceptions. + +| Table | E3FP | GemNet | MolAE | MolSpectra | UniMol | FMG | MACE | +|---|---|---|---|---|---|---|---| +| 1 Geometry | pending (rotation embeddings) | full 16-shard run pending | pending (rotation embeddings) | pending (rotation embeddings) | pending (rotation embeddings) | not in table | not in table | +| 2 Chirality (zero-shot) | reproducible | reproducible | reproducible | reproducible | reproducible | reproducible | reproducible | +| 3 / 6 / 7 Energy (zero-shot) | reproducible | reproducible | reproducible | reproducible | reproducible | partially | partially | +| 4 Chirality correlation | pending (evaluation code) | pending (evaluation code) | pending (evaluation code) | pending (evaluation code) | pending (evaluation code) | not in table | not in table | +| 5 Chirality fine-tuning | not in table | pending | pending | pending | pending | pending | pending | +| 8 / 9 rMD17 fine-tuning | pending | pending | pending | pending | pending | pending | pending | + +Further notes: + +- **Embedding extraction.** The published embeddings are the files used for the paper. Extraction + scripts are not part of this repository; the per-model status (known, partially known, unknown) is + listed in [docs/EMBEDDINGS.md](docs/EMBEDDINGS.md). +- **Table 1.** The published rows come from two runs: Spearman, Kendall, CKA, isotonic R² and + Torsion-SP from a 10 % molecule sample (its key list is in `reproduce/table1_geometry/`), LIE@k and + AS from all molecules. The per-molecule outputs of both runs for all five models are published in + the embeddings repository (`results/rotation/`). GemNet is the only model whose rotation embeddings + were kept, and a full 16-shard recomputation has not been run yet; `reproduce/table1_geometry/` + currently verifies one shard against the backed-up per-molecule outputs. In the full published run, + two molecules could not be processed and the embedding cursor was not advanced, so the molecules + after them in shards 1 and 2 (6.2 % of the total) were scored with embeddings shifted by 3 and 7 + rows; this affects the LIE@k and AS columns. `--replicate-offset-drift` reproduces that behaviour. + See [docs/metrics/geometry.md](docs/metrics/geometry.md). +- **Table 4.** The backed-up summary behind the Spearman, Kendall and CKA rows is published + (`results/chirality/chirality_metrics_summary.csv` in the embeddings repository); outputs for the + OPD rows were not found in our backups. The code for this table is not in the release. +- **Fine-tuning (Tables 5, 8, 9).** Fine-tuning code and checkpoints are not part of this release. + Checkpoints for the chirality fine-tuning (Table 5) are being prepared and will be added later; no + checkpoints are planned for the rMD17 tables (8, 9). For the rMD17 tables, the train/test indices we + found correspond to the official split 01 ([splits/rmd17/](splits/rmd17/README.md)). + +## Dataset structure + +| Config | Rows | Fields | +|--------|------|--------| +| `chirality` | 14,903 (52,391 conformers) | key, mol_id, en_id, n_conformers, offset, mol_blocks | +| `rotation` | 1,559,779 (10,097,643 conformers) | key, shard, n_conformers, offset (per shard), mol_blocks, torsion_deg | +| `traj_frames` | 999,988 | mol_type, frame_idx, mol_block | +| `traj_energies` | 10 | mol_type, n_frames, energies (kcal/mol) | + +`mol_blocks` stores MolBlock strings with 3D coordinates; rebuild RDKit molecules with +`three_dbench.datasets.serialization.mol_from_block`. Converters for the original raw files are +available as `python -m three_dbench convert {chirality,traj,rotation}` (the raw files are not +distributed). ## Documentation -- [docs/USAGE.md](docs/USAGE.md) — end-to-end usage guide -- [docs/EMBEDDINGS.md](docs/EMBEDDINGS.md) — embedding format specifications -- [CONTRIBUTING.md](CONTRIBUTING.md) — development setup and contribution guidelines - -## Contributing - -See [CONTRIBUTING.md](CONTRIBUTING.md) for how to set up a development environment, -run tests, and submit changes. - -```bash -make dev # Install with dev dependencies + pre-commit -make test # Run test suite -make lint # Check code style +- [docs/USAGE.md](docs/USAGE.md): CLI and Python API +- [docs/EMBEDDINGS.md](docs/EMBEDDINGS.md): embedding formats and the published baseline embeddings +- [docs/METRICS.md](docs/METRICS.md): metric definitions (`paper` and `v2`) +- [reproduce/README.md](reproduce/README.md): reproducing the paper tables +- [CONTRIBUTING.md](CONTRIBUTING.md): development setup + +## Citation + +```bibtex +@inproceedings{wang2026threedcs, + title = {3{DCS}: Datasets and Benchmark for Evaluating Conformational Sensitivity in Molecular Representations}, + author = {Wang, Xi and Zhang, Yang and Zhang, Yingjia and Cai, Yejia and Wang, Shengjie}, + booktitle = {The Fourteenth International Conference on Learning Representations (ICLR)}, + year = {2026}, + url = {https://openreview.net/forum?id=JAb0y8lkqL} +} ``` +See also [CITATION.cff](CITATION.cff). When you use the trajectory data or the rMD17 splits, please +also cite rMD17 (Christensen & von Lilienfeld, 2020). + ## License -This project is licensed under the MIT License. See [LICENSE](LICENSE) for details. +The code is released under the MIT License (see [LICENSE](LICENSE)). The datasets on Hugging Face +are released under CC BY-SA 4.0; see the dataset cards for upstream terms (ChEMBL, rMD17). diff --git a/docs/EMBEDDINGS.md b/docs/EMBEDDINGS.md index 4296049..0f1cc56 100644 --- a/docs/EMBEDDINGS.md +++ b/docs/EMBEDDINGS.md @@ -1,119 +1,98 @@ -# Embedding Generation Guide +# Embeddings -This guide explains what inputs 3DCS provides and what embedding formats are accepted. +This page describes (1) the embedding formats the evaluators accept and (2) the published baseline +embeddings in [`EscheWang/3dcs-embeddings`](https://huggingface.co/datasets/EscheWang/3dcs-embeddings). -## Overview +## 1. Inputs provided by 3DCS -3DCS evaluates embeddings that represent 3D molecular conformations. For each task, the -dataset contains RDKit MolBlocks with 3D coordinates. Your workflow is: +Each dataset row stores RDKit MolBlocks with 3D coordinates (`three_dbench.datasets.serialization.mol_from_block`). -1. Load the HF dataset for a task -2. Reconstruct RDKit molecules from MolBlocks -3. Call your model to produce a fixed-length vector per conformer -4. Save the embeddings in one of the supported formats +| Config | Row | Conformer order | +|---|---|---| +| `chirality` | one stereoisomer of a parent molecule: `key`, `mol_id`, `en_id`, `n_conformers`, `offset`, `mol_blocks` | `offset` is a global index (rows in dataset order) | +| `rotation` | one molecule: `key`, `shard`, `n_conformers`, `offset`, `mol_blocks`, `torsion_deg` | `offset` restarts at 0 in every shard; shards appear in string order `0, 1, 10, …, 15, 2, …, 9` | +| `traj_frames` | one rMD17 frame: `mol_type`, `frame_idx`, `mol_block` | `frame_idx` = row of the rMD17 `.npz` | +| `traj_energies` | one molecule: `mol_type`, `n_frames`, `energies` (kcal/mol) | aligned with `frame_idx` | -The embedding dimension must be fixed for a given model. +## 2. Accepted formats -## Inputs provided by 3DCS +- **NPZ**: `np.savez(path, arr_0=embeddings)` or any key; pass `--embedding-key`. Without a key the + loader tries `arr_0`, `embeddings`, `gemnet`, `mol_feature`, then the first numeric array (string + side-arrays such as FMG's `smiles` are skipped). +- **NPY**: `np.save(path, embeddings)` (memory-mapped for the rotation by-shard layout). +- **PKL**: a list of RDKit `ExplicitBitVect` (fingerprints, Tanimoto distance) or a dict; without a key + the entries `e3fp`, `embeddings`, `arr_0` are tried. +- A directory holding exactly one embedding file can be passed instead of the file. -- Chirality dataset rows contain: `key`, `mol_id`, `en_id`, `n_conformers`, `offset`, `mol_blocks` -- Rotation dataset rows contain: `key`, `shard`, `n_conformers`, `offset`, `mol_blocks`, `torsion_deg` -- Trajectory frames dataset rows contain: `mol_type`, `frame_idx`, `mol_block` -- Trajectory energies dataset rows contain: `mol_type`, `n_frames`, `energies` +| Task | Layout | Shape | +|---|---|---| +| Chirality | flat, dataset row order | `(52391, dim)` or a list of 52,391 fingerprints | +| Trajectory | directory with `rmd17_.npz` (or `.pkl` fingerprints), or a dict `{mol_type: array}` | `(n_frames, dim)` per molecule | +| Rotation | `--layout by-shard`: directory with one file per shard (`rotation_conformers_{shard}.npz`, or `--shard-file-pattern`), rows in per-shard offset order | `(conformers in shard, dim)` | +| Rotation | `--layout flat`: one array in dataset row order | `(10097643, dim)` | +| Rotation | `--layout by-key`: dict `{key: array}` in `.npz`/`.pkl` | `(n_conformers, dim)` per key | -MolBlocks include 3D coordinates. Use `three_dbench.datasets.serialization.mol_from_block` -to rebuild RDKit Mol objects. - -## Supported embedding formats - -### Common formats - -- NPZ: `np.savez(path, arr_0=embeddings)` or `np.savez(path, embeddings=embeddings)` -- NPY: `np.save(path, embeddings)` -- PKL: `pickle.dump(embeddings, f)` or a dict `{key: embeddings}` - -### Chirality - -- **Flat array** (default): shape `(total_conformers, dim)` aligned with dataset order -- **Fingerprint list**: a list of RDKit fingerprints aligned with dataset order - -Alignment is determined by `offset` and `n_conformers` in the HF dataset rows. - -### Rotation - -- **Flat array** (default): shape `(total_conformers, dim)` aligned with dataset order -- **By key dict**: `{key: np.ndarray of shape (n_conformers, dim)}` -- **Fingerprint list**: supported for flat layout - -### Trajectory - -- **Directory of NPZ files**: one file per molecule (e.g. `rmd17_aspirin.npz`) -- **Dict**: `{mol_type: np.ndarray of shape (n_frames, dim)}` - -## Example: chirality embedding generation - -```python -from pathlib import Path -import numpy as np -from datasets import load_from_disk -from three_dbench.datasets.serialization import mol_from_block - -ds = load_from_disk("data/hf/chirality") - -vectors = [] -for row in ds: - mols = [mol_from_block(b, sanitize=False) for b in row["mol_blocks"]] - # Replace this with your model call - for mol in mols: - vec = np.random.randn(256).astype(np.float32) - vectors.append(vec) - -embeddings = np.stack(vectors, axis=0) -np.savez("my_model_chirality.npz", arr_0=embeddings) -``` - -## Example: rotation embedding generation (by key) +Generating embeddings for the rotation dataset shard by shard keeps the per-shard layout: ```python -from pathlib import Path import numpy as np from datasets import load_from_disk from three_dbench.datasets.serialization import mol_from_block ds = load_from_disk("data/hf/rotation") -out = {} -for row in ds: - mols = [mol_from_block(b, sanitize=False) for b in row["mol_blocks"]] - vecs = [np.random.randn(256).astype(np.float32) for _ in mols] - out[row["key"]] = np.stack(vecs, axis=0) - -import pickle -with open("my_rotation_by_key.pkl", "wb") as f: - pickle.dump(out, f) +for shard in range(16): + part = ds.filter(lambda s: s == shard, input_columns=["shard"]).sort("offset") + vectors = [] + for row in part: + for block in row["mol_blocks"]: + vectors.append(your_model(mol_from_block(block))) # shape (dim,) + np.savez(f"embeddings/my_model/rotation_conformers_{shard}.npz", arr_0=np.stack(vectors)) ``` -## Example: trajectory embedding generation (per molecule) - -```python -import numpy as np -from datasets import load_from_disk -from three_dbench.datasets.serialization import mol_from_block - -frames = load_from_disk("data/hf/traj/frames") -mol_types = sorted(set(frames["mol_type"])) - -for mol_type in mol_types: - rows = frames.filter(lambda x: x["mol_type"] == mol_type) - vecs = [] - for row in rows: - mol = mol_from_block(row["mol_block"], sanitize=False) - vecs.append(np.random.randn(256).astype(np.float32)) - embeddings = np.stack(vecs, axis=0) - np.savez(f"embeddings/{mol_type}.npz", arr_0=embeddings) -``` - -## Tips - -- Use float32 for most models. Float16 is acceptable for very large datasets. -- Keep the embedding order identical to the dataset order. -- For rotation, MolBlocks are required to compute RMSD; do not skip them during conversion. +Check alignment before evaluating: for flat arrays the number of rows must equal the sum of +`n_conformers`; for by-shard files, each file must have `max(offset + n_conformers)` rows of its shard. + +## 3. Published baseline embeddings + +`python -m three_dbench download embeddings --task {chirality,traj,rotation,chirality_legacy_15218} [--models ...] [--results]` +downloads files listed in the repository's `manifest.csv` to `data/embeddings/` and checks +their SHA-256. The files keep their original bytes and array keys; only directory names were +normalised. `three_dbench.embeddings.published_embedding(task, model)` returns the path, key and +layout of each entry. + +| Path | Key | Shape | Used for | +|---|---|---|---| +| `chirality/e3fp/sampled_chi.pkl` | dict entry `e3fp` (also `morgan`) | 52,391 RDKit bit vectors (1024 bits) | Table 2 | +| `chirality/gemnet/sampled_feature.npz` | `gemnet` | (52391, 128) float32 | Table 2 | +| `chirality/molae/1.npz` | `arr_0` | (52391, 512) | Table 2 | +| `chirality/molspectra/sampled_mol_feature.npz` | `arr_0` | (52391, 256) | Table 2 | +| `chirality/unimol/1.npz` | `arr_0` | (52391, 512) | Table 2 | +| `chirality/fmg/chirality_bench_conformers_noised_only_aslist_embed.npz` | `embeddings` (+ `smiles`) | (52391, 128) | Table 2 | +| `chirality/mace/chirality.npz` | `arr_0` | (52391, 256) | Table 2 | +| `chirality_legacy_15218/…` | see manifest | 15,218 conformers | earlier chirality set (Table 4) | +| `traj//rmd17_.npz` (`.pkl` for E3FP) | `gemnet` (GemNet), `embeddings` (FMG), `arr_0` (others); E3FP: pickled list | (100000, dim); azobenzene 99,988 | Tables 3, 6, 7 | +| `rotation/gemnet/rotation_conformers_{0..15}.npz` | `gemnet` | (conformers in shard, 128) | Table 1 | +| `rotation/fmg/rot_mol_list_0_embed.npz`, `rotation/mace/rot0.npz` | `embeddings` / `arr_0` | shard 0 only | not used in the paper | +| `results/chirality/`, `results/traj/`, `results/rotation/` | – | original metric outputs | reference values | + +Rotation embeddings for E3FP, UniMol, MolAE and MolSpectra are not available. For those models the +per-molecule metric outputs of the original runs (`results/rotation/metrics_all_0.1_1.json.gz`, +`results/rotation/metrics_sup_100.json.gz`) are published instead. + +### Extraction status + +The extraction scripts used for the paper are not part of this repository. What is known about each +set of embeddings: + +| Model | Dimension | Status | What is known | +|---|---|---|---| +| E3FP | 1024 bits | known | `e3fp` 1.2.7, `fprints_from_mol(mol, fprint_params=dict(bits=1024, level=5, radius_multiplier=1.5, stereo=True, include_disconnected=True, rdkit_invariants=True, first=1, counts=False))`, hydrogens kept. Recomputing from the original RDKit molecules reproduces 3,000/3,000 sampled chirality fingerprints; starting from the HF MolBlocks, about 5 % of fingerprints differ. | +| GemNet (GemNet-Q) | 128 | partially known | A GemNet implementation in the authors' files returns 128-d molecule embeddings by averaging final-layer atom features, but it postdates the published embeddings and its use for these files is not confirmed. The GemNet-Q weights are not released. | +| UniMol | 512 | unknown | Output format only. | +| MolAE | 512 | unknown | Output format only. | +| MolSpectra | 256 | unknown | Output format only. | +| MACE | 256 | unknown | Output format only. | +| FMG | 128 | unknown | Output format only (third-party model: Dumitrescu et al., ICLR 2025). | + +Embeddings produced with a re-implemented extractor may differ from these files; compare against the +published files before using them to reproduce the tables. diff --git a/docs/METRICS.md b/docs/METRICS.md new file mode 100644 index 0000000..3c98108 --- /dev/null +++ b/docs/METRICS.md @@ -0,0 +1,30 @@ +# Metrics + +3DCS evaluates a molecular representation along three axes. Each evaluator supports two metric +versions: + +- `--metric-version paper` (default): the definitions that produced the published numbers. Where + the implementation used for the paper differs from the text of the paper, this version follows + the implementation, so that the tables can be regenerated. +- `--metric-version v2`: definitions that follow the text of the paper (or correct a degenerate + case), with the rationale given on each page. + +| Axis | Dataset (`EscheWang/3dcs` config) | Paper tables | Definitions | +|---|---|---|---| +| Geometry | `rotation` | Table 1 | [metrics/geometry.md](metrics/geometry.md) | +| Chirality | `chirality` | Tables 2, 4 | [metrics/chirality.md](metrics/chirality.md) | +| Energy | `traj_energies` (+ `traj_frames` for embedding generation) | Tables 3, 6, 7 | [metrics/energy.md](metrics/energy.md) | + +Common conventions: + +- Representation distances `Δ` are computed within one molecule (all conformers of a rotation + molecule, all stereoisomer conformers of a chirality parent, or one trajectory window). RDKit bit + vectors always use the Tanimoto distance. +- The chirality evaluator uses the Euclidean distance by default (`--distance euclidean`), which is + what the published Table 2 used; `--distance cosine` is available. The geometry evaluator reports + both cosine and Euclidean spaces (`--metrics`); Table 1 uses cosine. The energy evaluator uses the + cosine distance. +- Scores are averaged per molecule (or per trajectory window) and then over molecules; non-finite + per-molecule values are excluded from the mean. `summary.csv` reports the number of finite values. + +The reproduction scripts and the reference values for each table are in [../reproduce/](../reproduce/README.md). diff --git a/docs/USAGE.md b/docs/USAGE.md index 141979e..448c591 100644 --- a/docs/USAGE.md +++ b/docs/USAGE.md @@ -1,284 +1,195 @@ -# Usage Guide +# Usage guide -This guide describes dataset conversion, embedding formats, and evaluation workflows. -For embedding generation details, see `docs/EMBEDDINGS.md`. +This guide covers downloading data, the `evaluate` options of each benchmark, and the Python API. +Embedding formats are described in [EMBEDDINGS.md](EMBEDDINGS.md), metric definitions in +[METRICS.md](METRICS.md), and the reproduction of the paper tables in +[../reproduce/README.md](../reproduce/README.md). -## Quick demo +Default data and output directories are resolved relative to `$THREE_DBENCH_HOME` if it is set, and +relative to the current working directory otherwise. Importing the package does not create files. -The fastest way to see 3DCS in action is to run the bundled demo with pre-computed -GemNet embeddings: +## 1. Download ```bash -pip install -e . -python examples/demo.py all +python -m three_dbench download dataset --task {chirality,traj,rotation,all} [--out data/hf] [--revision REV] [--overwrite] +python -m three_dbench download embeddings --task {chirality,traj,rotation,chirality_legacy_15218,all} \ + [--models gemnet unimol ...] [--results] [--out data/embeddings] [--revision REV] [--no-verify] [--dry-run] ``` -This downloads the HF dataset and evaluates the included fixture embeddings for both -chirality and trajectory benchmarks. See `examples/demo.py` for details. +`download dataset` loads the configs of [`EscheWang/3dcs`](https://huggingface.co/datasets/EscheWang/3dcs) +and writes them with `save_to_disk`: -## 1. Dataset conversion +| Task | Config | Written to | +|---|---|---| +| `chirality` | `chirality` | `data/hf/chirality` | +| `traj` | `traj_energies`, `traj_frames` | `data/hf/traj/energies`, `data/hf/traj/frames` | +| `rotation` | `rotation` | `data/hf/rotation` (~7.5 GB download; ~25 GB on disk) | -Convert the raw RDKit molecule data into Hugging Face datasets: +Existing directories are kept unless `--overwrite` is given. -```bash -python -m three_dbench convert chirality \ - --input-pkl data/chirality/chirality_bench_conformers_noised_only.pkl \ - --output-dir data/hf/chirality - -python -m three_dbench convert traj \ - --mol-pkl-dir data/traj/mol_pkl \ - --energy-dir data/traj/npz_data \ - --output-dir data/hf/traj - -python -m three_dbench convert rotation \ - --lmdb-root data/rotation/results \ - --output-dir data/hf/rotation -``` - -Use `--no-mol-blocks` to skip MolBlock storage when disk space is limited. Rotation -evaluation requires MolBlocks to compute RMSD. - -## 2. Embedding formats - -### Chirality - -- **Flat array**: one embedding per conformer, aligned with the dataset order -- Use the `offset` field in the dataset rows to verify alignment if needed -- Supported files: `.npz`, `.npy`, `.pkl` -- Use `--embedding-key` for NPZ or pickled dicts - -### Rotation - -- **Flat array** (default): embeddings aligned with `offset` and `n_conformers` -- **By key**: a dict mapping `key` to a `(n_conformers, dim)` array -- Supported files: `.npz` or `.pkl` dicts for by-key layout +`download embeddings` reads `manifest.csv` of +[`EscheWang/3dcs-embeddings`](https://huggingface.co/datasets/EscheWang/3dcs-embeddings), downloads +the files under `//` (plus `results//` with `--results`) to `/`, and +verifies each file's SHA-256 (files that are already present with the right checksum are not +downloaded again). `--dry-run` lists the selected files and their sizes. -### Trajectory +Equivalent Python: -- Directory of `rmd17_*.npz` files (default) -- Or a dict mapping `mol_type` to `(n_frames, dim)` arrays +```python +from three_dbench.download import download_dataset, download_embeddings -## 3. Run evaluation +download_dataset("chirality", "data/hf") +download_embeddings("rotation", "data/embeddings", models=["gemnet"]) +``` -### Chirality +## 2. Chirality ```bash python -m three_dbench evaluate chirality \ --dataset-dir data/hf/chirality \ - --embeddings data/chirality/unimol/1.npz \ - --embedding-key arr_0 \ - --model-name unimol + --embeddings data/embeddings/chirality/unimol/1.npz --embedding-key arr_0 \ + --model-name unimol --output-dir results/chirality/unimol ``` -### Rotation +Options (see [metrics/chirality.md](metrics/chirality.md)): -```bash -python -m three_dbench evaluate rotation \ - --dataset-dir data/hf/rotation \ - --embeddings /path/to/rotation_embeddings.npz \ - --embedding-key arr_0 \ - --model-name my_model -``` +| Option | Default | Meaning | +|---|---|---| +| `--distance {euclidean,cosine}` | `euclidean` | distance for continuous embeddings; `euclidean` reproduces Table 2. Fingerprints always use Tanimoto. | +| `--metric-version {paper,v2}` | `paper` | metric definitions | +| `--unsup-kmax` | `n-1` | largest k for the best-k silhouette | +| `--per-mol-min-n` | 2 | minimum conformers per molecule | +| `--max-molecules` | all | evaluate the first N molecules (testing) | +| `--do-unsup-when-single-en` | off | also compute the unsupervised metrics for parents with a single stereoisomer | +| `--n-jobs` | 1 | worker processes (`-1` = all CPUs) | -To use a dict keyed by rotation entry IDs: +Outputs: `_per_molecule.json`, `summary.csv`. -```bash -python -m three_dbench evaluate rotation \ - --dataset-dir data/hf/rotation \ - --embeddings /path/to/rotation_by_key.pkl \ - --layout by-key \ - --model-name my_model -``` - -### Trajectory +## 3. Trajectory (energy) ```bash python -m three_dbench evaluate traj \ --dataset-dir data/hf/traj/energies \ - --embeddings data/traj/results/unimol \ - --embedding-key arr_0 \ - --model-name unimol -``` - -## 4. End-to-end examples (download from HF and evaluate) - -The scripts under `examples/` will download the dataset from HF if it is not cached -locally, then run evaluation using existing embeddings. - -### Chirality - -```bash -python examples/run_chirality_from_hf.py \ - --repo-id EscheWang/3dcs \ - --config chirality \ - --embeddings data/chirality/unimol/1.npz \ - --embedding-key arr_0 \ - --model-name unimol \ - --output-dir results/chirality/unimol + --embeddings data/embeddings/traj/unimol --embedding-key arr_0 \ + --model-name unimol --n-jobs 16 --output-dir results/traj/unimol ``` -### Trajectory +`--embeddings` is a directory with one `rmd17_.npz` (or `.pkl` fingerprint list) per +molecule, or a single `.npz`/`.pkl` dict keyed by molecule. -```bash -python examples/run_traj_from_hf.py \ - --repo-id EscheWang/3dcs \ - --config traj_energies \ - --embeddings-dir data/traj/results/unimol \ - --embedding-key arr_0 \ - --model-name unimol \ - --output-dir results/traj/unimol -``` +Options (see [metrics/energy.md](metrics/energy.md)): -### Rotation +| Option | Default | Meaning | +|---|---|---| +| `--window-scheme {legacy,shared}` | `legacy` | window sampling; `legacy` reproduces the published runs | +| `--n-samples`, `--window`, `--random-seed` | 100, 2000, 2025 | windows per molecule, frames per window, seed | +| `--metric-embed {cosine,euclidean,tanimoto}` | cosine (Tanimoto for fingerprints) | representation distance | +| `--metric-version {paper,v2}` | `paper` | metric definitions | +| `--molecules` | all | subset of rMD17 molecules | +| `--energy-precision-check {error,warn,ignore}` | `error` | action when the energies look float32-quantized | +| `--time-ordered` | off | treat the frames as a time series (enables the `v2` TS / smoothness metrics; rMD17 frames are not ordered) | +| `--legacy-traj-len` | 100000 | trajectory length assumed by the `legacy` window scheme | +| `--block-size` | 4096 | block size of the pairwise distance computation | +| `--n-jobs` | 1 | worker processes (`-1` = all CPUs) | -```bash -python examples/run_rotation_from_hf.py \ - --repo-id EscheWang/3dcs \ - --config rotation \ - --embeddings-dir data/rotation/results/gemnet \ - --embedding-key gemnet \ - --model-name gemnet \ - --output-dir results/rotation/gemnet -``` +Outputs: `details.csv`, `summary.csv`, `config.json`. -#### Rotation quick test +## 4. Rotation (geometry) ```bash -python examples/run_rotation_from_hf.py \ - --repo-id EscheWang/3dcs \ - --config rotation \ - --embeddings-dir data/rotation/results/gemnet \ - --embedding-key gemnet \ - --model-name gemnet \ - --output-dir results/rotation/gemnet_quick \ - --shards 0 \ - --max-keys 200 +python -m three_dbench evaluate rotation \ + --dataset-dir data/hf/rotation \ + --embeddings data/embeddings/rotation/gemnet --layout by-shard --embedding-key gemnet \ + --model-name gemnet --metrics cosine --n-jobs 24 --output-dir results/rotation/gemnet ``` -## 5. End-to-end with your own embeddings - -This section shows the full pipeline when you generate embeddings with your own model. -It includes download, embedding generation, format validation, and evaluation. - -### Step 1: Download datasets from HF +Embedding layouts (details in [metrics/geometry.md](metrics/geometry.md#data-and-alignment)): -```bash -# Chirality dataset -python - <<'PY' -from datasets import load_dataset - -ds = load_dataset("EscheWang/3dcs", name="chirality", split="train") -ds.save_to_disk("data/hf/chirality") -PY +| `--layout` | `--embeddings` | Alignment | +|---|---|---| +| `by-shard` (default for a directory) | directory with one file per shard (`rotation_conformers_{shard}.npz`, `rot{shard}.npz`, … or `--shard-file-pattern`) | per-shard `offset` of each row | +| `flat` (default for a file) | one `.npz`/`.npy`/`.pkl` array in dataset row order | cumulative `n_conformers` over the dataset rows | +| `by-key` | `.npz`/`.pkl` dict keyed by `key` | by key | -# Trajectory energies -python - <<'PY' -from datasets import load_dataset +The published dataset stores per-shard offsets (they restart at 0 in every shard). Datasets written +by `convert rotation` store global offsets; `--offset-mode auto` (default) detects both. -ds = load_dataset("EscheWang/3dcs", name="traj_energies", split="train") -ds.save_to_disk("data/hf/traj/energies") -PY +Options: -# Rotation dataset -python - <<'PY' -from datasets import load_dataset +| Option | Default | Meaning | +|---|---|---| +| `--metrics` | `cosine euclidean` | distance spaces for continuous embeddings (Table 1 uses cosine) | +| `--metric-version {paper,v2}` | `paper` | metric definitions | +| `--lie-k`, `--lie-self {include,exclude}` | from the metric version | override LIE@k | +| `--as-variant` | from the metric version | override angular smoothness (`mean_delta_circular`, `median_delta_circular`, `median_halfdelta_circular`, `median_dz_linear`) | +| `--extra-metrics` | off | also distance correlation, Mantel, stress, triplet order (slow) | +| `--shards` | all shards with embeddings | shard ids to evaluate | +| `--molecule-list FILE` | – | evaluate only these keys (one per line, `.gz` allowed), e.g. `reproduce/table1_geometry/sampled_molecules_seed2027.txt` | +| `--sample-ratio R --sample-seed S` | – , 2027 | per-shard random sample (`default_rng(S + shard)`); does not regenerate the published 10 % sample | +| `--min-conformers` | 2 | skip molecules with fewer conformers | +| `--max-keys` | – | evaluate at most N molecules (testing) | +| `--n-jobs` | 1 | worker processes (Linux, fork) | +| `--replicate-offset-drift` | off | by-shard only: also compute `__offset_drift` columns with the embedding shift of the published full run (Table 1 LIE@k / AS) | -ds = load_dataset("EscheWang/3dcs", name="rotation", split="train") -ds.save_to_disk("data/hf/rotation") -PY -``` +Outputs: `_per_key.parquet` (one row per molecule and distance space), `summary.csv` +(mean, median, number of finite values per metric) and `config.json` (definitions, selection counts, +molecules that could not be evaluated, runtime). -### Step 2: Generate embeddings +A full GemNet run over the 16 shards (1,464,495 molecules) with 24 workers is expected to take about +20–25 minutes per metric version for the cosine space. -Use MolBlocks from the HF datasets to feed your model and generate fixed-length vectors. -Detailed formats and examples are in `docs/EMBEDDINGS.md`. +`examples/run_rotation_from_hf.py` wraps the same evaluation and can also build a flat `.npy` cache +of the selected shards (`--flat-cache DIR`). -Minimal skeleton for chirality: +## 5. Python API ```python -from datasets import load_from_disk -from three_dbench.datasets.serialization import mol_from_block -import numpy as np - -ds = load_from_disk("data/hf/chirality") -vectors = [] +from pathlib import Path -for row in ds: - mols = [mol_from_block(b, sanitize=False) for b in row["mol_blocks"]] - for mol in mols: - vec = your_model(mol) # shape (dim,) - vectors.append(vec) +from three_dbench.benchmarks import evaluate_chirality_embeddings, evaluate_rotation_embeddings +from three_dbench.benchmarks.rotation import ShardedEmbeddings +from three_dbench.embeddings import load_embeddings, published_embedding -embeddings = np.stack(vectors, axis=0) -np.savez("embeddings/my_model_chirality.npz", arr_0=embeddings) +spec = published_embedding("rotation", "gemnet") # path, key, layout of the published files +result = evaluate_rotation_embeddings( + dataset_dir=Path("data/hf/rotation"), + embeddings_by_shard=ShardedEmbeddings.from_directory(spec.local_path("data/embeddings"), key=spec.key), + metrics=["cosine"], + metric_version="v2", + shards=[0], + n_jobs=8, +) +print(result["summary"]) ``` -### Step 3: Validate embedding alignment +Per-molecule geometry metrics can be computed directly with +`three_dbench.rotation.metrics.compute_geometry_metrics(D, Delta, torsion_deg=..., spec=PRESETS["v2"], Z=...)`. -Embedding arrays must align with the dataset order. For flat arrays, the total number -of vectors must equal the sum of `n_conformers` in the dataset. +## 6. Generating your own embeddings -```python -from datasets import load_from_disk -import numpy as np +Load a dataset with `datasets.load_from_disk`, rebuild molecules with +`three_dbench.datasets.serialization.mol_from_block`, and write one vector per conformer in the +order described in [EMBEDDINGS.md](EMBEDDINGS.md). For rotation, writing one file per shard in +per-shard offset order lets you use `--layout by-shard`. -ds = load_from_disk("data/hf/chirality") -expected = int(sum(ds["n_conformers"])) -arr = np.load("embeddings/my_model_chirality.npz")["arr_0"] -assert arr.shape[0] == expected -``` +Common pitfalls: -### Step 4: Run evaluation +- A flat array must have exactly one row per conformer, in dataset row order. +- For rotation, do not use the stored `offset` as a global index: it restarts in every shard. +- Rotation evaluation needs the MolBlocks (RMSD); do not convert with `--no-mol-blocks`. +- Energies must be float64 (`EscheWang/3dcs` config `traj_energies`); the trajectory evaluator checks + for float32 quantization. -```bash -python -m three_dbench evaluate chirality \ - --dataset-dir data/hf/chirality \ - --embeddings embeddings/my_model_chirality.npz \ - --embedding-key arr_0 \ - --model-name my_model \ - --output-dir results/chirality/my_model -``` +## 7. Converting raw data -### Trajectory with custom embeddings +The converters used to build the Hugging Face datasets are kept for completeness; the raw inputs are +not distributed. ```bash -python -m three_dbench evaluate traj \ - --dataset-dir data/hf/traj/energies \ - --embeddings embeddings/traj_my_model \ - --embedding-key arr_0 \ - --model-name my_model \ - --output-dir results/traj/my_model -``` - -`embeddings/traj_my_model` should contain one `rmd17_*.npz` per molecule. - -### Rotation with custom embeddings - -For rotation, embeddings must be aligned with `offset` and `n_conformers` in the -rotation dataset. If you generate per-key embeddings, build a dict mapping `key` to -`(n_conformers, dim)` and save as a pickle. - -```bash -python -m three_dbench evaluate rotation \ - --dataset-dir data/hf/rotation \ - --embeddings embeddings/rotation_by_key.pkl \ - --layout by-key \ - --model-name my_model \ - --output-dir results/rotation/my_model +python -m three_dbench convert chirality --input-pkl data/chirality/chirality_bench_conformers_noised_only.pkl --output-dir data/hf/chirality +python -m three_dbench convert traj --mol-pkl-dir data/traj/mol_pkl --energy-dir data/traj/npz_data --output-dir data/hf/traj +python -m three_dbench convert rotation --lmdb-root data/rotation/results --output-dir data/hf/rotation ``` -### Common pitfalls - -- If the evaluation errors with a length mismatch, check the total number of conformers. -- Rotation evaluation requires MolBlocks; do not use `--no-mol-blocks` during conversion. -- Trajectory evaluation uses energy windows; make sure embeddings cover all frames. - -## 6. Outputs - -Each evaluation writes: - -- `summary.csv`: aggregated statistics -- `details.csv` or `*_per_molecule.json`: per-sample metrics -- `config.json` (trajectory) - -Output directory defaults to `results/{task}/{model_name}` unless overridden. +`convert rotation` writes global offsets in numeric shard order, which differs from the published +dataset (per-shard offsets); the evaluator accepts both. diff --git a/docs/metrics/chirality.md b/docs/metrics/chirality.md new file mode 100644 index 0000000..7a43b00 --- /dev/null +++ b/docs/metrics/chirality.md @@ -0,0 +1,315 @@ +# Chirality metrics (Table 2) + +This page documents how `python -m three_dbench evaluate chirality` computes the zero-shot chirality +metrics, which settings produced the published Table 2, and what the `v2` definitions change. + +- Code: `src/three_dbench/chirality/evaluation.py` (metrics, per-molecule loop) and + `src/three_dbench/benchmarks/chirality.py` (dataset loading, validation, output files). +- Reproduction script: `reproduce/table2_chirality/run.sh`, expected values in + `reproduce/table2_chirality/expected.csv`. + +## 1. Options at a glance + +| Option | Values | Default | Default reproduces the published Table 2? | +|---|---|---|---| +| `--distance` | `euclidean`, `cosine` | `euclidean` | yes | +| `--metric-version` | `paper`, `v2` | `paper` | yes | +| `--unsup-kmax` | `n-1` or an integer >= 2 | `n-1` | yes | +| `--embedding-key` | NPZ array name or pickle dict entry | none | use the key listed in the table in section 6 | +| `--n-jobs` | integer (`-1` = all CPUs) | `1` | results do not depend on it | + +The Python API takes the same options: `evaluate_chirality_embeddings(..., distance=, metric_version=, unsup_kmax=, n_jobs=)` +and `evaluate_en_separation_from_counts(...)`. `unsup_kmax=None` means `n - 1`. + +Each run writes `_per_molecule.json`, `summary.csv` (the same 18 columns as the original +Sept-2025 output) and `config.json`. `config.json` records the options, the library versions and the +number of molecules behind each mean (`coverage`). + +## 2. Data and evaluation unit + +- Dataset: `load_dataset("EscheWang/3dcs", name="chirality", split="train")`. It has 14,903 rows, + one per stereoisomer (`key = ::en_`), with 52,391 conformers and 3,903 molecules. +- Embeddings: one row per conformer, in dataset row order. Row `i` of the dataset owns embedding rows + `offset[i] : offset[i] + n_conformers[i]`. The evaluator checks three things before any metric is + computed, and raises an error naming the problem if one fails: + - the number of embedding rows equals `sum(n_conformers)`; + - `offset` is the running sum of `n_conformers`; + - keys are unique and embeddings are finite. +- Unit of evaluation: a molecule (`mol_id`), which pools the conformers of all its stereoisomer rows. + The label is `en_id`, the index of the stereoisomer within the molecule. There are up to 5 per + molecule, and they include diastereomers, not only enantiomer pairs. +- Aggregation: every summary value is the unweighted mean over molecules of the finite per-molecule + values. NaN values are skipped. + +Molecule populations in the published protocol (`paper`, identical for all 7 models): + +| Population | Molecules | +|---|---| +| all molecules (`n_molecules` in `summary.csv`) | 3,903 | +| skipped, fewer than 2 conformers (`skip_small`) | 19 | +| skipped, one stereoisomer only (`skip_single_en`) | 42 | +| supervised metrics computed (`supervised+unsup`) | 3,842 | +| of which every stereoisomer has exactly one conformer | 1,019 | +| ES-AUC and SCI finite (at least one same-class pair) | 2,823 | +| NN@1-Acc finite | 3,842 (the 1,019 molecules above score 0 by construction) | +| Hopkins finite (continuous embeddings, n >= 10) | 1,764 (including 11 single-stereoisomer molecules) | +| SCI_unsup finite (n >= 3) | 3,576 | + +## 3. Representation distance (`--distance`) + +| | Definition | Code | +|---|---|---| +| `euclidean` (default) | `Delta_ij = ||z_i - z_j||_2` via `sklearn.metrics.pairwise_distances` (float32 input stays float32) | `euclidean_distances` | +| `cosine` | `Delta_ij = 1 - / ((||z_i|| + 1e-12)(||z_j|| + 1e-12))` in float64, with the diagonal set to exactly 0 | `cosine_distances` | +| RDKit fingerprints | Tanimoto distance `1 - |F_i & F_j| / |F_i | F_j|` (RDKit `BulkTanimotoSimilarity`), whatever `--distance` is | `tanimoto_distance_matrix` | + +- **The published Table 2 was computed with Euclidean distance.** The paper text (§4.1 and App. C.2) + says cosine distance is the default for learned representations. The released evaluator + (`chirality/evaluation.py`, `distance_matrix_for_subset`) used Euclidean distance. Recomputing + Table 2 with Euclidean distance matches all published cells to within 0.001, except for the sign of + GemNet SCI (section 7). Cosine distance does not match: for example, MolAE SCI is 0.189 against the + printed 0.115, and GemNet ES-AUC is 0.602 against 0.577. +- App. C.2 also says distance matrices are normalised to [0, 1]. Normalisation is not applied, and it + would not change any chirality metric. Each metric is computed within one molecule, and each is + invariant to rescaling that molecule's `Delta` by a positive constant: ES-AUC is rank based, NN@1 + uses an argmin, and silhouette, DBI and clarity are ratios. +- The diagonal of the cosine matrix is set to 0. Without that, the 1e-12 guard leaves diagonal values + around 1e-12, and `sklearn.metrics.silhouette_score(metric="precomputed")` rejects the matrix. The + code then silently falls back to a hand-written silhouette that skips points in singleton classes + instead of scoring them 0. Without the fix, cosine SCI changes for 6 (MolSpectra, large norms) to 959 + (FMG) molecules per model; for example, MolAE gives 0.1938 instead of 0.1895. Cosine SCI is not + published. +- In `paper` mode, `--distance` changes ES-AUC, NN@1-Acc, SCI, DBI and clarity. Hopkins and SCI_unsup + are still computed on the raw vectors with Euclidean distance, as in the published code. In `v2` + they follow the selected distance (section 4). + +## 4. Metric definitions: `paper` and `v2` + +`paper` is the code path that produced the published numbers. `v2` changes a definition only where +the published code departs from App. C.5 of the paper, or where a per-molecule value is fixed by +construction rather than by the representation. Every such case is listed below with the measured +evidence. The remaining metrics are identical in both versions. + +Notation: a molecule has `n` conformers with labels `y`, representation distances `Delta`, and +vectors `X` (rows L2-normalised when `v2` is combined with `--distance cosine`). + +### ES-AUC (identical in both versions) + +`ESA-AUC = ROC-AUC({(Delta_ij, 1[y_i != y_j])}_{i= 3` and only singleton classes get `S = 0` and therefore `DBI = 0`, the best possible + score, whatever the representation. + +### Hopkins statistic + +- **paper** (`hopkins_statistic`): computed on the raw vectors with Euclidean distance. + - `m = max(10, int(0.1 n))` points are drawn uniformly in the bounding box of `X`, and `m` data + points are sampled without replacement. Both use `numpy.random.default_rng(0)` for every molecule. + - `H = sum u_i / (sum u_i + sum w_i)`, where `u` are nearest-data distances of the uniform points and + `w` are nearest-other-point distances of the sampled points. + - The value is NaN for `n < 10` and for fingerprints. + - It is also computed for molecules with a single stereoisomer (11 molecules with `n >= 10`). +- **v2**: the same estimator and the same `n >= 10` threshold, with two changes. + 1. It is computed on the L2-normalised vectors when `--distance cosine` is used. + 2. It is reported only for the molecules that enter the supervised metrics, so all Table 2 columns + describe the same molecule set. The single-stereoisomer molecules become NaN. +- **Why `n < 10` stays NaN in v2.** On the published embeddings this estimator depends strongly on + `n`. With the sample size capped at `n` (`m = min(n, max(10, int(0.1 n)))`), the mean Hopkins value + for molecules with 2 conformers is 0.35 for every model. It rises steadily with `n`: + + | Conformers per molecule | 2 | 3 | 5 | 7 | 9 | 10 | >= 12 | + |---|---|---|---|---|---|---|---| + | Molecules | 276 | 110 | 719 | 165 | 151 | 138 | 1,512 | + | GemNet | 0.349 | 0.430 | 0.496 | 0.520 | 0.545 | 0.553 | 0.599 | + | MolAE | 0.359 | 0.429 | 0.485 | 0.520 | 0.539 | 0.552 | 0.611 | + | FMG | 0.349 | 0.460 | 0.552 | 0.613 | 0.652 | 0.674 | 0.765 | + + Adding the 2,139 molecules with `n < 10` would mostly add this size effect to the mean. `v2` + therefore keeps the threshold, and `config.json` reports how many molecules contribute + (`coverage.n_finite_hopkins`). The Hopkins mean is best read as a relative score between + representations on the same molecule set. It is not an absolute test for clusters. + +### SCI_unsup (best-k silhouette) + +- **paper**: for `k = 2 .. kmax`, labels come from `sklearn.cluster.KMeans(n_clusters=k, n_init=10, + random_state=0)` on the raw `X`. The silhouette is computed on `X` with Euclidean distance, and the + largest silhouette over `k` is reported. + - Fingerprints use a PAM k-medoids on the Tanimoto matrix (`pam_kmedoids`, `default_rng(0)`), with + the silhouette on that matrix. + - `kmax = n - 1` (`--unsup-kmax n-1`, the default). +- **v2**: the silhouette is computed on the selected `Delta`, as App. C.5 writes + `silhouette(Delta, k-means_k)`. + - With `--distance cosine`, k-means runs on L2-normalised vectors. + - With `--distance euclidean` the value equals `paper` up to floating-point rounding. + - With `--do-unsup-when-single-en`, the single-stereoisomer branch uses the same deterministic PAM. + In `paper` mode that branch uses `sklearn_extra.KMedoids` when it is importable, and falls back to + PAM otherwise. +- **`kmax` and the published run.** The first public release (0.1.0) hard-coded + `kmax = min(10, n - 1)`, with the comment "Limit kmax to 10 for speed". The Sept-2025 run that + produced Table 2 did not have this cap: + - its per-molecule output contains `k_unsup` values up to 82 for E3FP and 16 for MolAE; + - with `kmax = n - 1` the current code reproduces those per-molecule values, except for 3 molecules + whose KMeans optimum depends on the BLAS kernel (section 7); + - with the cap, SCI_unsup differs by up to 3.9e-4 (E3FP 0.033437 against 0.033825). + + Both settings are within 0.001 of every printed SCI_unsup value. `--unsup-kmax 10` restores the + 0.1.0 behaviour. (In 0.1.0 the opt-in single-stereoisomer branch used `k <= min(50, n - 1)`; it + now follows `--unsup-kmax` as well.) + +### Clarity (not in the paper) + +`boundary_clarity_from_D` and `clarity_unsup` are unchanged. They are written to `summary.csv` for +continuity with the original output files. + +## 5. Expected values + +All values below come from the published embeddings (section 6) with the current code, on +numpy 2.4.6, scikit-learn 1.9.1, rdkit 2026.03.6 and an AMD EPYC 7513. `expected.csv` holds 6-decimal +values for all 7 models × 4 variants, with a tolerance of 0.001. + +The `euclidean` column is the published protocol. Every cell is within 0.001 of the printed value, +except for GemNet SCI (sign). E3FP is a fingerprint, so its cosine rows equal its Euclidean rows and its +Hopkins value is NaN. In `v2`, NN@1-Acc is averaged over 2,823 molecules instead of 3,842 and Hopkins +over 1,753 instead of 1,764. The other columns use the same molecules in both versions. + +| Metric | Model | Paper | `euclidean` (published protocol) | `cosine` | `v2_euclidean` | `v2_cosine` | +|---|---|---|---|---|---|---| +| ES-AUC | E3FP | 0.486 | 0.485935 | 0.485935 | 0.485935 | 0.485935 | +| ES-AUC | GemNet | 0.577 | 0.577410 | 0.601837 | 0.577410 | 0.601837 | +| ES-AUC | MolAE | 0.782 | 0.782544 | 0.781942 | 0.782544 | 0.781942 | +| ES-AUC | MolSpectra | 0.545 | 0.544744 | 0.543114 | 0.544744 | 0.543114 | +| ES-AUC | UniMol | 0.622 | 0.622998 | 0.622620 | 0.622998 | 0.622620 | +| ES-AUC | FMG | 0.706 | 0.705652 | 0.712555 | 0.705652 | 0.712555 | +| ES-AUC | MACE | 0.485 | 0.485611 | 0.484971 | 0.485611 | 0.484971 | +| NN@1-Acc | E3FP | 0.178 | 0.177939 | 0.177939 | 0.256400 | 0.256400 | +| NN@1-Acc | GemNet | 0.292 | 0.291641 | 0.312251 | 0.415357 | 0.444804 | +| NN@1-Acc | MolAE | 0.497 | 0.497546 | 0.497357 | 0.709640 | 0.709405 | +| NN@1-Acc | MolSpectra | 0.235 | 0.235250 | 0.235108 | 0.337786 | 0.337668 | +| NN@1-Acc | UniMol | 0.339 | 0.339582 | 0.338899 | 0.481977 | 0.480923 | +| NN@1-Acc | FMG | 0.412 | 0.411765 | 0.422004 | 0.587976 | 0.601655 | +| NN@1-Acc | MACE | 0.199 | 0.199113 | 0.200708 | 0.283468 | 0.285157 | +| Hopkins | E3FP | – | NaN | NaN | NaN | NaN | +| Hopkins | GemNet | 0.593 | 0.592812 | 0.592812 | 0.592823 | 0.577979 | +| Hopkins | MolAE | 0.602 | 0.602659 | 0.602659 | 0.602748 | 0.602690 | +| Hopkins | MolSpectra | 0.533 | 0.532642 | 0.532642 | 0.532736 | 0.533677 | +| Hopkins | UniMol | 0.559 | 0.559572 | 0.559572 | 0.559674 | 0.559664 | +| Hopkins | FMG | 0.752 | 0.751741 | 0.751741 | 0.752106 | 0.751049 | +| Hopkins | MACE | 0.637 | 0.637379 | 0.637379 | 0.637289 | 0.637111 | +| SCI | E3FP | -0.012 | -0.012543 | -0.012543 | -0.012543 | -0.012543 | +| SCI | GemNet | 0.015 (sign typo) | -0.015151 | 0.004663 | -0.015151 | 0.004663 | +| SCI | MolAE | 0.115 | 0.115274 | 0.189480 | 0.115274 | 0.189480 | +| SCI | MolSpectra | -0.020 | -0.020326 | -0.037315 | -0.020326 | -0.037315 | +| SCI | UniMol | 0.012 | 0.011623 | 0.021010 | 0.011623 | 0.021010 | +| SCI | FMG | 0.117 | 0.117174 | 0.158030 | 0.117174 | 0.158030 | +| SCI | MACE | -0.094 | -0.093877 | -0.151823 | -0.093877 | -0.151823 | +| SCI_unsup | E3FP | 0.033 | 0.033825 | 0.033825 | 0.033825 | 0.033825 | +| SCI_unsup | GemNet | 0.272 | 0.271961 | 0.271961 | 0.271961 | 0.380451 | +| SCI_unsup | MolAE | 0.247 | 0.247099 | 0.247099 | 0.247099 | 0.408498 | +| SCI_unsup | MolSpectra | 0.127 | 0.127115 | 0.127115 | 0.127115 | 0.232817 | +| SCI_unsup | UniMol | 0.152 | 0.152108 | 0.152108 | 0.152108 | 0.270063 | +| SCI_unsup | FMG | 0.509 | 0.509364 | 0.509364 | 0.509364 | 0.682689 | +| SCI_unsup | MACE | 0.369 | 0.369336 | 0.369336 | 0.369336 | 0.566272 | + +## 6. Published embeddings and keys + +The files are the original bytes from the authors' backup, published at `EscheWang/3dcs-embeddings` +(see `docs/EMBEDDINGS.md`). SHA-256 values are listed in `reproduce/table2_chirality/models.csv`. + +| Model | File (repo path) | `--embedding-key` | Content | +|---|---|---|---| +| E3FP | `chirality/e3fp/sampled_chi.pkl` | `e3fp` | pickled dict, `e3fp`: list of 52,391 RDKit `ExplicitBitVect` (1024 bits); `morgan`: 2048-bit Morgan (not used) | +| GemNet | `chirality/gemnet/sampled_feature.npz` | `gemnet` | float32 (52391, 128) | +| MolAE | `chirality/molae/1.npz` | `arr_0` | float32 (52391, 512) | +| MolSpectra | `chirality/molspectra/sampled_mol_feature.npz` | `arr_0` | float32 (52391, 256) | +| UniMol | `chirality/unimol/1.npz` | `arr_0` | float32 (52391, 512) | +| FMG | `chirality/fmg/chirality_bench_conformers_noised_only_aslist_embed.npz` | `embeddings` | float32 (52391, 128); the same file also stores `smiles` | +| MACE | `chirality/mace/chirality.npz` | `arr_0` | float32 (52391, 256) | + +- **E3FP.** Reading the pickle requires RDKit. The fingerprints were regenerated bit-exactly (3,000 of + 3,000 random conformers) from the original RDKit molecules with e3fp 1.2.7: + + ```python + fprints_from_mol(mol, fprint_params=dict(bits=1024, level=5, radius_multiplier=1.5, stereo=True, + include_disconnected=True, rdkit_invariants=True, first=1, counts=False)) + ``` + + Starting from the HF MolBlocks instead gives the same bits for 2,846 of 3,000 conformers. +- **Other models.** The extraction settings (checkpoint, layer, pooling) of the other published + embeddings have not been recovered. The files are provided so that the published numbers can be + recomputed. They do not document how the embeddings were produced. + +## 7. Known differences between the paper and the recomputed values + +- **GemNet SCI.** The paper prints `0.015`. The recomputed value, and the Sept-2025 output, is + `-0.015151`, so the minus sign is missing in the table. +- **Rounding.** The printed values mix rounding and truncation to three decimals. The largest gap is + UniMol ES-AUC: 0.622998 is printed as 0.622. Every other cell is within 0.001 of the recomputed value. +- **Hopkins for E3FP** is printed as "–". Hopkins is not defined for fingerprints, and the evaluator + returns NaN. +- **Sept-2025 outputs.** For E3FP, GemNet, MolAE, MolSpectra and UniMol, the backup holds the original + per-molecule outputs (`en_sep_results/*.json`). With the defaults, ES-AUC, NN@1-Acc, SCI, DBI and + Hopkins are bit-identical for every molecule of all five models. + - SCI_unsup is bit-identical for E3FP, MolAE and MolSpectra. + - It differs for 1 GemNet molecule and 2 UniMol molecules, which changes the means by +2.4e-6 and + +2.0e-6. On these near-tied silhouette landscapes the KMeans local optimum depends on the OpenBLAS + kernel. + - The unpublished clarity columns differ by less than 2e-5, because of a numpy version difference + (they match under numpy 2.2 and 2.3). +- **FMG and MACE** have no original metric output in the backup. Their expected values are recomputed + from the published embeddings. + +## 8. Performance + +The run time is dominated by the best-k KMeans scan up to `k = n - 1` (at most 99). On 22 processes +(`--n-jobs 22`, AMD EPYC 7513, shared machine), one model and variant takes about 40–65 s for E3FP and +1–3 min for the continuous embeddings. + +Molecules are evaluated independently with fixed seeds, in batches run by joblib (`loky`) with one +BLAS/OpenMP thread per worker. The per-molecule outputs for `--n-jobs 8` were bit-identical to +`--n-jobs 1` on the first 300 molecules for UniMol and E3FP, and the full 22-process runs are +bit-identical to the phase-1 sharded runs of the released evaluator. diff --git a/docs/metrics/energy.md b/docs/metrics/energy.md new file mode 100644 index 0000000..122acae --- /dev/null +++ b/docs/metrics/energy.md @@ -0,0 +1,252 @@ +# Energy (trajectory) benchmark metrics + +This page defines the metrics behind the zero-shot energy tables of the paper (Table 3 in the main +text, Tables 6 and 7 in Appendix B.2). It covers the data, the sampling protocol, and each metric in +its two implementations: + +- `--metric-version paper` (the default) is the implementation that produced the published numbers. +- `--metric-version v2` follows the definitions written in Appendix C.3/C.6. Where the appendix + leaves a choice open, v2 makes that choice explicit. + +Both versions share the same windows and the same aggregation. `reproduce/energy_tables_3_6_7/` +contains the end-to-end script and the expected values for both. + +Code map: + +| Component | Location | +|---|---| +| CLI | `python -m three_dbench evaluate traj` (`src/three_dbench/__main__.py`) | +| Benchmark driver | `src/three_dbench/benchmarks/trajectory.py::evaluate_trajectory_embeddings` | +| Windows, distances, version dispatch | `src/three_dbench/traj/protocol.py` | +| `paper` metrics | `src/three_dbench/traj/evaluation.py` (`compute_energy_metrics_from_condensed`, `thresholded_smoothness`, `ks_wasserstein_against_energy_diff`) | +| `v2` metrics | `src/three_dbench/traj/metrics_v2.py::compute_energy_metrics_v2` | +| Shared estimators | `src/three_dbench/common/metrics.py` (`spearman_correlation`, `kendall_correlation`, `isotonic_r2`, `cka_rbf`) | +| Embedding loading | `src/three_dbench/traj/io.py::load_traj_embeddings` | +| Energy precision check | `src/three_dbench/datasets/traj.py::detect_quantized_energies` | +| Legacy runner used for the paper | `src/three_dbench/traj/evaluation.py::run_trajectory_benchmark` (numerically unchanged; the CLI reproduces it bit for bit, see `tests/test_traj_legacy_equivalence.py`) | + +## 1. Data + +**Energies.** The benchmark uses the 10 molecules of rMD17 with 100,000 frames each; azobenzene +has 99,988. The values are absolute total energies in kcal/mol, about −4×10⁵, stored as **float64**. +Download them with `load_dataset("EscheWang/3dcs", name="traj_energies", split="train")` or from the +standalone repo `EscheWang/3dcs-traj-energies`. Either source can be converted from the original +rMD17 npz files with `python -m three_dbench convert traj`. + +**Earlier revision.** An earlier revision of `traj_energies` stored a float32 cast of these energies. +The schema was declared float64, but each molecule held only 1,096–3,334 distinct values, with a +resolution of 1/64–1/256 kcal/mol. Do not use that revision (see the dataset card). With those values: +- Pooled CKA means drop by up to about 40%, e.g. GemNet 0.0163 → 0.0113. +- Smoothness and KS shift by about 0.001. +- Table 7 no longer reproduces. + +The evaluator therefore checks the precision of the energies of every evaluated molecule (option +`--energy-precision-check`, default `error`). A molecule is flagged as quantized when either: +- fewer than 50% of its frames have distinct energies, or +- every value is float32-representable **and** the float32 step at the largest magnitude is ≥ 10⁻⁴ + of the inter-quartile range. + +Relative energies stored in float32 are not flagged. + +**Frame order.** rMD17 frames are **not in simulation-time order**, based on checks on the rMD17 npz +files: +- `old_indices` is not monotonic. +- The lag-1 correlation of the energies is 0.002–0.004 (aspirin, ethanol, uracil). +- The median |ΔE| between consecutive frames equals the median over random pairs, e.g. aspirin + 5.847 vs 5.834 kcal/mol. + +Metrics defined along consecutive frames (TS and Smoothness) are therefore computed on effectively +random pairs of frames. See §4. + +**Embeddings.** `EscheWang/3dcs-embeddings` holds the backed-up files as +`traj//rmd17_.{npz,pkl}`, one file per molecule, with rows in frame order: + +| Model | Format | Key / size | +|---|---|---| +| E3FP | pickle | list of RDKit `ExplicitBitVect` (1024 bits) | +| GemNet | npz | `gemnet` (128) | +| MolAE | npz | `arr_0` (512) | +| MolSpectra | npz | `arr_0` (256) | +| UniMol | npz | `arr_0` (512) | +| FMG | npz | `embeddings` (128), plus `smiles` | +| MACE | npz | `arr_0` (256) | + +`load_traj_embeddings` accepts: +- a directory with one `rmd17_.{npz,npy,pkl}` per molecule, or +- a single `.npz`/`.pkl` keyed by molecule. + +Each molecule is loaded only when it is evaluated. + +## 2. Protocol (shared by both metric versions) + +**Windows** (`--window-scheme`, `traj/protocol.py::draw_window_starts`): + +- `legacy` (default, published): every molecule draws its starts from a fresh generator, so all + molecules share the same 100 starts: + + ```python + starts = numpy.random.default_rng(2025).integers(0, 100_000 - 2000, size=100) + ``` + + - The first three starts are 43852, 97456 and 97273, and the largest is 97636, so no window + runs past the 99,988 azobenzene frames. + - A molecule's windows do not depend on which other molecules are evaluated (`--molecules`). + - Molecules are processed in the legacy order `aspirin, azobenzene, benzene, ethanol, malonaldehyde, + toluene, naphthalene, paracetamol, salicylic, uracil`. This order fixes the floating-point + summation order of the summary. + - This is exactly `traj/evaluation.py::run_once_for_molecule`. +- `shared` (the 0.1.0 CLI): one generator is shared across molecules in dataset row order, and each + molecule draws `integers(0, n_frames − 2000, size=100)`. Only aspirin gets the published windows. + +Defaults are `--n-samples 100 --window 2000 --random-seed 2025`, i.e. 1,000 windows and +n = 2,000 frames per window. Every metric uses all n(n−1)/2 = 1,999,000 frame pairs of a window. + +**Representation distance Δ** (`traj/protocol.py::window_distances`): +- *Vectors* (cosine, default): Δᵢⱼ = (1 − cos(zᵢ, zⱼ))/2 ∈ [0, 1]. It is computed in float32 from + unit-normalised vectors and stored as **float16** in `paper` and **float32** in `v2`. + `--metric-embed euclidean` gives ‖ẑᵢ − ẑⱼ‖²/4 on unit vectors. +- *Fingerprints* (Tanimoto, automatic for bit-vector pickles): Δᵢⱼ = 1 − |Fᵢ∩Fⱼ| / |Fᵢ∪Fⱼ|, with + RDKit `BulkTanimotoSimilarity`. It uses the same storage dtype rule. Dense 0/1 arrays with + `--metric-embed tanimoto` give identical values, with one exception: for two all-zero fingerprints + the dense path always returns similarity 0, while RDKit returns 1.0 up to version 2025.09 and 0.0 + from 2026.03 on. The published E3FP fingerprints contain no all-zero vector (checked for all + 999,988 trajectory frames and all 52,391 chirality conformers), so the tables do not depend on + this. + +**Aggregation.** For every metric, the per-window values of all molecules are pooled, non-finite +values are dropped, and the result is reported as mean ± 1.96·sd(ddof = 1)/√n, with n = 1,000. + +**Determinism.** BLAS pools are limited to one thread per worker, and `--n-jobs` workers evaluate +windows in parallel with order-preserving results. On one machine, repeated runs are bitwise +identical, and so are runs with different `--n-jobs`. Across machines, float32 BLAS kernels can +change per-window values slightly: up to ~4×10⁻⁵, and up to ~3×10⁻⁴ for TS, were observed between +the original run and a rerun on different hardware. + +## 3. Notation + +For one window: Eᵢ is the energy of frame i (float64), dEᵢⱼ = |Eᵢ − Eⱼ|, Δᵢⱼ is the representation +distance, and all sums and quantiles run over the pairs i < j unless stated otherwise. Q_p(x) +denotes the p-quantile. ε = 10⁻¹². + +## 4. Metric definitions + +### Spearman ρ and Kendall τ (keys `spearman`, `kendall`) + +| | Definition | +|---|---| +| paper | `scipy.stats.spearmanr` / `kendalltau` (τ-b) between vec(dE) and vec(Δ). dE is cast to float32 first (`vector_to_absdiff_condensed`), and Δ is float16. | +| v2 | Same estimators, with dE in float64 and Δ in float32. | + +### CKA with RBF kernels (key `cka_rbf`) + +K⁽ᴰ⁾ᵢⱼ = exp(−dE²ᵢⱼ / (2σ_D² + ε)) and K⁽Δ⁾ᵢⱼ = exp(−Δ²ᵢⱼ / (2σ_Δ² + ε)), with unit diagonals. +With H = I − 11ᵀ/n: + +CKA = ⟨HK⁽ᴰ⁾H, HK⁽Δ⁾H⟩_F / (‖HK⁽ᴰ⁾H‖_F ‖HK⁽Δ⁾H‖_F). + +| | Bandwidth | +|---|---| +| paper | **One shared** σ = √median{d² : d ∈ vec(dE) ∪ vec(Δ), d > 0} over the concatenation of both distance sets (`cka_rbf(..., share_sigma=True)`). | +| v2 | **Separate** median heuristics σ_D = √median{dE² > 0} and σ_Δ = √median{Δ² > 0} (Appendix C.3). | + +*Rationale for v2:* dE is in kcal/mol (median ≈ 6) and Δ ∈ [0, 1] (median ≈ 10⁻⁴–10⁻³ for the +continuous models). The shared median is therefore ≈ 0.003, far below almost every energy difference, and the +energy kernel degenerates to an indicator of near-tied energies. This is also why the published CKA +values depend strongly on the precision of the energies. + +### Isotonic R² (key `iso_R2`) + +| | Definition | +|---|---| +| paper | Fits a non-decreasing Δ̂ = f(dE) and returns 1 − Σ(Δ − Δ̂)² / (Σ(Δ − Δ̄)² + ε). A constant representation scores 1.0. | +| v2 | Fits a non-decreasing dÊ = f(Δ) and returns 1 − Σ(dE − dÊ)² / (Σ(dE − dĒ)² + ε), as in Appendix C.3 ("mapping representation distances onto the reference values"). The result is NaN when Δ is constant. | + +### Energy-jump sensitivity EJS(λ) (keys `EJS_lam{0p1,0p5,1,2,3}`, `EJS` = λ = 2; counts `EJS_num_jumps_lam*`) + +θ(λ) = λ·σ̂_d, J_λ = {(i, j) : dEᵢⱼ > θ(λ)}, τ = Q_0.75(vec Δ) over all pairs, and + +EJS(λ) = |{(i, j) ∈ J_λ : Δᵢⱼ > τ}| / |J_λ|, for λ ∈ {0.1, 0.5, 1, 2, 3}. + +| | σ̂_d | +|---|---| +| paper | Robust half-normal scale σ̂_d = median(dE) / (√2·erf⁻¹(0.5) + ε) ≈ median(dE)/0.6745. | +| v2 | σ̂_d = √(mean(dE²) + ε) (Appendix C.6). | + +Table 7 reports the mean ± CI of |J_λ| for each λ. It depends only on the energies and the windows, +not on the representation. The published counts, e.g. 102,542.184 ± 492.257 at λ = 2, are +reproduced exactly with the `paper` σ̂_d, float64 energies and legacy windows. The appendix σ̂_d +gives 94,275.340 ± 163.305 at λ = 2. + +### EJS–ROC (keys `ROC_AUC`, `PR_AUC`) + +The labels are yᵢⱼ = 1{dEᵢⱼ > 2σ̂_rms} with σ̂_rms = √(mean(dE²) + ε), and the scores are sᵢⱼ = Δᵢⱼ. +The AUC is computed with `sklearn.metrics.roc_auc_score` (PR-AUC with `average_precision_score`). + +Both versions use this definition. It already matched the appendix in `paper`. + +### Thresholded smoothness TS (key `TS`) + +| | Definition | +|---|---| +| paper | Uses **all pairs** i < j with dEᵢⱼ ≥ T_E, where T_E = 0.2·Q̃_E. Q̃_E is the 0.9-quantile of \|Eᵢ − Eⱼ\| over ≤ 200,000 random index pairs (`default_rng(0)`, i ∈ [0, n−1), j ∈ [1, n), pairs with i < j kept) plus ε, and Q_Z = Q_0.9(vec Δ) + ε. TS = mean exp(−(Δᵢⱼ/Q_Z) / (dEᵢⱼ/Q̃_E + ε)). | +| v2 | Uses **consecutive frames** k → k+1 along the declared time order (Appendix C.6). Q_Z = Q_0.9(vec Δ) + ε and Q_E = Q_0.9(vec dE) + ε over all pairs (exact). T_E = 0.2·Q_E; the appendix leaves T_E unspecified, and 0.2 is the relative threshold of the released code. K = {k : \|E_{k+1} − E_k\| > T_E}. TS = mean over k ∈ K of exp(−(Δ_{k,k+1}/Q_Z) / (\|E_{k+1} − E_k\|/Q_E + ε)). **It is computed only with `--time-ordered`; otherwise it is NaN.** | + +### Smoothness (key `Smoothness`; Table 6) + +The final paper does not define this metric. Its two definitions here are: + +| | Definition | +|---|---| +| paper | Consecutive frames in file order, in raw units: Smoothness = mean_k exp(−Δ_{k,k+1} / (\|E_{k+1} − E_k\| + 10⁻⁸)). Δ is dimensionless and \|ΔE\| is in kcal/mol. | +| v2 | The scale-normalised TS expression over **all** consecutive segments (T_E = 0): mean_k exp(−(Δ_{k,k+1}/Q_Z) / (\|E_{k+1} − E_k\|/Q_E + ε)). **It is computed only with `--time-ordered`; otherwise it is NaN.** | + +*Rationale for v2:* both quantities describe how the representation changes along a trajectory, which +requires time-ordered frames. rMD17 frames are not time-ordered (§1). The `paper` values for rMD17 +therefore average over pairs of frames that are effectively random, and v2 does not report them. + +### Distributional divergence KS / W1 (keys `KS`, `W1`) + +| | Definition | +|---|---| +| paper | Two-sample KS statistic `scipy.stats.ks_2samp(vec Δ, vec dE)` and `wasserstein_distance(vec Δ, vec dE)`. Δ ∈ [0, 1] is compared directly with dE in kcal/mol, so KS is close to 1 for every continuous representation (≥ 0.976 in Table 6), and the value mostly reflects the unit mismatch. | +| v2 | The same statistics between the dimensionless quantities vec(Δ)/Q_Z and vec(dE)/Q_E, with Q_Z and Q_E the 0.9-quantiles above. **Smaller is better** (more similar distribution shapes). | + +### Distance correlation (key `dCor`, not reported in the paper) + +Both versions return the double-centred distance correlation of the two n × n matrices, clipped to [0, 1]. + +## 5. Table mapping + +| Table cell | Summary key | +|---|---| +| Table 3: Spearman, Kendall, CKA, iso R², EJS, EJS-ROCAUC, TS, KS | `spearman`, `kendall`, `cka_rbf`, `iso_R2`, `EJS`, `ROC_AUC`, `TS`, `KS` (means) | +| Table 6: the 13 rows, mean ± CI | the Table 3 keys plus `EJS_lam0p1` … `EJS_lam3` and `Smoothness` | +| Table 7: jumps at 0.1σ … 3σ | `EJS_num_jumps_lam0p1` … `EJS_num_jumps_lam3` | + +`reproduce/energy_tables_3_6_7/collect.py` performs this mapping. `expected.csv` in the same folder +lists, for every cell, the printed value, the value recomputed with this code, and notes on cells +where the two differ. + +## 6. Usage + +```bash +# Published protocol (defaults: legacy windows, paper metrics, 100 x 2000-frame windows, seed 2025) +python -m three_dbench evaluate traj \ + --dataset-dir data/hf/traj/energies \ + --embeddings embeddings/traj/gemnet --embedding-key gemnet \ + --model-name GemNet --output-dir results/traj/gemnet --n-jobs 16 + +# E3FP fingerprints (pickled RDKit bit vectors): Tanimoto distance is selected automatically +python -m three_dbench evaluate traj --dataset-dir data/hf/traj/energies \ + --embeddings embeddings/traj/e3fp --model-name E3FP --n-jobs 16 + +# Corrected definitions, subset of molecules +python -m three_dbench evaluate traj --dataset-dir data/hf/traj/energies \ + --embeddings embeddings/traj/unimol --metric-version v2 --molecules aspirin ethanol --n-jobs 8 +``` + +Each run writes: +- `details.csv`: one row per molecule × window × metric. +- `summary.csv`: model, metric, mean, ci95, n. +- `config.json`: all options, window starts, the sha256 of each molecule's float64 energies, and + package versions. diff --git a/docs/metrics/geometry.md b/docs/metrics/geometry.md new file mode 100644 index 0000000..c9df3d1 --- /dev/null +++ b/docs/metrics/geometry.md @@ -0,0 +1,162 @@ +# Geometry metrics (rotation dataset, Table 1) + +This page documents how `python -m three_dbench evaluate rotation` computes the geometry +metrics, which definitions were used for the published Table 1, and what `--metric-version v2` +changes. Chirality and energy metrics are described in [chirality.md](chirality.md) and +[energy.md](energy.md); [../METRICS.md](../METRICS.md) is the index. + +## Data and alignment + +- Dataset: `EscheWang/3dcs`, config `rotation`: 1,559,779 molecules (rows), 10,097,643 + conformers, 16 shards. Each row has `key`, `shard`, `n_conformers`, `offset`, `mol_blocks` + and `torsion_deg`. +- **`offset` is per shard.** It restarts at 0 in every shard, and the rows are ordered by the + shard id as a string (`0, 1, 10, 11, …, 15, 2, …, 9`). The initial release (0.1.0) sliced a single + flat array with this offset, which picks the wrong conformers for every shard other than 0. + The evaluator now supports three layouts: + + | `--layout` | Embeddings | Slicing | + |---|---|---| + | `by-shard` (default for a directory) | one file per shard, e.g. `rotation_conformers_{shard}.npz` (the layout of `EscheWang/3dcs-embeddings`) | file of `row.shard`, rows `offset : offset + n_conformers` | + | `flat` (default for a file) | one array in **dataset row order** | cumulative sum of `n_conformers` over the dataset rows | + | `by-key` | dict `{key: (n_conformers, dim)}` | by key | + + Datasets written by the original converter (`convert rotation`) store global offsets in numeric + shard order; `--offset-mode auto` detects this for complete datasets and converts them to per-shard + offsets. For a filtered subset of such a dataset, pass `--offset-mode global`. +- Reference distance `D`: RMSD after optimal alignment, `rdMolAlign.GetBestRMS` on heavy atoms + (`Chem.RemoveHs`), symmetry-aware, for all conformer pairs of a molecule. +- Representation distance `Δ`: cosine distance `1 − cos` (not rescaled), Euclidean distance, or + Tanimoto distance for RDKit bit vectors. `--metrics` selects the spaces; Table 1 uses cosine + (Tanimoto for E3FP). +- Molecules with fewer than 2 conformers are skipped (`--min-conformers 2`), as in the published + runs (1,464,495 of 1,559,779 molecules qualify). +- Molecules whose conformers cannot be merged for RMSD (different heavy-atom counts after + `RemoveHs`) are skipped and listed in `config.json` (`failed_keys`). On the published data this + happens for `1-R5B2G8_6-R7B1G14_0_22` (shard 1) and, according to the backup outputs, + `6-R8B2G15_35-R2B1G6_0_36` (shard 2); the published full run also has no result for these two. +- Aggregation: one value per molecule; `summary.csv` reports the mean and median over molecules + with a finite value. + +Outputs: `_per_key.parquet` (one row per molecule and distance space), `summary.csv` and +`config.json` (definitions, selection counts, failures, runtime). + +## Definitions by metric version + +`--metric-version paper` is the default. The Python API also exposes `legacy` (the behaviour of +`compute_all_geometry_metrics` in release 0.1.0) for comparison. + +| Metric (output key) | `paper` (published runs) | `v2` (paper text) | `legacy` (release 0.1.0) | +|---|---|---|---| +| Spearman (`A1_spearman`) | Spearman of upper-triangular `D` vs `Δ` | same | same | +| Kendall (`A2_kendall`) | as Spearman, **only for molecules with ≥ 11 conformers** (NaN otherwise) | all molecules with ≥ 2 pairs | all molecules | +| CKA (`G_cka_rbf`) | `K = exp(−d² / (median(d) + 1e-12)²)` per matrix (median over the upper triangle), HKH centring | `K = exp(−d² / 2σ²)`, `σ² = median` of the positive squared distances, per matrix (median heuristic, Appendix C.3) | as v2 | +| Isotonic R² (`J_isotonic_R2`) | fit `D̂ = f_iso(Δ)`, R² on `D`; no guard (a single pair gives 1.0) | fit `D̂ = f_iso(Δ)`, R² on `D`; NaN for < 2 pairs or constant `Δ` | fit `Δ̂ = f_iso(D)`, R² on `Δ` (a constant representation scores 1.0) | +| LIE@k (`H_LIE@k`) | k = 10, neighbour set `argpartition(D)[:, :k]` (**includes the conformer itself**), `Δ` rescaled to [0, 1] (`(1 − cos)/2` for cosine) | k = 3 nearest **other** conformers (Table 1 caption, Appendix C.4); molecules with ≥ 3 conformers | k = 10 including self, `Δ` not rescaled | +| Torsion-SP (`torsion_sp`) | Spearman of `Δ` vs circular torsion distance `min(|φi − φj|, 2π − |φi − φj|)` | same | same | +| AS (`AS`) | **median** over consecutive conformers sorted by torsion, **including the wrap-around step**, of `((1 − cos)/2) / Δφ` (radians) | **median** of `‖z_{i+1} − z_i‖ / |φ_{i+1} − φ_i|` (radians) over consecutive conformers sorted by torsion, no wrap-around step (Appendix C.4); Tanimoto distance for bit vectors | **mean** of `Δ / Δφ` including the wrap-around step | + +Individual choices can be overridden: `--lie-k`, `--lie-self {include,exclude}`, +`--as-variant {mean_delta_circular,median_delta_circular,median_halfdelta_circular,median_dz_linear}`. +`--extra-metrics` adds distance correlation, Mantel r/p, Kruskal stress and triplet order +preservation (not in Table 1; they dominate the runtime). + +Notes on `v2`: + +- **LIE**: the neighbour set of the paper definition does not contain the conformer itself. + With the conformer included, one of the k "neighbours" has distance 0 in both spaces, which + dilutes the score, and k = 10 exceeds the conformer count of most molecules (median 6). +- **AS**: the appendix defines AS as the median representation change per angular increment along + the scan. `‖z_{i+1} − z_i‖` is in the units of the embedding, so AS values are comparable + between conformers of one model but not across models with different embedding scales. + The wrap-around step (largest torsion back to the smallest) is excluded because conformers do not + necessarily cover the full circle after redundancy removal. +- **Isotonic R²**: the appendix fits `D` as a monotone function of `Δ`. The reverse fit rewards a + constant representation with R² = 1. +- **Kendall**: no conformer-count threshold. +- The `paper` column is kept as the default so that the published numbers can be regenerated. + +## Provenance of Table 1 + +The scripts that produced Table 1 (`eval_geo_single.py`, `eval_geo_single_sup.py`) are not +available. The `paper` definitions above were recovered by recomputing GemNet metrics from the +published GemNet rotation embeddings and matching them, molecule by molecule, against the backed-up +per-molecule outputs of the two original runs (the per-molecule JSON files are published in +`EscheWang/3dcs-embeddings` under `results/rotation/`): + +| Table 1 rows | Original run | Molecules | Backup file | +|---|---|---|---| +| Spearman, Kendall, CKA, Isotonic R², Torsion-SP | `eval_geo_single.py --sample-ratio 0.1 --sample-seed 2027 --pairs-cap 100` | 146,389 (10 % sample) | `metrics_all_0.1_1.json.gz` (= `all_metric.csv`) | +| LIE@k, AS | `eval_geo_single_sup.py --sample-ratio 1 --sample-seed 2027 --pairs-cap 100` | 1,464,493 | `metrics_sup_100.json.gz` | + +The 10 % sample cannot be regenerated from the seed because the sampling code is not available. +Its molecule keys, read from the backup file, are distributed as +[`reproduce/table1_geometry/sampled_molecules_seed2027.txt`](../../reproduce/table1_geometry/sampled_molecules_seed2027.txt) +(146,389 keys in dataset order; use it with `--molecule-list`). `--sample-ratio/--sample-seed` +implement a documented per-shard sampler (`numpy.random.default_rng(seed + shard)`), which does not +reproduce that list. + +### Per-molecule agreement (GemNet, cosine) + +Recomputed with `--metric-version paper` from the HF MolBlocks and the published embeddings. + +| Metric | Molecules compared | Max. abs. difference | Difference of the mean | +|---|---|---|---| +| Spearman | 16,717 (10 % sample, shards 0–1) | 2.1e-2 (54 molecules > 1e-6) | 4.7e-7 | +| Kendall | 3,003 | 1.4e-3 | −3.9e-7 | +| CKA | 18,262 | 4.9e-5 | −2.3e-8 | +| Isotonic R² | 18,262 | 2.6e-3 | −2.9e-7 | +| Torsion-SP | 16,717 | 1.0e-2 (1 molecule > 1e-6) | 6.2e-7 | +| LIE@k | 91,499 (all of shard 0) | 9.9e-2 (99th percentile 1.9e-5) | 3.6e-7 | +| AS | 91,499 | 2.0e-3 (31 molecules > 1e-6) | −1.2e-8 | + +The remaining per-molecule differences come from the reference distances: the HF MolBlocks store +coordinates with 4 decimals, so RMSD values differ from the original coordinates by about 1e-5 Å, +which can reorder near-tied pairs. LIE is sensitive for GemNet because some GemNet cosine distances +are of the order of the 1e-12 stabiliser. + +Two further properties of the published runs were found this way: + +- **Kendall** is finite for every sampled molecule with ≥ 11 conformers and NaN for every molecule + with ≤ 10 conformers (24,198 of 146,389 molecules have a value). The exact rule of the original + script is not recoverable; any pair-count threshold between 46 and 55 selects the same molecules. +- **Embedding offsets in the full run.** In `metrics_sup_100.json.gz`, the molecules of shard 1 after + `1-R5B2G8_6-R7B1G14_0_22` (local offset 69,515, 3 conformers) and of shard 2 after + `6-R8B2G15_35-R2B1G6_0_36` (local offset 560,074, 7 conformers) were evaluated with embeddings + shifted back by 3 and 7 rows: the failing molecule was skipped without advancing the embedding + cursor. For GemNet this reproduces 530/530 and 67/67 sampled molecules after the failure in + shards 1 and 2 (LIE and AS), while the correctly aligned embeddings do not. 91,093 of the + 1,464,493 molecules (6.2 %) are affected. The 10 % run is not affected (neither molecule is in the + sample), and E3FP is not affected (its fingerprints are stored per molecule). + `--replicate-offset-drift` (by-shard layout) recomputes all metrics with this shift and stores + them as `__offset_drift` columns; the regular columns always use aligned embeddings. + +For the four learned models, the effect on the published LIE@k and AS means can be estimated from +the backup outputs by averaging over the unaffected molecules only (an estimate; exact aligned +values require the embeddings, which are available for GemNet only): + +| Model | LIE@k published | LIE@k, unaffected molecules | AS published | AS, unaffected molecules | +|---|---|---|---|---| +| E3FP | 0.3238 | 0.3239 | 2.7576 | 2.7850 | +| GemNet | 0.3901 | 0.3662 | 0.001825 | 0.001166 | +| MolAE | 0.3489 | 0.3321 | 0.004755 | 0.004022 | +| MolSpectra | 0.2379 | 0.2254 | 0.021443 | 0.020741 | +| UniMol | 0.3059 | 0.2863 | 0.005987 | 0.005291 | + +(For E3FP the small differences reflect the subset only.) + +### Not reproduced + +- The Euclidean-space LIE@k values of the full run (not used in Table 1). In that run AS is the same + in both spaces (computed from the cosine distance). +- Rotation embeddings for E3FP, UniMol, MolAE and MolSpectra are not available, so their Table 1 + values can only be compared with the backed-up per-molecule outputs. + +## Runtime + +GemNet, 24 worker processes on the A100 box, cosine and Euclidean spaces: shard 0 (91,499 +molecules) took 94 s with `--metric-version paper`; shards 0–1 (182,981 molecules) took 188 s +(`paper`) and 191 s (`v2`) while another 24-process job shared the machine. RMSD takes about 3 ms per +molecule. A full run over the 16 shards (1,464,495 molecules) is therefore expected to take +roughly 25 minutes per metric version with 24 workers; `--replicate-offset-drift` adds a second pass +over the affected molecules. `--extra-metrics` is about 20× slower (Mantel permutations). diff --git a/examples/demo.py b/examples/demo.py index ae78c18..c1c58d9 100644 --- a/examples/demo.py +++ b/examples/demo.py @@ -9,6 +9,7 @@ python examples/demo.py trajectory python examples/demo.py all """ + from __future__ import annotations import argparse diff --git a/examples/run_chirality_from_hf.py b/examples/run_chirality_from_hf.py index 7e1d036..92d5a3d 100644 --- a/examples/run_chirality_from_hf.py +++ b/examples/run_chirality_from_hf.py @@ -1,7 +1,13 @@ -"""Download chirality dataset from HF and run evaluation. +"""Download the chirality dataset from HF and evaluate one embedding file. Requires: pip install -e . + +Example (published UniMol embedding, Table 2 protocol):: + + python examples/run_chirality_from_hf.py \ + --embeddings data/embeddings/chirality/unimol/1.npz --embedding-key arr_0 --model-name unimol """ + from __future__ import annotations import argparse @@ -9,26 +15,30 @@ from datasets import load_dataset -from three_dbench.benchmarks import evaluate_chirality_embeddings -from three_dbench.embeddings import load_embeddings +from three_dbench.benchmarks.chirality import evaluate_chirality_embeddings, load_chirality_embeddings ROOT = Path(__file__).resolve().parents[1] def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Run chirality evaluation from HF datasets") - parser.add_argument("--repo-id", type=str, required=True, help="HF dataset repo, e.g. EscheWang/3dcs") + parser.add_argument("--repo-id", type=str, default="EscheWang/3dcs", help="HF dataset repo") parser.add_argument("--config", type=str, default="chirality", help="Dataset config name") + parser.add_argument("--revision", type=str, default=None, help="Optional HF revision (commit hash)") parser.add_argument("--dataset-dir", type=Path, default=ROOT / "data" / "hf" / "chirality") parser.add_argument( "--embeddings", type=Path, - default=ROOT / "data" / "chirality" / "unimol" / "1.npz", - help="Embedding file path", + default=ROOT / "data" / "embeddings" / "chirality" / "unimol" / "1.npz", + help="Embedding file: one row per conformer in dataset order, or a pickled fingerprint list/dict", ) - parser.add_argument("--embedding-key", type=str, default="arr_0") + parser.add_argument("--embedding-key", type=str, default="arr_0", help="NPZ key or pickle dict entry (e3fp)") parser.add_argument("--model-name", type=str, default="unimol") - parser.add_argument("--output-dir", type=Path, default=ROOT / "results" / "chirality" / "unimol") + parser.add_argument("--output-dir", type=Path, default=None) + parser.add_argument("--distance", choices=["euclidean", "cosine"], default="euclidean") + parser.add_argument("--metric-version", choices=["paper", "v2"], default="paper") + parser.add_argument("--unsup-kmax", type=int, default=None, help="Best-k silhouette cap (default n-1)") + parser.add_argument("--n-jobs", type=int, default=1) return parser.parse_args() @@ -36,7 +46,7 @@ def main() -> None: args = parse_args() if not args.dataset_dir.exists(): - ds = load_dataset(args.repo_id, name=args.config, split="train") + ds = load_dataset(args.repo_id, name=args.config, split="train", revision=args.revision) args.dataset_dir.parent.mkdir(parents=True, exist_ok=True) ds.save_to_disk(str(args.dataset_dir)) print(f"Saved HF dataset to {args.dataset_dir}") @@ -46,14 +56,23 @@ def main() -> None: if not args.embeddings.exists(): raise FileNotFoundError(f"Embeddings not found: {args.embeddings}") - embeddings = load_embeddings(args.embeddings, key=args.embedding_key) - evaluate_chirality_embeddings( + output_dir = ( + args.output_dir or ROOT / "results" / "chirality" / f"{args.model_name}_{args.distance}_{args.metric_version}" + ) + embeddings = load_chirality_embeddings(args.embeddings, key=args.embedding_key) + _, summary = evaluate_chirality_embeddings( dataset_dir=args.dataset_dir, embeddings=embeddings, - output_dir=args.output_dir, + output_dir=output_dir, model_name=args.model_name, + distance=args.distance, + metric_version=args.metric_version, + unsup_kmax=args.unsup_kmax, + n_jobs=args.n_jobs, ) - print(f"Report saved to {args.output_dir}") + for k in ("ESA_AUC_mean", "NN1_acc_mean", "hopkins_mean", "sil_sup_mean", "sil_unsup_mean"): + print(f"{k}: {summary[k]:.6f}") + print(f"Report saved to {output_dir}") if __name__ == "__main__": diff --git a/examples/run_rotation_from_hf.py b/examples/run_rotation_from_hf.py index fd5d06f..3e14e4c 100644 --- a/examples/run_rotation_from_hf.py +++ b/examples/run_rotation_from_hf.py @@ -1,104 +1,61 @@ -"""Download rotation dataset from HF and run evaluation. +"""Download the rotation dataset from HF (if needed) and evaluate per-shard embeddings. Requires: pip install -e . + +The published GemNet rotation embeddings (``EscheWang/3dcs-embeddings``, ``rotation/gemnet``) hold +one file per shard, ``rotation_conformers_{shard}.npz``; rows of shard ``s`` are aligned with the +per-shard ``offset`` column of the HF rotation config. Example:: + + python -m three_dbench download dataset --task rotation + python -m three_dbench download embeddings --task rotation --models gemnet + python examples/run_rotation_from_hf.py --shards 0 --max-keys 200 --n-jobs 4 + +``--flat-cache`` additionally writes the selected shards as one ``.npy`` in dataset row order +(``numpy.lib.format.open_memmap``) and evaluates that flat array instead. """ + from __future__ import annotations import argparse from pathlib import Path -from typing import Iterable, List - -import numpy as np -from datasets import load_dataset from three_dbench.benchmarks import evaluate_rotation_embeddings +from three_dbench.benchmarks.rotation import ShardedEmbeddings, build_rotation_index, write_flat_embeddings from three_dbench.embeddings import EmbeddingArray -ROOT = Path(__file__).resolve().parents[1] +ROOT = Path.cwd() def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Run rotation evaluation from HF datasets") - parser.add_argument("--repo-id", type=str, required=True, help="HF dataset repo, e.g. EscheWang/3dcs") + parser.add_argument("--repo-id", type=str, default="EscheWang/3dcs", help="HF dataset repo") parser.add_argument("--config", type=str, default="rotation", help="Dataset config name") parser.add_argument("--dataset-dir", type=Path, default=ROOT / "data" / "hf" / "rotation") parser.add_argument( "--embeddings-dir", type=Path, - default=ROOT / "data" / "rotation" / "results" / "gemnet", - help="Directory containing rotation_conformers_*.npz", + default=ROOT / "data" / "embeddings" / "rotation" / "gemnet", + help="Directory with one embedding file per shard (e.g. rotation_conformers_{shard}.npz)", ) - parser.add_argument("--embedding-key", type=str, default="gemnet") + parser.add_argument("--embedding-key", type=str, default=None, help="NPZ key (default: auto)") + parser.add_argument("--shard-file-pattern", type=str, default=None) parser.add_argument("--model-name", type=str, default="gemnet") parser.add_argument("--output-dir", type=Path, default=ROOT / "results" / "rotation" / "gemnet") - parser.add_argument( - "--shards", - type=int, - nargs="*", - default=None, - help="Optional shard IDs to evaluate (e.g. --shards 0 1 2).", - ) - parser.add_argument( - "--max-keys", - type=int, - default=None, - help="Optional cap on number of dataset keys for a quick test.", - ) + parser.add_argument("--shards", type=int, nargs="*", default=None, help="Shard IDs to evaluate") + parser.add_argument("--max-keys", type=int, default=None, help="Evaluate at most N molecules") + parser.add_argument("--molecule-list", type=Path, default=None, help="File with one key per line") + parser.add_argument("--sample-ratio", type=float, default=None) + parser.add_argument("--sample-seed", type=int, default=2027) + parser.add_argument("--metric-version", type=str, choices=["paper", "v2"], default="paper") + parser.add_argument("--metrics", type=str, nargs="*", default=["cosine", "euclidean"]) + parser.add_argument("--n-jobs", type=int, default=1) + parser.add_argument("--flat-cache", type=Path, default=None, help="Directory for an optional flat .npy cache") return parser.parse_args() -def _load_embedding_npz(path: Path, key: str) -> np.ndarray: - with np.load(path) as data: - if key in data.files: - return data[key] - if "arr_0" in data.files: - return data["arr_0"] - return data[data.files[0]] - - -def _concat_embeddings(emb_dir: Path, shards: Iterable[int], key: str, cache_path: Path) -> np.ndarray: - if cache_path.exists(): - return np.load(cache_path, mmap_mode="r") - - arrays = [] - total = 0 - dim = None - dtype = None - for shard in shards: - path = emb_dir / f"rotation_conformers_{shard}.npz" - arr = _load_embedding_npz(path, key) - arrays.append(arr) - total += arr.shape[0] - if dim is None: - dim = arr.shape[1] - dtype = arr.dtype - - if dim is None or dtype is None: - raise ValueError("No embeddings loaded.") - - cache_path.parent.mkdir(parents=True, exist_ok=True) - mm = np.memmap(cache_path, mode="w+", dtype=dtype, shape=(total, dim)) - cursor = 0 - for arr in arrays: - mm[cursor : cursor + arr.shape[0]] = arr - cursor += arr.shape[0] - mm.flush() - return np.load(cache_path, mmap_mode="r") - - -def _reindex_offsets(rows: List[dict]) -> List[dict]: - cursor = 0 - out = [] - for row in rows: - row = dict(row) - row["offset"] = int(cursor) - cursor += int(row["n_conformers"]) - out.append(row) - return out - - def main() -> None: args = parse_args() + from datasets import load_dataset, load_from_disk if not args.dataset_dir.exists(): ds = load_dataset(args.repo_id, name=args.config, split="train") @@ -107,44 +64,46 @@ def main() -> None: print(f"Saved HF dataset to {args.dataset_dir}") else: print(f"Using cached dataset at {args.dataset_dir}") - - from datasets import load_from_disk, Dataset - ds = load_from_disk(str(args.dataset_dir)) - if args.shards: - shard_set = set(args.shards) - ds = ds.filter(lambda row: row["shard"] in shard_set) - rows = ds.to_list() - rows = _reindex_offsets(rows) - ds = Dataset.from_list(rows) - - tmp_dir = args.dataset_dir.parent / "rotation_tmp" - tmp_dir.mkdir(parents=True, exist_ok=True) - - shards = args.shards or list(range(16)) - cache_path = tmp_dir / f"rotation_{args.model_name}_all.npy" - embeddings = _concat_embeddings(args.embeddings_dir, shards, args.embedding_key, cache_path) - - if args.max_keys is not None: - ds = ds.select(range(min(args.max_keys, len(ds)))) - rows = _reindex_offsets(ds.to_list()) - ds = Dataset.from_list(rows) - - local_dir = tmp_dir / ("rotation_subset" if args.shards or args.max_keys else "rotation_full") - ds.save_to_disk(str(local_dir)) - emb = EmbeddingArray(array=embeddings, kind="vector") - - total = int(sum(ds["n_conformers"])) - if total > embeddings.shape[0]: - raise ValueError(f"Embedding length {embeddings.shape[0]} < total conformers {total}") - - evaluate_rotation_embeddings( - dataset_dir=local_dir, - embeddings=emb, + sharded = ShardedEmbeddings.from_directory( + args.embeddings_dir, key=args.embedding_key, pattern=args.shard_file_pattern + ) + shards = args.shards if args.shards is not None else sharded.shards + common = dict( output_dir=args.output_dir, model_name=args.model_name, + metrics=args.metrics, + metric_version=args.metric_version, + molecule_list=args.molecule_list, + sample_ratio=args.sample_ratio, + sample_seed=args.sample_seed, + max_keys=args.max_keys, + n_jobs=args.n_jobs, + progress=True, ) + + if args.flat_cache is None: + evaluate_rotation_embeddings(dataset=ds, embeddings_by_shard=sharded, shards=shards, **common) + else: + # Flat layout: restrict the dataset to the requested shards first, so that the flat + # array covers exactly the rows of the evaluated dataset, in dataset row order. + shard_set = set(shards) + subset = ds.filter(lambda s: s in shard_set, input_columns=["shard"]) + index = build_rotation_index(subset) + tag = "-".join(str(s) for s in sorted(shard_set)) + cache = args.flat_cache / f"rotation_{args.model_name}_{args.embedding_key or 'auto'}_shards{tag}.npy" + if cache.exists(): + import numpy as np + + flat = np.load(cache, mmap_mode="r") + print(f"Using flat cache {cache}") + else: + flat = write_flat_embeddings(index, sharded, cache) + print(f"Wrote flat cache {cache} {flat.shape}") + if flat.shape[0] != int(index["n_conformers"].sum()): + raise ValueError(f"Flat cache {cache} does not match the dataset; delete it and rerun.") + evaluate_rotation_embeddings(dataset=subset, embeddings=EmbeddingArray(array=flat, kind="vector"), **common) print(f"Report saved to {args.output_dir}") diff --git a/examples/run_traj_from_hf.py b/examples/run_traj_from_hf.py index ad498e9..d159b4a 100644 --- a/examples/run_traj_from_hf.py +++ b/examples/run_traj_from_hf.py @@ -1,7 +1,12 @@ -"""Download trajectory energies from HF and run evaluation. +"""Download trajectory energies from HF and run the energy (trajectory) evaluation. + +Defaults reproduce the published protocol (legacy windows, paper metric definitions, float64 +energies). Embeddings for the paper baselines are in the ``EscheWang/3dcs-embeddings`` dataset under +``traj//rmd17_.{npz,pkl}``; see ``reproduce/energy_tables_3_6_7/run.sh``. Requires: pip install -e . """ + from __future__ import annotations import argparse @@ -10,30 +15,36 @@ from datasets import load_dataset from three_dbench.benchmarks import evaluate_trajectory_embeddings -from three_dbench.embeddings import load_embeddings_dir +from three_dbench.traj.io import load_traj_embeddings ROOT = Path(__file__).resolve().parents[1] def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Run trajectory evaluation from HF datasets") - parser.add_argument("--repo-id", type=str, required=True, help="HF dataset repo, e.g. EscheWang/3dcs") + parser.add_argument("--repo-id", type=str, default="EscheWang/3dcs", help="HF dataset repo") parser.add_argument("--config", type=str, default="traj_energies", help="Dataset config name") + parser.add_argument("--revision", type=str, default=None, help="HF dataset revision (default: main)") parser.add_argument("--dataset-dir", type=Path, default=ROOT / "data" / "hf" / "traj" / "energies") parser.add_argument( "--embeddings-dir", type=Path, default=ROOT / "data" / "traj" / "results" / "unimol", - help="Directory of rmd17_*.npz files", + help="Directory of rmd17_*.npz (vectors) or rmd17_*.pkl (RDKit fingerprints) files", ) - parser.add_argument("--embedding-key", type=str, default="arr_0") + parser.add_argument("--embedding-key", type=str, default=None, help="NPZ key (default: arr_0 / only array)") parser.add_argument("--model-name", type=str, default="unimol") parser.add_argument("--output-dir", type=Path, default=ROOT / "results" / "traj" / "unimol") parser.add_argument("--n-samples", type=int, default=100) parser.add_argument("--window", type=int, default=2000) - parser.add_argument("--metric-embed", type=str, default="cosine") + parser.add_argument("--metric-embed", type=str, default=None, choices=["cosine", "euclidean", "tanimoto"]) parser.add_argument("--block-size", type=int, default=4096) parser.add_argument("--random-seed", type=int, default=2025) + parser.add_argument("--window-scheme", choices=["legacy", "shared"], default="legacy") + parser.add_argument("--metric-version", choices=["paper", "v2"], default="paper") + parser.add_argument("--n-jobs", type=int, default=1) + parser.add_argument("--molecules", type=str, nargs="*", default=None) + parser.add_argument("--energy-precision-check", choices=["error", "warn", "ignore"], default="error") return parser.parse_args() @@ -41,7 +52,7 @@ def main() -> None: args = parse_args() if not args.dataset_dir.exists(): - ds = load_dataset(args.repo_id, name=args.config, split="train") + ds = load_dataset(args.repo_id, name=args.config, split="train", revision=args.revision) args.dataset_dir.parent.mkdir(parents=True, exist_ok=True) ds.save_to_disk(str(args.dataset_dir)) print(f"Saved HF dataset to {args.dataset_dir}") @@ -51,7 +62,7 @@ def main() -> None: if not args.embeddings_dir.exists(): raise FileNotFoundError(f"Embeddings directory not found: {args.embeddings_dir}") - emb_dict = load_embeddings_dir(args.embeddings_dir, file_glob="rmd17_*.npz", key=args.embedding_key) + emb_dict = load_traj_embeddings(args.embeddings_dir, key=args.embedding_key) evaluate_trajectory_embeddings( dataset_dir=args.dataset_dir, embeddings_by_mol=emb_dict, @@ -62,6 +73,11 @@ def main() -> None: metric_embed=args.metric_embed, block_size=args.block_size, random_seed=args.random_seed, + window_scheme=args.window_scheme, + metric_version=args.metric_version, + n_jobs=args.n_jobs, + molecules=args.molecules, + energy_precision_check=args.energy_precision_check, ) print(f"Report saved to {args.output_dir}") diff --git a/pyproject.toml b/pyproject.toml index a1d23f4..31c358d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,13 @@ readme = "README.md" license = {text = "MIT"} requires-python = ">=3.9" keywords = ["3d", "benchmark", "chemistry", "evaluation", "3dcs", "molecular-embeddings"] -authors = [{ name = "3DCS Team" }] +authors = [ + { name = "Xi Wang" }, + { name = "Yang Zhang" }, + { name = "Yingjia Zhang" }, + { name = "Yejia Cai" }, + { name = "Shengjie Wang" }, +] classifiers = [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", @@ -27,7 +33,8 @@ dependencies = [ "scipy>=1.9", "pandas>=1.5", "scikit-learn>=1.2", - "scikit-learn-extra>=0.3", + "joblib>=1.2", + "rdkit>=2023.9", "lmdb>=1.4", "tqdm>=4.66", "matplotlib!=3.6.1,>=3.4", @@ -35,9 +42,13 @@ dependencies = [ "python-dateutil>=2.8", "datasets>=2.18", "pyarrow>=12", + "huggingface_hub>=0.20", ] [project.optional-dependencies] +# Optional KMedoids backend for chirality unsupervised metrics (not used by default; the +# scikit-learn-extra 0.3.0 wheels are built against numpy 1.x and fail to import with numpy 2). +kmedoids = ["scikit-learn-extra>=0.3"] dev = [ "pytest>=7.0", "pytest-cov>=4.0", @@ -51,6 +62,9 @@ three-dbench = "three_dbench.__main__:main" [project.urls] Homepage = "https://github.com/ComDec/3DCS" Documentation = "https://github.com/ComDec/3DCS/tree/main/docs" +Paper = "https://openreview.net/forum?id=JAb0y8lkqL" +Datasets = "https://huggingface.co/datasets/EscheWang/3dcs" +Embeddings = "https://huggingface.co/datasets/EscheWang/3dcs-embeddings" Issues = "https://github.com/ComDec/3DCS/issues" [tool.setuptools] diff --git a/reproduce/README.md b/reproduce/README.md new file mode 100644 index 0000000..0340dad --- /dev/null +++ b/reproduce/README.md @@ -0,0 +1,54 @@ +# Reproducing the paper tables + +Each directory recomputes one or more tables of the ICLR 2026 paper from the released datasets +(`EscheWang/3dcs`), the published embeddings (`EscheWang/3dcs-embeddings`) and this code. + +| Directory | Tables | Command | Notes | +|---|---|---|---| +| [`table1_geometry/`](table1_geometry/README.md) | 1 | `bash reproduce/table1_geometry/run.sh` | GemNet only (the other rotation embeddings are not available); `QUICK=1` runs shard 1 | +| [`table2_chirality/`](table2_chirality/README.md) | 2 | `bash reproduce/table2_chirality/run.sh` | 7 models; Euclidean (published) and cosine / v2 variants | +| [`energy_tables_3_6_7/`](energy_tables_3_6_7/README.md) | 3, 6, 7 | `bash reproduce/energy_tables_3_6_7/run.sh` | 7 models; float64 energies, legacy windows | + +Every directory contains: + +- `run.sh`: downloads what it needs, runs `python -m three_dbench evaluate …`, writes `results.csv` + and calls `compare.py`. The header of each script lists its environment variables (output + directory, number of workers, reuse of local copies). +- `expected.csv`: one row per table cell and variant. + +## File formats + +`expected.csv` + +| Column | Meaning | +|---|---| +| `table` | table id (`1`, `2`, `3`, …; `1-shard1` for the quick Table 1 check) | +| `model` | lower-case model name (`e3fp`, `gemnet`, `molae`, `molspectra`, `unimol`, `fmg`, `mace`) | +| `metric` | metric id used by the table's `collect` script | +| `variant` | e.g. `paper`, `euclidean`, `cosine`, `v2` (see the table's README) | +| `paper_value` | value as printed in the paper (empty if the variant is not in the paper) | +| `expected_value` | value recomputed with this code or taken from the original outputs, 6 decimals; empty if not available yet | +| `tolerance` | allowed absolute difference (default 0.001) | +| `notes` | provenance and known differences from the printed value (rounding vs truncation, cells that cannot be recomputed, …) | + +`results.csv`: `table,model,metric,variant,value`. + +## Comparing + +```bash +python reproduce/compare.py --expected reproduce/table1_geometry/expected.csv --results results/reproduce/table1_geometry/results.csv +``` + +Status per expected row: + +- `PASS`: `|value − expected_value| ≤ tolerance`; +- `FAIL`: larger difference or a non-finite value; +- `MISSING`: no result row, e.g. a model whose embeddings are not published (a failure with `--strict`); +- `NO_EXPECTED`: the reference value is not available yet (never a failure). + +The exit code is 1 if any row fails and 2 if no row could be compared. `--tables` and `--variants` +restrict the comparison. + +The `expected_value` is compared, not the printed `paper_value`: printed values are rounded or, in +some cells, truncated, and a few printed cells differ from the recomputed values for reasons listed in +`notes`. diff --git a/reproduce/compare.py b/reproduce/compare.py new file mode 100755 index 0000000..5a2bb4d --- /dev/null +++ b/reproduce/compare.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python +"""Compare recomputed results with the expected values of a paper table. + +usage: python reproduce/compare.py --expected --results [--strict] + +expected.csv columns: table,model,metric,variant,paper_value,expected_value,tolerance,notes +results.csv columns: table,model,metric,variant,value + +Rows are matched on (table, model, metric, variant). Status per expected row: + PASS |value - expected_value| <= tolerance (tolerance defaults to 0.001) + FAIL the difference exceeds the tolerance, or the value is not finite + MISSING no result row (e.g. a model whose embeddings are not available); a failure with --strict + NO_EXPECTED expected_value is empty (reference value not available yet); never a failure +Exit code: 0 if no FAIL, 1 if any FAIL, 2 if no row could be compared. +""" + +from __future__ import annotations + +import argparse +import csv +import math +import sys +from pathlib import Path + +KEY = ("table", "model", "metric", "variant") +DEFAULT_TOLERANCE = 0.001 + + +def _float(text): + if text is None: + return None + text = str(text).strip() + if text == "": + return None + try: + return float(text) + except ValueError: + return None + + +def _read(path: Path, required: tuple[str, ...]) -> list[dict]: + with Path(path).open(newline="", encoding="utf-8") as fh: + reader = csv.DictReader(fh) + missing = set(required) - set(reader.fieldnames or []) + if missing: + raise SystemExit(f"{path}: missing columns {sorted(missing)}") + return [{k: (v.strip() if isinstance(v, str) else v) for k, v in row.items()} for row in reader] + + +def compare(expected_rows: list[dict], result_rows: list[dict], *, strict: bool = False) -> tuple[list[dict], int]: + results: dict[tuple, dict] = {} + for row in result_rows: + key = tuple(row[k] for k in KEY) + if key in results: + raise SystemExit(f"duplicate result row for {key}") + results[key] = row + report = [] + n_fail = 0 + n_compared = 0 + for exp in expected_rows: + key = tuple(exp[k] for k in KEY) + expected = _float(exp.get("expected_value")) + tol = _float(exp.get("tolerance")) + tol = DEFAULT_TOLERANCE if tol is None else tol + res = results.get(key) + value = _float(res.get("value")) if res is not None else None + diff = None + if expected is None: + status = "NO_EXPECTED" + elif res is None: + status = "MISSING" + if strict: + n_fail += 1 + status = "MISSING(FAIL)" + else: + n_compared += 1 + if value is None or not math.isfinite(value): + status = "FAIL" + n_fail += 1 + else: + diff = value - expected + if abs(diff) <= tol: + status = "PASS" + else: + status = "FAIL" + n_fail += 1 + report.append( + { + **{k: exp[k] for k in KEY}, + "paper_value": exp.get("paper_value", ""), + "expected_value": "" if expected is None else f"{expected:.6f}", + "value": "" if value is None else f"{value:.6f}", + "diff": "" if diff is None else f"{diff:+.2e}", + "tolerance": f"{tol:g}", + "status": status, + } + ) + unmatched = sorted(set(results) - {tuple(e[k] for k in KEY) for e in expected_rows}) + for key in unmatched: + print(f"note: result row without expected value: {key}", file=sys.stderr) + return report, (1 if n_fail else (2 if n_compared == 0 else 0)) + + +def _print_table(report: list[dict]) -> None: + cols = [ + "table", + "model", + "metric", + "variant", + "paper_value", + "expected_value", + "value", + "diff", + "tolerance", + "status", + ] + widths = {c: max(len(c), *(len(str(r[c])) for r in report)) if report else len(c) for c in cols} + print(" ".join(c.ljust(widths[c]) for c in cols)) + print(" ".join("-" * widths[c] for c in cols)) + for r in report: + print(" ".join(str(r[c]).ljust(widths[c]) for c in cols)) + + +def main(argv=None) -> int: + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--expected", type=Path, required=True) + ap.add_argument("--results", type=Path, required=True) + ap.add_argument("--strict", action="store_true", help="treat MISSING rows as failures") + ap.add_argument("--tables", nargs="*", default=None, help="only compare these table ids") + ap.add_argument("--variants", nargs="*", default=None, help="only compare these variants") + args = ap.parse_args(argv) + + expected = _read(args.expected, KEY + ("expected_value",)) + results = _read(args.results, KEY + ("value",)) + if args.tables: + expected = [r for r in expected if r["table"] in args.tables] + results = [r for r in results if r["table"] in args.tables] + if args.variants: + expected = [r for r in expected if r["variant"] in args.variants] + results = [r for r in results if r["variant"] in args.variants] + report, code = compare(expected, results, strict=args.strict) + _print_table(report) + counts: dict[str, int] = {} + for r in report: + counts[r["status"]] = counts.get(r["status"], 0) + 1 + print("\n" + ", ".join(f"{k}: {v}" for k, v in sorted(counts.items()))) + if code == 2: + print("No expected row had a matching result; nothing was compared.", file=sys.stderr) + return code + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/reproduce/energy_tables_3_6_7/README.md b/reproduce/energy_tables_3_6_7/README.md new file mode 100644 index 0000000..80560a7 --- /dev/null +++ b/reproduce/energy_tables_3_6_7/README.md @@ -0,0 +1,43 @@ +# Tables 3, 6 and 7: zero-shot energy benchmark (rMD17) + +```bash +bash reproduce/energy_tables_3_6_7/run.sh # published definitions +N_JOBS=24 METRIC_VERSIONS="paper v2" bash reproduce/energy_tables_3_6_7/run.sh # plus corrected definitions +``` + +`run.sh` does four things: + +1. Downloads the float64 rMD17 energies (`EscheWang/3dcs`, config `traj_energies`). +2. Downloads the backed-up trajectory embeddings of the 7 baselines (`EscheWang/3dcs-embeddings`, + `traj//`, about 7 GB). +3. Runs `python -m three_dbench evaluate traj` once per model with the published protocol: legacy + windows, 100 × 2,000 frames per molecule, seed 2025, cosine distance (Tanimoto for E3FP). +4. Writes `out/results.csv` (`table,model,metric,variant,value`) with `collect.py` and compares it + with `expected.csv` using `reproduce/compare.py`. + +Existing local copies can be reused with `ENERGY_DATASET_DIR=...` and `EMB_ROOT=...` (see the header +of `run.sh`). Runs whose `summary.csv` already exists are skipped unless `FORCE=1`. + +**Runtime.** On a shared 128-thread box with `N_JOBS=24`, one metric version took about 50 minutes +for the 7 models (1,000 windows per model). See `timings.tsv` in the output folder. + +**Files.** + +| File | Content | +|---|---| +| `expected.csv` | For every published cell (variant `paper`): the printed value, the value recomputed with this code (6 decimals), a tolerance of 0.001 and notes. Variant `v2` rows hold the corrected definitions, which have no printed counterpart. | +| `paper_values.csv` | Values as printed in the final paper. Table 7 is written without thousands separators. | +| `make_expected.py` | Regenerates `expected.csv` from a reference `results.csv` and `paper_values.csv`, classifying each cell as rounding to the printed value, matching only by truncation, or differing. | +| `collect.py` | Maps `summary.csv` keys to table cells (see `docs/metrics/energy.md` §5). | + +**Where the recomputed values differ from the printed tables.** The `notes` column of +`expected.csv` records each case: + +- Table 3 KS for E3FP (0.916) and MolSpectra (0.977) differ from the Table 6 values for the same + cells (0.913, 0.976). The recomputation reproduces the Table 6 values. +- Some Table 3 cells are printed truncated rather than rounded, e.g. UniMol EJS 0.30155 → 0.301. +- MACE and FMG columns are only partially reproducible from the backed-up embeddings. Most of their + published 95% CIs are 4–5 times wider than a 1,000-window run gives, and the published FMG + Smoothness (0.972 ± 0.002) is not attainable with these embeddings. +- The v2 `TS` and `Smoothness` are not reported, because rMD17 frames are not time-ordered + (`docs/metrics/energy.md` §1). diff --git a/reproduce/energy_tables_3_6_7/collect.py b/reproduce/energy_tables_3_6_7/collect.py new file mode 100644 index 0000000..055a7ff --- /dev/null +++ b/reproduce/energy_tables_3_6_7/collect.py @@ -0,0 +1,115 @@ +"""Collect Table 3/6/7 cells from per-model trajectory runs into results.csv. + +Input: ``///summary.csv`` written by ``python -m three_dbench evaluate traj``. +Output columns: ``table,model,metric,variant,value`` (variant = metric version: ``paper`` or ``v2``). + +Cell naming + Table 3: metric in {Spearman, Kendall, CKA, isoR2, EJS, EJS_ROCAUC, TS, KS} (means). + Table 6: ``:mean`` and ``:ci95`` for rows Spearman, Kendall, CKA, isoR2, EJS(sigma) for + l in {0.1, 0.5, 1, 2, 3}, ROC_AUC, Smoothness, TS, KS. + Table 7: model ``all``; ``jumps(sigma):mean`` / ``:ci95``. The counts depend only on the energies and + windows, so they are identical for every model; the script checks this. +Non-finite values (v2 TS/Smoothness on rMD17, which is not time-ordered) are omitted. +""" + +from __future__ import annotations + +import argparse +import math +from pathlib import Path + +import pandas as pd + +DISPLAY = { + "e3fp": "E3FP", + "gemnet": "GemNet", + "molae": "MolAE", + "molspectra": "MolSpectra", + "unimol": "UniMol", + "fmg": "FMG", + "mace": "MACE", +} +TABLE3_MODELS = ["E3FP", "GemNet", "MolAE", "MolSpectra", "UniMol", "FMG", "MACE"] +TABLE6_MODELS = ["E3FP", "GemNet", "MolAE", "MolSpectra", "MACE", "FMG"] +TABLE3_ROWS = [ + ("Spearman", "spearman"), + ("Kendall", "kendall"), + ("CKA", "cka_rbf"), + ("isoR2", "iso_R2"), + ("EJS", "EJS"), + ("EJS_ROCAUC", "ROC_AUC"), + ("TS", "TS"), + ("KS", "KS"), +] +LAMBDAS = [("0.1", "0p1"), ("0.5", "0p5"), ("1", "1"), ("2", "2"), ("3", "3")] +TABLE6_ROWS = ( + [("Spearman", "spearman"), ("Kendall", "kendall"), ("CKA", "cka_rbf"), ("isoR2", "iso_R2")] + + [(f"EJS({lam}sigma)", f"EJS_lam{tag}") for lam, tag in LAMBDAS] + + [("ROC_AUC", "ROC_AUC"), ("Smoothness", "Smoothness"), ("TS", "TS"), ("KS", "KS")] +) +TABLE7_ROWS = [(f"jumps({lam}sigma)", f"EJS_num_jumps_lam{tag}") for lam, tag in LAMBDAS] + + +def _finite(x: float) -> bool: + return x is not None and not (isinstance(x, float) and math.isnan(x)) + + +def collect(runs: Path) -> pd.DataFrame: + rows = [] + for version_dir in sorted(p for p in runs.iterdir() if p.is_dir()): + variant = version_dir.name + summaries = {} + for model_dir in sorted(p for p in version_dir.iterdir() if p.is_dir()): + summary_path = model_dir / "summary.csv" + if not summary_path.exists(): + continue + df = pd.read_csv(summary_path) + name = DISPLAY.get(model_dir.name, model_dir.name) + summaries[name] = df.set_index("metric") + for name, s in summaries.items(): + if name in TABLE3_MODELS: + for cell, metric in TABLE3_ROWS: + if metric in s.index and _finite(s.loc[metric, "mean"]): + rows.append(("3", name, cell, variant, float(s.loc[metric, "mean"]))) + if name in TABLE6_MODELS: + for cell, metric in TABLE6_ROWS: + if metric in s.index and _finite(s.loc[metric, "mean"]): + rows.append(("6", name, f"{cell}:mean", variant, float(s.loc[metric, "mean"]))) + rows.append(("6", name, f"{cell}:ci95", variant, float(s.loc[metric, "ci95"]))) + for cell, metric in TABLE7_ROWS: + values = {n: (float(s.loc[metric, "mean"]), float(s.loc[metric, "ci95"])) for n, s in summaries.items()} + if not values: + continue + distinct = set(values.values()) + if len(distinct) != 1: + raise SystemExit(f"Table 7 {cell} ({variant}) differs between models: {values}") + mean, ci = distinct.pop() + rows.append(("7", "all", f"{cell}:mean", variant, mean)) + rows.append(("7", "all", f"{cell}:ci95", variant, ci)) + return pd.DataFrame(rows, columns=["table", "model", "metric", "variant", "value"]) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--runs", type=Path, required=True) + parser.add_argument("--out", type=Path, required=True) + parser.add_argument("--expected", type=Path, default=None, help="expected.csv to subset") + parser.add_argument( + "--expected-out", type=Path, default=None, help="write the expected rows for the computed cells" + ) + args = parser.parse_args() + results = collect(args.runs) + args.out.parent.mkdir(parents=True, exist_ok=True) + results.to_csv(args.out, index=False, float_format="%.10g") + print(f"wrote {len(results)} cells to {args.out}") + if args.expected is not None and args.expected_out is not None and args.expected.exists(): + expected = pd.read_csv(args.expected, dtype={"table": str}) + keys = set(map(tuple, results[["table", "model", "metric", "variant"]].astype(str).values)) + mask = [tuple(map(str, r)) in keys for r in expected[["table", "model", "metric", "variant"]].values] + expected[mask].to_csv(args.expected_out, index=False) + missing = len(expected[expected["variant"].isin(results["variant"].unique())]) - int(sum(mask)) + print(f"wrote {int(sum(mask))} expected rows to {args.expected_out} ({missing} expected cells not computed)") + + +if __name__ == "__main__": + main() diff --git a/reproduce/energy_tables_3_6_7/expected.csv b/reproduce/energy_tables_3_6_7/expected.csv new file mode 100644 index 0000000..24daf32 --- /dev/null +++ b/reproduce/energy_tables_3_6_7/expected.csv @@ -0,0 +1,414 @@ +table,model,metric,variant,paper_value,expected_value,tolerance,notes +3,E3FP,Spearman,paper,0.026,0.026068,0.001,recomputed value rounds to the printed value +3,E3FP,Kendall,paper,0.018,0.018248,0.001,recomputed value rounds to the printed value +3,E3FP,CKA,paper,0.011,0.010683,0.001,recomputed value rounds to the printed value +3,E3FP,isoR2,paper,0.002,0.001507,0.001,recomputed value rounds to the printed value +3,E3FP,EJS,paper,0.184,0.183794,0.001,recomputed value rounds to the printed value +3,E3FP,EJS_ROCAUC,paper,0.531,0.531304,0.001,recomputed value rounds to the printed value +3,E3FP,TS,paper,0.385,0.385347,0.001,recomputed value rounds to the printed value +3,E3FP,KS,paper,0.916,0.913498,0.001,recomputed value does not match the printed value; Table 3 prints 0.916 while Table 6 prints 0.913 for the same mean; the Table 3 value is carried over from the submission draft and is not produced by any backed-up run +3,FMG,Spearman,paper,0.015,0.013975,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +3,FMG,Kendall,paper,0.009,0.009321,0.001,recomputed value rounds to the printed value; Table 3 prints 0.009 while Table 6 prints 0.010 for the same mean +3,FMG,CKA,paper,0.011,0.011691,0.001,printed value is the 3-dp truncation (not rounding) of the recomputed value; Table 3 prints 0.011 while Table 6 prints 0.012 for the same mean +3,FMG,isoR2,paper,0.001,0.000822,0.001,recomputed value rounds to the printed value +3,FMG,EJS,paper,0.269,0.266977,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +3,FMG,EJS_ROCAUC,paper,0.517,0.515996,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +3,FMG,TS,paper,0.582,0.582899,0.001,printed value is the 3-dp truncation (not rounding) of the recomputed value; Table 3 prints 0.582 while Table 6 prints 0.583 for the same mean +3,FMG,KS,paper,0.999,0.999827,0.001,printed value is the 3-dp truncation (not rounding) of the recomputed value; Table 3 prints 0.999 while Table 6 prints 1.000 for the same mean +3,GemNet,Spearman,paper,0.078,0.077526,0.001,recomputed value rounds to the printed value +3,GemNet,Kendall,paper,0.052,0.051807,0.001,recomputed value rounds to the printed value +3,GemNet,CKA,paper,0.016,0.016325,0.001,recomputed value rounds to the printed value +3,GemNet,isoR2,paper,0.013,0.013470,0.001,recomputed value rounds to the printed value +3,GemNet,EJS,paper,0.356,0.355988,0.001,recomputed value rounds to the printed value +3,GemNet,EJS_ROCAUC,paper,0.592,0.591777,0.001,recomputed value rounds to the printed value +3,GemNet,TS,paper,0.444,0.443715,0.001,recomputed value rounds to the printed value +3,GemNet,KS,paper,0.998,0.998367,0.001,recomputed value rounds to the printed value +3,MACE,Spearman,paper,0.236,0.236143,0.001,recomputed value rounds to the printed value +3,MACE,Kendall,paper,0.159,0.159662,0.001,printed value is the 3-dp truncation (not rounding) of the recomputed value; Table 3 prints 0.159 while Table 6 prints 0.160 for the same mean +3,MACE,CKA,paper,0.017,0.017391,0.001,recomputed value rounds to the printed value +3,MACE,isoR2,paper,0.080,0.080723,0.001,printed value is the 3-dp truncation (not rounding) of the recomputed value; Table 3 prints 0.080 while Table 6 prints 0.083 for the same mean +3,MACE,EJS,paper,0.578,0.572063,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +3,MACE,EJS_ROCAUC,paper,0.764,0.760630,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +3,MACE,TS,paper,0.424,0.424343,0.001,recomputed value rounds to the printed value +3,MACE,KS,paper,0.999,0.999448,0.001,recomputed value rounds to the printed value +3,MolAE,Spearman,paper,0.039,0.038868,0.001,recomputed value rounds to the printed value +3,MolAE,Kendall,paper,0.026,0.025930,0.001,recomputed value rounds to the printed value +3,MolAE,CKA,paper,0.024,0.024077,0.001,recomputed value rounds to the printed value +3,MolAE,isoR2,paper,0.003,0.002824,0.001,recomputed value rounds to the printed value +3,MolAE,EJS,paper,0.294,0.293714,0.001,recomputed value rounds to the printed value +3,MolAE,EJS_ROCAUC,paper,0.545,0.545296,0.001,recomputed value rounds to the printed value +3,MolAE,TS,paper,0.346,0.346272,0.001,recomputed value rounds to the printed value +3,MolAE,KS,paper,0.998,0.998199,0.001,recomputed value rounds to the printed value +3,MolSpectra,Spearman,paper,0.023,0.022909,0.001,recomputed value rounds to the printed value +3,MolSpectra,Kendall,paper,0.015,0.015282,0.001,recomputed value rounds to the printed value +3,MolSpectra,CKA,paper,0.006,0.006444,0.001,recomputed value rounds to the printed value +3,MolSpectra,isoR2,paper,0.001,0.001481,0.001,recomputed value rounds to the printed value +3,MolSpectra,EJS,paper,0.271,0.270563,0.001,recomputed value rounds to the printed value +3,MolSpectra,EJS_ROCAUC,paper,0.526,0.526507,0.001,printed value is the 3-dp truncation (not rounding) of the recomputed value; Table 3 prints 0.526 while Table 6 prints 0.527 for the same mean +3,MolSpectra,TS,paper,0.409,0.408949,0.001,recomputed value rounds to the printed value +3,MolSpectra,KS,paper,0.977,0.976430,0.001,recomputed value does not match the printed value; Table 3 prints 0.977 while Table 6 prints 0.976 for the same mean; the Table 3 value is carried over from the submission draft and is not produced by any backed-up run +3,UniMol,Spearman,paper,0.043,0.042610,0.001,recomputed value rounds to the printed value +3,UniMol,Kendall,paper,0.028,0.028426,0.001,recomputed value rounds to the printed value +3,UniMol,CKA,paper,0.019,0.018778,0.001,recomputed value rounds to the printed value +3,UniMol,isoR2,paper,0.003,0.003016,0.001,recomputed value rounds to the printed value +3,UniMol,EJS,paper,0.301,0.301547,0.001,printed value is the 3-dp truncation (not rounding) of the recomputed value +3,UniMol,EJS_ROCAUC,paper,0.549,0.549794,0.001,printed value is the 3-dp truncation (not rounding) of the recomputed value +3,UniMol,TS,paper,0.356,0.356314,0.001,recomputed value rounds to the printed value +3,UniMol,KS,paper,0.997,0.996900,0.001,recomputed value rounds to the printed value +6,E3FP,Spearman:mean,paper,0.026,0.026068,0.001,recomputed value rounds to the printed value +6,E3FP,Spearman:ci95,paper,0.001,0.001085,0.001,recomputed value rounds to the printed value +6,E3FP,Kendall:mean,paper,0.018,0.018248,0.001,recomputed value rounds to the printed value +6,E3FP,Kendall:ci95,paper,0.001,0.000751,0.001,recomputed value rounds to the printed value +6,E3FP,CKA:mean,paper,0.011,0.010683,0.001,recomputed value rounds to the printed value +6,E3FP,CKA:ci95,paper,0.000,0.000383,0.001,recomputed value rounds to the printed value +6,E3FP,isoR2:mean,paper,0.002,0.001507,0.001,recomputed value rounds to the printed value +6,E3FP,isoR2:ci95,paper,0.000,0.000084,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(0.1sigma):mean,paper,0.151,0.150918,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(0.1sigma):ci95,paper,0.006,0.005522,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(0.5sigma):mean,paper,0.155,0.154587,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(0.5sigma):ci95,paper,0.006,0.005622,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(1sigma):mean,paper,0.162,0.161624,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(1sigma):ci95,paper,0.006,0.005834,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(2sigma):mean,paper,0.184,0.183794,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(2sigma):ci95,paper,0.007,0.006687,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(3sigma):mean,paper,0.217,0.217036,0.001,recomputed value rounds to the printed value +6,E3FP,EJS(3sigma):ci95,paper,0.009,0.008698,0.001,recomputed value rounds to the printed value +6,E3FP,ROC_AUC:mean,paper,0.531,0.531304,0.001,recomputed value rounds to the printed value +6,E3FP,ROC_AUC:ci95,paper,0.001,0.001400,0.001,recomputed value rounds to the printed value +6,E3FP,Smoothness:mean,paper,0.847,0.846836,0.001,recomputed value rounds to the printed value +6,E3FP,Smoothness:ci95,paper,0.002,0.002126,0.001,recomputed value rounds to the printed value +6,E3FP,TS:mean,paper,0.385,0.385347,0.001,recomputed value rounds to the printed value +6,E3FP,TS:ci95,paper,0.010,0.010232,0.001,recomputed value rounds to the printed value +6,E3FP,KS:mean,paper,0.913,0.913498,0.001,recomputed value rounds to the printed value +6,E3FP,KS:ci95,paper,0.001,0.000972,0.001,recomputed value rounds to the printed value +6,FMG,Spearman:mean,paper,0.015,0.013975,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,FMG,Spearman:ci95,paper,0.004,0.000781,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,FMG,Kendall:mean,paper,0.010,0.009321,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,FMG,Kendall:ci95,paper,0.003,0.000521,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,FMG,CKA:mean,paper,0.012,0.011691,0.001,recomputed value rounds to the printed value +6,FMG,CKA:ci95,paper,0.001,0.000167,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,FMG,isoR2:mean,paper,0.001,0.000822,0.001,recomputed value rounds to the printed value +6,FMG,isoR2:ci95,paper,0.000,0.000064,0.001,recomputed value rounds to the printed value +6,FMG,EJS(0.1sigma):mean,paper,0.250,0.250204,0.001,recomputed value rounds to the printed value +6,FMG,EJS(0.1sigma):ci95,paper,0.000,0.000031,0.001,recomputed value rounds to the printed value +6,FMG,EJS(0.5sigma):mean,paper,0.253,0.252309,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,FMG,EJS(0.5sigma):ci95,paper,0.001,0.000168,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,FMG,EJS(1sigma):mean,paper,0.257,0.256173,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,FMG,EJS(1sigma):ci95,paper,0.002,0.000420,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,FMG,EJS(2sigma):mean,paper,0.269,0.266977,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,FMG,EJS(2sigma):ci95,paper,0.006,0.001189,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,FMG,EJS(3sigma):mean,paper,0.283,0.281762,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,FMG,EJS(3sigma):ci95,paper,0.016,0.002975,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,FMG,ROC_AUC:mean,paper,0.517,0.515996,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,FMG,ROC_AUC:ci95,paper,0.005,0.000930,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,FMG,Smoothness:mean,paper,0.972,0.999889,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value; published FMG Smoothness 0.972 +/- 0.002 is not attainable with the backed-up FMG embeddings: every one of the 1000 per-window values is >= 0.999 +6,FMG,Smoothness:ci95,paper,0.002,0.000007,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs; published FMG Smoothness 0.972 +/- 0.002 is not attainable with the backed-up FMG embeddings: every one of the 1000 per-window values is >= 0.999 +6,FMG,TS:mean,paper,0.583,0.582899,0.001,recomputed value rounds to the printed value +6,FMG,TS:ci95,paper,0.007,0.001634,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,FMG,KS:mean,paper,1.000,0.999827,0.001,recomputed value rounds to the printed value +6,FMG,KS:ci95,paper,0.000,0.000015,0.001,recomputed value rounds to the printed value +6,GemNet,Spearman:mean,paper,0.078,0.077526,0.001,recomputed value rounds to the printed value +6,GemNet,Spearman:ci95,paper,0.002,0.002243,0.001,recomputed value rounds to the printed value +6,GemNet,Kendall:mean,paper,0.052,0.051807,0.001,recomputed value rounds to the printed value +6,GemNet,Kendall:ci95,paper,0.002,0.001504,0.001,recomputed value rounds to the printed value +6,GemNet,CKA:mean,paper,0.016,0.016325,0.001,recomputed value rounds to the printed value +6,GemNet,CKA:ci95,paper,0.000,0.000417,0.001,recomputed value rounds to the printed value +6,GemNet,isoR2:mean,paper,0.013,0.013470,0.001,recomputed value rounds to the printed value +6,GemNet,isoR2:ci95,paper,0.001,0.000981,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(0.1sigma):mean,paper,0.252,0.251834,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(0.1sigma):ci95,paper,0.000,0.000067,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(0.5sigma):mean,paper,0.263,0.263241,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(0.5sigma):ci95,paper,0.000,0.000446,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(1sigma):mean,paper,0.286,0.285514,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(1sigma):ci95,paper,0.001,0.001190,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(2sigma):mean,paper,0.356,0.355988,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(2sigma):ci95,paper,0.004,0.003577,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(3sigma):mean,paper,0.451,0.450997,0.001,recomputed value rounds to the printed value +6,GemNet,EJS(3sigma):ci95,paper,0.007,0.007127,0.001,recomputed value rounds to the printed value +6,GemNet,ROC_AUC:mean,paper,0.592,0.591777,0.001,recomputed value rounds to the printed value +6,GemNet,ROC_AUC:ci95,paper,0.003,0.002668,0.001,recomputed value rounds to the printed value +6,GemNet,Smoothness:mean,paper,0.998,0.998238,0.001,recomputed value rounds to the printed value +6,GemNet,Smoothness:ci95,paper,0.000,0.000188,0.001,recomputed value rounds to the printed value +6,GemNet,TS:mean,paper,0.444,0.443715,0.001,recomputed value rounds to the printed value +6,GemNet,TS:ci95,paper,0.004,0.003603,0.001,recomputed value rounds to the printed value +6,GemNet,KS:mean,paper,0.998,0.998367,0.001,recomputed value rounds to the printed value +6,GemNet,KS:ci95,paper,0.000,0.000172,0.001,recomputed value rounds to the printed value +6,MACE,Spearman:mean,paper,0.236,0.236143,0.001,recomputed value rounds to the printed value +6,MACE,Spearman:ci95,paper,0.011,0.002361,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,Kendall:mean,paper,0.160,0.159662,0.001,recomputed value rounds to the printed value +6,MACE,Kendall:ci95,paper,0.008,0.001626,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,CKA:mean,paper,0.017,0.017391,0.001,recomputed value rounds to the printed value +6,MACE,CKA:ci95,paper,0.001,0.000285,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,isoR2:mean,paper,0.083,0.080723,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,MACE,isoR2:ci95,paper,0.010,0.002210,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,EJS(0.1sigma):mean,paper,0.255,0.255266,0.001,recomputed value rounds to the printed value +6,MACE,EJS(0.1sigma):ci95,paper,0.000,0.000079,0.001,recomputed value rounds to the printed value +6,MACE,EJS(0.5sigma):mean,paper,0.286,0.286281,0.001,recomputed value rounds to the printed value +6,MACE,EJS(0.5sigma):ci95,paper,0.002,0.000527,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,EJS(1sigma):mean,paper,0.350,0.349974,0.001,recomputed value rounds to the printed value +6,MACE,EJS(1sigma):ci95,paper,0.007,0.001425,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,EJS(2sigma):mean,paper,0.578,0.572063,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,MACE,EJS(2sigma):ci95,paper,0.019,0.004105,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,EJS(3sigma):mean,paper,0.845,0.837292,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,MACE,EJS(3sigma):ci95,paper,0.025,0.005381,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,ROC_AUC:mean,paper,0.764,0.760630,0.001,recomputed value does not match the printed value; MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no backed-up run reproduces this published value +6,MACE,ROC_AUC:ci95,paper,0.011,0.002222,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,Smoothness:mean,paper,0.999,0.999394,0.001,recomputed value rounds to the printed value +6,MACE,Smoothness:ci95,paper,0.000,0.000031,0.001,recomputed value rounds to the printed value +6,MACE,TS:mean,paper,0.424,0.424343,0.001,recomputed value rounds to the printed value +6,MACE,TS:ci95,paper,0.014,0.003126,0.001,recomputed value does not match the printed value; published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly 40-47 windows); no backed-up run reproduces the published MACE/FMG CIs +6,MACE,KS:mean,paper,0.999,0.999448,0.001,recomputed value rounds to the printed value +6,MACE,KS:ci95,paper,0.000,0.000022,0.001,recomputed value rounds to the printed value +6,MolAE,Spearman:mean,paper,0.039,0.038868,0.001,recomputed value rounds to the printed value +6,MolAE,Spearman:ci95,paper,0.001,0.001260,0.001,recomputed value rounds to the printed value +6,MolAE,Kendall:mean,paper,0.026,0.025930,0.001,recomputed value rounds to the printed value +6,MolAE,Kendall:ci95,paper,0.001,0.000841,0.001,recomputed value rounds to the printed value +6,MolAE,CKA:mean,paper,0.024,0.024077,0.001,recomputed value rounds to the printed value +6,MolAE,CKA:ci95,paper,0.000,0.000342,0.001,recomputed value rounds to the printed value +6,MolAE,isoR2:mean,paper,0.003,0.002824,0.001,recomputed value rounds to the printed value +6,MolAE,isoR2:ci95,paper,0.000,0.000180,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(0.1sigma):mean,paper,0.251,0.250616,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(0.1sigma):ci95,paper,0.000,0.000043,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(0.5sigma):mean,paper,0.256,0.255715,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(0.5sigma):ci95,paper,0.000,0.000266,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(1sigma):mean,paper,0.265,0.265368,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(1sigma):ci95,paper,0.001,0.000683,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(2sigma):mean,paper,0.294,0.293714,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(2sigma):ci95,paper,0.002,0.001914,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(3sigma):mean,paper,0.328,0.327981,0.001,recomputed value rounds to the printed value +6,MolAE,EJS(3sigma):ci95,paper,0.004,0.004323,0.001,recomputed value rounds to the printed value +6,MolAE,ROC_AUC:mean,paper,0.545,0.545296,0.001,recomputed value rounds to the printed value +6,MolAE,ROC_AUC:ci95,paper,0.001,0.001443,0.001,recomputed value rounds to the printed value +6,MolAE,Smoothness:mean,paper,0.996,0.996316,0.001,recomputed value rounds to the printed value +6,MolAE,Smoothness:ci95,paper,0.000,0.000125,0.001,recomputed value rounds to the printed value +6,MolAE,TS:mean,paper,0.346,0.346272,0.001,recomputed value rounds to the printed value +6,MolAE,TS:ci95,paper,0.001,0.001449,0.001,recomputed value rounds to the printed value +6,MolAE,KS:mean,paper,0.998,0.998199,0.001,recomputed value rounds to the printed value +6,MolAE,KS:ci95,paper,0.000,0.000061,0.001,recomputed value rounds to the printed value +6,MolSpectra,Spearman:mean,paper,0.023,0.022909,0.001,recomputed value rounds to the printed value +6,MolSpectra,Spearman:ci95,paper,0.001,0.001254,0.001,recomputed value rounds to the printed value +6,MolSpectra,Kendall:mean,paper,0.015,0.015282,0.001,recomputed value rounds to the printed value +6,MolSpectra,Kendall:ci95,paper,0.001,0.000836,0.001,recomputed value rounds to the printed value +6,MolSpectra,CKA:mean,paper,0.006,0.006444,0.001,recomputed value rounds to the printed value +6,MolSpectra,CKA:ci95,paper,0.000,0.000169,0.001,recomputed value rounds to the printed value +6,MolSpectra,isoR2:mean,paper,0.001,0.001481,0.001,recomputed value rounds to the printed value +6,MolSpectra,isoR2:ci95,paper,0.000,0.000174,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(0.1sigma):mean,paper,0.250,0.250276,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(0.1sigma):ci95,paper,0.000,0.000041,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(0.5sigma):mean,paper,0.253,0.252824,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(0.5sigma):ci95,paper,0.000,0.000258,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(1sigma):mean,paper,0.258,0.257560,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(1sigma):ci95,paper,0.001,0.000658,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(2sigma):mean,paper,0.271,0.270563,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(2sigma):ci95,paper,0.002,0.001780,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(3sigma):mean,paper,0.289,0.289048,0.001,recomputed value rounds to the printed value +6,MolSpectra,EJS(3sigma):ci95,paper,0.004,0.003845,0.001,recomputed value rounds to the printed value +6,MolSpectra,ROC_AUC:mean,paper,0.527,0.526507,0.001,recomputed value rounds to the printed value +6,MolSpectra,ROC_AUC:ci95,paper,0.001,0.001402,0.001,recomputed value rounds to the printed value +6,MolSpectra,Smoothness:mean,paper,0.974,0.973790,0.001,recomputed value rounds to the printed value +6,MolSpectra,Smoothness:ci95,paper,0.001,0.000667,0.001,recomputed value rounds to the printed value +6,MolSpectra,TS:mean,paper,0.409,0.408949,0.001,recomputed value rounds to the printed value +6,MolSpectra,TS:ci95,paper,0.004,0.004161,0.001,recomputed value rounds to the printed value +6,MolSpectra,KS:mean,paper,0.976,0.976430,0.001,recomputed value rounds to the printed value +6,MolSpectra,KS:ci95,paper,0.001,0.000974,0.001,recomputed value rounds to the printed value +7,all,jumps(0.1sigma):mean,paper,1839169.111,1839169.111000,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +7,all,jumps(0.1sigma):ci95,paper,34.666,34.665787,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +7,all,jumps(0.5sigma):mean,paper,1232341.449,1232341.449000,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +7,all,jumps(0.5sigma):ci95,paper,61.925,61.924894,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +7,all,jumps(1sigma):mean,paper,638505.706,638505.706000,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +7,all,jumps(1sigma):ci95,paper,197.971,197.970570,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +7,all,jumps(2sigma):mean,paper,102542.184,102542.184000,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +7,all,jumps(2sigma):ci95,paper,492.257,492.257172,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +7,all,jumps(3sigma):mean,paper,8929.385,8929.385000,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +7,all,jumps(3sigma):ci95,paper,167.468,167.468492,0.001,recomputed value rounds to the printed value; model-independent; printed with thousands separators in the paper +3,E3FP,Spearman,v2,,0.026068,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,E3FP,Kendall,v2,,0.018248,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,E3FP,CKA,v2,,0.016007,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,E3FP,isoR2,v2,,0.002812,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,E3FP,EJS,v2,,0.184661,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,E3FP,EJS_ROCAUC,v2,,0.531304,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,E3FP,KS,v2,,0.530219,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,FMG,Spearman,v2,,0.013975,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,FMG,Kendall,v2,,0.009319,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,FMG,CKA,v2,,0.009316,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,FMG,isoR2,v2,,0.000924,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,FMG,EJS,v2,,0.267582,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,FMG,EJS_ROCAUC,v2,,0.515996,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,FMG,KS,v2,,0.186257,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,GemNet,Spearman,v2,,0.077526,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,GemNet,Kendall,v2,,0.051798,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,GemNet,CKA,v2,,0.041768,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,GemNet,isoR2,v2,,0.012737,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,GemNet,EJS,v2,,0.359672,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,GemNet,EJS_ROCAUC,v2,,0.591777,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,GemNet,KS,v2,,0.159024,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MACE,Spearman,v2,,0.236143,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MACE,Kendall,v2,,0.159639,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MACE,CKA,v2,,0.175913,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MACE,isoR2,v2,,0.083043,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MACE,EJS,v2,,0.583026,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MACE,EJS_ROCAUC,v2,,0.760630,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MACE,KS,v2,,0.173223,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolAE,Spearman,v2,,0.038868,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolAE,Kendall,v2,,0.025923,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolAE,CKA,v2,,0.031395,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolAE,isoR2,v2,,0.003183,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolAE,EJS,v2,,0.295529,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolAE,EJS_ROCAUC,v2,,0.545296,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolAE,KS,v2,,0.325364,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolSpectra,Spearman,v2,,0.022909,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolSpectra,Kendall,v2,,0.015279,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolSpectra,CKA,v2,,0.013705,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolSpectra,isoR2,v2,,0.001810,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolSpectra,EJS,v2,,0.271520,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolSpectra,EJS_ROCAUC,v2,,0.526507,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,MolSpectra,KS,v2,,0.261547,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,UniMol,Spearman,v2,,0.042610,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,UniMol,Kendall,v2,,0.028420,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,UniMol,CKA,v2,,0.032049,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,UniMol,isoR2,v2,,0.003165,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,UniMol,EJS,v2,,0.303481,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +3,UniMol,EJS_ROCAUC,v2,,0.549794,0.001,v2 metric 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definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(0.1sigma):mean,v2,,0.150929,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(0.1sigma):ci95,v2,,0.005522,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(0.5sigma):mean,v2,,0.154680,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(0.5sigma):ci95,v2,,0.005623,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(1sigma):mean,v2,,0.161898,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(1sigma):ci95,v2,,0.005840,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(2sigma):mean,v2,,0.184661,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(2sigma):ci95,v2,,0.006718,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(3sigma):mean,v2,,0.219369,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,EJS(3sigma):ci95,v2,,0.008872,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,ROC_AUC:mean,v2,,0.531304,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,ROC_AUC:ci95,v2,,0.001400,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,KS:mean,v2,,0.530219,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,E3FP,KS:ci95,v2,,0.008080,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,Spearman:mean,v2,,0.013975,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,Spearman:ci95,v2,,0.000781,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,Kendall:mean,v2,,0.009319,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,Kendall:ci95,v2,,0.000520,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,CKA:mean,v2,,0.009316,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,CKA:ci95,v2,,0.000441,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,isoR2:mean,v2,,0.000924,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,isoR2:ci95,v2,,0.000065,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(0.1sigma):mean,v2,,0.250330,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(0.1sigma):ci95,v2,,0.000028,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(0.5sigma):mean,v2,,0.252497,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(0.5sigma):ci95,v2,,0.000171,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(1sigma):mean,v2,,0.256485,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(1sigma):ci95,v2,,0.000432,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(2sigma):mean,v2,,0.267582,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(2sigma):ci95,v2,,0.001228,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(3sigma):mean,v2,,0.282903,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,EJS(3sigma):ci95,v2,,0.003169,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,ROC_AUC:mean,v2,,0.515996,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,FMG,ROC_AUC:ci95,v2,,0.000930,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart 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counterpart +6,GemNet,isoR2:ci95,v2,,0.000859,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,GemNet,EJS(0.1sigma):mean,v2,,0.252056,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,GemNet,EJS(0.1sigma):ci95,v2,,0.000070,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,GemNet,EJS(0.5sigma):mean,v2,,0.263785,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,GemNet,EJS(0.5sigma):ci95,v2,,0.000468,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,GemNet,EJS(1sigma):mean,v2,,0.286809,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,GemNet,EJS(1sigma):ci95,v2,,0.001253,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,GemNet,EJS(2sigma):mean,v2,,0.359672,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart 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(docs/metrics/energy.md); no published counterpart +6,MACE,Kendall:mean,v2,,0.159639,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MACE,Kendall:ci95,v2,,0.001625,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MACE,CKA:mean,v2,,0.175913,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MACE,CKA:ci95,v2,,0.001936,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MACE,isoR2:mean,v2,,0.083043,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MACE,isoR2:ci95,v2,,0.001556,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MACE,EJS(0.1sigma):mean,v2,,0.255564,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MACE,EJS(0.1sigma):ci95,v2,,0.000084,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart 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metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,isoR2:mean,v2,,0.003183,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,isoR2:ci95,v2,,0.000162,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(0.1sigma):mean,v2,,0.250940,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(0.1sigma):ci95,v2,,0.000043,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(0.5sigma):mean,v2,,0.256201,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(0.5sigma):ci95,v2,,0.000277,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(1sigma):mean,v2,,0.266207,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(1sigma):ci95,v2,,0.000713,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(2sigma):mean,v2,,0.295529,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(2sigma):ci95,v2,,0.002003,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(3sigma):mean,v2,,0.331006,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,EJS(3sigma):ci95,v2,,0.004616,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,ROC_AUC:mean,v2,,0.545296,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,ROC_AUC:ci95,v2,,0.001443,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,KS:mean,v2,,0.325364,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolAE,KS:ci95,v2,,0.003224,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,Spearman:mean,v2,,0.022909,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,Spearman:ci95,v2,,0.001254,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,Kendall:mean,v2,,0.015279,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,Kendall:ci95,v2,,0.000836,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,CKA:mean,v2,,0.013705,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,CKA:ci95,v2,,0.000523,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,isoR2:mean,v2,,0.001810,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,isoR2:ci95,v2,,0.000164,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(0.1sigma):mean,v2,,0.250465,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(0.1sigma):ci95,v2,,0.000041,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(0.5sigma):mean,v2,,0.253109,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(0.5sigma):ci95,v2,,0.000268,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(1sigma):mean,v2,,0.258028,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(1sigma):ci95,v2,,0.000686,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(2sigma):mean,v2,,0.271520,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(2sigma):ci95,v2,,0.001857,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(3sigma):mean,v2,,0.290743,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,EJS(3sigma):ci95,v2,,0.004092,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,ROC_AUC:mean,v2,,0.526507,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,ROC_AUC:ci95,v2,,0.001402,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,KS:mean,v2,,0.261547,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +6,MolSpectra,KS:ci95,v2,,0.006749,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(0.1sigma):mean,v2,,1836048.939000,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(0.1sigma):ci95,v2,,156.743292,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(0.5sigma):mean,v2,,1218701.243000,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(0.5sigma):ci95,v2,,625.179967,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(1sigma):mean,v2,,620312.428000,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(1sigma):ci95,v2,,591.198447,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(2sigma):mean,v2,,94275.340000,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(2sigma):ci95,v2,,163.304862,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(3sigma):mean,v2,,7494.103000,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart +7,all,jumps(3sigma):ci95,v2,,105.603263,0.001,v2 metric definitions (docs/metrics/energy.md); no published counterpart diff --git a/reproduce/energy_tables_3_6_7/make_expected.py b/reproduce/energy_tables_3_6_7/make_expected.py new file mode 100644 index 0000000..39b8559 --- /dev/null +++ b/reproduce/energy_tables_3_6_7/make_expected.py @@ -0,0 +1,137 @@ +"""Build expected.csv from a reference results.csv and the printed paper values (maintainer tool). + + python make_expected.py --results out/results.csv --paper paper_values.csv --out expected.csv + +expected_value is the recomputed value rounded to 6 decimals. For variant ``paper`` each cell is +classified against the printed value (3 decimals): it rounds to the printed value, matches it only by +truncation, or differs from it. Known issues are added to ``notes``. +""" + +from __future__ import annotations + +import argparse +import math +from decimal import ROUND_DOWN, ROUND_HALF_UP, Decimal +from pathlib import Path + +import pandas as pd + +TOLERANCE = 0.001 + +T3_VS_T6 = { # Table 3 cells printed differently from the same Table 6 mean + ("MACE", "Kendall"): ("0.159", "0.160"), + ("MACE", "isoR2"): ("0.080", "0.083"), + ("FMG", "Kendall"): ("0.009", "0.010"), + ("FMG", "CKA"): ("0.011", "0.012"), + ("FMG", "TS"): ("0.582", "0.583"), + ("FMG", "KS"): ("0.999", "1.000"), + ("MolSpectra", "EJS_ROCAUC"): ("0.526", "0.527"), + ("MolSpectra", "KS"): ("0.977", "0.976"), + ("E3FP", "KS"): ("0.916", "0.913"), +} + + +def _decimals(printed: str) -> int: + return len(printed.split(".")[1]) if "." in printed else 0 + + +def classify(value: float, printed: str) -> str: + q = Decimal(1).scaleb(-_decimals(printed)) + v = Decimal(repr(value)) + if v.quantize(q, rounding=ROUND_HALF_UP) == Decimal(printed): + return "round" + if v.quantize(q, rounding=ROUND_DOWN) == Decimal(printed): + return "trunc" + return "mismatch" + + +def notes_for(table: str, model: str, metric: str, value: float, printed: str | None) -> str: + if printed is None: + return "" + status = classify(value, printed) + notes = [] + if status == "round": + notes.append("recomputed value rounds to the printed value") + elif status == "trunc": + notes.append("printed value is the 3-dp truncation (not rounding) of the recomputed value") + else: + notes.append("recomputed value does not match the printed value") + + base = metric.split(":")[0] + stat = metric.split(":")[1] if ":" in metric else "mean" + if table == "3" and (model, base) in T3_VS_T6: + t3, t6 = T3_VS_T6[(model, base)] + notes.append(f"Table 3 prints {t3} while Table 6 prints {t6} for the same mean") + if table == "3" and base == "KS" and model in ("E3FP", "MolSpectra"): + notes.append( + "the Table 3 value is carried over from the submission draft and is not produced by any backed-up run" + ) + if model in ("MACE", "FMG") and status == "mismatch": + if stat == "ci95": + notes.append( + "published MACE/FMG CIs are about 4-5x wider than this 1000-window run (consistent with roughly " + "40-47 windows); no backed-up run reproduces the published MACE/FMG CIs" + ) + else: + notes.append( + "MACE/FMG energy columns are only partially reproducible from the backed-up embeddings; no " + "backed-up run reproduces this published value" + ) + if model == "FMG" and base == "Smoothness": + notes.append( + "published FMG Smoothness 0.972 +/- 0.002 is not attainable with the backed-up FMG embeddings: " + "every one of the 1000 per-window values is >= 0.999" + ) + if table == "7": + notes.append("model-independent; printed with thousands separators in the paper") + return "; ".join(notes) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--results", type=Path, required=True) + parser.add_argument("--paper", type=Path, required=True) + parser.add_argument("--out", type=Path, required=True) + args = parser.parse_args() + + results = pd.read_csv(args.results, dtype={"table": str}) + paper = pd.read_csv(args.paper, dtype={"table": str, "paper_value": str}) + printed = {(r.table, r.model, r.metric): r.paper_value for r in paper.itertuples()} + + rows = [] + for r in results.itertuples(): + if not math.isfinite(r.value): + continue + pv = printed.get((r.table, r.model, r.metric)) if r.variant == "paper" else None + note = notes_for(r.table, r.model, r.metric, float(r.value), pv) + if r.variant != "paper": + note = f"{r.variant} metric definitions (docs/metrics/energy.md); no published counterpart" + rows.append( + { + "table": r.table, + "model": r.model, + "metric": r.metric, + "variant": r.variant, + "paper_value": pv if pv is not None else "", + "expected_value": f"{float(r.value):.6f}", + "tolerance": TOLERANCE, + "notes": note, + "_status": classify(float(r.value), pv) if pv is not None else "", + } + ) + missing = set(printed) - {(r["table"], r["model"], r["metric"]) for r in rows if r["variant"] == "paper"} + if missing: + raise SystemExit(f"printed cells without a recomputed value: {sorted(missing)[:10]}") + out = pd.DataFrame(rows) + order = {"paper": 0, "v2": 1} + out["_v"] = out["variant"].map(order) + out["_t"] = out["table"].astype(int) + out = out.sort_values(["_v", "_t"], kind="stable").drop(columns=["_v", "_t"]) + counts = out.loc[out["_status"] != "", "_status"].value_counts() + out = out.drop(columns=["_status"]) + out.to_csv(args.out, index=False) + print(f"wrote {len(out)} rows to {args.out}; paper cells: {counts.to_dict()}") + + +if __name__ == "__main__": + main() diff --git a/reproduce/energy_tables_3_6_7/paper_values.csv b/reproduce/energy_tables_3_6_7/paper_values.csv new file mode 100644 index 0000000..2b0f3a8 --- /dev/null +++ b/reproduce/energy_tables_3_6_7/paper_values.csv @@ -0,0 +1,223 @@ +table,model,metric,paper_value +3,E3FP,Spearman,0.026 +3,GemNet,Spearman,0.078 +3,MolAE,Spearman,0.039 +3,MolSpectra,Spearman,0.023 +3,UniMol,Spearman,0.043 +3,FMG,Spearman,0.015 +3,MACE,Spearman,0.236 +3,E3FP,Kendall,0.018 +3,GemNet,Kendall,0.052 +3,MolAE,Kendall,0.026 +3,MolSpectra,Kendall,0.015 +3,UniMol,Kendall,0.028 +3,FMG,Kendall,0.009 +3,MACE,Kendall,0.159 +3,E3FP,CKA,0.011 +3,GemNet,CKA,0.016 +3,MolAE,CKA,0.024 +3,MolSpectra,CKA,0.006 +3,UniMol,CKA,0.019 +3,FMG,CKA,0.011 +3,MACE,CKA,0.017 +3,E3FP,isoR2,0.002 +3,GemNet,isoR2,0.013 +3,MolAE,isoR2,0.003 +3,MolSpectra,isoR2,0.001 +3,UniMol,isoR2,0.003 +3,FMG,isoR2,0.001 +3,MACE,isoR2,0.080 +3,E3FP,EJS,0.184 +3,GemNet,EJS,0.356 +3,MolAE,EJS,0.294 +3,MolSpectra,EJS,0.271 +3,UniMol,EJS,0.301 +3,FMG,EJS,0.269 +3,MACE,EJS,0.578 +3,E3FP,EJS_ROCAUC,0.531 +3,GemNet,EJS_ROCAUC,0.592 +3,MolAE,EJS_ROCAUC,0.545 +3,MolSpectra,EJS_ROCAUC,0.526 +3,UniMol,EJS_ROCAUC,0.549 +3,FMG,EJS_ROCAUC,0.517 +3,MACE,EJS_ROCAUC,0.764 +3,E3FP,TS,0.385 +3,GemNet,TS,0.444 +3,MolAE,TS,0.346 +3,MolSpectra,TS,0.409 +3,UniMol,TS,0.356 +3,FMG,TS,0.582 +3,MACE,TS,0.424 +3,E3FP,KS,0.916 +3,GemNet,KS,0.998 +3,MolAE,KS,0.998 +3,MolSpectra,KS,0.977 +3,UniMol,KS,0.997 +3,FMG,KS,0.999 +3,MACE,KS,0.999 +6,E3FP,Spearman:mean,0.026 +6,E3FP,Spearman:ci95,0.001 +6,GemNet,Spearman:mean,0.078 +6,GemNet,Spearman:ci95,0.002 +6,MolAE,Spearman:mean,0.039 +6,MolAE,Spearman:ci95,0.001 +6,MolSpectra,Spearman:mean,0.023 +6,MolSpectra,Spearman:ci95,0.001 +6,MACE,Spearman:mean,0.236 +6,MACE,Spearman:ci95,0.011 +6,FMG,Spearman:mean,0.015 +6,FMG,Spearman:ci95,0.004 +6,E3FP,Kendall:mean,0.018 +6,E3FP,Kendall:ci95,0.001 +6,GemNet,Kendall:mean,0.052 +6,GemNet,Kendall:ci95,0.002 +6,MolAE,Kendall:mean,0.026 +6,MolAE,Kendall:ci95,0.001 +6,MolSpectra,Kendall:mean,0.015 +6,MolSpectra,Kendall:ci95,0.001 +6,MACE,Kendall:mean,0.160 +6,MACE,Kendall:ci95,0.008 +6,FMG,Kendall:mean,0.010 +6,FMG,Kendall:ci95,0.003 +6,E3FP,CKA:mean,0.011 +6,E3FP,CKA:ci95,0.000 +6,GemNet,CKA:mean,0.016 +6,GemNet,CKA:ci95,0.000 +6,MolAE,CKA:mean,0.024 +6,MolAE,CKA:ci95,0.000 +6,MolSpectra,CKA:mean,0.006 +6,MolSpectra,CKA:ci95,0.000 +6,MACE,CKA:mean,0.017 +6,MACE,CKA:ci95,0.001 +6,FMG,CKA:mean,0.012 +6,FMG,CKA:ci95,0.001 +6,E3FP,isoR2:mean,0.002 +6,E3FP,isoR2:ci95,0.000 +6,GemNet,isoR2:mean,0.013 +6,GemNet,isoR2:ci95,0.001 +6,MolAE,isoR2:mean,0.003 +6,MolAE,isoR2:ci95,0.000 +6,MolSpectra,isoR2:mean,0.001 +6,MolSpectra,isoR2:ci95,0.000 +6,MACE,isoR2:mean,0.083 +6,MACE,isoR2:ci95,0.010 +6,FMG,isoR2:mean,0.001 +6,FMG,isoR2:ci95,0.000 +6,E3FP,EJS(0.1sigma):mean,0.151 +6,E3FP,EJS(0.1sigma):ci95,0.006 +6,GemNet,EJS(0.1sigma):mean,0.252 +6,GemNet,EJS(0.1sigma):ci95,0.000 +6,MolAE,EJS(0.1sigma):mean,0.251 +6,MolAE,EJS(0.1sigma):ci95,0.000 +6,MolSpectra,EJS(0.1sigma):mean,0.250 +6,MolSpectra,EJS(0.1sigma):ci95,0.000 +6,MACE,EJS(0.1sigma):mean,0.255 +6,MACE,EJS(0.1sigma):ci95,0.000 +6,FMG,EJS(0.1sigma):mean,0.250 +6,FMG,EJS(0.1sigma):ci95,0.000 +6,E3FP,EJS(0.5sigma):mean,0.155 +6,E3FP,EJS(0.5sigma):ci95,0.006 +6,GemNet,EJS(0.5sigma):mean,0.263 +6,GemNet,EJS(0.5sigma):ci95,0.000 +6,MolAE,EJS(0.5sigma):mean,0.256 +6,MolAE,EJS(0.5sigma):ci95,0.000 +6,MolSpectra,EJS(0.5sigma):mean,0.253 +6,MolSpectra,EJS(0.5sigma):ci95,0.000 +6,MACE,EJS(0.5sigma):mean,0.286 +6,MACE,EJS(0.5sigma):ci95,0.002 +6,FMG,EJS(0.5sigma):mean,0.253 +6,FMG,EJS(0.5sigma):ci95,0.001 +6,E3FP,EJS(1sigma):mean,0.162 +6,E3FP,EJS(1sigma):ci95,0.006 +6,GemNet,EJS(1sigma):mean,0.286 +6,GemNet,EJS(1sigma):ci95,0.001 +6,MolAE,EJS(1sigma):mean,0.265 +6,MolAE,EJS(1sigma):ci95,0.001 +6,MolSpectra,EJS(1sigma):mean,0.258 +6,MolSpectra,EJS(1sigma):ci95,0.001 +6,MACE,EJS(1sigma):mean,0.350 +6,MACE,EJS(1sigma):ci95,0.007 +6,FMG,EJS(1sigma):mean,0.257 +6,FMG,EJS(1sigma):ci95,0.002 +6,E3FP,EJS(2sigma):mean,0.184 +6,E3FP,EJS(2sigma):ci95,0.007 +6,GemNet,EJS(2sigma):mean,0.356 +6,GemNet,EJS(2sigma):ci95,0.004 +6,MolAE,EJS(2sigma):mean,0.294 +6,MolAE,EJS(2sigma):ci95,0.002 +6,MolSpectra,EJS(2sigma):mean,0.271 +6,MolSpectra,EJS(2sigma):ci95,0.002 +6,MACE,EJS(2sigma):mean,0.578 +6,MACE,EJS(2sigma):ci95,0.019 +6,FMG,EJS(2sigma):mean,0.269 +6,FMG,EJS(2sigma):ci95,0.006 +6,E3FP,EJS(3sigma):mean,0.217 +6,E3FP,EJS(3sigma):ci95,0.009 +6,GemNet,EJS(3sigma):mean,0.451 +6,GemNet,EJS(3sigma):ci95,0.007 +6,MolAE,EJS(3sigma):mean,0.328 +6,MolAE,EJS(3sigma):ci95,0.004 +6,MolSpectra,EJS(3sigma):mean,0.289 +6,MolSpectra,EJS(3sigma):ci95,0.004 +6,MACE,EJS(3sigma):mean,0.845 +6,MACE,EJS(3sigma):ci95,0.025 +6,FMG,EJS(3sigma):mean,0.283 +6,FMG,EJS(3sigma):ci95,0.016 +6,E3FP,ROC_AUC:mean,0.531 +6,E3FP,ROC_AUC:ci95,0.001 +6,GemNet,ROC_AUC:mean,0.592 +6,GemNet,ROC_AUC:ci95,0.003 +6,MolAE,ROC_AUC:mean,0.545 +6,MolAE,ROC_AUC:ci95,0.001 +6,MolSpectra,ROC_AUC:mean,0.527 +6,MolSpectra,ROC_AUC:ci95,0.001 +6,MACE,ROC_AUC:mean,0.764 +6,MACE,ROC_AUC:ci95,0.011 +6,FMG,ROC_AUC:mean,0.517 +6,FMG,ROC_AUC:ci95,0.005 +6,E3FP,Smoothness:mean,0.847 +6,E3FP,Smoothness:ci95,0.002 +6,GemNet,Smoothness:mean,0.998 +6,GemNet,Smoothness:ci95,0.000 +6,MolAE,Smoothness:mean,0.996 +6,MolAE,Smoothness:ci95,0.000 +6,MolSpectra,Smoothness:mean,0.974 +6,MolSpectra,Smoothness:ci95,0.001 +6,MACE,Smoothness:mean,0.999 +6,MACE,Smoothness:ci95,0.000 +6,FMG,Smoothness:mean,0.972 +6,FMG,Smoothness:ci95,0.002 +6,E3FP,TS:mean,0.385 +6,E3FP,TS:ci95,0.010 +6,GemNet,TS:mean,0.444 +6,GemNet,TS:ci95,0.004 +6,MolAE,TS:mean,0.346 +6,MolAE,TS:ci95,0.001 +6,MolSpectra,TS:mean,0.409 +6,MolSpectra,TS:ci95,0.004 +6,MACE,TS:mean,0.424 +6,MACE,TS:ci95,0.014 +6,FMG,TS:mean,0.583 +6,FMG,TS:ci95,0.007 +6,E3FP,KS:mean,0.913 +6,E3FP,KS:ci95,0.001 +6,GemNet,KS:mean,0.998 +6,GemNet,KS:ci95,0.000 +6,MolAE,KS:mean,0.998 +6,MolAE,KS:ci95,0.000 +6,MolSpectra,KS:mean,0.976 +6,MolSpectra,KS:ci95,0.001 +6,MACE,KS:mean,0.999 +6,MACE,KS:ci95,0.000 +6,FMG,KS:mean,1.000 +6,FMG,KS:ci95,0.000 +7,all,jumps(0.1sigma):mean,1839169.111 +7,all,jumps(0.1sigma):ci95,34.666 +7,all,jumps(0.5sigma):mean,1232341.449 +7,all,jumps(0.5sigma):ci95,61.925 +7,all,jumps(1sigma):mean,638505.706 +7,all,jumps(1sigma):ci95,197.971 +7,all,jumps(2sigma):mean,102542.184 +7,all,jumps(2sigma):ci95,492.257 +7,all,jumps(3sigma):mean,8929.385 +7,all,jumps(3sigma):ci95,167.468 diff --git a/reproduce/energy_tables_3_6_7/run.sh b/reproduce/energy_tables_3_6_7/run.sh new file mode 100755 index 0000000..eb885d5 --- /dev/null +++ b/reproduce/energy_tables_3_6_7/run.sh @@ -0,0 +1,120 @@ +#!/usr/bin/env bash +# Reproduce the zero-shot energy tables of the 3DCS paper (ICLR 2026): +# Table 3 (7 models, main text), Table 6 (6 models, mean +/- 95% CI) and Table 7 (energy-jump counts). +# +# Steps: download the float64 rMD17 energies (EscheWang/3dcs, config traj_energies) and the backed-up +# trajectory embeddings (EscheWang/3dcs-embeddings, traj//), run +# `python -m three_dbench evaluate traj` per model with the published protocol, collect the table cells +# into results.csv and compare them with expected.csv. +# +# Environment variables (all optional): +# PYTHON=python OUT=/out N_JOBS=16 MODELS="e3fp gemnet molae molspectra unimol fmg mace" +# METRIC_VERSIONS="paper" (add v2 for the corrected definitions: METRIC_VERSIONS="paper v2") +# ENERGY_DATASET_DIR=... use an existing save_to_disk copy of traj_energies instead of downloading +# EMB_ROOT=... use existing embeddings: EMB_ROOT//rmd17_.{npz,pkl} +# EMB_LAYOUT=hf|nyubox nyubox = original backup directory names (molspec, FMG) +# HF_DATASET_REPO, HF_DATASET_REVISION, HF_EMBEDDINGS_REPO, HF_EMBEDDINGS_REVISION +# FORCE=1 recompute models whose summary.csv already exists +# EXTRA_ARGS="..." extra CLI options appended last (e.g. "--molecules aspirin --n-samples 2" for a smoke test) +# Runtime: about 1000 windows x 7 models x ~4.5 CPU-s per window, i.e. ~25 min with N_JOBS=24 per metric version. +set -euo pipefail + +HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO="$(cd "$HERE/../.." && pwd)" +PYTHON="${PYTHON:-python}" +OUT="${OUT:-$HERE/out}" +N_JOBS="${N_JOBS:-16}" +MODELS="${MODELS:-e3fp gemnet molae molspectra unimol fmg mace}" +METRIC_VERSIONS="${METRIC_VERSIONS:-paper}" +HF_DATASET_REPO="${HF_DATASET_REPO:-EscheWang/3dcs}" +HF_DATASET_REVISION="${HF_DATASET_REVISION:-main}" +HF_EMBEDDINGS_REPO="${HF_EMBEDDINGS_REPO:-EscheWang/3dcs-embeddings}" +HF_EMBEDDINGS_REVISION="${HF_EMBEDDINGS_REVISION:-main}" +ENERGY_DATASET_DIR="${ENERGY_DATASET_DIR:-$OUT/data/traj_energies}" +EMB_LAYOUT="${EMB_LAYOUT:-hf}" +FORCE="${FORCE:-0}" +EXTRA_ARGS="${EXTRA_ARGS:-}" +# One BLAS thread per worker: no oversubscription and reproducible float32 BLAS results. +export OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 + +mkdir -p "$OUT" + +# model id -> " " +model_spec() { + case "$1" in + e3fp) echo "E3FP e3fp -" ;; + gemnet) echo "GemNet gemnet gemnet" ;; + molae) echo "MolAE molae arr_0" ;; + molspectra) echo "MolSpectra molspec arr_0" ;; + unimol) echo "UniMol unimol arr_0" ;; + fmg) echo "FMG FMG embeddings" ;; + mace) echo "MACE mace arr_0" ;; + *) echo "unknown model $1" >&2; return 1 ;; + esac +} + +# 1) float64 energies +if [ ! -d "$ENERGY_DATASET_DIR" ]; then + echo "== downloading $HF_DATASET_REPO (traj_energies, revision $HF_DATASET_REVISION)" + "$PYTHON" - "$HF_DATASET_REPO" "$HF_DATASET_REVISION" "$ENERGY_DATASET_DIR" <<'PY' +import sys +from datasets import load_dataset +repo, revision, out = sys.argv[1:4] +ds = load_dataset(repo, name="traj_energies", split="train", revision=revision) +ds.save_to_disk(out) +print(ds) +PY +fi + +# 2) embeddings +if [ -z "${EMB_ROOT:-}" ]; then + EMB_ROOT="$OUT/embeddings/traj" + EMB_LAYOUT=hf + echo "== downloading traj embeddings for: $MODELS from $HF_EMBEDDINGS_REPO" + "$PYTHON" - "$HF_EMBEDDINGS_REPO" "$HF_EMBEDDINGS_REVISION" "$OUT/embeddings" $MODELS <<'PY' +import sys +from huggingface_hub import snapshot_download +repo, revision, local_dir, *models = sys.argv[1:] +snapshot_download(repo_id=repo, repo_type="dataset", revision=revision, local_dir=local_dir, + allow_patterns=[f"traj/{m}/rmd17_*" for m in models]) +PY +fi + +# 3) evaluate +TIMINGS="$OUT/timings.tsv" +[ -f "$TIMINGS" ] || printf "metric_version\tmodel\tseconds\tn_jobs\n" > "$TIMINGS" +for version in $METRIC_VERSIONS; do + for model in $MODELS; do + read -r name nyubox_dir key <<<"$(model_spec "$model")" + if [ "$EMB_LAYOUT" = "nyubox" ]; then emb_dir="$EMB_ROOT/$nyubox_dir"; else emb_dir="$EMB_ROOT/$model"; fi + run_dir="$OUT/runs/$version/$model" + if [ -f "$run_dir/summary.csv" ] && [ "$FORCE" != "1" ]; then + echo "== $version/$model: summary.csv exists, skipping (FORCE=1 to recompute)" + continue + fi + key_args=() + [ "$key" != "-" ] && key_args=(--embedding-key "$key") + echo "== $version/$model ($name) from $emb_dir" + start=$(date +%s) + "$PYTHON" -m three_dbench evaluate traj \ + --dataset-dir "$ENERGY_DATASET_DIR" \ + --embeddings "$emb_dir" ${key_args[@]+"${key_args[@]}"} \ + --model-name "$name" \ + --output-dir "$run_dir" \ + --window-scheme legacy --n-samples 100 --window 2000 --random-seed 2025 \ + --metric-version "$version" \ + --n-jobs "$N_JOBS" $EXTRA_ARGS + printf "%s\t%s\t%s\t%s\n" "$version" "$model" "$(( $(date +%s) - start ))" "$N_JOBS" >> "$TIMINGS" + done +done + +# 4) collect table cells +"$PYTHON" "$HERE/collect.py" --runs "$OUT/runs" --out "$OUT/results.csv" \ + --expected "$HERE/expected.csv" --expected-out "$OUT/expected_selected.csv" + +# 5) compare with expected values +if [ -f "$REPO/reproduce/compare.py" ]; then + "$PYTHON" "$REPO/reproduce/compare.py" --expected "$OUT/expected_selected.csv" --results "$OUT/results.csv" +else + echo "reproduce/compare.py not found; results written to $OUT/results.csv" >&2 +fi diff --git a/reproduce/table1_geometry/README.md b/reproduce/table1_geometry/README.md new file mode 100644 index 0000000..d99a47d --- /dev/null +++ b/reproduce/table1_geometry/README.md @@ -0,0 +1,43 @@ +# Table 1: geometry (rotation dataset) + +```bash +bash reproduce/table1_geometry/run.sh # all 16 shards +QUICK=1 bash reproduce/table1_geometry/run.sh # shard 1 only +``` + +`run.sh`: + +1. downloads the `rotation` config of `EscheWang/3dcs` to `data/hf/rotation` and the GemNet rotation + embeddings (`rotation/gemnet/rotation_conformers_{0..15}.npz`) to `data/embeddings` + (skip with `SKIP_DOWNLOAD=1` when they are already there); +2. evaluates GemNet with `--metric-version paper --replicate-offset-drift` and with + `--metric-version v2` (cosine space, `--layout by-shard`); +3. writes `results.csv` with `collect.py`; +4. compares it with `expected.csv` (or `expected_quick.csv` for `QUICK=1`). + +## Variants + +| Variant | Rows | Definition | +|---|---|---| +| `paper` | all 7 | Spearman, Kendall, CKA, isotonic R², Torsion-SP: mean over the 146,389 molecules in `sampled_molecules_seed2027.txt`; LIE@k and AS: mean over all molecules, with the embedding-offset shift of the original full run (`__offset_drift` columns). This is Table 1. | +| `paper_aligned` | LIE@k, AS | same definitions, correctly aligned embeddings | +| `v2` | all 7 | `--metric-version v2`, mean over all evaluated molecules | + +Definitions, provenance and the offset shift are described in +[docs/metrics/geometry.md](../../docs/metrics/geometry.md). + +## Files + +| File | Content | +|---|---| +| `expected.csv` | Table 1 cells for the five models. `expected_value` for `paper` rows = mean of the original per-molecule outputs (`metrics_all_0.1_1.json.gz` for the sampled rows, `metrics_sup_100.json.gz` for LIE@k and AS). Only GemNet can be recomputed; rows of the other models report `MISSING`. The GemNet `paper_aligned` and `v2` reference values need a full 16-shard run and are left empty (`NO_EXPECTED`). | +| `expected_quick.csv` | shard 1 only: `paper` rows from the original outputs restricted to shard 1; `paper_aligned` and `v2` rows are regression values computed with this code. | +| `sampled_molecules_seed2027.txt` | the 146,389 molecule keys of the original 10 % sample run, in dataset order (read from the backup output; the sampling code is not available). SHA-256 `35da6a9ea84ee1112bd99c43028269cba9035ffa913f0eaafbfba7fab83163ac`. | +| `collect.py` | per-key parquet → `results.csv` | + +## Run time + +Measured on the A100 box with 24 worker processes (shared with another 24-process job), cosine space +only: shard 1 took 129 s with `--metric-version paper --replicate-offset-drift` (including the second +pass over the 80,901 shifted molecules) and 75 s with `--metric-version v2`. A full run is expected to +take about 20–25 min per metric version, plus downloads (dataset ~7.5 GB, embeddings ~5.2 GB). diff --git a/reproduce/table1_geometry/collect.py b/reproduce/table1_geometry/collect.py new file mode 100755 index 0000000..5c886b1 --- /dev/null +++ b/reproduce/table1_geometry/collect.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python +"""Turn rotation per-key outputs into results.csv rows for Table 1. + +Table 1 of the paper combines two original runs (see docs/metrics/geometry.md): +- Spearman, Kendall, CKA, isotonic R^2 and Torsion-SP are means over the 10 % molecule sample + (``sampled_molecules_seed2027.txt``); that run used correctly aligned embeddings; +- LIE@k and AS are means over all evaluated molecules of the full run, in which the embeddings of + shards 1 and 2 were misaligned after a molecule that failed (``__offset_drift`` columns, + written by ``evaluate rotation --replicate-offset-drift``). +Variant ``paper`` reproduces the published values; variant ``paper_aligned`` gives LIE@k and AS with +correctly aligned embeddings; variant ``v2`` gives all rows (means over all evaluated molecules) +with the corrected definitions. Means exclude non-finite per-molecule values. +""" + +from __future__ import annotations + +import argparse +import csv +from pathlib import Path + +import numpy as np +import pandas as pd + +HERE = Path(__file__).resolve().parent +ROWS = [ + ("spearman", "A1_spearman", "sample"), + ("kendall", "A2_kendall", "sample"), + ("cka", "G_cka_rbf", "sample"), + ("isotonic_r2", "J_isotonic_R2", "sample"), + ("lie_k", "H_LIE@k", "all_drift"), + ("torsion_sp", "torsion_sp", "sample"), + ("as", "AS", "all_drift"), +] + + +def _mean(values: pd.Series) -> float: + v = values.to_numpy(dtype=float) + v = v[np.isfinite(v)] + return float(v.mean()) if v.size else float("nan") + + +def collect(per_key: Path, *, model: str, variant: str, table: str, space: str, sample_keys: set[str] | None): + df = pd.read_parquet(per_key) + df = df[df["space"] == space] + rows = [] + + def add(name: str, var: str, series: pd.Series) -> None: + rows.append({"table": table, "model": model, "metric": name, "variant": var, "value": f"{_mean(series):.6f}"}) + + for name, col, subset in ROWS: + if variant != "paper": + add(name, variant, df[col]) + elif subset == "sample": + add(name, "paper", df.loc[df["key"].isin(sample_keys), col]) + else: + drift_col = f"{col}__offset_drift" + if drift_col not in df.columns: + raise SystemExit(f"{per_key}: missing {drift_col}; run evaluate rotation with --replicate-offset-drift") + add(name, "paper", df[drift_col]) + add(name, "paper_aligned", df[col]) + return rows + + +def main(argv=None) -> int: + ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--run", nargs=3, action="append", metavar=("VARIANT", "MODEL", "PER_KEY_PARQUET"), required=True) + ap.add_argument("--table", default="1") + ap.add_argument("--space", default="cosine") + ap.add_argument("--molecule-list", type=Path, default=HERE / "sampled_molecules_seed2027.txt") + ap.add_argument("--out", type=Path, required=True) + args = ap.parse_args(argv) + + sample_keys = None + if any(v == "paper" for v, _, _ in args.run): + with args.molecule_list.open() as fh: + sample_keys = {line.strip() for line in fh if line.strip() and not line.startswith("#")} + rows = [] + for variant, model, path in args.run: + rows.extend( + collect( + Path(path), model=model, variant=variant, table=args.table, space=args.space, sample_keys=sample_keys + ) + ) + args.out.parent.mkdir(parents=True, exist_ok=True) + with args.out.open("w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["table", "model", "metric", "variant", "value"]) + writer.writeheader() + writer.writerows(rows) + print(f"wrote {len(rows)} rows to {args.out}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/reproduce/table1_geometry/expected.csv b/reproduce/table1_geometry/expected.csv new file mode 100644 index 0000000..3794117 --- /dev/null +++ b/reproduce/table1_geometry/expected.csv @@ -0,0 +1,45 @@ +table,model,metric,variant,paper_value,expected_value,tolerance,notes +1,e3fp,spearman,paper,0.406,0.405619,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,gemnet,spearman,paper,0.560,0.559754,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); recomputed from the published GemNet rotation embeddings" +1,molae,spearman,paper,0.640,0.639965,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,molspectra,spearman,paper,0.682,0.682009,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,unimol,spearman,paper,0.697,0.697176,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,e3fp,kendall,paper,0.272,0.272296,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,gemnet,kendall,paper,0.336,0.335809,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); recomputed from the published GemNet rotation embeddings" +1,molae,kendall,paper,0.483,0.483252,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,molspectra,kendall,paper,0.506,0.505864,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,unimol,kendall,paper,0.563,0.562824,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,e3fp,cka,paper,0.757,0.757292,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,gemnet,cka,paper,0.813,0.813476,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); recomputed from the published GemNet rotation embeddings" +1,molae,cka,paper,0.862,0.862460,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,molspectra,cka,paper,0.904,0.903560,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,unimol,cka,paper,0.889,0.889347,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,e3fp,isotonic_r2,paper,0.521,0.521339,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,gemnet,isotonic_r2,paper,0.667,0.666678,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); recomputed from the published GemNet rotation embeddings" +1,molae,isotonic_r2,paper,0.734,0.734246,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,molspectra,isotonic_r2,paper,0.770,0.770137,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,unimol,isotonic_r2,paper,0.782,0.782243,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,e3fp,lie_k,paper,0.324,0.323791,0.001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,gemnet,lie_k,paper,0.390,0.390109,0.001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); recomputed from the published GemNet rotation embeddings; requires --replicate-offset-drift (shards 1 and 2 of the original run used shifted embeddings after a failed molecule; see docs/metrics/geometry.md)" +1,molae,lie_k,paper,0.349,0.348912,0.001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied; published value includes the embedding-offset shift of shards 1-2 (see docs/metrics/geometry.md)" +1,molspectra,lie_k,paper,0.238,0.237903,0.001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied; published value includes the embedding-offset shift of shards 1-2 (see docs/metrics/geometry.md)" +1,unimol,lie_k,paper,0.306,0.305915,0.001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied; published value includes the embedding-offset shift of shards 1-2 (see docs/metrics/geometry.md)" +1,e3fp,torsion_sp,paper,0.313,0.313169,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,gemnet,torsion_sp,paper,0.420,0.420297,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); recomputed from the published GemNet rotation embeddings" +1,molae,torsion_sp,paper,0.437,0.436622,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,molspectra,torsion_sp,paper,0.508,0.508405,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,unimol,torsion_sp,paper,0.504,0.504171,0.001,"mean over the 146,389 molecules of the 10% sample run (metrics_all_0.1_1.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied" +1,e3fp,as,paper,2.757,2.757644,0.001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied; paper value truncated (2.75764)" +1,gemnet,as,paper,0.0018,0.001825,0.00001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); recomputed from the published GemNet rotation embeddings; requires --replicate-offset-drift (shards 1 and 2 of the original run used shifted embeddings after a failed molecule; see docs/metrics/geometry.md)" +1,molae,as,paper,0.0048,0.004755,0.00001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied; published value includes the embedding-offset shift of shards 1-2 (see docs/metrics/geometry.md)" +1,molspectra,as,paper,0.0214,0.021443,0.00001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied; published value includes the embedding-offset shift of shards 1-2 (see docs/metrics/geometry.md)" +1,unimol,as,paper,0.006,0.005987,0.00001,"mean over the 1,464,493 molecules of the full run (metrics_sup_100.json.gz; cosine; E3FP Tanimoto); rotation embeddings for this model are not available; row compares only if results are supplied; published value includes the embedding-offset shift of shards 1-2 (see docs/metrics/geometry.md)" +1,gemnet,lie_k,paper_aligned,,,0.001,same definitions as paper with correctly aligned embeddings; reference value pending a full 16-shard run (mean over the molecules not affected by the offset shift: 0.366238) +1,gemnet,as,paper_aligned,,,0.00001,same definitions as paper with correctly aligned embeddings; reference value pending a full 16-shard run (mean over the molecules not affected by the offset shift: 0.001166) +1,gemnet,spearman,v2,,,0.001,"metric-version v2, mean over all evaluated molecules; reference value pending a full 16-shard run" +1,gemnet,kendall,v2,,,0.001,"metric-version v2, mean over all evaluated molecules; reference value pending a full 16-shard run" +1,gemnet,cka,v2,,,0.001,"metric-version v2, mean over all evaluated molecules; reference value pending a full 16-shard run" +1,gemnet,isotonic_r2,v2,,,0.001,"metric-version v2, mean over all evaluated molecules; reference value pending a full 16-shard run" +1,gemnet,lie_k,v2,,,0.001,"metric-version v2, mean over all evaluated molecules; reference value pending a full 16-shard run" +1,gemnet,torsion_sp,v2,,,0.001,"metric-version v2, mean over all evaluated molecules; reference value pending a full 16-shard run" +1,gemnet,as,v2,,,0.001,"metric-version v2, mean over all evaluated molecules; reference value pending a full 16-shard run" diff --git a/reproduce/table1_geometry/expected_quick.csv b/reproduce/table1_geometry/expected_quick.csv new file mode 100644 index 0000000..cd051d0 --- /dev/null +++ b/reproduce/table1_geometry/expected_quick.csv @@ -0,0 +1,17 @@ +table,model,metric,variant,paper_value,expected_value,tolerance,notes +1-shard1,gemnet,spearman,paper,,0.569728,0.001,"backup 10% run, mean over its 9,116 shard-1 molecules" +1-shard1,gemnet,kendall,paper,,0.342239,0.001,"backup 10% run, mean over its 9,116 shard-1 molecules" +1-shard1,gemnet,cka,paper,,0.818822,0.001,"backup 10% run, mean over its 9,116 shard-1 molecules" +1-shard1,gemnet,isotonic_r2,paper,,0.674854,0.001,"backup 10% run, mean over its 9,116 shard-1 molecules" +1-shard1,gemnet,lie_k,paper,,0.708220,0.001,"backup full run (with its embedding-offset shift), mean over 91,481 shard-1 molecules; use --replicate-offset-drift" +1-shard1,gemnet,torsion_sp,paper,,0.427353,0.001,"backup 10% run, mean over its 9,116 shard-1 molecules" +1-shard1,gemnet,as,paper,,0.010496,0.00001,"backup full run (with its embedding-offset shift), mean over 91,481 shard-1 molecules; use --replicate-offset-drift" +1-shard1,gemnet,lie_k,paper_aligned,,0.365596,0.001,"regression value computed with this code (A100 box, 24 workers); aligned embeddings, all 91,481 evaluated shard-1 molecules" +1-shard1,gemnet,as,paper_aligned,,0.001037,0.00001,"regression value computed with this code (A100 box, 24 workers); aligned embeddings, all 91,481 evaluated shard-1 molecules" +1-shard1,gemnet,spearman,v2,,0.561983,0.001,"regression value computed with this code (A100 box, 24 workers); metric-version v2, all 91,481 evaluated shard-1 molecules" +1-shard1,gemnet,kendall,v2,,0.447778,0.001,"regression value computed with this code (A100 box, 24 workers); metric-version v2, all 91,481 evaluated shard-1 molecules" +1-shard1,gemnet,cka,v2,,0.763413,0.001,"regression value computed with this code (A100 box, 24 workers); metric-version v2, all 91,481 evaluated shard-1 molecules" +1-shard1,gemnet,isotonic_r2,v2,,0.637885,0.001,"regression value computed with this code (A100 box, 24 workers); metric-version v2, all 91,481 evaluated shard-1 molecules" +1-shard1,gemnet,lie_k,v2,,0.325691,0.001,"regression value computed with this code (A100 box, 24 workers); metric-version v2, all 91,481 evaluated shard-1 molecules" +1-shard1,gemnet,torsion_sp,v2,,0.421115,0.001,"regression value computed with this code (A100 box, 24 workers); metric-version v2, all 91,481 evaluated shard-1 molecules" +1-shard1,gemnet,as,v2,,1.814237,0.001,"regression value computed with this code (A100 box, 24 workers); metric-version v2, all 91,481 evaluated shard-1 molecules" diff --git a/reproduce/table1_geometry/run.sh b/reproduce/table1_geometry/run.sh new file mode 100755 index 0000000..9cbd7a4 --- /dev/null +++ b/reproduce/table1_geometry/run.sh @@ -0,0 +1,77 @@ +#!/usr/bin/env bash +# Reproduce Table 1 (geometry, rotation dataset) for GemNet and compare with expected.csv. +# +# bash reproduce/table1_geometry/run.sh # all 16 shards (compares with expected.csv) +# QUICK=1 bash reproduce/table1_geometry/run.sh # shard 1 only (compares with expected_quick.csv) +# +# Environment variables: +# NJOBS worker processes (default 24) +# DATA data root for downloads (default data) +# OUT output directory (default results/reproduce/table1_geometry[_quick]) +# SKIP_DOWNLOAD=1 use data already present under $DATA/hf/rotation and $DATA/embeddings/rotation/gemnet +# +# Downloads: EscheWang/3dcs config "rotation" (~7.5 GB parquet, ~25 GB after save_to_disk) and the +# GemNet rotation embeddings from EscheWang/3dcs-embeddings (16 files, ~5.2 GB). +# Measured on 24 CPU workers (A100 box, cosine space only): about 50-90 s per shard per metric +# version, so both full runs take roughly 30-50 min (QUICK=1: about 3-5 min), plus download time. +# Shard 1 is used for QUICK=1 because it contains one of the two molecules that fail and therefore +# exercises both per-shard offsets and --replicate-offset-drift. +# +# Only GemNet can be recomputed: rotation embeddings for E3FP, UniMol, MolAE and MolSpectra are +# not available, so their expected.csv rows report MISSING (their reference values come from the +# original per-molecule metric outputs). +set -euo pipefail + +HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +ROOT="$(cd "$HERE/../.." && pwd)" +NJOBS="${NJOBS:-24}" +DATA="${DATA:-data}" +QUICK="${QUICK:-0}" +PY="${PYTHON:-python}" + +if [[ "$QUICK" == "1" ]]; then + OUT="${OUT:-results/reproduce/table1_geometry_quick}" + SHARD_ARGS=(--shards 1) + TABLE="1-shard1" + EXPECTED="$HERE/expected_quick.csv" +else + OUT="${OUT:-results/reproduce/table1_geometry}" + SHARD_ARGS=() + TABLE="1" + EXPECTED="$HERE/expected.csv" +fi +mkdir -p "$OUT" + +echo "== 1/4 download dataset and embeddings" +if [[ "${SKIP_DOWNLOAD:-0}" != "1" ]]; then + $PY -m three_dbench download dataset --task rotation --out "$DATA/hf" + $PY -m three_dbench download embeddings --task rotation --models gemnet --out "$DATA/embeddings" +fi + +for VERSION in paper v2; do + echo "== 2/4 evaluate GemNet (metric-version $VERSION)" + EXTRA=() + [[ "$VERSION" == "paper" ]] && EXTRA=(--replicate-offset-drift) + $PY -m three_dbench evaluate rotation \ + --dataset-dir "$DATA/hf/rotation" \ + --embeddings "$DATA/embeddings/rotation/gemnet" \ + --layout by-shard \ + --embedding-key gemnet \ + --model-name gemnet \ + --metrics cosine \ + --metric-version "$VERSION" \ + --n-jobs "$NJOBS" \ + ${SHARD_ARGS[@]+"${SHARD_ARGS[@]}"} \ + ${EXTRA[@]+"${EXTRA[@]}"} \ + --output-dir "$OUT/gemnet_$VERSION" +done + +echo "== 3/4 collect results" +$PY "$HERE/collect.py" \ + --run paper gemnet "$OUT/gemnet_paper/gemnet_per_key.parquet" \ + --run v2 gemnet "$OUT/gemnet_v2/gemnet_per_key.parquet" \ + --table "$TABLE" \ + --out "$OUT/results.csv" + +echo "== 4/4 compare with $(basename "$EXPECTED")" +$PY "$ROOT/reproduce/compare.py" --expected "$EXPECTED" --results "$OUT/results.csv" diff --git a/reproduce/table1_geometry/sampled_molecules_seed2027.txt b/reproduce/table1_geometry/sampled_molecules_seed2027.txt new file mode 100644 index 0000000..1335329 --- /dev/null +++ b/reproduce/table1_geometry/sampled_molecules_seed2027.txt @@ -0,0 +1,146389 @@ +0-R0B0G12_16-R10B3G0_1_23 +0-R0B1G12_18-R13B3G3_0_19 +0-R0B1G12_20-R16B1G0_0_19 +0-R0B1G12_21-R9B0G0_0_22 +0-R0B1G12_23-R17B0G2_0_24 +0-R0B1G12_28-R17B5G2_0_25 +0-R0B1G12_29-R16B3G0_0_19 +0-R0B1G12_30-R16B4G0_0_24 +0-R0B1G12_31-R11B3G0_0_21 +0-R0B1G12_34-R13B2G3_0_20 +0-R0B1G12_37-R2B1G4_0_23 +0-R0B1G12_38-R5B2G8_0_24 +0-R0B1G12_7-R5B1G8_0_19 +0-R0B1G12_7-R6B3G10_0_19 +0-R0B1G12_8-R3B2G0_0_19 +0-R0B1G4_14-R18B2G0_0_25 +0-R0B1G4_15-R15B1G2_0_19 +0-R0B1G4_18-R17B3G2_0_19 +0-R0B1G4_25-R16B3G0_0_21 +0-R0B1G4_31-R0B4G6_0_21 +0-R0B1G4_33-R15B2G2_0_19 +0-R0B1G4_34-R3B1G0_0_23 +0-R0B1G4_6-R14B2G0_0_22 +0-R0B1G6_12-R17B1G2_0_19 +0-R0B1G6_16-R3B0G0_0_21 +0-R0B1G6_2-R2B2G6_0_22 +0-R0B1G6_2-R7B0G14_0_22 +0-R0B1G6_22-R11B3G0_0_19 +0-R0B1G6_25-R16B3G0_0_21 +0-R0B1G6_25-R8B3G15_0_21 +0-R0B1G6_34-R17B2G0_0_20 +0-R0B1G6_35-R11B2G0_0_22 +0-R0B1G6_4-R0B0G6_0_22 +0-R0B1G6_5-R2B2G4_0_19 +0-R0B1G6_6-R12B0G0_0_22 +0-R0B1G6_8-R13B0G3_0_23 +0-R0B1G6_9-R3B0G0_0_24 +0-R0B2G12_17-R0B3G6_0_19 +0-R0B2G12_19-R18B3G0_0_19 +0-R0B2G12_2-R15B0G2_0_22 +0-R0B2G12_21-R0B2G12_0_19 +0-R0B2G12_23-R0B4G12_0_19 +0-R0B2G12_26-R13B1G3_0_19 +0-R0B2G12_28-R18B4G0_0_22 +0-R0B2G12_29-R0B3G6_0_19 +0-R0B2G12_29-R10B3G0_0_19 +0-R0B2G12_3-R13B4G3_0_19 +0-R0B2G12_3-R17B0G0_0_23 +0-R0B2G12_30-R9B4G0_0_19 +0-R0B2G12_31-R17B3G2_0_21 +0-R0B2G12_37-R15B3G2_0_20 +0-R0B2G4_16-R17B0G2_0_21 +0-R0B2G4_17-R10B2G0_0_19 +0-R0B2G4_2-R2B2G4_0_22 +0-R0B2G4_21-R15B2G2_0_19 +0-R0B2G4_23-R14B4G0_0_19 +0-R0B2G4_26-R18B1G0_0_19 +0-R0B2G4_28-R6B4G10_0_22 +0-R0B2G4_29-R16B3G0_0_19 +0-R0B2G4_3-R3B3G0_0_19 +0-R0B2G4_32-R17B4G0_0_19 +0-R0B2G4_37-R9B2G0_0_20 +0-R0B2G6_15-R2B3G6_0_19 +0-R0B2G6_2-R6B0G10_0_22 +0-R0B2G6_2-R6B0G10_0_25 +0-R0B2G6_21-R2B2G4_0_19 +0-R0B2G6_3-R3B3G0_0_23 +0-R0B2G6_34-R1B2G1_0_20 +0-R0B2G6_37-R1B3G1_0_20 +0-R0B2G6_4-R13B3G3_0_19 +0-R0B2G6_9-R0B4G6_0_19 +0-R0B3G12_4-R15B3G2_0_22 +0-R0B3G4_4-R14B3G0_0_19 +0-R0B3G4_4-R9B3G0_0_22 +0-R0B3G6_2-R6B2G10_0_19 +0-R0B3G6_3-R10B4G0_0_19 +0-R0B3G6_4-R3B3G0_0_22 +0-R10B1G0_11-R12B3G0_0_7 +0-R10B1G0_17-R4B0G1_0_13 +0-R10B1G0_2-R1B2G1_0_10 +0-R10B1G0_24-R12B3G0_0_11 +0-R10B1G0_24-R1B3G1_0_7 +0-R10B1G0_30-R10B3G0_0_7 +0-R10B1G0_34-R16B3G0_0_8 +0-R10B1G0_34-R6B3G10_0_8 +0-R10B1G0_35-R13B1G3_0_7 +0-R10B1G0_38-R8B1G15_0_12 +0-R10B1G0_4-R17B3G0_0_10 +0-R10B2G0_17-R18B2G0_0_7 +0-R10B2G0_18-R18B3G0_0_7 +0-R10B2G0_19-R6B3G10_0_7 +0-R10B2G0_23-R13B4G3_0_7 +0-R10B2G0_23-R17B3G2_0_7 +0-R10B2G0_34-R18B2G0_0_8 +0-R10B2G0_34-R7B3G14_0_8 +0-R10B2G0_7-R6B1G10_0_7 +0-R10B3G0_1-R4B3G1_0_7 +0-R11B1G0_14-R0B0G6_0_15 +0-R11B1G0_18-R15B0G2_0_12 +0-R11B1G0_19-R5B0G8_0_12 +0-R11B1G0_2-R0B2G4_0_12 +0-R11B1G0_20-R0B0G4_0_12 +0-R11B1G0_30-R2B4G4_0_9 +0-R11B1G0_31-R2B4G6_0_14 +0-R11B1G0_33-R14B2G0_0_12 +0-R11B1G0_39-R12B1G0_0_13 +0-R11B1G0_39-R7B1G14_0_13 +0-R11B1G0_5-R14B1G0_0_9 +0-R11B1G0_6-R6B0G10_0_12 +0-R11B1G0_9-R13B3G3_0_9 +0-R11B2G0_1-R17B3G2_0_9 +0-R11B2G0_2-R11B3G0_0_9 +0-R11B2G0_23-R18B3G0_0_9 +0-R11B2G0_29-R2B3G4_0_9 +0-R11B2G0_3-R13B3G3_0_9 +0-R11B2G0_34-R13B2G3_0_10 +0-R11B2G0_7-R5B1G8_0_9 +0-R11B3G0_2-R0B0G6_0_12 +0-R11B3G0_3-R17B0G0_0_13 +0-R12B1G0_15-R5B3G8_0_7 +0-R12B1G0_2-R17B0G0_0_10 +0-R12B1G0_2-R5B2G8_0_10 +0-R12B1G0_20-R1B2G1_0_7 +0-R12B1G0_26-R0B1G6_0_7 +0-R12B1G0_30-R1B4G1_0_12 +0-R12B1G0_30-R8B0G15_0_12 +0-R12B1G0_33-R17B0G0_0_10 +0-R12B1G0_38-R16B2G0_0_12 +0-R12B1G0_39-R12B4G0_0_8 +0-R12B1G0_39-R13B1G3_0_11 +0-R12B1G0_5-R16B2G0_0_7 +0-R12B1G0_5-R7B2G14_0_7 +0-R12B1G0_6-R17B2G2_0_7 +0-R12B1G0_7-R18B0G0_0_10 +0-R12B1G0_9-R17B4G0_0_7 +0-R12B1G0_9-R2B4G4_0_7 +0-R12B2G0_14-R14B2G0_0_7 +0-R12B2G0_17-R7B3G14_0_7 +0-R12B2G0_2-R1B1G1_0_13 +0-R12B2G0_25-R17B3G0_0_9 +0-R12B2G0_3-R10B0G0_0_11 +0-R12B2G0_31-R16B3G0_0_9 +0-R12B2G0_33-R5B2G8_0_7 +0-R12B2G0_38-R10B1G0_0_12 +0-R12B2G0_4-R18B3G0_0_7 +0-R12B3G0_1-R4B3G1_0_7 +0-R12B3G0_2-R3B2G0_0_7 +0-R12B3G0_2-R8B3G15_0_7 +0-R13B1G3_14-R15B0G2_0_15 +0-R13B1G3_18-R7B1G14_0_9 +0-R13B1G3_23-R12B4G0_0_14 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+9-R4B3G1_20-R0B2G6_0_18 +9-R4B3G1_21-R0B2G12_0_18 +9-R4B3G1_22-R4B0G1_0_21 +9-R4B3G1_29-R0B7G4_0_23 +9-R4B3G1_32-R1B4G1_0_18 +9-R4B3G1_33-R0B0G12_0_21 +9-R4B3G1_33-R3B1G0_0_18 +9-R4B3G1_38-R11B1G0_0_23 +9-R4B3G1_38-R4B1G1_0_23 +9-R4B3G1_39-R17B1G0_0_22 +9-R4B4G1_15-R6B3G10_0_18 +9-R4B4G1_16-R10B3G0_0_20 +9-R4B4G1_18-R1B1G1_0_18 +9-R4B4G1_30-R7B4G14_0_18 +9-R4B4G1_37-R0B3G6_0_19 +9-R4B4G1_39-R2B2G4_0_19 +9-R5B0G8_16-R0B3G6_3_26 +9-R5B3G8_15-R15B0G2_0_25 +9-R5B3G8_16-R12B0G0_0_24 +9-R5B3G8_19-R1B0G1_0_25 +9-R5B3G8_19-R5B1G8_0_22 +9-R5B3G8_19-R5B3G8_0_22 +9-R5B3G8_21-R7B1G14_0_22 +9-R5B3G8_22-R3B3G0_0_22 +9-R5B3G8_26-R9B1G0_0_22 +9-R5B3G8_29-R11B3G0_0_22 +9-R5B3G8_29-R16B0G0_0_27 +9-R5B3G8_31-R4B2G1_0_27 +9-R5B3G8_32-R0B3G12_0_22 +9-R5B3G8_33-R3B0G0_0_25 +9-R5B3G8_33-R8B2G15_0_25 +9-R5B3G8_37-R0B1G6_0_26 +9-R5B3G8_37-R13B3G3_0_23 +9-R5B3G8_39-R18B4G0_0_23 +9-R5B4G8_18-R12B1G0_0_22 +9-R5B4G8_24-R7B2G14_0_22 +9-R5B4G8_28-R9B5G0_0_25 +9-R5B4G8_36-R3B3G0_0_23 +9-R5B4G8_37-R13B3G3_0_23 +9-R5B4G8_38-R11B1G0_0_27 +9-R5B4G8_38-R2B1G4_0_27 +9-R6B3G10_12-R10B1G0_0_26 +9-R6B3G10_14-R14B2G0_0_32 +9-R6B3G10_16-R17B0G0_0_28 +9-R6B3G10_18-R7B1G14_0_26 +9-R6B3G10_24-R14B3G0_0_26 +9-R6B3G10_32-R6B4G10_0_26 +9-R6B3G10_33-R11B2G0_0_26 +9-R6B3G10_35-R2B1G6_0_26 +9-R6B3G10_35-R4B2G1_0_26 +9-R6B4G10_22-R2B3G6_0_26 +9-R6B4G10_30-R16B4G0_0_26 +9-R6B4G10_32-R3B3G0_0_26 +9-R6B4G10_33-R2B1G6_0_26 +9-R6B4G10_34-R5B3G8_0_27 +9-R7B3G14_14-R13B2G3_0_34 +9-R7B3G14_16-R13B0G3_0_36 +9-R7B3G14_35-R8B0G15_0_37 +9-R7B3G14_36-R15B3G2_0_35 +9-R7B3G14_37-R7B3G14_0_35 +9-R7B4G14_11-R11B3G0_0_34 +9-R7B4G14_19-R5B1G8_0_34 +9-R7B4G14_33-R17B1G0_0_34 +9-R8B3G15_14-R13B2G3_0_42 +9-R8B3G15_16-R4B3G1_0_38 +9-R8B3G15_19-R6B3G10_0_36 +9-R8B3G15_20-R1B1G1_0_36 +9-R8B3G15_21-R10B0G0_0_39 +9-R8B3G15_22-R11B0G0_0_39 +9-R8B3G15_23-R10B4G0_0_36 +9-R8B3G15_27-R1B3G1_0_38 +9-R8B3G15_28-R2B5G6_0_39 +9-R8B3G15_31-R17B3G2_0_38 +9-R8B3G15_31-R5B2G8_0_41 +9-R8B3G15_33-R4B0G1_0_39 +9-R8B3G15_34-R14B2G0_0_37 +9-R8B4G15_17-R8B2G15_0_36 +9-R8B4G15_20-R10B1G0_0_36 +9-R8B4G15_20-R14B2G0_0_36 +9-R8B4G15_30-R0B4G4_0_36 +9-R8B4G15_31-R13B3G3_0_38 +9-R9B3G0_16-R2B3G6_0_10 +9-R9B3G0_23-R6B4G10_0_13 +9-R9B3G0_24-R14B3G0_0_12 +9-R9B3G0_29-R14B7G0_0_13 +9-R9B3G0_32-R13B4G3_0_8 +9-R9B3G0_34-R18B2G0_0_9 +9-R9B3G0_39-R16B1G0_0_12 +9-R9B4G0_20-R11B1G0_0_8 +9-R9B4G0_22-R1B1G1_0_8 +9-R9B4G0_31-R17B4G2_0_10 +9-R9B4G0_32-R3B4G0_0_8 +9-R9B4G0_33-R17B2G0_0_8 +9-R9B4G0_37-R9B3G0_0_9 diff --git a/reproduce/table2_chirality/README.md b/reproduce/table2_chirality/README.md new file mode 100644 index 0000000..2435e7b --- /dev/null +++ b/reproduce/table2_chirality/README.md @@ -0,0 +1,55 @@ +# Table 2: zero-shot chirality + +This directory recomputes Table 2 of the paper from the published embeddings. + +```bash +pip install -e . +bash reproduce/table2_chirality/run.sh # all 7 models x 4 variants +VARIANTS=euclidean N_JOBS=16 bash reproduce/table2_chirality/run.sh # published protocol only +``` + +`run.sh` runs four steps: + +1. It saves the `chirality` config of `EscheWang/3dcs` to `data/hf/chirality`. +2. It downloads the seven files listed in `models.csv` from `EscheWang/3dcs-embeddings` into + `data/embeddings/` (about 382 MB) and checks their SHA-256. +3. It runs `python -m three_dbench evaluate chirality` for each model and variant. +4. It writes `results/reproduce/table2_chirality/results.csv` and compares it with `expected.csv` + using `reproduce/compare.py`. + +To use local copies of the files, set `DATASET_DIR`, `EMB_ROOT` (a directory laid out as +`chirality//`) and `SKIP_DOWNLOAD=1`. The header of `run.sh` lists all variables. + +| Variant | CLI options | In the paper? | +|---|---|---| +| `euclidean` | `--distance euclidean --metric-version paper` (the defaults) | yes, this is Table 2 | +| `cosine` | `--distance cosine --metric-version paper` | no | +| `v2_euclidean` | `--distance euclidean --metric-version v2` | no | +| `v2_cosine` | `--distance cosine --metric-version v2` | no | + +All variants scan the best-k silhouette up to `k = n - 1` (`--unsup-kmax n-1`, the default). + +## What to expect + +- **`euclidean` against the paper.** + - All published cells are within 0.001 of `expected_value`, apart from GemNet SCI. The paper + prints +0.015; the computed value is −0.015151. + - The printed table mixes rounding and truncation. UniMol ES-AUC is 0.622998 and is printed as 0.622. + - E3FP Hopkins is "–" in the paper, because Hopkins is not defined for fingerprints. That cell is not + listed in `expected.csv`. +- **`euclidean` against the Sept-2025 outputs** (`en_sep_results`, in the embeddings repo under + `results/chirality/`). These outputs exist for E3FP, GemNet, MolAE, MolSpectra and UniMol. + - ES-AUC, NN@1-Acc, SCI, Hopkins and DBI are bit-identical for every molecule. + - SCI_unsup is bit-identical for E3FP, MolAE and MolSpectra. + - GemNet and UniMol differ by about 2e-6, because KMeans local optima depend on the BLAS kernel. +- **FMG and MACE** have no original metric output. Their values are recomputed from the published + embeddings. +- **Other variants.** The `cosine` and `v2_*` rows are not in the paper. They are given so that the + alternative definitions can be checked. See `docs/metrics/chirality.md` for the definitions and the + reasons behind them. + +## Run time + +On 22 processes of an AMD EPYC 7513, each model and variant takes about 40–65 s for E3FP and 1–3 min +for the other models, so all 28 runs take roughly 40–60 min. With `N_JOBS=1`, expect roughly 10–20 +times longer. diff --git a/reproduce/table2_chirality/expected.csv b/reproduce/table2_chirality/expected.csv new file mode 100644 index 0000000..79dabcd --- /dev/null +++ b/reproduce/table2_chirality/expected.csv @@ -0,0 +1,137 @@ +table,model,metric,variant,paper_value,expected_value,tolerance,notes +table2,e3fp,ES-AUC,euclidean,0.486,0.485935,0.001,"paper value = round(x, 3); |expected-paper|=0.000065; bit-identical to backup en_sep_results/summary.csv (Sep 2025)" +table2,e3fp,NN@1-Acc,euclidean,0.178,0.177939,0.001,"paper value = round(x, 3); |expected-paper|=0.000061; bit-identical to backup en_sep_results/summary.csv (Sep 2025)" +table2,e3fp,SCI,euclidean,-0.012,-0.012543,0.001,paper value = truncation of x; |expected-paper|=0.000543; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,e3fp,SCI_unsup,euclidean,0.033,0.033825,0.001,paper value = truncation of x; |expected-paper|=0.000825; bit-identical to backup en_sep_results/summary.csv (Sep 2025); best-k silhouette scans k = 2..n-1 (release 0.1.0 capped k at 10) +table2,gemnet,ES-AUC,euclidean,0.577,0.577410,0.001,round and truncation agree; |expected-paper|=0.000410; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,gemnet,NN@1-Acc,euclidean,0.292,0.291641,0.001,"paper value = round(x, 3); |expected-paper|=0.000359; bit-identical to backup en_sep_results/summary.csv (Sep 2025)" +table2,gemnet,Hopkins,euclidean,0.593,0.592812,0.001,"paper value = round(x, 3); |expected-paper|=0.000188; bit-identical to backup en_sep_results/summary.csv (Sep 2025)" +table2,gemnet,SCI,euclidean,0.015,-0.015151,0.001,paper prints +0.015; the computed value and the Sep-2025 output are -0.0152 (sign typo in the paper); bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,gemnet,SCI_unsup,euclidean,0.272,0.271961,0.001,"paper value = round(x, 3); |expected-paper|=0.000039; backup en_sep_results/summary.csv = 0.271958887 (diff +2.41e-06; KMeans local optimum is BLAS-kernel dependent); best-k silhouette scans k = 2..n-1 (release 0.1.0 capped k at 10)" +table2,molae,ES-AUC,euclidean,0.782,0.782544,0.001,paper value = truncation of x; |expected-paper|=0.000544; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,molae,NN@1-Acc,euclidean,0.497,0.497546,0.001,paper value = truncation of x; |expected-paper|=0.000546; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,molae,Hopkins,euclidean,0.602,0.602659,0.001,paper value = truncation of x; |expected-paper|=0.000659; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,molae,SCI,euclidean,0.115,0.115274,0.001,round and truncation agree; |expected-paper|=0.000274; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,molae,SCI_unsup,euclidean,0.247,0.247099,0.001,round and truncation agree; |expected-paper|=0.000099; bit-identical to backup en_sep_results/summary.csv (Sep 2025); best-k silhouette scans k = 2..n-1 (release 0.1.0 capped k at 10) +table2,molspectra,ES-AUC,euclidean,0.545,0.544744,0.001,"paper value = round(x, 3); |expected-paper|=0.000256; bit-identical to backup en_sep_results/summary.csv (Sep 2025)" +table2,molspectra,NN@1-Acc,euclidean,0.235,0.235250,0.001,round and truncation agree; |expected-paper|=0.000250; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,molspectra,Hopkins,euclidean,0.533,0.532642,0.001,"paper value = round(x, 3); |expected-paper|=0.000358; bit-identical to backup en_sep_results/summary.csv (Sep 2025)" +table2,molspectra,SCI,euclidean,-0.020,-0.020326,0.001,round and truncation agree; |expected-paper|=0.000326; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,molspectra,SCI_unsup,euclidean,0.127,0.127115,0.001,round and truncation agree; |expected-paper|=0.000115; bit-identical to backup en_sep_results/summary.csv (Sep 2025); best-k silhouette scans k = 2..n-1 (release 0.1.0 capped k at 10) +table2,unimol,ES-AUC,euclidean,0.622,0.622998,0.001,paper value = truncation of x; |expected-paper|=0.000998; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,unimol,NN@1-Acc,euclidean,0.339,0.339582,0.001,paper value = truncation of x; |expected-paper|=0.000582; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,unimol,Hopkins,euclidean,0.559,0.559572,0.001,paper value = truncation of x; |expected-paper|=0.000572; bit-identical to backup en_sep_results/summary.csv (Sep 2025) +table2,unimol,SCI,euclidean,0.012,0.011623,0.001,"paper value = round(x, 3); |expected-paper|=0.000377; bit-identical to backup en_sep_results/summary.csv (Sep 2025)" +table2,unimol,SCI_unsup,euclidean,0.152,0.152108,0.001,round and truncation agree; |expected-paper|=0.000108; backup en_sep_results/summary.csv = 0.152106160 (diff +1.98e-06; KMeans local optimum is BLAS-kernel dependent); best-k silhouette scans k = 2..n-1 (release 0.1.0 capped k at 10) +table2,fmg,ES-AUC,euclidean,0.706,0.705652,0.001,"paper value = round(x, 3); |expected-paper|=0.000348; no original metric output in the backup; recomputed from the published embedding" +table2,fmg,NN@1-Acc,euclidean,0.412,0.411765,0.001,"paper value = round(x, 3); |expected-paper|=0.000235; no original metric output in the backup; recomputed from the published embedding" +table2,fmg,Hopkins,euclidean,0.752,0.751741,0.001,"paper value = round(x, 3); |expected-paper|=0.000259; no original metric output in the backup; recomputed from the published embedding" +table2,fmg,SCI,euclidean,0.117,0.117174,0.001,round and truncation agree; |expected-paper|=0.000174; no original metric output in the backup; recomputed from the published embedding +table2,fmg,SCI_unsup,euclidean,0.509,0.509364,0.001,round and truncation agree; |expected-paper|=0.000364; no original metric output in the backup; recomputed from the published embedding; best-k silhouette scans k = 2..n-1 (release 0.1.0 capped k at 10) +table2,mace,ES-AUC,euclidean,0.485,0.485611,0.001,paper value = truncation of x; |expected-paper|=0.000611; no original metric output in the backup; recomputed from the published embedding +table2,mace,NN@1-Acc,euclidean,0.199,0.199113,0.001,round and truncation agree; |expected-paper|=0.000113; no original metric output in the backup; recomputed from the published embedding +table2,mace,Hopkins,euclidean,0.637,0.637379,0.001,round and truncation agree; |expected-paper|=0.000379; no original metric output in the backup; recomputed from the published embedding +table2,mace,SCI,euclidean,-0.094,-0.093877,0.001,"paper value = round(x, 3); |expected-paper|=0.000123; no original metric output in the backup; recomputed from the published embedding" +table2,mace,SCI_unsup,euclidean,0.369,0.369336,0.001,round and truncation agree; |expected-paper|=0.000336; no original metric output in the backup; recomputed from the published embedding; best-k silhouette scans k = 2..n-1 (release 0.1.0 capped k at 10) +table2,e3fp,ES-AUC,cosine,,0.485935,0.001,"not in the paper; paper definitions, --distance cosine; fingerprints always use Tanimoto distance" +table2,e3fp,NN@1-Acc,cosine,,0.177939,0.001,"not in the paper; paper definitions, --distance cosine; fingerprints always use Tanimoto distance" +table2,e3fp,SCI,cosine,,-0.012543,0.001,"not in the paper; paper definitions, --distance cosine; fingerprints always use Tanimoto distance" +table2,e3fp,SCI_unsup,cosine,,0.033825,0.001,"not in the paper; paper definitions, --distance cosine; fingerprints always use Tanimoto distance" +table2,gemnet,ES-AUC,cosine,,0.601837,0.001,"not in the paper; paper definitions, --distance cosine" +table2,gemnet,NN@1-Acc,cosine,,0.312251,0.001,"not in the paper; paper definitions, --distance cosine" +table2,gemnet,Hopkins,cosine,,0.592812,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,gemnet,SCI,cosine,,0.004663,0.001,"not in the paper; paper definitions, --distance cosine" +table2,gemnet,SCI_unsup,cosine,,0.271961,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,molae,ES-AUC,cosine,,0.781942,0.001,"not in the paper; paper definitions, --distance cosine" +table2,molae,NN@1-Acc,cosine,,0.497357,0.001,"not in the paper; paper definitions, --distance cosine" +table2,molae,Hopkins,cosine,,0.602659,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,molae,SCI,cosine,,0.189480,0.001,"not in the paper; paper definitions, --distance cosine" +table2,molae,SCI_unsup,cosine,,0.247099,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,molspectra,ES-AUC,cosine,,0.543114,0.001,"not in the paper; paper definitions, --distance cosine" +table2,molspectra,NN@1-Acc,cosine,,0.235108,0.001,"not in the paper; paper definitions, --distance cosine" +table2,molspectra,Hopkins,cosine,,0.532642,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,molspectra,SCI,cosine,,-0.037315,0.001,"not in the paper; paper definitions, --distance cosine" +table2,molspectra,SCI_unsup,cosine,,0.127115,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,unimol,ES-AUC,cosine,,0.622620,0.001,"not in the paper; paper definitions, --distance cosine" +table2,unimol,NN@1-Acc,cosine,,0.338899,0.001,"not in the paper; paper definitions, --distance cosine" +table2,unimol,Hopkins,cosine,,0.559572,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,unimol,SCI,cosine,,0.021010,0.001,"not in the paper; paper definitions, --distance cosine" +table2,unimol,SCI_unsup,cosine,,0.152108,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,fmg,ES-AUC,cosine,,0.712555,0.001,"not in the paper; paper definitions, --distance cosine" +table2,fmg,NN@1-Acc,cosine,,0.422004,0.001,"not in the paper; paper definitions, --distance cosine" +table2,fmg,Hopkins,cosine,,0.751741,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,fmg,SCI,cosine,,0.158030,0.001,"not in the paper; paper definitions, --distance cosine" +table2,fmg,SCI_unsup,cosine,,0.509364,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,mace,ES-AUC,cosine,,0.484971,0.001,"not in the paper; paper definitions, --distance cosine" +table2,mace,NN@1-Acc,cosine,,0.200708,0.001,"not in the paper; paper definitions, --distance cosine" +table2,mace,Hopkins,cosine,,0.637379,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,mace,SCI,cosine,,-0.151823,0.001,"not in the paper; paper definitions, --distance cosine" +table2,mace,SCI_unsup,cosine,,0.369336,0.001,"not in the paper; paper definitions, --distance cosine; paper definitions compute this on raw vectors (Euclidean), independent of --distance" +table2,e3fp,ES-AUC,v2_euclidean,,0.485935,0.001,"not in the paper; v2 definitions, --distance euclidean; fingerprints always use Tanimoto distance" +table2,e3fp,NN@1-Acc,v2_euclidean,,0.256400,0.001,"not in the paper; v2 definitions, --distance euclidean; fingerprints always use Tanimoto distance" +table2,e3fp,SCI,v2_euclidean,,-0.012543,0.001,"not in the paper; v2 definitions, --distance euclidean; fingerprints always use Tanimoto distance" +table2,e3fp,SCI_unsup,v2_euclidean,,0.033825,0.001,"not in the paper; v2 definitions, --distance euclidean; fingerprints always use Tanimoto distance" +table2,gemnet,ES-AUC,v2_euclidean,,0.577410,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,gemnet,NN@1-Acc,v2_euclidean,,0.415357,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,gemnet,Hopkins,v2_euclidean,,0.592823,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,gemnet,SCI,v2_euclidean,,-0.015151,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,gemnet,SCI_unsup,v2_euclidean,,0.271961,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molae,ES-AUC,v2_euclidean,,0.782544,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molae,NN@1-Acc,v2_euclidean,,0.709640,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molae,Hopkins,v2_euclidean,,0.602748,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molae,SCI,v2_euclidean,,0.115274,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molae,SCI_unsup,v2_euclidean,,0.247099,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molspectra,ES-AUC,v2_euclidean,,0.544744,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molspectra,NN@1-Acc,v2_euclidean,,0.337786,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molspectra,Hopkins,v2_euclidean,,0.532736,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molspectra,SCI,v2_euclidean,,-0.020326,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,molspectra,SCI_unsup,v2_euclidean,,0.127115,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,unimol,ES-AUC,v2_euclidean,,0.622998,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,unimol,NN@1-Acc,v2_euclidean,,0.481977,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,unimol,Hopkins,v2_euclidean,,0.559674,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,unimol,SCI,v2_euclidean,,0.011623,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,unimol,SCI_unsup,v2_euclidean,,0.152108,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,fmg,ES-AUC,v2_euclidean,,0.705652,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,fmg,NN@1-Acc,v2_euclidean,,0.587976,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,fmg,Hopkins,v2_euclidean,,0.752106,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,fmg,SCI,v2_euclidean,,0.117174,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,fmg,SCI_unsup,v2_euclidean,,0.509364,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,mace,ES-AUC,v2_euclidean,,0.485611,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,mace,NN@1-Acc,v2_euclidean,,0.283468,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,mace,Hopkins,v2_euclidean,,0.637289,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,mace,SCI,v2_euclidean,,-0.093877,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,mace,SCI_unsup,v2_euclidean,,0.369336,0.001,"not in the paper; v2 definitions, --distance euclidean" +table2,e3fp,ES-AUC,v2_cosine,,0.485935,0.001,"not in the paper; v2 definitions, --distance cosine; fingerprints always use Tanimoto distance" +table2,e3fp,NN@1-Acc,v2_cosine,,0.256400,0.001,"not in the paper; v2 definitions, --distance cosine; fingerprints always use Tanimoto distance" +table2,e3fp,SCI,v2_cosine,,-0.012543,0.001,"not in the paper; v2 definitions, --distance cosine; fingerprints always use Tanimoto distance" +table2,e3fp,SCI_unsup,v2_cosine,,0.033825,0.001,"not in the paper; v2 definitions, --distance cosine; fingerprints always use Tanimoto distance" +table2,gemnet,ES-AUC,v2_cosine,,0.601837,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,gemnet,NN@1-Acc,v2_cosine,,0.444804,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,gemnet,Hopkins,v2_cosine,,0.577979,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,gemnet,SCI,v2_cosine,,0.004663,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,gemnet,SCI_unsup,v2_cosine,,0.380451,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molae,ES-AUC,v2_cosine,,0.781942,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molae,NN@1-Acc,v2_cosine,,0.709405,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molae,Hopkins,v2_cosine,,0.602690,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molae,SCI,v2_cosine,,0.189480,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molae,SCI_unsup,v2_cosine,,0.408498,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molspectra,ES-AUC,v2_cosine,,0.543114,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molspectra,NN@1-Acc,v2_cosine,,0.337668,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molspectra,Hopkins,v2_cosine,,0.533677,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molspectra,SCI,v2_cosine,,-0.037315,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,molspectra,SCI_unsup,v2_cosine,,0.232817,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,unimol,ES-AUC,v2_cosine,,0.622620,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,unimol,NN@1-Acc,v2_cosine,,0.480923,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,unimol,Hopkins,v2_cosine,,0.559664,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,unimol,SCI,v2_cosine,,0.021010,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,unimol,SCI_unsup,v2_cosine,,0.270063,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,fmg,ES-AUC,v2_cosine,,0.712555,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,fmg,NN@1-Acc,v2_cosine,,0.601655,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,fmg,Hopkins,v2_cosine,,0.751049,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,fmg,SCI,v2_cosine,,0.158030,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,fmg,SCI_unsup,v2_cosine,,0.682689,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,mace,ES-AUC,v2_cosine,,0.484971,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,mace,NN@1-Acc,v2_cosine,,0.285157,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,mace,Hopkins,v2_cosine,,0.637111,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,mace,SCI,v2_cosine,,-0.151823,0.001,"not in the paper; v2 definitions, --distance cosine" +table2,mace,SCI_unsup,v2_cosine,,0.566272,0.001,"not in the paper; v2 definitions, --distance cosine" diff --git a/reproduce/table2_chirality/make_results.py b/reproduce/table2_chirality/make_results.py new file mode 100644 index 0000000..069b584 --- /dev/null +++ b/reproduce/table2_chirality/make_results.py @@ -0,0 +1,51 @@ +"""Collect Table 2 summaries written by run.sh into results.csv (table,model,metric,variant,value).""" + +from __future__ import annotations + +import argparse +import csv +import math +from pathlib import Path + +METRICS = [ # (paper row label, summary.csv column) + ("ES-AUC", "ESA_AUC_mean"), + ("NN@1-Acc", "NN1_acc_mean"), + ("Hopkins", "hopkins_mean"), + ("SCI", "sil_sup_mean"), + ("SCI_unsup", "sil_unsup_mean"), +] +MODELS = ["e3fp", "gemnet", "molae", "molspectra", "unimol", "fmg", "mace"] +VARIANTS = ["euclidean", "cosine", "v2_euclidean", "v2_cosine"] + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--runs", type=Path, required=True, help="OUT_DIR of run.sh (//summary.csv)") + ap.add_argument("--out", type=Path, required=True) + args = ap.parse_args() + + rows = [] + for variant in VARIANTS: + for model in MODELS: + path = args.runs / variant / model / "summary.csv" + if not path.exists(): + continue + with path.open() as f: + summary = next(csv.DictReader(f)) + for label, col in METRICS: + raw = (summary.get(col) or "").strip() + value = float(raw) if raw else math.nan # pandas writes NaN as an empty field + if math.isnan(value): # e.g. Hopkins for fingerprints ("-" in the paper) + continue + rows.append({"table": "table2", "model": model, "metric": label, "variant": variant, "value": value}) + args.out.parent.mkdir(parents=True, exist_ok=True) + with args.out.open("w", newline="") as f: + w = csv.DictWriter(f, fieldnames=["table", "model", "metric", "variant", "value"]) + w.writeheader() + for r in rows: + w.writerow({**r, "value": repr(r["value"])}) + print(f"wrote {len(rows)} rows to {args.out}") + + +if __name__ == "__main__": + main() diff --git a/reproduce/table2_chirality/models.csv b/reproduce/table2_chirality/models.csv new file mode 100644 index 0000000..d278e93 --- /dev/null +++ b/reproduce/table2_chirality/models.csv @@ -0,0 +1,8 @@ +model,path,key,size_bytes,sha256,source_nyubox_path +e3fp,chirality/e3fp/sampled_chi.pkl,e3fp,11358037,a1c176b2ff392be59e64f03f83120ba34ae0d0b8fd7abddf90a0130a42a9b920,3DBench/data/chirality/fingerprint/sampled_chi.pkl +gemnet,chirality/gemnet/sampled_feature.npz,gemnet,26824458,8271b6b005fbd7fc77dbaab8aa94a6744afeeadcc25c2f04f1ce2adbe8093d39,3DBench/data/chirality/gemnet/sampled_feature.npz +molae,chirality/molae/1.npz,arr_0,107297032,2c93b4593f730e78bca1ff7bec1ef8643a933659621b18700c21e91a47451ff0,3DBench/data/chirality/molae/1.npz +molspectra,chirality/molspectra/sampled_mol_feature.npz,arr_0,53648648,4b57ba17c0feb81b16af9758e5450a3d14b562c7dd93fa4620b7357e6477463b,3DBench/data/chirality/molspectra/sampled_mol_feature.npz +unimol,chirality/unimol/1.npz,arr_0,107297032,78e72a78167019419a82c0c3529982b8ad135ae6122f9a111eb913158cebd584,3DBench/data/chirality/unimol/1.npz +fmg,chirality/fmg/chirality_bench_conformers_noised_only_aslist_embed.npz,embeddings,26128283,9425f0561de194d66ebc31169c3b3fcee094b3412e643e6e635d9e74281b3b43,3DBench/data/chirality/fmg/chirality_bench_conformers_noised_only_aslist_embed.npz +mace,chirality/mace/chirality.npz,arr_0,49661969,d677df043092c46d29f3d3b52a1145e975a4f0ca362029a15890fce65d6552a4,3DBench/data/chirality/mace/chirality.npz diff --git a/reproduce/table2_chirality/run.sh b/reproduce/table2_chirality/run.sh new file mode 100755 index 0000000..8efa960 --- /dev/null +++ b/reproduce/table2_chirality/run.sh @@ -0,0 +1,145 @@ +#!/usr/bin/env bash +# Reproduce Table 2 (zero-shot chirality) of the 3DCS paper from the published embeddings. +# +# bash reproduce/table2_chirality/run.sh +# +# Steps: (1) download the HF chirality config of EscheWang/3dcs, (2) download the 7 Table-2 +# embedding files from EscheWang/3dcs-embeddings and check their SHA-256 (models.csv), (3) run +# `python -m three_dbench evaluate chirality` for every model and variant, (4) write results.csv +# and compare it with expected.csv (reproduce/compare.py). Only rows present in results.csv can +# pass: restricting VARIANTS/MODELS makes the comparison report the other rows as missing. +# +# Environment variables (all optional): +# PYTHON python interpreter (default: python) +# DATASET_DIR save_to_disk dir of the chirality config (default: /data/hf/chirality) +# EMB_ROOT root holding chirality// (default: /data/embeddings) +# OUT_DIR per-run outputs (default: /results/reproduce/table2_chirality) +# RESULTS_CSV collected results (default: $OUT_DIR/results.csv) +# VARIANTS subset of: euclidean cosine v2_euclidean v2_cosine (default: all four) +# MODELS subset of: e3fp gemnet molae molspectra unimol fmg mace (default: all seven) +# N_JOBS worker processes per evaluation (default: 8) +# HF_DATASET_REVISION / HF_EMBEDDINGS_REVISION pin Hub revisions (default: main) +# SKIP_DOWNLOAD=1 use DATASET_DIR / EMB_ROOT as they are; SKIP_SHA256=1 skip the checksum check +# +# Variant "euclidean" is the published protocol (--distance euclidean --metric-version paper). +# Runtime with N_JOBS=22 on an AMD EPYC 7513: ~1-2 min per model and variant (E3FP ~40 s). +set -euo pipefail + +HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "$HERE/../.." && pwd)" +PYTHON="${PYTHON:-python}" +DATASET_DIR="${DATASET_DIR:-$REPO_ROOT/data/hf/chirality}" +EMB_ROOT="${EMB_ROOT:-$REPO_ROOT/data/embeddings}" +OUT_DIR="${OUT_DIR:-$REPO_ROOT/results/reproduce/table2_chirality}" +VARIANTS="${VARIANTS:-euclidean cosine v2_euclidean v2_cosine}" +MODELS="${MODELS:-e3fp gemnet molae molspectra unimol fmg mace}" +N_JOBS="${N_JOBS:-8}" +RESULTS_CSV="${RESULTS_CSV:-$OUT_DIR/results.csv}" +export HF_DATASET_REVISION="${HF_DATASET_REVISION:-}" HF_EMBEDDINGS_REVISION="${HF_EMBEDDINGS_REVISION:-}" + +# Use the code of this checkout even if another copy of three_dbench is installed. +export PYTHONPATH="$REPO_ROOT/src${PYTHONPATH:+:$PYTHONPATH}" +"$PYTHON" -c "import three_dbench, sys; print('three_dbench from', three_dbench.__file__); sys.exit(0)" + +if [[ "${SKIP_DOWNLOAD:-0}" != "1" ]]; then + echo "== [1/4] dataset: EscheWang/3dcs (config chirality) -> $DATASET_DIR" + DATASET_DIR="$DATASET_DIR" "$PYTHON" - <<'PY' +import os +from pathlib import Path +out = Path(os.environ["DATASET_DIR"]) +if out.exists() and any(out.iterdir()): + print(f" {out} exists, skipping download") +else: + from datasets import load_dataset + kw = {"name": "chirality", "split": "train"} + if os.environ.get("HF_DATASET_REVISION"): + kw["revision"] = os.environ["HF_DATASET_REVISION"] + ds = load_dataset("EscheWang/3dcs", **kw) + out.parent.mkdir(parents=True, exist_ok=True) + ds.save_to_disk(str(out)) + print(f" saved {len(ds)} rows / {sum(ds['n_conformers'])} conformers") +PY + + echo "== [2/4] embeddings: EscheWang/3dcs-embeddings -> $EMB_ROOT" + EMB_ROOT="$EMB_ROOT" MODELS="$MODELS" MODELS_CSV="$HERE/models.csv" "$PYTHON" - <<'PY' +import csv, os +from pathlib import Path +root = Path(os.environ["EMB_ROOT"]) +wanted = set(os.environ["MODELS"].split()) +rows = [r for r in csv.DictReader(open(os.environ["MODELS_CSV"])) if r["model"] in wanted] +missing = [r for r in rows if not (root / r["path"]).exists()] +if not missing: + print(" all files present, skipping download") +else: + from huggingface_hub import hf_hub_download + rev = os.environ.get("HF_EMBEDDINGS_REVISION") or None + for r in missing: + print(f" downloading {r['path']} ({int(r['size_bytes']) / 1e6:.1f} MB)") + hf_hub_download("EscheWang/3dcs-embeddings", r["path"], repo_type="dataset", revision=rev, local_dir=str(root)) +PY +fi + +if [[ "${SKIP_SHA256:-0}" != "1" ]]; then + EMB_ROOT="$EMB_ROOT" MODELS="$MODELS" MODELS_CSV="$HERE/models.csv" "$PYTHON" - <<'PY' +import csv, hashlib, os, sys +from pathlib import Path +root = Path(os.environ["EMB_ROOT"]) +wanted = set(os.environ["MODELS"].split()) +bad = [] +for r in csv.DictReader(open(os.environ["MODELS_CSV"])): + if r["model"] not in wanted: + continue + p = root / r["path"] + if not p.exists(): + bad.append(f"{p}: missing"); continue + h = hashlib.sha256() + with p.open("rb") as f: + for block in iter(lambda: f.read(1 << 22), b""): + h.update(block) + if h.hexdigest() != r["sha256"]: + bad.append(f"{p}: sha256 {h.hexdigest()} != {r['sha256']}") +if bad: + sys.exit("embedding check failed:\n " + "\n ".join(bad)) +print(" sha256 OK for", len(wanted), "models") +PY +fi + +echo "== [3/4] evaluate (variants: $VARIANTS; models: $MODELS; N_JOBS=$N_JOBS)" +key_of() { awk -F, -v m="$1" '$1==m {print $3}' "$HERE/models.csv"; } +path_of() { awk -F, -v m="$1" '$1==m {print $2}' "$HERE/models.csv"; } +for variant in $VARIANTS; do + case "$variant" in + euclidean) dist=euclidean; version=paper ;; + cosine) dist=cosine; version=paper ;; + v2_euclidean) dist=euclidean; version=v2 ;; + v2_cosine) dist=cosine; version=v2 ;; + *) echo "unknown variant $variant" >&2; exit 2 ;; + esac + for model in $MODELS; do + out="$OUT_DIR/$variant/$model" + if [[ -f "$out/summary.csv" && "${FORCE:-0}" != "1" ]]; then + echo " $variant/$model: $out/summary.csv exists, skipping (FORCE=1 to recompute)"; continue + fi + mkdir -p "$out" + start=$(date +%s) + "$PYTHON" -m three_dbench evaluate chirality \ + --dataset-dir "$DATASET_DIR" \ + --embeddings "$EMB_ROOT/$(path_of "$model")" \ + --embedding-key "$(key_of "$model")" \ + --model-name "$model" \ + --output-dir "$out" \ + --distance "$dist" \ + --metric-version "$version" \ + --unsup-kmax n-1 \ + --n-jobs "$N_JOBS" > "$out/log.txt" 2>&1 || { echo "FAILED: $variant/$model (see $out/log.txt)" >&2; exit 1; } + echo " $variant/$model: $(( $(date +%s) - start )) s $(grep '^ES-AUC' "$out/log.txt" || true)" + done +done + +echo "== [4/4] results.csv" +"$PYTHON" "$HERE/make_results.py" --runs "$OUT_DIR" --out "$RESULTS_CSV" +if [[ -f "$REPO_ROOT/reproduce/compare.py" ]]; then + "$PYTHON" "$REPO_ROOT/reproduce/compare.py" --expected "$HERE/expected.csv" --results "$RESULTS_CSV" +else + echo "reproduce/compare.py not found; compare $RESULTS_CSV with $HERE/expected.csv manually" +fi diff --git a/splits/rmd17/README.md b/splits/rmd17/README.md new file mode 100644 index 0000000..12e47fb --- /dev/null +++ b/splits/rmd17/README.md @@ -0,0 +1,55 @@ +# rMD17 train/test splits + +These are the five official train/test index splits distributed with the revised MD17 dataset +(rMD17; Christensen & von Lilienfeld, 2020, ). +The files are copied byte for byte from the rMD17 release (`splits/index_{train,test}_0{1..5}.csv`); +we did not modify them. + +| File | Rows | Content | +|---|---|---| +| `index_train_0{1..5}.csv` | 1000 each | 0-based frame indices into each `rmd17_.npz` | +| `index_test_0{1..5}.csv` | 1000 each | 0-based frame indices, disjoint from the matching train split | + +The same indices apply to all ten molecules. In the 3DCS trajectory data (`EscheWang/3dcs`, +configs `traj_frames` / `traj_energies`), frame `frame_idx` of `rmd17_` is row `frame_idx` +of the rMD17 `.npz`, so these indices can be used directly on the HF data. + +## Use in the paper + +- The zero-shot energy benchmark (Tables 3, 6 and 7) does **not** use these splits; it samples + windows over all frames. +- The fine-tuning inputs for Tables 8 and 9 that we found in our backups are exactly split **01** + (`index_train_01.csv` / `index_test_01.csv`, 1000/1000 frames, no validation split) for all ten + molecules. Fine-tuning code and checkpoints for rMD17 are not part of this repository. + +## SHA-256 + +``` +67f2c51e7c00bbc1f9fc5ba90863e5d526e6bca0d885bb68f9a1df4a5b7825cb index_train_01.csv +65bfd73039f32a73586b84d799b96cea4fb4809a642611a7e8e8d94b9a5982f8 index_test_01.csv +437d61f05031ae35ff3bcf6aa22b2de4e56f9cfa10af169a5c604871b11056fc index_train_02.csv +161f85238146e9398f84b22d250c350cb51916af5f93a51b0b8e0abb671883d8 index_test_02.csv +9d87806d98b902298568844b3a3bf694de51c2266066f2d7cd90810bcf4e2524 index_train_03.csv +2d5dbebdf942ead1b44fa496e037a6a6c4814d1615907e5f42ca2f354dca3925 index_test_03.csv +8d4d44298dbb15895472b7da8c0429c14449e88390450ecc202822d50a36b6e6 index_train_04.csv +b6716781813dfc569e820f45f9741d18b4c8fb1ac71a7a17b21a6780a9b37d24 index_test_04.csv +daa7e1351047d67a9a5d91ee44968ce708138e0f4f7a47fc38cc9c8d109fd636 index_train_05.csv +9cd07efd184dced6320b8656cf9efef2a59534d16eec85e536a2b04aa40775fe index_test_05.csv +``` + +## License and citation + +rMD17 is published on figshare under CC0; see the rMD17 record for its terms. If you use these +splits, please cite rMD17: + +```bibtex +@article{christensen2020role, + title = {On the role of gradients for machine learning of molecular energies and forces}, + author = {Christensen, Anders S. and von Lilienfeld, O. Anatole}, + journal = {Machine Learning: Science and Technology}, + volume = {1}, + number = {4}, + pages = {045018}, + year = {2020} +} +``` diff --git a/splits/rmd17/index_test_01.csv b/splits/rmd17/index_test_01.csv new file mode 100644 index 0000000..621053d --- /dev/null +++ b/splits/rmd17/index_test_01.csv @@ -0,0 +1,1000 @@ +92 +172 +406 +530 +549 +694 +1104 +1170 +1356 +1415 +1454 +1566 +1866 +2009 +2089 +2406 +2599 +2605 +2664 +2665 +2759 +2780 +3076 +3109 +3240 +3326 +3370 +3508 +3590 +3908 +3992 +4329 +4526 +4571 +4586 +4720 +4817 +4822 +4823 +4877 +4940 +4957 +4961 +4978 +5036 +5077 +5154 +5207 +5311 +5404 +5539 +5694 +5792 +5820 +5828 +5829 +5978 +6146 +6177 +6248 +6566 +6575 +6576 +6864 +6918 +7060 +7408 +7539 +7652 +7696 +7711 +7721 +7741 +7836 +7866 +7920 +7927 +7952 +7992 +8053 +8189 +8292 +8411 +8447 +8762 +8787 +8797 +8832 +9300 +9338 +9443 +9548 +9625 +9643 +9750 +9862 +9926 +10304 +10376 +10381 +10555 +10870 +10911 +10914 +10922 +11293 +11296 +11328 +11455 +11727 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b/src/three_dbench/__main__.py index 01a33cd..03a8846 100644 --- a/src/three_dbench/__main__.py +++ b/src/three_dbench/__main__.py @@ -5,7 +5,7 @@ import argparse from pathlib import Path -from three_dbench.embeddings import load_embeddings, load_embeddings_dict, load_embeddings_dir +from three_dbench.embeddings import load_embeddings, load_embeddings_dict from three_dbench.utils.paths import DATA_ROOT, RESULTS_ROOT @@ -32,19 +32,135 @@ def _parse_args() -> argparse.Namespace: evaluate.add_argument("--output-dir", type=Path, default=None, help="Output directory") evaluate.add_argument("--metrics", type=str, nargs="*", default=None, help="Rotation distance metrics") evaluate.add_argument( - "--layout", type=str, choices=["flat", "by-key"], default="flat", help="Rotation embedding layout" + "--layout", + type=str, + choices=["flat", "by-shard", "by-key"], + default=None, + help="Rotation embedding layout (default: by-shard for a directory, flat for a file)", ) + evaluate.add_argument( + "--offset-mode", + type=str, + choices=["auto", "per-shard", "global"], + default="auto", + help="Rotation: how the dataset 'offset' column is interpreted", + ) + evaluate.add_argument("--shard-file-pattern", type=str, default=None, help="Rotation by-shard file pattern") + evaluate.add_argument("--shards", type=int, nargs="*", default=None, help="Rotation shard IDs to evaluate") + evaluate.add_argument("--molecule-list", type=Path, default=None, help="Rotation: file with one key per line") + evaluate.add_argument("--sample-ratio", type=float, default=None, help="Rotation: fraction of molecules per shard") + evaluate.add_argument("--sample-seed", type=int, default=2027, help="Rotation: seed for --sample-ratio") + evaluate.add_argument("--min-conformers", type=int, default=2, help="Rotation: skip molecules with fewer") + evaluate.add_argument("--max-keys", type=int, default=None, help="Rotation: evaluate at most N molecules") + evaluate.add_argument( + "--metric-version", + type=str, + choices=["paper", "v2"], + default="paper", + help="All tasks: 'paper' reproduces the published numbers (default), 'v2' uses the corrected " + "definitions documented in docs/METRICS.md", + ) + evaluate.add_argument("--lie-k", type=int, default=None, help="Rotation: override k of LIE@k") + evaluate.add_argument( + "--lie-self", + type=str, + choices=["include", "exclude"], + default=None, + help="Rotation: include the conformer itself in its LIE neighbourhood", + ) + evaluate.add_argument( + "--as-variant", + type=str, + default=None, + choices=["mean_delta_circular", "median_delta_circular", "median_halfdelta_circular", "median_dz_linear"], + help="Rotation: override the angular smoothness definition", + ) + evaluate.add_argument("--extra-metrics", action="store_true", help="Rotation: also dCor, Mantel, stress, triplets") + evaluate.add_argument( + "--replicate-offset-drift", + action="store_true", + help="Rotation (by-shard): also report metrics with the embedding-cursor drift of the published full run", + ) + evaluate.add_argument("--n-jobs", type=int, default=1, help="All tasks: worker processes (-1 = all CPUs)") evaluate.add_argument("--n-samples", type=int, default=100, help="Trajectory samples per molecule") evaluate.add_argument("--window", type=int, default=2000, help="Trajectory window size") - evaluate.add_argument("--metric-embed", type=str, default="cosine", help="Trajectory distance metric") + evaluate.add_argument( + "--metric-embed", + type=str, + choices=["cosine", "euclidean", "tanimoto"], + default=None, + help="Trajectory distance (default: tanimoto for fingerprint pickles, cosine for vectors)", + ) evaluate.add_argument("--block-size", type=int, default=4096, help="Trajectory distance block size") evaluate.add_argument("--random-seed", type=int, default=2025, help="Trajectory random seed") + evaluate.add_argument( + "--window-scheme", + choices=["legacy", "shared"], + default="legacy", + help="Trajectory windows: legacy = per-molecule reseeding as in the paper (default); shared = 0.1.0 CLI", + ) + evaluate.add_argument("--molecules", type=str, nargs="*", default=None, help="Trajectory molecule subset") + evaluate.add_argument( + "--energy-precision-check", + choices=["error", "warn", "ignore"], + default="error", + help="Action when trajectory energies look quantized (e.g. float32-cast)", + ) + evaluate.add_argument( + "--time-ordered", action="store_true", help="Trajectory frames are time-ordered (v2 TS/Smoothness)" + ) + evaluate.add_argument( + "--legacy-traj-len", type=int, default=100_000, help="Trajectory length assumed by the legacy window scheme" + ) evaluate.add_argument("--per-mol-min-n", type=int, default=2, help="Chirality minimum conformers per molecule") evaluate.add_argument("--max-molecules", type=int, default=None, help="Chirality max molecules for testing") evaluate.add_argument("--do-unsup-when-single-en", action="store_true", help="Chirality unsupervised metrics") + # --- chirality options (defaults reproduce the published Table 2; see docs/metrics/chirality.md) --- + evaluate.add_argument( + "--distance", + choices=["euclidean", "cosine"], + default="euclidean", + help="Chirality: distance for continuous embeddings (default euclidean = published Table 2); " + "RDKit fingerprints always use Tanimoto", + ) + evaluate.add_argument( + "--unsup-kmax", + type=_parse_unsup_kmax, + default=None, + help="Chirality: largest k for the best-k silhouette (SCI_unsup); 'n-1' (default, published run) or an int", + ) + + download = subparsers.add_parser("download", help="Download datasets or published embeddings from Hugging Face") + download.add_argument("what", choices=["dataset", "embeddings"], help="What to download") + download.add_argument( + "--task", + required=True, + choices=["chirality", "traj", "rotation", "chirality_legacy_15218", "all"], + help="Benchmark task", + ) + download.add_argument("--models", type=str, nargs="*", default=None, help="Embeddings: model directories") + download.add_argument("--out", type=Path, default=None, help="Output root (default: data/hf or data/embeddings)") + download.add_argument("--repo-id", type=str, default=None, help="Override the HF repo id") + download.add_argument("--revision", type=str, default=None, help="HF revision (branch, tag or commit)") + download.add_argument("--results", action="store_true", help="Embeddings: also fetch results// files") + download.add_argument("--no-verify", action="store_true", help="Embeddings: skip SHA-256 verification") + download.add_argument("--dry-run", action="store_true", help="Embeddings: list files without downloading") + download.add_argument("--overwrite", action="store_true", help="Dataset: replace an existing save_to_disk dir") return parser.parse_args() +def _parse_unsup_kmax(value: str): + if value.lower() in {"n-1", "none", "all"}: + return None + try: + k = int(value) + except ValueError as exc: + raise argparse.ArgumentTypeError(f"--unsup-kmax must be 'n-1' or an integer >= 2, got {value!r}") from exc + if k < 2: + raise argparse.ArgumentTypeError(f"--unsup-kmax must be >= 2, got {k}") + return k + + def _convert_dataset(args: argparse.Namespace) -> None: include_mols = not args.no_mol_blocks if args.task == "chirality": @@ -85,53 +201,75 @@ def _evaluate_embeddings(args: argparse.Namespace) -> None: output_dir = RESULTS_ROOT / args.task / args.model_name if args.task == "chirality": - from three_dbench.benchmarks import evaluate_chirality_embeddings + from three_dbench.benchmarks.chirality import evaluate_chirality_embeddings, load_chirality_embeddings - embeddings = load_embeddings(args.embeddings, key=args.embedding_key) - evaluate_chirality_embeddings( + embeddings = load_chirality_embeddings(args.embeddings, key=args.embedding_key) + _, summary = evaluate_chirality_embeddings( dataset_dir=args.dataset_dir, embeddings=embeddings, output_dir=output_dir, model_name=args.model_name, per_mol_min_n=args.per_mol_min_n, do_unsup_when_single_en=args.do_unsup_when_single_en, + unsup_kmax=args.unsup_kmax, max_molecules=args.max_molecules, + distance=args.distance, + metric_version=args.metric_version, + n_jobs=args.n_jobs, + ) + print( + "ES-AUC {ESA_AUC_mean:.6f} NN@1-Acc {NN1_acc_mean:.6f} Hopkins {hopkins_mean:.6f} " + "SCI {sil_sup_mean:.6f} SCI_unsup {sil_unsup_mean:.6f}".format(**summary) ) print(f"Chirality report saved to {output_dir}") return if args.task == "rotation": from three_dbench.benchmarks import evaluate_rotation_embeddings + from three_dbench.benchmarks.rotation import ShardedEmbeddings metrics = args.metrics or ["cosine", "euclidean"] - if args.layout == "by-key": + layout = args.layout or ("by-shard" if args.embeddings.is_dir() else "flat") + common = { + "dataset_dir": args.dataset_dir, + "output_dir": output_dir, + "model_name": args.model_name, + "metrics": metrics, + "metric_version": args.metric_version, + "lie_k": args.lie_k, + "lie_include_self": None if args.lie_self is None else args.lie_self == "include", + "as_variant": args.as_variant, + "extra_metrics": True if args.extra_metrics else None, + "offset_mode": args.offset_mode, + "shards": args.shards, + "molecule_list": args.molecule_list, + "sample_ratio": args.sample_ratio, + "sample_seed": args.sample_seed, + "min_conformers": args.min_conformers, + "max_keys": args.max_keys, + "n_jobs": args.n_jobs, + "progress": True, + "replicate_offset_drift": args.replicate_offset_drift, + } + if layout == "by-key": emb_dict = load_embeddings_dict(args.embeddings, key=args.embedding_key) - evaluate_rotation_embeddings( - dataset_dir=args.dataset_dir, - embeddings_by_key=emb_dict, - output_dir=output_dir, - model_name=args.model_name, - metrics=metrics, + evaluate_rotation_embeddings(embeddings_by_key=emb_dict, **common) + elif layout == "by-shard": + sharded = ShardedEmbeddings.from_directory( + args.embeddings, key=args.embedding_key, pattern=args.shard_file_pattern ) + evaluate_rotation_embeddings(embeddings_by_shard=sharded, **common) else: embeddings = load_embeddings(args.embeddings, key=args.embedding_key) - evaluate_rotation_embeddings( - dataset_dir=args.dataset_dir, - embeddings=embeddings, - output_dir=output_dir, - model_name=args.model_name, - metrics=metrics, - ) + evaluate_rotation_embeddings(embeddings=embeddings, **common) print(f"Rotation report saved to {output_dir}") return if args.task == "traj": from three_dbench.benchmarks import evaluate_trajectory_embeddings + from three_dbench.traj.io import load_traj_embeddings - if args.embeddings.is_dir(): - emb_dict = load_embeddings_dir(args.embeddings, file_glob="rmd17_*.npz", key=args.embedding_key) - else: - emb_dict = load_embeddings_dict(args.embeddings, key=args.embedding_key) + emb_dict = load_traj_embeddings(args.embeddings, key=args.embedding_key) evaluate_trajectory_embeddings( dataset_dir=args.dataset_dir, embeddings_by_mol=emb_dict, @@ -142,17 +280,52 @@ def _evaluate_embeddings(args: argparse.Namespace) -> None: metric_embed=args.metric_embed, block_size=args.block_size, random_seed=args.random_seed, + window_scheme=args.window_scheme, + metric_version=args.metric_version, + n_jobs=args.n_jobs, + molecules=args.molecules, + energy_precision_check=args.energy_precision_check, + time_ordered=args.time_ordered, + legacy_traj_len=args.legacy_traj_len, ) print(f"Trajectory report saved to {output_dir}") return +def _download(args: argparse.Namespace) -> None: + from three_dbench import download + + if args.what == "dataset": + if args.task == "chirality_legacy_15218": + raise SystemExit("The 15,218-conformer chirality set is only published as embeddings.") + download.download_dataset( + args.task, + args.out or (DATA_ROOT / "hf"), + repo_id=args.repo_id or download.DATASET_REPO_ID, + revision=args.revision, + overwrite=args.overwrite, + ) + else: + download.download_embeddings( + args.task, + args.out or (DATA_ROOT / "embeddings"), + models=args.models, + include_results=args.results, + repo_id=args.repo_id or download.EMBEDDINGS_REPO_ID, + revision=args.revision, + verify=not args.no_verify, + dry_run=args.dry_run, + ) + + def main() -> None: args = _parse_args() if args.command == "convert": _convert_dataset(args) elif args.command == "evaluate": _evaluate_embeddings(args) + elif args.command == "download": + _download(args) if __name__ == "__main__": diff --git a/src/three_dbench/benchmarks/chirality.py b/src/three_dbench/benchmarks/chirality.py index fa00065..21b8cc9 100644 --- a/src/three_dbench/benchmarks/chirality.py +++ b/src/three_dbench/benchmarks/chirality.py @@ -1,50 +1,174 @@ -"""Chirality benchmark evaluation for user-provided embeddings.""" +"""Chirality benchmark evaluation for user-provided embeddings. + +Defaults reproduce the published Table 2 protocol: Euclidean distance for continuous embeddings, +Tanimoto distance for RDKit fingerprints, ``metric_version="paper"`` and an unbounded best-k +silhouette scan (``unsup_kmax=None`` -> ``k <= n - 1``). See ``docs/metrics/chirality.md``. +""" from __future__ import annotations import json +import pickle +import platform from collections import OrderedDict from pathlib import Path +from typing import Any +import numpy as np import pandas as pd -from three_dbench.chirality.evaluation import evaluate_en_separation_from_counts +from three_dbench.chirality.evaluation import ( + DISTANCES, + METRIC_VERSIONS, + coverage_from_rows, + evaluate_en_separation_from_counts, +) from three_dbench.datasets.chirality import load_chirality_dataset -from three_dbench.embeddings import EmbeddingArray +from three_dbench.embeddings import EmbeddingArray, load_embeddings def _key_counts_from_dataset(dataset) -> dict[str, int]: - counts = OrderedDict() - for row in dataset: - counts[row["key"]] = int(row["n_conformers"]) - return counts + """Ordered ``key -> n_conformers`` mapping, validated against the dataset ``offset`` column.""" + columns = set(getattr(dataset, "column_names", []) or []) + if {"key", "n_conformers"} <= columns: + keys = list(dataset["key"]) + counts = [int(c) for c in dataset["n_conformers"]] + offsets = list(dataset["offset"]) if "offset" in columns else None + else: # generic iterable of row dicts + rows = list(dataset) + keys = [r["key"] for r in rows] + counts = [int(r["n_conformers"]) for r in rows] + offsets = [r["offset"] for r in rows] if rows and "offset" in rows[0] else None + + out: dict[str, int] = OrderedDict() + for k, c in zip(keys, counts): + if k in out: + raise ValueError(f"Duplicate key {k!r} in the chirality dataset; keys must be unique.") + out[k] = c + + if offsets is not None: + expected = np.concatenate([[0], np.cumsum(counts)[:-1]]) if counts else np.zeros(0, dtype=int) + bad = np.flatnonzero(np.asarray(offsets, dtype=np.int64) != expected) + if bad.size: + i = int(bad[0]) + raise ValueError( + f"Dataset 'offset' column is not the running sum of 'n_conformers' (first mismatch at row {i}: " + f"offset={offsets[i]}, expected {int(expected[i])}). Was the dataset re-ordered or filtered? " + "Embeddings are aligned by dataset row order." + ) + return out + + +def _available_keys(path: Path) -> list[str] | None: + try: + if path.suffix.lower() == ".npz": + with np.load(path) as data: + return list(data.files) + if path.suffix.lower() == ".pkl": + with path.open("rb") as f: + payload = pickle.load(f) + return sorted(map(str, payload)) if isinstance(payload, dict) else None + except Exception: + return None + return None + + +def load_chirality_embeddings(path: Path, key: str | None = None) -> EmbeddingArray: + """Load chirality embeddings (NPZ/NPY array or pickled list of RDKit fingerprints). + + Thin wrapper around :func:`three_dbench.embeddings.load_embeddings` that lists the available keys + when the key is missing or ambiguous. The published E3FP file (``chirality/e3fp/sampled_chi.pkl``) + is a dict with ``'e3fp'`` (Table 2, 1024-bit) and ``'morgan'`` fingerprint lists: pass + ``--embedding-key e3fp``. + """ + path = Path(path) + try: + return load_embeddings(path, key=key) + except (ValueError, KeyError) as exc: + keys = _available_keys(path) + if keys is None: + raise + raise type(exc)( + f"{exc} [{path}: available keys {keys}; pass --embedding-key, e.g. 'e3fp' for the published E3FP file]" + ) from exc + + +def _embedding_description(arr: Any) -> dict: + if isinstance(arr, np.ndarray): + return {"type": "ndarray", "shape": list(arr.shape), "dtype": str(arr.dtype)} + first = arr[0] if len(arr) else None + desc = {"type": type(arr).__name__, "length": len(arr), "element": type(first).__name__} + if first is not None and hasattr(first, "GetNumBits"): + desc["n_bits"] = int(first.GetNumBits()) + return desc + + +def _versions() -> dict: + out = {"python": platform.python_version()} + for mod in ("numpy", "scipy", "sklearn", "rdkit", "three_dbench"): + try: + m = __import__(mod) + out[mod] = getattr(m, "__version__", "unknown") + except Exception: + out[mod] = None + return out def evaluate_chirality_embeddings( *, dataset_dir: Path, - embeddings: EmbeddingArray, + embeddings: EmbeddingArray | np.ndarray | list, output_dir: Path | None = None, model_name: str = "custom", per_mol_min_n: int = 2, do_unsup_when_single_en: bool = False, - unsup_kmax: int = 50, + unsup_kmax: int | None = None, max_molecules: int | None = None, + distance: str = "euclidean", + metric_version: str = "paper", + n_jobs: int = 1, + progress: bool = False, ) -> tuple[dict, dict]: - """Evaluate chirality embeddings against the HF dataset.""" + """Evaluate chirality embeddings against the HF dataset. + + Args: + dataset_dir: ``save_to_disk`` directory of the chirality dataset + (``load_dataset("EscheWang/3dcs", name="chirality", split="train")``). + embeddings: one row per conformer in dataset order (array) or a list of RDKit fingerprints. + output_dir: if given, writes ``{model_name}_per_molecule.json``, ``summary.csv`` and ``config.json``. + distance: ``"euclidean"`` (default, published Table 2) or ``"cosine"``; fingerprints always + use Tanimoto distance. + metric_version: ``"paper"`` (default, published definitions) or ``"v2"``. + unsup_kmax: best-k silhouette upper bound; ``None`` = ``n - 1`` (published run). + n_jobs: worker processes over molecules (``-1`` = all CPUs); results do not depend on it. + + Returns: + ``({"rows": per_molecule_rows}, summary)``. + """ + if distance not in DISTANCES: + raise ValueError(f"distance must be one of {DISTANCES}, got {distance!r}") + if metric_version not in METRIC_VERSIONS: + raise ValueError(f"metric_version must be one of {METRIC_VERSIONS}, got {metric_version!r}") + + array = embeddings.array if isinstance(embeddings, EmbeddingArray) else embeddings dataset = load_chirality_dataset(dataset_dir) key_to_counts = _key_counts_from_dataset(dataset) rows, summary = evaluate_en_separation_from_counts( key_to_counts, - embeddings.array, + array, per_mol_min_n=per_mol_min_n, do_unsup_when_single_en=do_unsup_when_single_en, unsup_kmax=unsup_kmax, max_molecules=max_molecules, + distance=distance, + metric_version=metric_version, + n_jobs=n_jobs, + progress=progress, ) if output_dir is not None: + output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) per_mol_path = output_dir / f"{model_name}_per_molecule.json" with per_mol_path.open("w", encoding="utf-8") as f: @@ -55,4 +179,24 @@ def evaluate_chirality_embeddings( summary_path = output_dir / "summary.csv" summary_df.to_csv(summary_path, index=False) + is_fp = not isinstance(array, np.ndarray) + config = { + "model_name": model_name, + "dataset_dir": str(dataset_dir), + "n_keys": len(key_to_counts), + "n_conformers": int(sum(key_to_counts.values())), + "embeddings": _embedding_description(array), + "distance": "tanimoto (fingerprints)" if is_fp else distance, + "distance_requested": distance, + "metric_version": metric_version, + "unsup_kmax": "n-1" if unsup_kmax is None else int(unsup_kmax), + "per_mol_min_n": per_mol_min_n, + "do_unsup_when_single_en": do_unsup_when_single_en, + "max_molecules": max_molecules, + "coverage": coverage_from_rows(rows), + "versions": _versions(), + } + with (output_dir / "config.json").open("w", encoding="utf-8") as f: + json.dump(config, f, indent=2) + return {"rows": rows}, summary diff --git a/src/three_dbench/benchmarks/rotation.py b/src/three_dbench/benchmarks/rotation.py index f823406..7c3c019 100644 --- a/src/three_dbench/benchmarks/rotation.py +++ b/src/three_dbench/benchmarks/rotation.py @@ -1,119 +1,662 @@ -"""Rotation benchmark evaluation for user-provided embeddings.""" +"""Rotation (geometry) benchmark evaluation for user-provided embeddings. + +Embedding layouts +----------------- +``flat`` + One array (or fingerprint list) with one row per conformer, in the **row order of the + dataset**. The start of each molecule is the cumulative sum of ``n_conformers`` over the + dataset rows; the stored ``offset`` column is not used for flat arrays. +``by-shard`` + A directory with one file per shard (e.g. ``rotation_conformers_{shard}.npz``, the layout of + ``EscheWang/3dcs-embeddings``). Rows of shard ``s`` are sliced from the file of shard ``s`` + using the **per-shard** offset. The published HF dataset stores per-shard offsets (they + restart at 0 in every shard, and shards appear in string order 0, 1, 10, ..., 15, 2, ..., 9); + datasets written by the original converter store global offsets, which are converted to + per-shard offsets automatically. +``by-key`` + A dict ``{key: array of shape (n_conformers, dim)}``. +""" from __future__ import annotations +import gzip import json -from collections.abc import Iterable +import math +import os +import re +import time +from collections import OrderedDict +from collections.abc import Iterable, Mapping, Sequence +from dataclasses import dataclass from pathlib import Path +from typing import Any import numpy as np import pandas as pd -from three_dbench.datasets.rotation import load_rotation_dataset -from three_dbench.datasets.serialization import blocks_to_mols from three_dbench.embeddings import EmbeddingArray -from three_dbench.rotation.evaluation import compute_all_geometry_metrics, rmsd_matrix_from_one_mol +from three_dbench.embeddings.io import select_array_key + +LAYOUTS = ("flat", "by-shard", "by-key") +OFFSET_MODES = ("auto", "per-shard", "global") +SHARD_FILE_PATTERNS = ( + "rotation_conformers_{shard}.npz", + "rot_mol_list_{shard}_embed.npz", + "rot{shard}.npz", + "rot_{shard}.npz", + "{shard}.npz", + "rotation_conformers_{shard}.npy", + "rot_{shard}.pkl", +) +TABLE1_METRICS = ("A1_spearman", "A2_kendall", "G_cka_rbf", "J_isotonic_R2", "H_LIE@k", "torsion_sp", "AS") + + +# --------------------------------------------------------------------------- +# Index / offsets +# --------------------------------------------------------------------------- + + +def build_rotation_index(dataset: Any, *, offset_mode: str = "auto") -> pd.DataFrame: + """Return a per-row index with flat and per-shard offsets. + + Columns: ``row`` (dataset row), ``key``, ``shard``, ``n_conformers``, ``offset`` (as stored), + ``flat_offset`` (cumulative sum of ``n_conformers`` in dataset row order) and ``local_offset`` + (offset within the row's shard). ``offset_mode`` selects how the stored offsets are + interpreted. ``auto`` treats them as global only when they equal the cumulative conformer count + of a multi-shard dataset (the layout written by ``convert rotation``); otherwise they are taken + as per-shard offsets (the layout of ``EscheWang/3dcs``). Pass ``global`` explicitly for a subset + of a dataset with global offsets. + """ + if offset_mode not in OFFSET_MODES: + raise ValueError(f"offset_mode must be one of {OFFSET_MODES}, got {offset_mode!r}") + cols = [c for c in ("key", "shard", "n_conformers", "offset") if c in dataset.column_names] + meta = dataset.select_columns(cols).to_pandas() + n_conf = meta["n_conformers"].to_numpy(dtype=np.int64) + idx = pd.DataFrame( + { + "row": np.arange(len(n_conf), dtype=np.int64), + "key": meta["key"].astype(str).to_numpy(), + "n_conformers": n_conf, + } + ) + idx["shard"] = meta["shard"].to_numpy(dtype=np.int64) if "shard" in meta else 0 + stored = meta["offset"].to_numpy(dtype=np.int64) if "offset" in meta else None + flat = np.concatenate([[0], np.cumsum(n_conf)[:-1]]).astype(np.int64) if len(n_conf) else np.zeros(0, np.int64) + idx["flat_offset"] = flat + idx["offset"] = stored if stored is not None else flat + + mode = offset_mode + if mode == "auto": + if stored is None or (np.array_equal(stored, flat) and idx["shard"].nunique() > 1): + mode = "global" + else: + mode = "per-shard" + if mode == "per-shard": + idx["local_offset"] = idx["offset"] + else: + shard_start = idx.groupby("shard", sort=False)["offset"].transform("min") + idx["local_offset"] = idx["offset"] - shard_start + idx.attrs["offset_mode"] = mode + return idx + + +def validate_per_shard_offsets(index: pd.DataFrame) -> None: + """Check that the conformer ranges of different rows do not overlap within a shard (raises ValueError).""" + for shard, grp in index.groupby("shard", sort=False): + g = grp.sort_values("local_offset") + start = g["local_offset"].to_numpy() + end = start + g["n_conformers"].to_numpy() + if (start < 0).any() or (start[1:] < end[:-1]).any(): + raise ValueError( + f"Conformer ranges of shard {shard} overlap or are negative; check --offset-mode " + "(per-shard for EscheWang/3dcs, global for datasets written by 'convert rotation')." + ) + + +# --------------------------------------------------------------------------- +# Molecule selection +# --------------------------------------------------------------------------- + + +def read_molecule_list(path: str | Path) -> list[str]: + """Read one key per line (``#`` comments and blank lines ignored; ``.gz`` supported).""" + path = Path(path) + opener = gzip.open if path.suffix == ".gz" else open + keys = [] + with opener(path, "rt", encoding="utf-8") as fh: + for line in fh: + line = line.strip() + if line and not line.startswith("#"): + keys.append(line) + return keys + + +def select_rows( + index: pd.DataFrame, + *, + shards: Iterable[int] | None = None, + molecule_list: str | Path | Iterable[str] | None = None, + sample_ratio: float | None = None, + sample_seed: int = 2027, + min_conformers: int = 2, + max_keys: int | None = None, +) -> tuple[pd.DataFrame, dict]: + """Select dataset rows to evaluate; returns the selected index and selection statistics. + + ``sample_ratio`` draws ``ceil(ratio * n_eligible)`` molecules per shard with + ``numpy.random.default_rng(sample_seed + shard)``. This is a documented, reproducible + sampler; it does **not** regenerate the 10 % sample of the published run, which is + distributed as an explicit list (``reproduce/table1_geometry/sampled_molecules_seed2027.txt``) + for use with ``molecule_list``. + """ + stats: dict[str, Any] = {"n_rows_dataset": int(len(index))} + sel = index + if shards is not None: + shard_set = {int(s) for s in shards} + sel = sel[sel["shard"].isin(shard_set)] + stats["shards"] = sorted(shard_set) + if molecule_list is not None: + keys = read_molecule_list(molecule_list) if isinstance(molecule_list, (str, Path)) else list(molecule_list) + key_set = set(keys) + present = set(index["key"]) + stats["molecule_list_size"] = len(key_set) + stats["molecule_list_missing"] = len(key_set - present) + sel = sel[sel["key"].isin(key_set)] + n_before = len(sel) + sel = sel[sel["n_conformers"] >= int(min_conformers)] + stats["skipped_min_conformers"] = int(n_before - len(sel)) + if sample_ratio is not None: + if not (0.0 < sample_ratio <= 1.0): + raise ValueError("sample_ratio must be in (0, 1].") + parts = [] + for shard, grp in sel.groupby("shard", sort=False): + n_take = max(1, min(len(grp), int(math.ceil(len(grp) * sample_ratio)))) + rng = np.random.default_rng(int(sample_seed) + int(shard)) + take = np.sort(rng.choice(len(grp), size=n_take, replace=False)) + parts.append(grp.iloc[take]) + sel = pd.concat(parts) if parts else sel.iloc[:0] + sel = sel.sort_values("row") + stats["sample_ratio"] = sample_ratio + stats["sample_seed"] = sample_seed + if max_keys is not None: + sel = sel.iloc[: int(max_keys)] + stats["n_selected"] = int(len(sel)) + return sel, stats + + +# --------------------------------------------------------------------------- +# Embedding sources +# --------------------------------------------------------------------------- + + +def _load_array_file(path: Path, key: str | None) -> np.ndarray | list: + suffix = path.suffix.lower() + if suffix == ".npz": + with np.load(path, allow_pickle=False) as data: + return np.asarray(data[select_array_key(data, key)]) + if suffix == ".npy": + return np.load(path, mmap_mode="r") + if suffix == ".pkl": + import pickle + + with path.open("rb") as fh: + payload = pickle.load(fh) + if isinstance(payload, dict): + if key is None: + raise ValueError(f"{path} holds a dict; pass an embedding key.") + payload = payload[key] + return payload + raise ValueError(f"Unsupported embedding file type: {path}") + + +def find_shard_files(directory: Path, pattern: str | None = None) -> dict[int, Path]: + """Map shard id -> file for a by-shard embedding directory.""" + directory = Path(directory) + patterns = [pattern] if pattern else list(SHARD_FILE_PATTERNS) + for pat in patterns: + regex = re.compile("^" + re.escape(pat).replace(re.escape("{shard}"), r"(\d+)") + "$") + found = {} + for p in directory.iterdir(): + m = regex.match(p.name) + if m: + found[int(m.group(1))] = p + if found: + return dict(sorted(found.items())) + raise FileNotFoundError(f"No per-shard embedding files found in {directory} (patterns: {patterns})") + + +class ShardedEmbeddings: + """Lazily loaded per-shard embedding arrays (small LRU cache).""" + + def __init__( + self, + files: Mapping[int, str | Path], + *, + key: str | None = None, + cache_size: int = 2, + arrays: Mapping[int, Any] | None = None, + ) -> None: + self.files = {int(k): Path(v) for k, v in files.items()} + self.key = key + self.cache_size = max(1, int(cache_size)) + self._cache: OrderedDict[int, Any] = OrderedDict() + self._fixed = dict(arrays) if arrays is not None else {} + + @classmethod + def from_directory(cls, directory: str | Path, *, key: str | None = None, pattern: str | None = None): + return cls(find_shard_files(Path(directory), pattern), key=key) + + @classmethod + def from_arrays(cls, arrays: Mapping[int, Any]): + return cls({}, arrays=arrays) + + @property + def shards(self) -> list[int]: + return sorted(set(self.files) | set(self._fixed)) + + def get(self, shard: int): + shard = int(shard) + if shard in self._fixed: + return self._fixed[shard] + if shard in self._cache: + self._cache.move_to_end(shard) + return self._cache[shard] + if shard not in self.files: + raise KeyError(f"No embedding file for shard {shard}; available shards: {self.shards}") + arr = _load_array_file(self.files[shard], self.key) + self._cache[shard] = arr + while len(self._cache) > self.cache_size: + self._cache.popitem(last=False) + return arr + + +def write_flat_embeddings(index: pd.DataFrame, sharded: ShardedEmbeddings, path: str | Path) -> np.ndarray: + """Write per-shard embeddings as one ``.npy`` array in **dataset row order**. + + ``index`` is the output of :func:`build_rotation_index` for the dataset that will be + evaluated with the flat layout (so its ``flat_offset`` column defines the target rows). + The file is created with :func:`numpy.lib.format.open_memmap` (a valid ``.npy`` header), and + the returned array is a read-only memory map of it. + """ + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + order = index.sort_values("row") + total = int(order["n_conformers"].sum()) + first = sharded.get(int(order["shard"].iloc[0])) + if _is_fingerprint(first): + raise ValueError("write_flat_embeddings supports numeric arrays only.") + out = np.lib.format.open_memmap(path, mode="w+", dtype=first.dtype, shape=(total, first.shape[1])) + shard_col = order["shard"].to_numpy() + starts = np.flatnonzero(np.r_[True, shard_col[1:] != shard_col[:-1]]) + ends = np.r_[starts[1:], len(order)] + local = order["local_offset"].to_numpy() + counts = order["n_conformers"].to_numpy() + flat = order["flat_offset"].to_numpy() + for a, b in zip(starts, ends): + arr = sharded.get(int(shard_col[a])) + lo = int(local[a]) + hi = int(local[b - 1] + counts[b - 1]) + if not np.array_equal(local[a + 1 : b], (local[a:b] + counts[a:b])[:-1]): + raise ValueError(f"Rows of shard {shard_col[a]} are not contiguous in dataset order.") + pos = int(flat[a]) + out[pos : pos + (hi - lo)] = arr[lo:hi] + out.flush() + del out + return np.load(path, mmap_mode="r") -def _slice_flat_embeddings(embeddings: EmbeddingArray, offset: int, count: int): - if isinstance(embeddings.array, np.ndarray): - return embeddings.array[offset : offset + count] - return embeddings.array[offset : offset + count] +@dataclass +class _Source: + layout: str + flat: EmbeddingArray | None = None + sharded: ShardedEmbeddings | None = None + by_key: Mapping[str, Any] | None = None -def _aggregate_metric(values: Iterable[float | None]) -> tuple[float, float]: - arr = np.asarray([v for v in values if v is not None and np.isfinite(v)], dtype=float) - if arr.size == 0: - return float("nan"), float("nan") - return float(arr.mean()), float(np.median(arr)) +def _is_fingerprint(obj: Any) -> bool: + return isinstance(obj, (list, tuple)) + + +def _slice(source: _Source, row: dict): + n = int(row["n_conformers"]) + if source.layout == "flat": + start = int(row["flat_offset"]) + arr = source.flat.array + return arr[start : start + n], source.flat.kind == "fingerprint" or _is_fingerprint(arr) + if source.layout == "by-shard": + arr = source.sharded.get(int(row["shard"])) + start = int(row["local_offset"]) + if start + n > len(arr): + raise ValueError( + f"Shard {row['shard']} embeddings have {len(arr)} rows but key {row['key']} needs rows " + f"[{start}, {start + n}). Check the offset layout (--offset-mode)." + ) + return arr[start : start + n], _is_fingerprint(arr) + Z = source.by_key[row["key"]] + if not _is_fingerprint(Z) and len(Z) != n: + raise ValueError(f"Key {row['key']}: {len(Z)} embeddings for {n} conformers") + return Z, _is_fingerprint(Z) + + +# --------------------------------------------------------------------------- +# Worker +# --------------------------------------------------------------------------- + +_STATE: dict[str, Any] = {} + + +def _pin_threads() -> None: + for var in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS", "VECLIB_MAXIMUM_THREADS"): + os.environ[var] = "1" + try: # pragma: no cover - optional dependency + from threadpoolctl import threadpool_limits + + threadpool_limits(1) + except Exception: + pass + + +def _evaluate_rows(rows: list[dict]) -> list[dict]: + from three_dbench.datasets.serialization import blocks_to_mols + from three_dbench.rotation.evaluation import ( + pairwise_distances_from_embeddings, + pairwise_distances_from_fingerprint, + rmsd_matrix_from_one_mol, + ) + from three_dbench.rotation.metrics import compute_geometry_metrics + + dataset = _STATE["dataset"] + source: _Source = _STATE["source"] + spec = _STATE["spec"] + metrics: Sequence[str] = _STATE["metrics"] + out = [] + for row in rows: + rec = dataset[int(row["row"])] + if not rec["mol_blocks"]: + raise ValueError("Rotation dataset is missing MolBlocks. Rebuild without --no-mol-blocks.") + Z, is_fp = _slice(source, row) + base = {"key": row["key"], "shard": int(row["shard"]), "n_conformers": int(row["n_conformers"])} + try: + mols = blocks_to_mols(rec["mol_blocks"], sanitize=False) + D = rmsd_matrix_from_one_mol(mols) + except (ValueError, RuntimeError) as exc: + # e.g. conformers whose heavy-atom counts differ after RemoveHs; reported, not fatal + out.append({**base, "space": None, "error": f"{type(exc).__name__}: {exc}"}) + continue + deg = rec.get("torsion_deg") + if is_fp: + Delta = pairwise_distances_from_fingerprint(list(Z)) + vals = compute_geometry_metrics(D, Delta, torsion_deg=deg, spec=spec, Z=None, delta_metric="tanimoto") + out.append({**base, "space": "tanimoto", **vals}) + continue + Zd = np.asarray(Z, dtype=np.float64) + for metric in metrics: + Delta = pairwise_distances_from_embeddings(Zd, metric=metric) + vals = compute_geometry_metrics(D, Delta, torsion_deg=deg, spec=spec, Z=Zd, delta_metric=metric) + out.append({**base, "space": metric, **vals}) + return out + + +def _run_rows(records: list[dict], *, n_jobs: int, chunk_size: int, progress: bool, t0: float) -> list[dict]: + chunks = [records[i : i + chunk_size] for i in range(0, len(records), chunk_size)] + results: list[dict] = [] + if n_jobs <= 1 or len(chunks) <= 1: + for i, chunk in enumerate(chunks): + results.extend(_evaluate_rows(chunk)) + if progress and (i + 1) % 20 == 0: + print(f"[rotation] {len(results)} rows done, {time.time() - t0:.0f}s", flush=True) + return results + import multiprocessing as mp + from concurrent.futures import ProcessPoolExecutor + + try: + ctx = mp.get_context("fork") + except ValueError as exc: # pragma: no cover - platforms without fork + raise RuntimeError("n_jobs > 1 requires the 'fork' start method (Linux).") from exc + with ProcessPoolExecutor(max_workers=int(n_jobs), mp_context=ctx, initializer=_pin_threads) as ex: + for i, part in enumerate(ex.map(_evaluate_rows, chunks)): + results.extend(part) + if progress and (i + 1) % 50 == 0: + print(f"[rotation] {i + 1}/{len(chunks)} chunks, {time.time() - t0:.0f}s", flush=True) + return results + + +def _offset_drift_records(selected: pd.DataFrame, failed_keys: set[str]) -> tuple[list[dict], dict]: + """Rows after a failed molecule of the same shard, with the shifted per-shard offset.""" + records: list[dict] = [] + info: dict[str, Any] = {} + for shard, grp in selected.groupby("shard", sort=False): + g = grp.sort_values("local_offset") + failed = g["key"].isin(failed_keys).to_numpy() + if not failed.any(): + continue + shift = np.cumsum(np.where(failed, g["n_conformers"].to_numpy(), 0)) + shift_before = np.concatenate([[0], shift[:-1]]) + affected = (shift_before > 0) & ~failed + sub = g[affected].copy() + sub["local_offset"] = sub["local_offset"].to_numpy() - shift_before[affected] + records.extend(sub[["row", "key", "shard", "n_conformers", "flat_offset", "local_offset"]].to_dict("records")) + info[str(int(shard))] = { + "failed_keys": g.loc[failed, "key"].tolist(), + "first_failed_local_offset": int(g.loc[failed, "local_offset"].iloc[0]), + "n_affected_molecules": int(affected.sum()), + "max_shift_rows": int(shift[-1]), + } + return records, info + + +def _merge_offset_drift(per_key: pd.DataFrame, drift_rows: list[dict]) -> pd.DataFrame: + metric_cols = [c for c in per_key.columns if c not in {"key", "shard", "n_conformers", "space"}] + out = per_key.copy() + for c in metric_cols: + out[f"{c}__offset_drift"] = out[c] + if not drift_rows: + return out + drift = pd.DataFrame(drift_rows).set_index(["key", "space"]) + pos = out.set_index(["key", "space"]).index + hit = pos.isin(drift.index) + aligned = drift.reindex(pos[hit]) + for c in metric_cols: + out.loc[hit, f"{c}__offset_drift"] = aligned[c].to_numpy() + return out + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + + +def summarize_rotation_results(per_key: pd.DataFrame, *, model_name: str, metric_version: str) -> pd.DataFrame: + """Mean / median over molecules of every metric (non-finite values excluded).""" + rows = [] + if per_key.empty: + return pd.DataFrame(columns=["model", "metric_version", "space", "metric", "mean", "median", "n", "n_finite"]) + metric_cols = [c for c in per_key.columns if c not in {"key", "shard", "n_conformers", "space"}] + for space, grp in per_key.groupby("space", sort=False): + for col in metric_cols: + vals = grp[col].to_numpy(dtype=np.float64) + fin = vals[np.isfinite(vals)] + rows.append( + { + "model": model_name, + "metric_version": metric_version, + "space": space, + "metric": col, + "mean": float(fin.mean()) if fin.size else float("nan"), + "median": float(np.median(fin)) if fin.size else float("nan"), + "n": int(vals.size), + "n_finite": int(fin.size), + } + ) + return pd.DataFrame(rows) def evaluate_rotation_embeddings( *, - dataset_dir: Path, + dataset_dir: Path | None = None, + dataset: Any = None, embeddings: EmbeddingArray | None = None, - embeddings_by_key: dict[str, np.ndarray] | None = None, + embeddings_by_shard: ShardedEmbeddings | Mapping[int, Any] | None = None, + embeddings_by_key: Mapping[str, Any] | None = None, output_dir: Path | None = None, model_name: str = "custom", metrics: Iterable[str] = ("cosine", "euclidean"), -) -> dict[str, dict[str, dict[str, float]]]: - """Evaluate rotation embeddings using the HF dataset.""" - if embeddings is None and embeddings_by_key is None: - raise ValueError("Provide either embeddings or embeddings_by_key.") + metric_version: str = "paper", + lie_k: int | None = None, + lie_include_self: bool | None = None, + as_variant: str | None = None, + extra_metrics: bool | None = None, + offset_mode: str = "auto", + shards: Iterable[int] | None = None, + molecule_list: str | Path | Iterable[str] | None = None, + sample_ratio: float | None = None, + sample_seed: int = 2027, + min_conformers: int = 2, + max_keys: int | None = None, + n_jobs: int = 1, + chunk_size: int = 256, + progress: bool = False, + replicate_offset_drift: bool = False, +) -> dict[str, Any]: + """Evaluate rotation embeddings against the HF rotation dataset. + + Exactly one of ``embeddings`` (flat, dataset row order), ``embeddings_by_shard`` (per-shard + arrays aligned with per-shard offsets) or ``embeddings_by_key`` must be given. + + Molecules whose RMSD matrix cannot be computed (e.g. conformers with different heavy-atom + counts after ``RemoveHs``) are skipped and listed in ``config["selection"]["failed_keys"]``. + + ``replicate_offset_drift`` (by-shard layout only) additionally recomputes every metric as the + published full geometry run did (Table 1 LIE@k / AS rows): in that run a molecule that failed + was skipped without advancing the per-shard embedding cursor, so every later molecule of the + same shard was evaluated with embeddings shifted back by the failed molecule's conformer count. + These values are stored in extra columns ``__offset_drift``; the regular columns always + use correctly aligned embeddings. Use it with complete shards, since only failures among the + evaluated molecules are detected. - dataset = load_rotation_dataset(dataset_dir) - results: dict[str, dict[str, dict[str, float]]] = {} + Returns a dict with ``per_key`` (DataFrame, one row per key and distance space), + ``summary`` (DataFrame) and ``config`` (dict). When ``output_dir`` is given, writes + ``{model_name}_per_key.parquet``, ``summary.csv`` and ``config.json``. + """ + from three_dbench.rotation.metrics import resolve_metric_spec + + given = [x is not None for x in (embeddings, embeddings_by_shard, embeddings_by_key)] + if sum(given) != 1: + raise ValueError("Provide exactly one of embeddings, embeddings_by_shard or embeddings_by_key.") + if dataset is None: + if dataset_dir is None: + raise ValueError("Provide dataset_dir or dataset.") + from three_dbench.datasets.rotation import load_rotation_dataset + + dataset = load_rotation_dataset(dataset_dir) + spec = resolve_metric_spec( + metric_version, + lie_k=lie_k, + lie_include_self=lie_include_self, + as_variant=as_variant, + extra_metrics=extra_metrics, + ) metrics = list(metrics) - for row in dataset: - key = row["key"] - if not row["mol_blocks"]: - raise ValueError("Rotation dataset is missing MolBlocks. Rebuild without --no-mol-blocks.") - mols = blocks_to_mols(row["mol_blocks"], sanitize=False) - D = rmsd_matrix_from_one_mol(mols) - deg = row.get("torsion_deg") - - if embeddings_by_key is not None: - Z = embeddings_by_key[key] - entry_metrics = {} - for metric in metrics: - entry_metrics[metric] = compute_all_geometry_metrics( - D, - Z=Z, - Delta=None, - deg=deg, - delta_metric=metric, - embedding_type="vector", - ) - else: - assert embeddings is not None - emb = embeddings - offset = int(row["offset"]) - count = int(row["n_conformers"]) - Z = _slice_flat_embeddings(emb, offset, count) - entry_metrics = {} - if emb.kind == "fingerprint": - entry_metrics["fingerprint"] = compute_all_geometry_metrics( - D, - Z=Z, - Delta=None, - deg=deg, - embedding_type="fp", - ) - else: - for metric in metrics: - entry_metrics[metric] = compute_all_geometry_metrics( - D, - Z=Z, - Delta=None, - deg=deg, - delta_metric=metric, - embedding_type="vector", - ) - - results[key] = entry_metrics + t0 = time.time() + index = build_rotation_index(dataset, offset_mode=offset_mode) + if embeddings is not None: + source = _Source("flat", flat=embeddings) + total = int(index["n_conformers"].sum()) + if len(embeddings.array) < total: + raise ValueError(f"Flat embeddings have {len(embeddings.array)} rows < {total} conformers in the dataset.") + elif embeddings_by_shard is not None: + sharded = ( + embeddings_by_shard + if isinstance(embeddings_by_shard, ShardedEmbeddings) + else ShardedEmbeddings.from_arrays(embeddings_by_shard) + ) + validate_per_shard_offsets(index) + source = _Source("by-shard", sharded=sharded) + else: + source = _Source("by-key", by_key=embeddings_by_key) + + selected, stats = select_rows( + index, + shards=shards, + molecule_list=molecule_list, + sample_ratio=sample_ratio, + sample_seed=sample_seed, + min_conformers=min_conformers, + max_keys=max_keys, + ) + if source.layout == "by-shard": + missing = sorted(set(selected["shard"].unique()) - set(source.sharded.shards)) + if missing: + raise ValueError( + f"No embedding file for shards {missing}. Restrict the evaluation with shards=... " + f"(available: {source.sharded.shards})." + ) + selected = selected.sort_values(["shard", "local_offset"], kind="stable") + if replicate_offset_drift and source.layout != "by-shard": + raise ValueError("replicate_offset_drift requires the by-shard layout.") + records = selected[["row", "key", "shard", "n_conformers", "flat_offset", "local_offset"]].to_dict("records") + _STATE.update(dataset=dataset, source=source, spec=spec, metrics=metrics) + try: + results = _run_rows(records, n_jobs=n_jobs, chunk_size=chunk_size, progress=progress, t0=t0) + failures = [r for r in results if r.get("space") is None] + drift_results: list[dict] = [] + drift_info: dict[str, Any] = {} + if replicate_offset_drift and failures: + drift_records, drift_info = _offset_drift_records(selected, {r["key"] for r in failures}) + if progress: + print(f"[rotation] offset-drift pass over {len(drift_records)} molecules", flush=True) + drift_results = _run_rows(drift_records, n_jobs=n_jobs, chunk_size=chunk_size, progress=progress, t0=t0) + finally: + _STATE.clear() + + per_key = pd.DataFrame([r for r in results if r.get("space") is not None]) + stats["n_failed"] = len(failures) + stats["failed_keys"] = {r["key"]: r["error"] for r in failures} + if failures: + print(f"[rotation] {len(failures)} molecule(s) could not be evaluated (see config.json: failed_keys)") + if replicate_offset_drift and not per_key.empty: + per_key = _merge_offset_drift(per_key, [r for r in drift_results if r.get("space") is not None]) + stats["offset_drift"] = drift_info + summary = summarize_rotation_results(per_key, model_name=model_name, metric_version=metric_version) + config = { + "model_name": model_name, + "metric_version": metric_version, + "metric_spec": spec.to_dict(), + "distance_spaces": metrics, + "layout": source.layout, + "offset_mode": index.attrs.get("offset_mode"), + "selection": stats, + "n_jobs": int(n_jobs), + "runtime_seconds": round(time.time() - t0, 2), + } + try: + from three_dbench import __version__ + + config["three_dbench_version"] = __version__ + except Exception: # pragma: no cover + pass if output_dir is not None: + output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) - out_json = output_dir / f"{model_name}_per_key.json" - out_json.write_text(json.dumps(results, indent=2), encoding="utf-8") - - summary_rows = [] - for metric_name in next(iter(results.values())).keys(): - metric_keys = next(iter(results.values()))[metric_name].keys() - for metric_key in metric_keys: - values = [results[k][metric_name].get(metric_key) for k in results] - mean_val, med_val = _aggregate_metric(values) - summary_rows.append( - { - "model": model_name, - "metric": f"{metric_name}:{metric_key}", - "mean": mean_val, - "median": med_val, - "n": len(values), - } - ) - summary_df = pd.DataFrame(summary_rows) - summary_df.to_csv(output_dir / "summary.csv", index=False) + per_key.to_parquet(output_dir / f"{model_name}_per_key.parquet", index=False) + summary.to_csv(output_dir / "summary.csv", index=False) + (output_dir / "config.json").write_text(json.dumps(config, indent=2), encoding="utf-8") + return {"per_key": per_key, "summary": summary, "config": config} - return results + +__all__ = [ + "LAYOUTS", + "OFFSET_MODES", + "TABLE1_METRICS", + "ShardedEmbeddings", + "build_rotation_index", + "evaluate_rotation_embeddings", + "find_shard_files", + "read_molecule_list", + "select_rows", + "summarize_rotation_results", + "validate_per_shard_offsets", + "write_flat_embeddings", +] diff --git a/src/three_dbench/benchmarks/trajectory.py b/src/three_dbench/benchmarks/trajectory.py index 53393a5..26af3cd 100644 --- a/src/three_dbench/benchmarks/trajectory.py +++ b/src/three_dbench/benchmarks/trajectory.py @@ -1,20 +1,38 @@ -"""Trajectory benchmark evaluation for user-provided embeddings.""" +"""Trajectory (energy) benchmark evaluation for user-provided embeddings. + +Defaults reproduce the protocol of the published energy tables (paper Tables 3, 6 and 7): +float64 energies, the ``legacy`` window scheme (per-molecule ``default_rng(2025)``, 100 windows of +2,000 frames drawn from ``[0, 98,000)``), ``paper`` metric definitions, cosine distance for vectors and +Tanimoto distance for fingerprints, and mean +/- 1.96 sd/sqrt(n) pooled over all molecule-windows. +See ``docs/metrics/energy.md``. +""" from __future__ import annotations +import hashlib import json -from collections.abc import Iterable +import multiprocessing as mp +import time +from collections.abc import Iterable, Iterator, Mapping, Sequence from pathlib import Path +from typing import Any import numpy as np import pandas as pd -from three_dbench.datasets.traj import load_traj_energy_dataset -from three_dbench.traj.evaluation import ( - compute_energy_metrics_from_condensed, - ks_wasserstein_against_energy_diff, - pairwise_distances_from_embeddings_large, - thresholded_smoothness, +from three_dbench.datasets.traj import check_energy_precision, detect_quantized_energies, load_traj_energy_dataset +from three_dbench.traj.io import embedding_length, resolve_embedding +from three_dbench.traj.protocol import ( + LEGACY_TRAJ_LEN, + METRIC_VERSIONS, + WINDOW_SCHEMES, + compute_window_metrics, + draw_window_starts, + order_molecules, + resolve_embed_metric, + resolve_molecules, + single_threaded_blas, + window_distances, ) @@ -28,64 +46,48 @@ def _mean_ci(values: Iterable[float]) -> tuple[float, float]: return mean, float(ci95) -def evaluate_trajectory_embeddings( - *, - dataset_dir: Path, - embeddings_by_mol: dict[str, np.ndarray], - output_dir: Path | None = None, - model_name: str = "custom", - n_samples: int = 100, - window: int = 2000, - metric_embed: str = "cosine", - block_size: int = 4096, - random_seed: int = 2025, -) -> tuple[pd.DataFrame, pd.DataFrame]: - """Evaluate trajectory embeddings against the HF energy dataset.""" - dataset = load_traj_energy_dataset(dataset_dir) - rng = np.random.default_rng(random_seed) - details_rows = [] - - for row in dataset: - mol_type = row["mol_type"] - energies = np.asarray(row["energies"], dtype=np.float64) - embeddings = embeddings_by_mol[mol_type] - if embeddings.shape[0] != energies.shape[0]: - raise ValueError(f"Length mismatch for {mol_type}: {embeddings.shape[0]} != {energies.shape[0]}") - - max_start = energies.shape[0] - window - if max_start <= 0: - raise ValueError(f"Window size {window} exceeds trajectory length for {mol_type}.") - starts = rng.integers(0, max_start, size=min(n_samples, max_start), endpoint=False) - - for start in starts: - end = int(start + window) - energy_window = energies[start:end] - Z = embeddings[start:end] - D = pairwise_distances_from_embeddings_large( - Z, - metric=metric_embed, - block_size=block_size, - out_mode="condensed", - progress=False, - ) - m1 = compute_energy_metrics_from_condensed(D, energy_window) - m2 = thresholded_smoothness(D, energy_window, eps=1e-12) - m3 = ks_wasserstein_against_energy_diff(D, energy_window) - metrics = {**m1, **m2, **m3} - - for metric, value in metrics.items(): - details_rows.append( - { - "mol_type": mol_type, - "sample_start": int(start), - "sample_end": int(end), - "model": model_name, - "metric": metric, - "value": float(value) if np.isscalar(value) else np.nan, - } - ) +def _window_rows(task: dict[str, Any]) -> list[dict[str, Any]]: + D = window_distances( + task["X"], + metric=task["metric"], + metric_version=task["metric_version"], + block_size=task["block_size"], + ) + metrics = compute_window_metrics( + D, + task["E"], + metric_version=task["metric_version"], + time_ordered=task["time_ordered"], + ) + return [ + { + "mol_type": task["mol_type"], + "sample_start": task["start"], + "sample_end": task["end"], + "model": task["model"], + "metric": metric, + "value": float(value) if np.isscalar(value) else np.nan, + } + for metric, value in metrics.items() + ] - details_df = pd.DataFrame(details_rows) + +_WORKER_LIMITS: Any = None + + +def _worker_init() -> None: + global _WORKER_LIMITS + try: + from threadpoolctl import threadpool_limits + + _WORKER_LIMITS = threadpool_limits(limits=1) + except ImportError: # pragma: no cover + _WORKER_LIMITS = None + + +def _summarize(details_df: pd.DataFrame) -> pd.DataFrame: + if details_df.empty: + return pd.DataFrame(columns=["model", "metric", "mean", "ci95", "n"]) grouped = details_df.groupby(["model", "metric"])["value"].apply(list).reset_index() summary_rows = [] for _, row in grouped.iterrows(): @@ -96,31 +98,184 @@ def evaluate_trajectory_embeddings( "metric": row["metric"], "mean": mean_val, "ci95": ci_val, - "n": len(row["value"]), + "n": int(np.isfinite(np.asarray(row["value"], dtype=float)).sum()), } ) - summary_df = pd.DataFrame(summary_rows) + return pd.DataFrame(summary_rows) - if output_dir is not None: - output_dir.mkdir(parents=True, exist_ok=True) - details_path = output_dir / "details.csv" - summary_path = output_dir / "summary.csv" - details_df.to_csv(details_path, index=False) - summary_df.to_csv(summary_path, index=False) - config_path = output_dir / "config.json" - config_path.write_text( - json.dumps( - { + +def evaluate_trajectory_embeddings( + *, + dataset_dir: Path, + embeddings_by_mol: Mapping[str, Any], + output_dir: Path | None = None, + model_name: str = "custom", + n_samples: int = 100, + window: int = 2000, + metric_embed: str | None = None, + block_size: int = 4096, + random_seed: int = 2025, + window_scheme: str = "legacy", + metric_version: str = "paper", + n_jobs: int = 1, + molecules: Sequence[str] | None = None, + energy_precision_check: str = "error", + time_ordered: bool = False, + legacy_traj_len: int = LEGACY_TRAJ_LEN, + verbose: bool = True, +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Evaluate trajectory embeddings against the energy dataset saved at ``dataset_dir``. + + Args: + dataset_dir: ``save_to_disk`` copy of the ``traj_energies`` config (columns ``mol_type``, + ``n_frames``, ``energies``). + embeddings_by_mol: ``{mol_type: array | list of RDKit bit vectors | zero-arg loader}``. + metric_embed: ``cosine`` / ``euclidean`` / ``tanimoto``; ``None`` picks Tanimoto for + fingerprint lists and cosine for vectors (the published setting). + window_scheme: ``legacy`` (published) or ``shared`` (0.1.0 CLI behaviour). + metric_version: ``paper`` (published definitions) or ``v2`` (appendix definitions). + n_jobs: worker processes (windows are evaluated in parallel; results are order-preserving). + molecules: optional subset (``aspirin`` or ``rmd17_aspirin``). + energy_precision_check: ``error`` / ``warn`` / ``ignore`` for quantized energies. + time_ordered: declare frames to be in simulation-time order (v2 ``TS``/``Smoothness`` are NaN + otherwise). rMD17 frames are not time-ordered. + legacy_traj_len: trajectory length assumed by the legacy scheme (100,000 for rMD17). + + Returns: + ``(details_df, summary_df)``; also written to ``output_dir`` as ``details.csv``, + ``summary.csv`` and ``config.json`` when given. + """ + if window_scheme not in WINDOW_SCHEMES: + raise ValueError(f"window_scheme must be one of {WINDOW_SCHEMES}, got {window_scheme!r}") + if metric_version not in METRIC_VERSIONS: + raise ValueError(f"metric_version must be one of {METRIC_VERSIONS}, got {metric_version!r}") + t0 = time.time() + dataset = load_traj_energy_dataset(dataset_dir) + dataset_mols = [str(m) for m in dataset["mol_type"]] + n_frames_all = {m: int(n) for m, n in zip(dataset_mols, dataset["n_frames"])} + + selected = resolve_molecules(molecules, dataset_mols) + missing = [m for m in selected if m not in embeddings_by_mol] + if missing: + raise KeyError(f"No embeddings for {missing}; provide them or restrict with molecules=/--molecules") + + starts_all = draw_window_starts( + n_frames_all if window_scheme == "shared" else {m: n_frames_all[m] for m in selected}, + window_scheme=window_scheme, + n_samples=n_samples, + window=window, + random_seed=random_seed, + legacy_traj_len=legacy_traj_len, + ) + ordered = order_molecules(selected, window_scheme) + + energies: dict[str, np.ndarray] = {} + reports = [] + energy_sha256: dict[str, str] = {} + row_index = {m: i for i, m in enumerate(dataset_mols)} + for mol_type in ordered: + row = dataset[row_index[mol_type]] + E = np.asarray(row["energies"], dtype=np.float64) + if E.shape[0] != n_frames_all[mol_type]: + raise ValueError(f"n_frames {n_frames_all[mol_type]} != len(energies) {E.shape[0]} for {mol_type}") + energies[mol_type] = E + energy_sha256[mol_type] = hashlib.sha256(np.ascontiguousarray(E).tobytes()).hexdigest() + reports.append(detect_quantized_energies(E, mol_type=mol_type)) + flagged = check_energy_precision(reports, policy=energy_precision_check) + + resolved_metric: dict[str, str] = {} + + def _tasks() -> Iterator[dict[str, Any]]: + for mol_type in ordered: + X_all = resolve_embedding(embeddings_by_mol[mol_type]) + E = energies[mol_type] + if embedding_length(X_all) != E.shape[0]: + raise ValueError(f"Length mismatch for {mol_type}: {embedding_length(X_all)} != {E.shape[0]}") + metric = resolve_embed_metric(metric_embed, X_all) + resolved_metric[mol_type] = metric + if verbose: + print( + f"[{model_name}] {mol_type}: {len(starts_all[mol_type])} windows x {window} frames " + f"({metric}, {metric_version}, {window_scheme})", + flush=True, + ) + for start in starts_all[mol_type]: + s = int(start) + e = s + int(window) + yield { + "mol_type": mol_type, + "start": s, + "end": e, "model": model_name, - "n_samples": n_samples, - "window": window, - "metric_embed": metric_embed, + "E": E[s:e], + "X": X_all[s:e], + "metric": metric, + "metric_version": metric_version, "block_size": block_size, - "random_seed": random_seed, - }, - indent=2, - ), - encoding="utf-8", - ) + "time_ordered": time_ordered, + } + + details_rows: list[dict[str, Any]] = [] + if n_jobs is None or n_jobs <= 1: + with single_threaded_blas(): + for task in _tasks(): + details_rows.extend(_window_rows(task)) + else: + with mp.get_context().Pool(processes=int(n_jobs), initializer=_worker_init) as pool: + for rows in pool.imap(_window_rows, _tasks(), chunksize=1): + details_rows.extend(rows) + + details_df = pd.DataFrame( + details_rows, columns=["mol_type", "sample_start", "sample_end", "model", "metric", "value"] + ) + summary_df = _summarize(details_df) + elapsed = time.time() - t0 + + if output_dir is not None: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + details_df.to_csv(output_dir / "details.csv", index=False) + summary_df.to_csv(output_dir / "summary.csv", index=False) + config = { + "model": model_name, + "n_samples": n_samples, + "window": window, + "metric_embed": metric_embed, + "metric_embed_resolved": resolved_metric, + "block_size": block_size, + "random_seed": random_seed, + "window_scheme": window_scheme, + "legacy_traj_len": legacy_traj_len if window_scheme == "legacy" else None, + "metric_version": metric_version, + "time_ordered": time_ordered, + "molecules": ordered, + "n_jobs": n_jobs, + "energy_precision_check": energy_precision_check, + "energy_precision_flagged": [r.as_dict() for r in flagged], + "energy_sha256": energy_sha256, + "window_starts": {m: [int(s) for s in starts_all[m]] for m in ordered}, + "dataset_dir": str(dataset_dir), + "elapsed_seconds": round(elapsed, 3), + "versions": _versions(), + } + (output_dir / "config.json").write_text(json.dumps(config, indent=2), encoding="utf-8") return details_df, summary_df + + +def _versions() -> dict[str, str]: + import platform + + import scipy + import sklearn + + out = {"python": platform.python_version(), "numpy": np.__version__, "scipy": scipy.__version__} + out["scikit-learn"] = sklearn.__version__ + out["pandas"] = pd.__version__ + try: + from importlib.metadata import version + + out["3dcs"] = version("3dcs") + except Exception: + pass + return out diff --git a/src/three_dbench/chirality/evaluation.py b/src/three_dbench/chirality/evaluation.py index e22a888..4853322 100644 --- a/src/three_dbench/chirality/evaluation.py +++ b/src/three_dbench/chirality/evaluation.py @@ -1,8 +1,29 @@ +"""Chirality (stereoisomer separation) metrics for 3DCS. + +The per-molecule protocol and every metric definition are documented in +``docs/metrics/chirality.md``. Two switches control the definitions: + +``distance`` + Representation distance used for continuous embeddings: ``"euclidean"`` (default; this is + what produced the published Table 2) or ``"cosine"``. RDKit fingerprints always use Tanimoto + distance, whatever ``distance`` is. + +``metric_version`` + ``"paper"`` (default) reproduces the code path that produced the published numbers. + ``"v2"`` applies the corrected definitions listed in ``docs/metrics/chirality.md`` + (tie-aware NN1 restricted to points with a same-class partner, centroid DBI, explicit Hopkins + population, best-k silhouette on the selected distance). + +Importing this module has no side effects (no directories are created, no files are read). +""" + +import functools import itertools import json import os import pickle import re +import warnings from collections.abc import Sequence from concurrent.futures import ProcessPoolExecutor, as_completed from dataclasses import dataclass @@ -13,9 +34,6 @@ import pandas as pd from tqdm import tqdm -from three_dbench.utils.paths import DATA_ROOT as GLOBAL_DATA_ROOT -from three_dbench.utils.paths import RESULTS_ROOT as GLOBAL_RESULTS_ROOT - # ===== RDKit fingerprint support ===== try: from rdkit import DataStructs as DS @@ -30,17 +48,25 @@ # ===== Optional sklearn support (preferred when available) ===== try: from sklearn.cluster import KMeans - from sklearn.metrics import davies_bouldin_score, pairwise_distances, roc_auc_score, silhouette_score + from sklearn.metrics import pairwise_distances, roc_auc_score, silhouette_score _HAS_SK = True except Exception: roc_auc_score = None silhouette_score = None KMeans = None - davies_bouldin_score = None pairwise_distances = None _HAS_SK = False +_silhouette_score = silhouette_score + +DISTANCES = ("euclidean", "cosine") +METRIC_VERSIONS = ("paper", "v2") +#: Hopkins statistic is only computed for molecules with at least this many conformers. +HOPKINS_MIN_N = 10 +#: Small constant added to between-cluster distances in the v2 Davies-Bouldin index (App. C.5). +DBI_EPS = 1e-12 + # ===================== 1) Parse key: mol_id + en_id ===================== _KEY_RE = re.compile(r"^(?P[^:]+)::en(?P\d+)(?:_.*)?$") @@ -68,15 +94,18 @@ class FlatIndex: mol_labels: np.ndarray # Global array of mol_ids -def build_flat_index(key_to_mols: dict[str, list[Any]]) -> FlatIndex: +def build_flat_index_from_counts(key_to_counts: dict[str, int]) -> FlatIndex: + """Build flat indices from a mapping of key to conformer counts (dataset row order).""" keys, counts, offsets = [], [], [] mol_idx_map: dict[str, list[int]] = {} en_labels = [] mol_labels = [] cursor = 0 - for k, mol_list in key_to_mols.items(): - n = len(mol_list) if mol_list is not None else 0 + for k, n in key_to_counts.items(): + n = int(n) + if n < 0: + raise ValueError(f"Negative conformer count {n} for key {k!r}.") keys.append(k) counts.append(n) offsets.append(cursor) @@ -97,33 +126,13 @@ def build_flat_index(key_to_mols: dict[str, list[Any]]) -> FlatIndex: return FlatIndex(keys, counts, offsets, mol_to_indices, en_labels, mol_labels) -def build_flat_index_from_counts(key_to_counts: dict[str, int]) -> FlatIndex: - """Build flat indices from a mapping of key to conformer counts.""" - keys, counts, offsets = [], [], [] - mol_idx_map: dict[str, list[int]] = {} - en_labels = [] - mol_labels = [] - - cursor = 0 - for k, n in key_to_counts.items(): - keys.append(k) - counts.append(int(n)) - offsets.append(cursor) - - mol_id, en_id = parse_key_en(k) - if n > 0: - idxs = list(range(cursor, cursor + n)) - mol_idx_map.setdefault(mol_id, []).extend(idxs) - en_labels.extend([en_id] * n) - mol_labels.extend([mol_id] * n) - - cursor += n +def build_flat_index(key_to_mols: dict[str, list[Any]]) -> FlatIndex: + """Build flat indices from a mapping of key to conformer lists.""" + return build_flat_index_from_counts(_counts_from_mols(key_to_mols)) - en_labels = np.asarray(en_labels, dtype=object) - mol_labels = np.asarray(mol_labels, dtype=object) - mol_to_indices = {mol: np.asarray(ix, dtype=int) for mol, ix in mol_idx_map.items()} - return FlatIndex(keys, counts, offsets, mol_to_indices, en_labels, mol_labels) +def _counts_from_mols(key_to_mols: dict[str, list[Any]]) -> dict[str, int]: + return {k: (len(v) if v is not None else 0) for k, v in key_to_mols.items()} # ===================== 3) Distance computation (continuous / fingerprint) ===================== @@ -195,10 +204,51 @@ def pairwise_distances_from_embeddings( return pairwise_distances(Z, metric=metric) -def distance_matrix_for_subset(embeddings: Union[np.ndarray, Sequence[Any]], idxs: np.ndarray, mode: str) -> np.ndarray: - """Return the distance matrix for the selected subset.""" +def cosine_distances(X: np.ndarray) -> np.ndarray: + """Cosine distance ``1 - cos(z_i, z_j)`` in float64. + + Rows are L2-normalised with a 1e-12 guard (``pairwise_distances_from_embeddings``) and the + diagonal is set to exactly 0, so that precomputed-distance routines such as + ``sklearn.metrics.silhouette_score`` accept the matrix. + """ + D = pairwise_distances_from_embeddings(X, metric="cosine", normalize_cosine=True) + np.fill_diagonal(D, 0.0) + return D + + +def l2_normalize_rows(X: np.ndarray) -> np.ndarray: + """Return ``X`` with unit-norm rows (1e-12 guard), keeping the input dtype.""" + X = np.asarray(X) + nrm = np.linalg.norm(X, axis=1, keepdims=True) + 1e-12 + return (X / nrm).astype(X.dtype, copy=False) + + +def _check_distance(distance: str) -> None: + if distance not in DISTANCES: + raise ValueError(f"Unknown distance {distance!r}; expected one of {DISTANCES}.") + + +def _check_metric_version(metric_version: str) -> None: + if metric_version not in METRIC_VERSIONS: + raise ValueError(f"Unknown metric_version {metric_version!r}; expected one of {METRIC_VERSIONS}.") + + +def distance_matrix_for_subset( + embeddings: Union[np.ndarray, Sequence[Any]], + idxs: np.ndarray, + mode: str, + distance: str = "euclidean", +) -> np.ndarray: + """Return the representation distance matrix Delta for the selected rows. + + Continuous embeddings use ``distance`` (``"euclidean"`` or ``"cosine"``); fingerprints always + use Tanimoto distance. + """ if mode == "continuous": + _check_distance(distance) X = embeddings[idxs] # (n,d) + if distance == "cosine": + return cosine_distances(X) return euclidean_distances(X) elif mode == "fingerprint": fps = [embeddings[i] for i in idxs] @@ -285,27 +335,46 @@ def _cluster_medoids_from_D(D: np.ndarray, y: np.ndarray) -> tuple[np.ndarray, n return np.asarray(medoids, dtype=int), np.asarray(S, dtype=float) -def davies_bouldin_from_D(D: np.ndarray, y: np.ndarray, mode: str) -> float: - """Compute the Davies-Bouldin index, falling back to a medoid approximation.""" +def davies_bouldin_from_D(D: np.ndarray, y: np.ndarray, mode: str, eps: float = 0.0) -> float: + """Medoid approximation of the Davies-Bouldin index on a distance matrix. + + This is the ``paper`` definition (the published code path; ``eps=0``). Cluster "centres" are + medoids of ``D`` and the scatter is the mean distance to the medoid. + """ y = np.asarray(y) if len(np.unique(y)) < 2 or D.shape[0] < 3: return np.nan - if mode == "continuous" and _HAS_SK and davies_bouldin_score is not None: - # Requires features rather than distances; fall back to the medoid version to avoid extra deps - pass - # medoid-DBI - np.unique(y) medoids, S = _cluster_medoids_from_D(D, y) # Distances between medoids M = D[np.ix_(medoids, medoids)].copy() np.fill_diagonal(M, np.inf) - R = (S[:, None] + S[None, :]) / M + R = (S[:, None] + S[None, :]) / (M + eps) np.fill_diagonal(R, -np.inf) DBI = np.mean(np.max(R, axis=1)) return float(DBI) +def davies_bouldin_centroid(X: np.ndarray, y: np.ndarray, eps: float = DBI_EPS) -> float: + """Davies-Bouldin index with Euclidean centroids, as written in App. C.5 (``v2``). + + ``S_i`` is the mean Euclidean distance of the members of class ``i`` to its centroid ``mu_i``, + ``M_ij = ||mu_i - mu_j||`` and ``R_ij = (S_i + S_j) / (M_ij + eps)``. + """ + y = np.asarray(y) + labels = np.unique(y) + if labels.size < 2 or X.shape[0] < 3: + return np.nan + X = np.asarray(X, dtype=np.float64) + cents = np.stack([X[y == c].mean(axis=0) for c in labels]) + S = np.array([np.linalg.norm(X[y == c] - cents[i], axis=1).mean() for i, c in enumerate(labels)]) + M = np.linalg.norm(cents[:, None, :] - cents[None, :, :], axis=2) + R = (S[:, None] + S[None, :]) / (M + eps) + np.fill_diagonal(R, -np.inf) + return float(np.mean(np.max(R, axis=1))) + + def nn1_leave_one_out_from_D(D: np.ndarray, y: np.ndarray) -> float: + """Leave-one-out 1-NN accuracy (``paper``): ties go to the lowest row index (``np.argmin``).""" n = D.shape[0] if n < 2 or len(np.unique(y)) < 2: return np.nan @@ -316,6 +385,30 @@ def nn1_leave_one_out_from_D(D: np.ndarray, y: np.ndarray) -> float: return float(np.mean(pred == y)) +def nn1_leave_one_out_v2(D: np.ndarray, y: np.ndarray) -> float: + """Tie-aware leave-one-out 1-NN accuracy over points that have a same-class partner (``v2``). + + For each point ``i`` whose class has at least one other member, the score is the fraction of + its exactly-tied nearest neighbours (``j != i``) that share its label, i.e. the expected + accuracy under uniformly random tie-breaking. Points whose class has no other member cannot be + classified correctly by construction and are excluded. Returns NaN when no point is eligible. + """ + y = np.asarray(y) + n = D.shape[0] + if n < 2 or np.unique(y).size < 2: + return np.nan + same = y[:, None] == y[None, :] + np.fill_diagonal(same, False) + eligible = same.any(axis=1) + if not eligible.any(): + return np.nan + D2 = np.array(D, dtype=np.float64, copy=True) + np.fill_diagonal(D2, np.inf) + tied = D2 == D2.min(axis=1, keepdims=True) + hits = (tied & same).sum(axis=1) / tied.sum(axis=1) + return float(np.mean(hits[eligible])) + + def boundary_clarity_from_D(D: np.ndarray, y: np.ndarray, q_intra: float = 0.90, q_inter: float = 0.10) -> float: y = np.asarray(y) if len(np.unique(y)) < 2: @@ -348,9 +441,14 @@ def boundary_clarity_from_D(D: np.ndarray, y: np.ndarray, q_intra: float = 0.90, # ===================== 5) Unsupervised clustering (KMeans / K-medoids) ===================== def hopkins_statistic(X: np.ndarray, m: Optional[int] = None, rng: Optional[np.random.Generator] = None) -> float: + """Hopkins statistic on raw vectors (Euclidean); NaN when ``n < HOPKINS_MIN_N``. + + ``m = max(10, int(0.1 n))`` uniform points are drawn in the bounding box of ``X`` and ``m`` data + points are sampled without replacement (``default_rng(0)`` unless ``rng`` is given). + """ rng = rng or np.random.default_rng(0) n, d = X.shape - if n < 10: + if n < HOPKINS_MIN_N: return np.nan if m is None: m = max(10, int(0.1 * n)) @@ -390,7 +488,6 @@ def total_cost(lab, meds): for _ in range(max_iter): improved = False for m_idx in range(k): - medoids[m_idx] for cand in range(n): if cand in medoids: continue @@ -408,26 +505,39 @@ def total_cost(lab, meds): return labels, medoids -# Optional third-party clustering libraries -try: - from sklearn_extra.cluster import KMedoids as _KMedoids -except Exception: - _KMedoids = None +@functools.cache +def _kmedoids_class(): + """Return ``sklearn_extra.cluster.KMedoids`` if importable (lazy; optional dependency).""" + try: + from sklearn_extra.cluster import KMedoids -try: - from sklearn.cluster import AgglomerativeClustering as _AgglomerativeClustering -except Exception: - _AgglomerativeClustering = None + return KMedoids + except Exception: + return None -try: - from sklearn.cluster import KMeans - from sklearn.metrics import silhouette_score as _silhouette_score - _HAS_SK = True -except Exception: - KMeans = None - _silhouette_score = None - _HAS_SK = False +@functools.cache +def _agglomerative_class(): + try: + from sklearn.cluster import AgglomerativeClustering + + return AgglomerativeClustering + except Exception: + return None + + +def resolve_unsup_kmax(n: int, unsup_kmax: Optional[int]) -> int: + """Largest k scanned by the best-k silhouette for a molecule with ``n`` conformers. + + ``None`` means ``n - 1`` (unbounded; the setting of the published run). An integer caps the scan + at ``min(unsup_kmax, n - 1)`` (the first public release hard-coded ``min(10, n - 1)``). + """ + if unsup_kmax is None: + return n - 1 + unsup_kmax = int(unsup_kmax) + if unsup_kmax < 2: + raise ValueError(f"unsup_kmax must be >= 2 or None, got {unsup_kmax}.") + return min(unsup_kmax, n - 1) def best_unsup_silhouette_from_D_or_X( @@ -439,10 +549,13 @@ def best_unsup_silhouette_from_D_or_X( n_init: int = 10, random_state: int = 0, fp_backend: str = "kmedoids", # {"kmedoids","agglomerative","custom"} + silhouette_on: str = "X", # {"X","D"}; continuous mode only ) -> tuple[float, Optional[int], Optional[np.ndarray]]: """Unified unsupervised interface across continuous and fingerprint modes. - - ``mode == "continuous"``: run sklearn KMeans on ``X`` with Euclidean silhouette. + - ``mode == "continuous"``: run sklearn KMeans on ``X``. The silhouette is computed on ``X`` with + Euclidean distance (``silhouette_on="X"``, ``paper``) or on the precomputed ``D`` + (``silhouette_on="D"``, ``v2``). - Otherwise, operate on the distance matrix ``D`` using the selected backend (``"kmedoids"``, ``"agglomerative"``, or ``"custom"``). @@ -452,6 +565,10 @@ def best_unsup_silhouette_from_D_or_X( if mode == "continuous": if X is None or not _HAS_SK or KMeans is None or _silhouette_score is None: return np.nan, None, None + if silhouette_on not in ("X", "D"): + raise ValueError(f"silhouette_on must be 'X' or 'D', got {silhouette_on!r}") + if silhouette_on == "D" and D is None: + raise ValueError("silhouette_on='D' requires D") n = X.shape[0] if n <= kmin: return np.nan, None, None @@ -459,9 +576,16 @@ def best_unsup_silhouette_from_D_or_X( best_s, best_k, best_lab = -1.0, None, None for k in range(kmin, max(kmin, kmax_eff) + 1): try: - km = KMeans(n_clusters=k, n_init=n_init, random_state=random_state) - lab = km.fit_predict(X) - s = _silhouette_score(X, lab, metric="euclidean") + with warnings.catch_warnings(): + # k close to n with duplicate rows: sklearn warns "Number of distinct clusters ... smaller + # than n_clusters"; the fit result is unchanged, only the log noise is suppressed. + warnings.filterwarnings("ignore", message="Number of distinct clusters") + km = KMeans(n_clusters=k, n_init=n_init, random_state=random_state) + lab = km.fit_predict(X) + if silhouette_on == "D": + s = _silhouette_score(D, lab, metric="precomputed") + else: + s = _silhouette_score(X, lab, metric="euclidean") if s > best_s: best_s, best_k, best_lab = s, k, lab except Exception: @@ -491,10 +615,11 @@ def _sil_from_D(dist: np.ndarray, labels: np.ndarray) -> float: # sklearn-extra KMedoids backend def _try_kmedoids(dist: np.ndarray, k: int) -> Optional[np.ndarray]: - if _KMedoids is None: + kmedoids_cls = _kmedoids_class() + if kmedoids_cls is None: return None try: - model = _KMedoids( + model = kmedoids_cls( n_clusters=k, metric="precomputed", method="pam", @@ -507,14 +632,15 @@ def _try_kmedoids(dist: np.ndarray, k: int) -> Optional[np.ndarray]: # sklearn Agglomerative backend (handles metric vs affinity kwarg changes) def _try_agglomerative(dist: np.ndarray, k: int) -> Optional[np.ndarray]: - if _AgglomerativeClustering is None: + agglo_cls = _agglomerative_class() + if agglo_cls is None: return None try: # Handle sklearn API differences: metric="precomputed" vs affinity="precomputed" try: - model = _AgglomerativeClustering(n_clusters=k, metric="precomputed", linkage="average") + model = agglo_cls(n_clusters=k, metric="precomputed", linkage="average") except TypeError: - model = _AgglomerativeClustering(n_clusters=k, affinity="precomputed", linkage="average") + model = agglo_cls(n_clusters=k, affinity="precomputed", linkage="average") lab = model.fit(dist).labels_ return lab except Exception: @@ -554,160 +680,141 @@ def _try_custom_pam(dist: np.ndarray, k: int) -> Optional[np.ndarray]: # ===================== 6) Evaluation interface (one row per molecule) ===================== -def evaluate_en_separation( - key_to_mols: dict[str, list[Any]], - embeddings: Union[np.ndarray, Sequence[Any]], - *, - per_mol_min_n: int = 2, - do_unsup_when_single_en: bool = False, # Run unsupervised metrics even with a single en-class - unsup_kmax: int = 50, - max_molecules: Optional[int] = None, # Limit number of molecules for quick testing -): - """Evaluate embeddings per molecule and return detailed and summary metrics.""" - flat = build_flat_index(key_to_mols) - N = sum(flat.counts) - - # Auto-detect embedding mode - if isinstance(embeddings, np.ndarray): - mode = "continuous" - if embeddings.shape[0] != N: - raise ValueError(f"Embeddings count {embeddings.shape[0]} != total molecules {N}.") - elif is_fingerprint_list(embeddings): - mode = "fingerprint" - if len(embeddings) != N: - raise ValueError(f"Fingerprint list length {len(embeddings)} != total molecules {N}.") - if not _HAS_RDKIT: - raise RuntimeError("RDKit is required to compute Tanimoto distance.") - else: - raise ValueError("Unrecognised embeddings: provide np.ndarray vectors or RDKit fingerprints.") - - rows = [] +@dataclass(frozen=True) +class ChiralitySettings: + """Per-molecule evaluation settings (see ``docs/metrics/chirality.md``).""" + + per_mol_min_n: int = 2 + do_unsup_when_single_en: bool = False + unsup_kmax: Optional[int] = None + distance: str = "euclidean" + metric_version: str = "paper" + + def validate(self) -> None: + _check_distance(self.distance) + _check_metric_version(self.metric_version) + if self.unsup_kmax is not None: + resolve_unsup_kmax(3, self.unsup_kmax) + + +def _empty_row(mol_id: str, n: int, mode_tag: str, embedding_mode: str, n_en: int, hop: float = np.nan) -> dict: + return { + "mol_id": mol_id, + "n": int(n), + "mode": mode_tag, + "ESA_AUC": np.nan, + "NN1_acc": np.nan, + "sil_sup": np.nan, + "DBI": np.nan, + "clarity": np.nan, + "hopkins": hop, + "sil_unsup": np.nan, + "k_unsup": np.nan, + "clarity_unsup": np.nan, + "n_en_classes": n_en, + "embedding_mode": embedding_mode, + } - cur_idx = 0 - mol_items = list(flat.mol_to_indices.items()) - if max_molecules is not None: - mol_items = mol_items[:max_molecules] - print(f"Quick test mode: processing only {len(mol_items)} molecules (out of {len(flat.mol_to_indices)})") +def evaluate_molecule( + mol_id: str, + sub_embeddings: Union[np.ndarray, Sequence[Any]], + y_en: np.ndarray, + mode: str, + settings: ChiralitySettings, +) -> dict: + """Compute all chirality metrics for one molecule. - for mol_id, idxs in tqdm(mol_items): - n = idxs.size - if n < per_mol_min_n: - rows.append( - { - "mol_id": mol_id, - "n": int(n), - "mode": "skip_small", - "ESA_AUC": np.nan, - "NN1_acc": np.nan, - "sil_sup": np.nan, - "DBI": np.nan, - "clarity": np.nan, - "hopkins": np.nan, - "sil_unsup": np.nan, - "k_unsup": np.nan, - "clarity_unsup": np.nan, - "n_en_classes": 0, - "embedding_mode": mode, - } - ) - continue + ``sub_embeddings`` holds the rows of this molecule (array ``(n, d)`` or list of fingerprints) + and ``y_en`` their stereoisomer labels, in dataset order. + """ + y_en = np.asarray(y_en, dtype=object) + n = int(len(y_en)) + v2 = settings.metric_version == "v2" + if n < settings.per_mol_min_n: + return _empty_row(mol_id, n, "skip_small", mode, n_en=0) - # Subset distance matrix - D = distance_matrix_for_subset(embeddings, idxs, mode) - y_en = flat.en_labels[idxs] - n_en = int(np.unique(y_en).size) + all_idx = np.arange(n) + D = distance_matrix_for_subset(sub_embeddings, all_idx, mode, distance=settings.distance) + n_en = int(np.unique(y_en).size) - # Hopkins statistic only applies in continuous mode; fingerprints return NaN - if mode == "continuous": - X_sub = embeddings[idxs] # for KMeans/Hopkins - hop = hopkins_statistic(X_sub) + if mode == "continuous": + X_raw = sub_embeddings + # v2: clustering / Hopkins geometry follows the selected distance (unit sphere for cosine) + X_sub = l2_normalize_rows(X_raw) if (v2 and settings.distance == "cosine") else X_raw + hop = hopkins_statistic(X_sub) + else: + X_raw = None + X_sub = None + hop = np.nan + + fp_backend_single = "custom" if v2 else "kmedoids" + silhouette_on = "D" if v2 else "X" + + if n_en >= 2: + auc = auc_diff_pairs_large_when_different(D, y_en) + sils = silhouette_with_labels_from_D(D, y_en) + clar = boundary_clarity_from_D(D, y_en) + if v2: + nn1 = nn1_leave_one_out_v2(D, y_en) + has_pair = bool(np.any(np.unique(y_en, return_counts=True)[1] >= 2)) + if not has_pair: + dbi = np.nan + elif mode == "continuous": + dbi = davies_bouldin_centroid(X_sub, y_en) + else: + dbi = davies_bouldin_from_D(D, y_en, mode=mode, eps=DBI_EPS) else: - X_sub = None - hop = np.nan - - # Supervised metrics (requires n_en >= 2) - if n_en >= 2: - auc = auc_diff_pairs_large_when_different(D, y_en) nn1 = nn1_leave_one_out_from_D(D, y_en) - sils = silhouette_with_labels_from_D(D, y_en) dbi = davies_bouldin_from_D(D, y_en, mode=mode) - clar = boundary_clarity_from_D(D, y_en) - # Optional unsupervised metrics even when supervised metrics are available - # Limit kmax to 10 for speed (most molecules have < 10 enantiomers anyway) - kmax_unsup = min(10, D.shape[0] - 1) - silu, k_star, lab_star = best_unsup_silhouette_from_D_or_X( - mode=mode, D=D, X=X_sub, kmin=2, kmax=kmax_unsup, fp_backend="custom" - ) - clar_unsup = boundary_clarity_from_D(D, lab_star) if lab_star is not None else np.nan - - rows.append( - { - "mol_id": mol_id, - "n": int(n), - "mode": "supervised+unsup", - "ESA_AUC": auc, - "NN1_acc": nn1, - "sil_sup": sils, - "DBI": dbi, - "clarity": clar, - "hopkins": hop, - "sil_unsup": silu, - "k_unsup": (np.nan if k_star is None else int(k_star)), - "clarity_unsup": clar_unsup, - "n_en_classes": n_en, - "embedding_mode": mode, - } - ) + kmax_unsup = resolve_unsup_kmax(n, settings.unsup_kmax) + silu, k_star, lab_star = best_unsup_silhouette_from_D_or_X( + mode=mode, D=D, X=X_sub, kmin=2, kmax=kmax_unsup, fp_backend="custom", silhouette_on=silhouette_on + ) + clar_unsup = boundary_clarity_from_D(D, lab_star) if lab_star is not None else np.nan + + return { + "mol_id": mol_id, + "n": int(n), + "mode": "supervised+unsup", + "ESA_AUC": auc, + "NN1_acc": nn1, + "sil_sup": sils, + "DBI": dbi, + "clarity": clar, + "hopkins": hop, + "sil_unsup": silu, + "k_unsup": (np.nan if k_star is None else int(k_star)), + "clarity_unsup": clar_unsup, + "n_en_classes": n_en, + "embedding_mode": mode, + } + + # Only one stereoisomer class present. + if not settings.do_unsup_when_single_en: + # v2: Hopkins is reported on the same molecule population as the supervised metrics. + return _empty_row(mol_id, n, "skip_single_en", mode, n_en=n_en, hop=(np.nan if v2 else hop)) + + kmax_unsup = resolve_unsup_kmax(n, settings.unsup_kmax) + silu, k_star, lab_star = best_unsup_silhouette_from_D_or_X( + mode=mode, D=D, X=X_sub, kmin=2, kmax=kmax_unsup, fp_backend=fp_backend_single, silhouette_on=silhouette_on + ) + clar_unsup = boundary_clarity_from_D(D, lab_star) if lab_star is not None else np.nan + row = _empty_row(mol_id, n, "unsupervised_only", mode, n_en=n_en, hop=(np.nan if v2 else hop)) + row.update( + { + "sil_unsup": silu, + "k_unsup": (np.nan if k_star is None else int(k_star)), + "clarity_unsup": clar_unsup, + } + ) + return row + + +def summarize_rows(rows: list[dict]) -> dict: + """Macro-average per-molecule rows (NaNs are skipped) into the summary dict.""" - else: - # Default: skip entirely when there is only one en-class - if not do_unsup_when_single_en: - rows.append( - { - "mol_id": mol_id, - "n": int(n), - "mode": "skip_single_en", - "ESA_AUC": np.nan, - "NN1_acc": np.nan, - "sil_sup": np.nan, - "DBI": np.nan, - "clarity": np.nan, - "hopkins": hop, - "sil_unsup": np.nan, - "k_unsup": np.nan, - "clarity_unsup": np.nan, - "n_en_classes": n_en, - "embedding_mode": mode, - } - ) - else: - silu, k_star, lab_star = best_unsup_silhouette_from_D_or_X( - mode=mode, D=D, X=X_sub, kmin=2, kmax=unsup_kmax - ) - clar_unsup = boundary_clarity_from_D(D, lab_star) if lab_star is not None else np.nan - rows.append( - { - "mol_id": mol_id, - "n": int(n), - "mode": "unsupervised_only", - "ESA_AUC": np.nan, - "NN1_acc": np.nan, - "sil_sup": np.nan, - "DBI": np.nan, - "clarity": np.nan, - "hopkins": hop, - "sil_unsup": silu, - "k_unsup": (np.nan if k_star is None else int(k_star)), - "clarity_unsup": clar_unsup, - "n_en_classes": n_en, - "embedding_mode": mode, - } - ) - cur_idx += 1 - - # Aggregate macro-average across molecules def _agg_mean(xs): a = np.asarray(xs, float) a = a[np.isfinite(a)] @@ -718,38 +825,143 @@ def _agg_med(xs): a = a[np.isfinite(a)] return float(np.median(a)) if a.size else np.nan - ESA = [r["ESA_AUC"] for r in rows] - NN1 = [r["NN1_acc"] for r in rows] - SIL = [r["sil_sup"] for r in rows] - DBI = [r["DBI"] for r in rows] - CLR = [r["clarity"] for r in rows] - HOP = [r["hopkins"] for r in rows] - SUS = [r["sil_unsup"] for r in rows] - KUS = [r["k_unsup"] for r in rows] - CUS = [r["clarity_unsup"] for r in rows] - - summary = { - "ESA_AUC_mean": _agg_mean(ESA), - "ESA_AUC_median": _agg_med(ESA), - "NN1_acc_mean": _agg_mean(NN1), - "NN1_acc_median": _agg_med(NN1), - "sil_sup_mean": _agg_mean(SIL), - "sil_sup_median": _agg_med(SIL), - "DBI_mean": _agg_mean(DBI), - "DBI_median": _agg_med(DBI), - "clarity_mean": _agg_mean(CLR), - "clarity_median": _agg_med(CLR), - "hopkins_mean": _agg_mean(HOP), - "hopkins_median": _agg_med(HOP), - "sil_unsup_mean": _agg_mean(SUS), - "sil_unsup_median": _agg_med(SUS), - "k_unsup_median": _agg_med(KUS), - "clarity_unsup_mean": _agg_mean(CUS), - "clarity_unsup_median": _agg_med(CUS), + def col(name): + return [r[name] for r in rows] + + return { + "ESA_AUC_mean": _agg_mean(col("ESA_AUC")), + "ESA_AUC_median": _agg_med(col("ESA_AUC")), + "NN1_acc_mean": _agg_mean(col("NN1_acc")), + "NN1_acc_median": _agg_med(col("NN1_acc")), + "sil_sup_mean": _agg_mean(col("sil_sup")), + "sil_sup_median": _agg_med(col("sil_sup")), + "DBI_mean": _agg_mean(col("DBI")), + "DBI_median": _agg_med(col("DBI")), + "clarity_mean": _agg_mean(col("clarity")), + "clarity_median": _agg_med(col("clarity")), + "hopkins_mean": _agg_mean(col("hopkins")), + "hopkins_median": _agg_med(col("hopkins")), + "sil_unsup_mean": _agg_mean(col("sil_unsup")), + "sil_unsup_median": _agg_med(col("sil_unsup")), + "k_unsup_median": _agg_med(col("k_unsup")), + "clarity_unsup_mean": _agg_mean(col("clarity_unsup")), + "clarity_unsup_median": _agg_med(col("clarity_unsup")), + "n_molecules": len(rows), + } + + +def coverage_from_rows(rows: list[dict]) -> dict: + """Number of molecules that contribute a finite value to each summary metric.""" + + def n_finite(name): + return int(np.isfinite(np.asarray([r[name] for r in rows], dtype=float)).sum()) + + modes = [r["mode"] for r in rows] + return { "n_molecules": len(rows), + "n_supervised": modes.count("supervised+unsup"), + "n_skip_small": modes.count("skip_small"), + "n_skip_single_en": modes.count("skip_single_en"), + "n_unsupervised_only": modes.count("unsupervised_only"), + **{f"n_finite_{k}": n_finite(k) for k in ("ESA_AUC", "NN1_acc", "sil_sup", "DBI", "hopkins", "sil_unsup")}, } - return rows, summary + +def _detect_mode(embeddings: Union[np.ndarray, Sequence[Any]], n_total: int, n_keys: int) -> str: + """Validate the embedding container against the dataset and return ``continuous``/``fingerprint``.""" + if isinstance(embeddings, np.ndarray): + if embeddings.ndim != 2: + raise ValueError( + f"Continuous embeddings must be a 2-D array (n_conformers, dim); got shape {embeddings.shape}." + ) + if embeddings.shape[0] != n_total: + raise ValueError( + f"Embedding rows ({embeddings.shape[0]}) != total conformers in the dataset ({n_total} = sum of " + f"n_conformers over {n_keys} keys). Embeddings must have exactly one row per conformer, in dataset " + "row order (see the dataset 'offset' column)." + ) + if not np.issubdtype(embeddings.dtype, np.number): + raise ValueError(f"Embeddings must be numeric; got dtype {embeddings.dtype}.") + n_bad = int((~np.isfinite(embeddings)).any(axis=1).sum()) + if n_bad: + raise ValueError( + f"Embeddings contain NaN/Inf in {n_bad} rows; the chirality metrics require finite values." + ) + return "continuous" + if is_fingerprint_list(embeddings): + if len(embeddings) != n_total: + raise ValueError( + f"Fingerprint list length ({len(embeddings)}) != total conformers in the dataset ({n_total} = sum " + f"of n_conformers over {n_keys} keys). Fingerprints must be one per conformer, in dataset row order." + ) + if not _HAS_RDKIT: + raise RuntimeError("RDKit is required to compute Tanimoto distance.") + return "fingerprint" + raise ValueError( + "Unrecognised embeddings: provide a 2-D numpy array (one row per conformer) or a list of RDKit " + f"fingerprints (e.g. ExplicitBitVect); got {type(embeddings).__name__}." + ) + + +def _take_rows(embeddings: Union[np.ndarray, Sequence[Any]], idxs: np.ndarray, mode: str): + if mode == "continuous": + return embeddings[idxs] + return [embeddings[i] for i in idxs] + + +# ---- parallel helpers (module-level so that they can be pickled) ---- +def _evaluate_batch(batch, mode: str, settings: ChiralitySettings) -> list[tuple[int, dict]]: + return [(pos, evaluate_molecule(mol_id, sub, labels, mode, settings)) for pos, mol_id, sub, labels in batch] + + +def _run_molecules( + tasks: list[tuple[str, np.ndarray]], + embeddings, + en_labels: np.ndarray, + mode: str, + settings: ChiralitySettings, + n_jobs: int, + progress: bool, +) -> list[dict]: + if n_jobs is None or n_jobs == 0: + n_jobs = 1 + if n_jobs < 0: + n_jobs = os.cpu_count() or 1 + n_jobs = int(min(n_jobs, max(1, len(tasks)))) + + if n_jobs == 1: + it = tqdm(tasks, disable=not progress, desc="molecules") + return [ + evaluate_molecule(mol_id, _take_rows(embeddings, idxs, mode), en_labels[idxs], mode, settings) + for mol_id, idxs in it + ] + + # Every molecule is evaluated independently with its own fixed seeds, so the split into batches + # and the number of workers do not change any per-molecule value. Each batch carries only the rows + # of its molecules; large molecules are spread round-robin over the batches. + from joblib import Parallel, delayed + + order = sorted(range(len(tasks)), key=lambda i: -int(tasks[i][1].size)) + n_batches = min(len(tasks), n_jobs * 8) + batches: list[list] = [[] for _ in range(n_batches)] + for j, i in enumerate(order): + mol_id, idxs = tasks[i] + batches[j % n_batches].append((i, mol_id, _take_rows(embeddings, idxs, mode), en_labels[idxs])) + + try: # joblib >= 1.3 + from joblib import parallel_config as _backend_config + except ImportError: # pragma: no cover + from joblib import parallel_backend as _backend_config + + jobs = (delayed(_evaluate_batch)(b, mode, settings) for b in batches) + # One BLAS/OpenMP thread per worker: avoids oversubscription; KMeans results do not depend on it. + with _backend_config("loky", inner_max_num_threads=1): + parts = Parallel(n_jobs=n_jobs)(jobs) + rows: list[Optional[dict]] = [None] * len(tasks) + for part in tqdm(parts, total=n_batches, disable=not progress, desc="batches"): + for pos, row in part: + rows[pos] = row + return rows # type: ignore[return-value] def evaluate_en_separation_from_counts( @@ -758,209 +970,111 @@ def evaluate_en_separation_from_counts( *, per_mol_min_n: int = 2, do_unsup_when_single_en: bool = False, - unsup_kmax: int = 50, + unsup_kmax: Optional[int] = None, max_molecules: Optional[int] = None, + distance: str = "euclidean", + metric_version: str = "paper", + n_jobs: int = 1, + progress: bool = False, ): - """Evaluate embeddings using only conformer counts per key.""" - flat = build_flat_index_from_counts(key_to_counts) - N = sum(flat.counts) + """Evaluate embeddings per molecule using only conformer counts per key. + + Args: + key_to_counts: ordered mapping ``key -> n_conformers`` in dataset row order. + embeddings: ``(sum(n_conformers), dim)`` array or list of RDKit fingerprints, dataset order. + per_mol_min_n: molecules with fewer conformers are skipped. + do_unsup_when_single_en: also run the best-k silhouette for single-stereoisomer molecules. + unsup_kmax: largest k for the best-k silhouette; ``None`` = ``n - 1`` (published setting). + max_molecules: evaluate only the first N molecules (quick tests). + distance: ``"euclidean"`` (published Table 2) or ``"cosine"``; ignored for fingerprints. + metric_version: ``"paper"`` (published definitions) or ``"v2"`` (corrected definitions). + n_jobs: worker processes (``-1`` = all CPUs). Results do not depend on ``n_jobs``. + progress: show a tqdm progress bar. + + Returns: + ``(rows, summary)``: one dict per molecule and the macro-averaged summary. + """ + settings = ChiralitySettings( + per_mol_min_n=per_mol_min_n, + do_unsup_when_single_en=do_unsup_when_single_en, + unsup_kmax=unsup_kmax, + distance=distance, + metric_version=metric_version, + ) + settings.validate() - if isinstance(embeddings, np.ndarray): - mode = "continuous" - if embeddings.shape[0] != N: - raise ValueError(f"Embeddings count {embeddings.shape[0]} != total molecules {N}.") - elif is_fingerprint_list(embeddings): - mode = "fingerprint" - if len(embeddings) != N: - raise ValueError(f"Fingerprint list length {len(embeddings)} != total molecules {N}.") - if not _HAS_RDKIT: - raise RuntimeError("RDKit is required to compute Tanimoto distance.") - else: - raise ValueError("Unrecognised embeddings: provide np.ndarray vectors or RDKit fingerprints.") + flat = build_flat_index_from_counts(key_to_counts) + n_total = sum(flat.counts) + mode = _detect_mode(embeddings, n_total, len(flat.keys)) - rows = [] mol_items = list(flat.mol_to_indices.items()) if max_molecules is not None: mol_items = mol_items[:max_molecules] print(f"Quick test mode: processing only {len(mol_items)} molecules (out of {len(flat.mol_to_indices)})") - for mol_id, idxs in mol_items: - n = idxs.size - if n < per_mol_min_n: - rows.append( - { - "mol_id": mol_id, - "n": int(n), - "mode": "skip_small", - "ESA_AUC": np.nan, - "NN1_acc": np.nan, - "sil_sup": np.nan, - "DBI": np.nan, - "clarity": np.nan, - "hopkins": np.nan, - "sil_unsup": np.nan, - "k_unsup": np.nan, - "clarity_unsup": np.nan, - "n_en_classes": 0, - "embedding_mode": mode, - } - ) - continue - - D = distance_matrix_for_subset(embeddings, idxs, mode) - y_en = flat.en_labels[idxs] - n_en = int(np.unique(y_en).size) - - if mode == "continuous": - X_sub = embeddings[idxs] - hop = hopkins_statistic(X_sub) - else: - X_sub = None - hop = np.nan - - if n_en >= 2: - auc = auc_diff_pairs_large_when_different(D, y_en) - nn1 = nn1_leave_one_out_from_D(D, y_en) - sils = silhouette_with_labels_from_D(D, y_en) - dbi = davies_bouldin_from_D(D, y_en, mode=mode) - clar = boundary_clarity_from_D(D, y_en) - - kmax_unsup = min(10, D.shape[0] - 1) - silu, k_star, lab_star = best_unsup_silhouette_from_D_or_X( - mode=mode, D=D, X=X_sub, kmin=2, kmax=kmax_unsup, fp_backend="custom" - ) - clar_unsup = boundary_clarity_from_D(D, lab_star) if lab_star is not None else np.nan - - rows.append( - { - "mol_id": mol_id, - "n": int(n), - "mode": "supervised+unsup", - "ESA_AUC": auc, - "NN1_acc": nn1, - "sil_sup": sils, - "DBI": dbi, - "clarity": clar, - "hopkins": hop, - "sil_unsup": silu, - "k_unsup": (np.nan if k_star is None else int(k_star)), - "clarity_unsup": clar_unsup, - "n_en_classes": n_en, - "embedding_mode": mode, - } - ) - else: - if not do_unsup_when_single_en: - rows.append( - { - "mol_id": mol_id, - "n": int(n), - "mode": "skip_single_en", - "ESA_AUC": np.nan, - "NN1_acc": np.nan, - "sil_sup": np.nan, - "DBI": np.nan, - "clarity": np.nan, - "hopkins": hop, - "sil_unsup": np.nan, - "k_unsup": np.nan, - "clarity_unsup": np.nan, - "n_en_classes": n_en, - "embedding_mode": mode, - } - ) - else: - silu, k_star, lab_star = best_unsup_silhouette_from_D_or_X( - mode=mode, D=D, X=X_sub, kmin=2, kmax=unsup_kmax - ) - clar_unsup = boundary_clarity_from_D(D, lab_star) if lab_star is not None else np.nan - rows.append( - { - "mol_id": mol_id, - "n": int(n), - "mode": "unsupervised_only", - "ESA_AUC": np.nan, - "NN1_acc": np.nan, - "sil_sup": np.nan, - "DBI": np.nan, - "clarity": np.nan, - "hopkins": hop, - "sil_unsup": silu, - "k_unsup": (np.nan if k_star is None else int(k_star)), - "clarity_unsup": clar_unsup, - "n_en_classes": n_en, - "embedding_mode": mode, - } - ) + rows = _run_molecules(mol_items, embeddings, flat.en_labels, mode, settings, n_jobs, progress) + return rows, summarize_rows(rows) - def _agg_mean(xs): - a = np.asarray(xs, float) - a = a[np.isfinite(a)] - return float(a.mean()) if a.size else np.nan - def _agg_med(xs): - a = np.asarray(xs, float) - a = a[np.isfinite(a)] - return float(np.median(a)) if a.size else np.nan +def evaluate_en_separation( + key_to_mols: dict[str, list[Any]], + embeddings: Union[np.ndarray, Sequence[Any]], + *, + per_mol_min_n: int = 2, + do_unsup_when_single_en: bool = False, # Run unsupervised metrics even with a single en-class + unsup_kmax: Optional[int] = None, + max_molecules: Optional[int] = None, # Limit number of molecules for quick testing + distance: str = "euclidean", + metric_version: str = "paper", + n_jobs: int = 1, + progress: bool = True, +): + """Evaluate embeddings per molecule from a ``key -> [Mol]`` mapping (see the ``_from_counts`` variant).""" + return evaluate_en_separation_from_counts( + _counts_from_mols(key_to_mols), + embeddings, + per_mol_min_n=per_mol_min_n, + do_unsup_when_single_en=do_unsup_when_single_en, + unsup_kmax=unsup_kmax, + max_molecules=max_molecules, + distance=distance, + metric_version=metric_version, + n_jobs=n_jobs, + progress=progress, + ) + + +# ========= Legacy driver (authors' original data/ layout; not used by the CLI) ========= +N_WORKERS = min(6, os.cpu_count() or 2) - ESA = [r["ESA_AUC"] for r in rows] - NN1 = [r["NN1_acc"] for r in rows] - SIL = [r["sil_sup"] for r in rows] - DBI = [r["DBI"] for r in rows] - CLR = [r["clarity"] for r in rows] - HOP = [r["hopkins"] for r in rows] - SUS = [r["sil_unsup"] for r in rows] - KUS = [r["k_unsup"] for r in rows] - CUS = [r["clarity_unsup"] for r in rows] - - summary = { - "ESA_AUC_mean": _agg_mean(ESA), - "ESA_AUC_median": _agg_med(ESA), - "NN1_acc_mean": _agg_mean(NN1), - "NN1_acc_median": _agg_med(NN1), - "sil_sup_mean": _agg_mean(SIL), - "sil_sup_median": _agg_med(SIL), - "DBI_mean": _agg_mean(DBI), - "DBI_median": _agg_med(DBI), - "clarity_mean": _agg_mean(CLR), - "clarity_median": _agg_med(CLR), - "hopkins_mean": _agg_mean(HOP), - "hopkins_median": _agg_med(HOP), - "sil_unsup_mean": _agg_mean(SUS), - "sil_unsup_median": _agg_med(SUS), - "k_unsup_median": _agg_med(KUS), - "clarity_unsup_mean": _agg_mean(CUS), - "clarity_unsup_median": _agg_med(CUS), - "n_molecules": len(rows), - } - return rows, summary +def _legacy_paths() -> tuple[Path, Path]: + from three_dbench.utils.paths import DATA_ROOT, RESULTS_ROOT + return DATA_ROOT / "chirality", RESULTS_ROOT / "chirality" / "en_sep_results" -# ========= Configuration ========= -DATA_ROOT = GLOBAL_DATA_ROOT / "chirality" -RESULT_ROOT = GLOBAL_RESULTS_ROOT / "chirality" -BASE_DICT_PKL = DATA_ROOT / "chirality_bench_conformers_noised_only.pkl" -OUT_DIR = RESULT_ROOT / "en_sep_results" # Output directory (per-model JSON + summary CSV) -N_WORKERS = min(6, os.cpu_count() or 2) -# Model definitions and loader settings (name, path, loader_type, key) -MODEL_SPECS = [ - ("molspectra", str(DATA_ROOT / "molspectra" / "sampled_mol_feature.npz"), "npz", "arr_0"), - ("unimol", str(DATA_ROOT / "unimol" / "1.npz"), "npz", "arr_0"), - ("gemnet", str(DATA_ROOT / "gemnet" / "sampled_feature.npz"), "npz", "gemnet"), - ("molae", str(DATA_ROOT / "molae" / "1.npz"), "npz", "arr_0"), - ("e3fp", str(DATA_ROOT / "fingerprint" / "sampled_chi.pkl"), "pkl_dict", "e3fp"), -] +def legacy_model_specs(data_root: Optional[Path] = None) -> list[tuple[str, str, str, str]]: + """(name, path, loader, key) of the published Table 2 inputs in the original ``data/chirality`` layout.""" + root = Path(data_root) if data_root is not None else _legacy_paths()[0] + return [ + ("molspectra", str(root / "molspectra" / "sampled_mol_feature.npz"), "npz", "arr_0"), + ("unimol", str(root / "unimol" / "1.npz"), "npz", "arr_0"), + ("gemnet", str(root / "gemnet" / "sampled_feature.npz"), "npz", "gemnet"), + ("molae", str(root / "molae" / "1.npz"), "npz", "arr_0"), + ("e3fp", str(root / "fingerprint" / "sampled_chi.pkl"), "pkl_dict", "e3fp"), + ("fmg", str(root / "fmg" / "chirality_bench_conformers_noised_only_aslist_embed.npz"), "npz", "embeddings"), + ("mace", str(root / "mace" / "chirality.npz"), "npz", "arr_0"), + ] -# ========= Existing evaluation function ========= -# Assumes ``evaluate_en_separation`` is imported and available. BASE_DICT = None # Read-only cache shared by worker processes def _worker_init(base_dict_pkl: str): """Load the base dictionary once per worker to avoid repeated large IPC transfers.""" + if rdBase is None: + raise RuntimeError("RDKit is required to load chirality conformer pickles.") if rdBase.rdkitVersion < "2023.09": raise RuntimeError( "RDKit >= 2023.09 is required to load chirality conformer pickles. " @@ -976,19 +1090,26 @@ def _load_array(path: str, loader: str, key: str): with np.load(path) as data: return data[key] elif loader == "pkl_dict": - d = pickle.load(open(path, "rb")) + with open(path, "rb") as f: + d = pickle.load(f) return d[key] else: raise ValueError(f"Unknown loader: {loader}") def _run_one( - model_name: str, path: str, loader: str, key: str, max_molecules: Optional[int] = None + model_name: str, + path: str, + loader: str, + key: str, + max_molecules: Optional[int] = None, + eval_kwargs: Optional[dict] = None, ) -> tuple[str, dict[str, Any], dict[str, Any]]: """Evaluate a single model and return ``(model_name, result_dict, summary_dict)``.""" arr = _load_array(path, loader, key) - # Delegate to the user-provided evaluation function - result, summary = evaluate_en_separation(BASE_DICT, arr, max_molecules=max_molecules) + result, summary = evaluate_en_separation( + BASE_DICT, arr, max_molecules=max_molecules, progress=False, **(eval_kwargs or {}) + ) return model_name, result, summary @@ -999,13 +1120,15 @@ def run_chirality_benchmark( output_dir: Optional[Path] = None, model_specs: Optional[list[tuple[str, str, str, str]]] = None, max_molecules: Optional[int] = None, # Quick test: limit number of molecules + **eval_kwargs, ) -> None: - """Execute the chirality benchmark with optional overrides.""" + """Execute the chirality benchmark over several models from the original pickle layout.""" + data_root, default_out = _legacy_paths() workers = max_workers or N_WORKERS - base_path = str(base_dict_path or BASE_DICT_PKL) - out_dir = output_dir or OUT_DIR + base_path = str(base_dict_path or (data_root / "chirality_bench_conformers_noised_only.pkl")) + out_dir = output_dir or default_out out_dir.mkdir(parents=True, exist_ok=True) - specs = model_specs or MODEL_SPECS + specs = model_specs or legacy_model_specs(data_root) summaries = {} # model_name -> summary dict (shared keys) json_paths = [] # Paths of written result JSON files @@ -1013,7 +1136,7 @@ def run_chirality_benchmark( # Submit jobs in parallel with ProcessPoolExecutor(max_workers=workers, initializer=_worker_init, initargs=(base_path,)) as ex: futs = { - ex.submit(_run_one, name, path, loader, key, max_molecules): (name, path) + ex.submit(_run_one, name, path, loader, key, max_molecules, eval_kwargs): (name, path) for (name, path, loader, key) in specs } @@ -1037,7 +1160,6 @@ def run_chirality_benchmark( # Aggregate summary -> CSV if summaries: - # Use any summary's key order for columns (keys are shared) any_summary = next(iter(summaries.values())) cols = list(any_summary.keys()) @@ -1045,7 +1167,6 @@ def run_chirality_benchmark( csv_path = out_dir / "summary.csv" df.to_csv(csv_path, index_label="model") - # Short report to stdout print(f"\nDone. JSON files: {len(json_paths)} written to {out_dir}") print(f"Summary CSV: {csv_path}") else: diff --git a/src/three_dbench/datasets/rotation.py b/src/three_dbench/datasets/rotation.py index c7bb579..d8612e1 100644 --- a/src/three_dbench/datasets/rotation.py +++ b/src/three_dbench/datasets/rotation.py @@ -7,7 +7,6 @@ from pathlib import Path import datasets -import lmdb from .serialization import mols_to_blocks @@ -25,6 +24,8 @@ def convert_rotation_lmdb_to_hf( torsion angles, and embedding offsets. This makes it possible to align flattened embedding arrays with the dataset order. """ + import lmdb # imported lazily so that loading the HF dataset does not require lmdb + fp_dir = lmdb_root / "fingerprint" deg_dir = lmdb_root / "sources" shard_ids = list(shards) if shards is not None else list(range(16)) diff --git a/src/three_dbench/datasets/traj.py b/src/three_dbench/datasets/traj.py index 03e9c32..e7eca48 100644 --- a/src/three_dbench/datasets/traj.py +++ b/src/three_dbench/datasets/traj.py @@ -3,6 +3,9 @@ from __future__ import annotations import pickle +import warnings +from collections.abc import Iterable +from dataclasses import asdict, dataclass from pathlib import Path import datasets @@ -10,6 +13,110 @@ from .serialization import mol_to_block +ENERGY_PRECISION_POLICIES = ("error", "warn", "ignore") + +# A dataset is flagged as quantized when fewer than this fraction of its frames carry a distinct energy ... +_MIN_UNIQUE_FRACTION = 0.5 +# ... or when every value is float32-representable and the float32 step at the energy magnitude is +# at least this fraction of the energy inter-quartile range. +_MAX_FLOAT32_STEP_OVER_IQR = 1e-4 +_MIN_FRAMES_FOR_CHECK = 50 + + +class QuantizedEnergiesError(ValueError): + """Raised when trajectory energies look quantized (e.g. a float32 cast of absolute energies).""" + + +@dataclass(frozen=True) +class EnergyPrecisionReport: + """Summary of the numerical resolution of one molecule's energies.""" + + mol_type: str + n_frames: int + n_unique: int + unique_fraction: float + float32_representable: bool + float32_step: float + iqr: float + quantized: bool + reason: str + + def as_dict(self) -> dict: + return asdict(self) + + +def detect_quantized_energies(energies: Iterable[float], mol_type: str = "") -> EnergyPrecisionReport: + """Heuristically detect quantized (low-resolution) energies. + + rMD17 stores absolute total energies of about -4e5 kcal/mol. Casting them to float32 leaves a + resolution of 1/64-1/256 kcal/mol and only ~1-3% distinct values per molecule, which silently + changes CKA, Smoothness and KS. Energies are flagged when + + * fewer than 50% of the frames carry a distinct value, or + * every value is exactly float32-representable *and* the float32 step at the largest magnitude + is at least 1e-4 of the inter-quartile range of the energies. + + Relative energies stored as float32 (small magnitude, fine float32 step) are not flagged. + """ + arr = np.asarray(energies, dtype=np.float64).reshape(-1) + arr = arr[np.isfinite(arr)] + n = int(arr.size) + if n < _MIN_FRAMES_FOR_CHECK: + return EnergyPrecisionReport(mol_type, n, n, 1.0, False, float("nan"), float("nan"), False, "too few frames") + + n_unique = int(np.unique(arr).size) + unique_fraction = n_unique / n + f32_repr = bool(np.array_equal(arr, arr.astype(np.float32).astype(np.float64))) + max_abs = float(np.max(np.abs(arr))) + step32 = float(np.spacing(np.float32(max_abs))) + q75, q25 = np.percentile(arr, [75.0, 25.0]) + iqr = float(q75 - q25) + + reasons = [] + if unique_fraction < _MIN_UNIQUE_FRACTION: + reasons.append(f"only {n_unique} distinct values for {n} frames ({unique_fraction:.2%})") + if f32_repr and (iqr <= 0.0 or step32 >= _MAX_FLOAT32_STEP_OVER_IQR * iqr): + reasons.append( + f"all values are float32-representable; float32 step {step32:.3g} at |E|~{max_abs:.3g} " + f"is >= {_MAX_FLOAT32_STEP_OVER_IQR:g} x IQR ({iqr:.3g})" + ) + quantized = bool(reasons) + return EnergyPrecisionReport( + mol_type=mol_type, + n_frames=n, + n_unique=n_unique, + unique_fraction=float(unique_fraction), + float32_representable=f32_repr, + float32_step=step32, + iqr=iqr, + quantized=quantized, + reason="; ".join(reasons), + ) + + +def check_energy_precision( + reports: Iterable[EnergyPrecisionReport], policy: str = "error" +) -> list[EnergyPrecisionReport]: + """Apply ``policy`` ("error", "warn", "ignore") to the quantized reports and return them.""" + if policy not in ENERGY_PRECISION_POLICIES: + raise ValueError(f"policy must be one of {ENERGY_PRECISION_POLICIES}, got {policy!r}") + flagged = [r for r in reports if r.quantized] + if not flagged or policy == "ignore": + return flagged + lines = "\n".join(f" - {r.mol_type}: {r.reason}" for r in flagged) + msg = ( + "Trajectory energies look quantized (for example, a float32 cast of absolute energies):\n" + f"{lines}\n" + "Published energy metrics (CKA, Smoothness, KS) were computed with float64 rMD17 energies and are not " + "reproducible from quantized values. Re-download the float64 energies (EscheWang/3dcs, config " + "'traj_energies', current revision) or convert the original rMD17 npz files with " + "`python -m three_dbench convert traj`. Use --energy-precision-check warn|ignore to proceed anyway." + ) + if policy == "error": + raise QuantizedEnergiesError(msg) + warnings.warn(msg, stacklevel=2) + return flagged + def convert_traj_frames_pkl_to_hf( mol_pkl_dir: Path, @@ -45,11 +152,19 @@ def _iter_rows(): def convert_traj_energy_npz_to_hf(energy_dir: Path, output_dir: Path) -> datasets.Dataset: - """Convert trajectory energies into a Hugging Face dataset.""" + """Convert trajectory energies into a Hugging Face dataset. + + Energies are stored as float64 without any cast, so the dataset holds exactly the values of the + source npz files (rMD17: absolute total energies in kcal/mol). + """ rows = [] for npz_path in sorted(energy_dir.glob("rmd17_*.npz")): mol_type = npz_path.stem - energies = np.load(npz_path, mmap_mode="r")["energies"].astype(np.float32) + with np.load(npz_path) as data: + energies = np.asarray(data["energies"], dtype=np.float64).reshape(-1) + report = detect_quantized_energies(energies, mol_type=mol_type) + if report.quantized: + warnings.warn(f"Source energies for {mol_type} look quantized: {report.reason}", stacklevel=2) rows.append( { "mol_type": mol_type, diff --git a/src/three_dbench/download.py b/src/three_dbench/download.py new file mode 100644 index 0000000..c2090bd --- /dev/null +++ b/src/three_dbench/download.py @@ -0,0 +1,236 @@ +"""Download the 3DCS datasets and the published baseline embeddings from Hugging Face. + +Datasets come from the ``EscheWang/3dcs`` configs and are written with ``save_to_disk`` to the +paths the evaluators expect:: + + data/hf/chirality (config chirality) + data/hf/traj/energies (config traj_energies) + data/hf/traj/frames (config traj_frames) + data/hf/rotation (config rotation, ~7.5 GB) + +Embeddings come from ``EscheWang/3dcs-embeddings``. Files are listed in its ``manifest.csv`` +(``path,task,model,size_bytes,sha256,...``), downloaded to ``/`` and verified +against the manifest SHA-256. +""" + +from __future__ import annotations + +import csv +import hashlib +import io +import shutil +from collections.abc import Iterable, Sequence +from dataclasses import dataclass +from pathlib import Path +from typing import Callable + +from three_dbench.embeddings.published import DATASET_REPO_ID, EMBEDDINGS_REPO_ID + +DATASET_TASK_CONFIGS: dict[str, dict[str, str]] = { + "chirality": {"chirality": "chirality"}, + "traj": {"traj_energies": "traj/energies", "traj_frames": "traj/frames"}, + "rotation": {"rotation": "rotation"}, +} +EMBEDDING_TASKS = ("chirality", "traj", "rotation", "chirality_legacy_15218") +MANIFEST_NAME = "manifest.csv" + + +class ChecksumError(RuntimeError): + """A downloaded file does not match the SHA-256 in the manifest.""" + + +@dataclass +class ManifestEntry: + path: str + task: str + model: str + size_bytes: int | None + sha256: str + row: dict + + +def sha256_file(path: Path, chunk_size: int = 1 << 22) -> str: + h = hashlib.sha256() + with Path(path).open("rb") as fh: + for block in iter(lambda: fh.read(chunk_size), b""): + h.update(block) + return h.hexdigest() + + +# --------------------------------------------------------------------------- +# Datasets +# --------------------------------------------------------------------------- + + +def download_dataset( + task: str, + out: Path = Path("data/hf"), + *, + repo_id: str = DATASET_REPO_ID, + revision: str | None = None, + configs: Sequence[str] | None = None, + overwrite: bool = False, + load_dataset_fn: Callable | None = None, +) -> dict[str, Path]: + """Download the dataset configs of ``task`` and ``save_to_disk`` them under ``out``. + + Returns ``{config: saved_path}``. Existing directories are kept unless ``overwrite``. + """ + tasks = list(DATASET_TASK_CONFIGS) if task == "all" else [task] + for t in tasks: + if t not in DATASET_TASK_CONFIGS: + raise ValueError(f"Unknown dataset task {t!r}; choose from {sorted(DATASET_TASK_CONFIGS)} or 'all'") + if load_dataset_fn is None: + from datasets import load_dataset as load_dataset_fn + saved: dict[str, Path] = {} + for t in tasks: + for config, rel in DATASET_TASK_CONFIGS[t].items(): + if configs is not None and config not in configs: + continue + target = Path(out) / rel + if target.exists() and any(target.iterdir()) and not overwrite: + print(f"[download] {config}: {target} exists, skipping (use --overwrite to replace)") + saved[config] = target + continue + print(f"[download] {repo_id} config={config} -> {target}") + kwargs = {"name": config, "split": "train"} + if revision is not None: + kwargs["revision"] = revision + ds = load_dataset_fn(repo_id, **kwargs) + if target.exists() and overwrite: + shutil.rmtree(target) + target.parent.mkdir(parents=True, exist_ok=True) + ds.save_to_disk(str(target)) + saved[config] = target + return saved + + +# --------------------------------------------------------------------------- +# Embeddings +# --------------------------------------------------------------------------- + + +def parse_manifest(text: str) -> list[ManifestEntry]: + reader = csv.DictReader(io.StringIO(text)) + missing = {"path", "sha256"} - set(reader.fieldnames or []) + if missing: + raise ValueError(f"manifest.csv lacks columns {sorted(missing)}") + entries = [] + for row in reader: + path = row["path"].strip() + parts = path.split("/") + size = row.get("size_bytes") + entries.append( + ManifestEntry( + path=path, + task=(row.get("task") or parts[0]).strip(), + model=(row.get("model") or (parts[1] if len(parts) > 2 else "")).strip(), + size_bytes=int(size) if size not in (None, "") else None, + sha256=(row.get("sha256") or "").strip().lower(), + row=row, + ) + ) + return entries + + +def _normalise_model(name: str) -> str: + return name.strip().lower() + + +def select_manifest_entries( + entries: Iterable[ManifestEntry], + *, + task: str, + models: Sequence[str] | None = None, + include_results: bool = False, +) -> list[ManifestEntry]: + """Filter manifest entries by path prefix (``//...``, ``results//...``).""" + tasks = list(EMBEDDING_TASKS) if task == "all" else [task] + wanted_models = {_normalise_model(m) for m in models} if models else None + out = [] + for e in entries: + parts = e.path.split("/") + top = parts[0] + if top in tasks: + model = parts[1] if len(parts) > 2 else "" + if wanted_models is None or _normalise_model(model) in wanted_models: + out.append(e) + elif include_results and top == "results" and len(parts) > 1 and (task == "all" or parts[1] in tasks): + out.append(e) + return out + + +def download_embeddings( + task: str, + out: Path = Path("data/embeddings"), + *, + models: Sequence[str] | None = None, + include_results: bool = False, + repo_id: str = EMBEDDINGS_REPO_ID, + revision: str | None = None, + verify: bool = True, + dry_run: bool = False, + hf_hub_download_fn: Callable | None = None, +) -> list[Path]: + """Download published embeddings listed in the repo manifest and verify their SHA-256. + + Files already present with the manifest checksum are not downloaded again. + Raises :class:`ChecksumError` if a downloaded file does not match. + """ + if task != "all" and task not in EMBEDDING_TASKS: + raise ValueError(f"Unknown embeddings task {task!r}; choose from {EMBEDDING_TASKS} or 'all'") + if hf_hub_download_fn is None: + from huggingface_hub import hf_hub_download as hf_hub_download_fn + out = Path(out) + out.mkdir(parents=True, exist_ok=True) + manifest_path = Path( + hf_hub_download_fn( + repo_id=repo_id, filename=MANIFEST_NAME, repo_type="dataset", revision=revision, local_dir=str(out) + ) + ) + entries = parse_manifest(manifest_path.read_text(encoding="utf-8")) + selected = select_manifest_entries(entries, task=task, models=models, include_results=include_results) + if not selected: + raise ValueError(f"No manifest entries for task={task!r}, models={models!r}") + total = sum(e.size_bytes or 0 for e in selected) + print(f"[download] {len(selected)} files, {total / 1e9:.2f} GB from {repo_id}") + paths = [] + for e in selected: + target = out / e.path + if dry_run: + print(f" {e.path} ({(e.size_bytes or 0) / 1e6:.1f} MB)") + continue + if target.exists() and verify and e.sha256 and sha256_file(target) == e.sha256: + print(f" ok (cached) {e.path}") + paths.append(target) + continue + local = Path( + hf_hub_download_fn( + repo_id=repo_id, filename=e.path, repo_type="dataset", revision=revision, local_dir=str(out) + ) + ) + if local.resolve() != target.resolve(): + target.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(local, target) + if verify and e.sha256: + digest = sha256_file(target) + if digest != e.sha256: + raise ChecksumError(f"{e.path}: sha256 {digest} != manifest {e.sha256}") + print(f" ok {e.path}") + else: + print(f" downloaded {e.path} (not verified)") + paths.append(target) + return paths + + +__all__ = [ + "ChecksumError", + "DATASET_TASK_CONFIGS", + "EMBEDDING_TASKS", + "ManifestEntry", + "download_dataset", + "download_embeddings", + "parse_manifest", + "select_manifest_entries", + "sha256_file", +] diff --git a/src/three_dbench/embeddings/__init__.py b/src/three_dbench/embeddings/__init__.py index deadf33..fd6ca2f 100644 --- a/src/three_dbench/embeddings/__init__.py +++ b/src/three_dbench/embeddings/__init__.py @@ -1,5 +1,15 @@ """Embedding IO helpers.""" -from .io import EmbeddingArray, load_embeddings, load_embeddings_dict, load_embeddings_dir +from .io import EmbeddingArray, load_embeddings, load_embeddings_dict, load_embeddings_dir, select_array_key +from .published import PUBLISHED_EMBEDDINGS, PublishedEmbedding, published_embedding -__all__ = ["EmbeddingArray", "load_embeddings", "load_embeddings_dict", "load_embeddings_dir"] +__all__ = [ + "PUBLISHED_EMBEDDINGS", + "EmbeddingArray", + "PublishedEmbedding", + "load_embeddings", + "load_embeddings_dict", + "load_embeddings_dir", + "published_embedding", + "select_array_key", +] diff --git a/src/three_dbench/embeddings/io.py b/src/three_dbench/embeddings/io.py index 0da4d0d..1ada47f 100644 --- a/src/three_dbench/embeddings/io.py +++ b/src/three_dbench/embeddings/io.py @@ -3,11 +3,20 @@ from __future__ import annotations import pickle +import warnings from dataclasses import dataclass from pathlib import Path import numpy as np +#: Array names tried, in order, when no key is given. These cover the files published in +#: ``EscheWang/3dcs-embeddings`` (``arr_0``, ``embeddings`` for FMG, ``gemnet``, ``mol_feature``). +DEFAULT_ARRAY_KEYS = ("arr_0", "embeddings", "gemnet", "mol_feature") +#: Dict entries tried, in order, for pickled dicts when no key is given (E3FP pickles hold +#: ``{"e3fp": [...], "morgan": [...]}``). +DEFAULT_PICKLE_KEYS = ("e3fp", "embeddings", "arr_0") +EMBEDDING_SUFFIXES = (".npz", ".npy", ".pkl") + @dataclass class EmbeddingArray: @@ -17,19 +26,53 @@ class EmbeddingArray: kind: str # "vector" or "fingerprint" -def _select_npz_key(data: np.lib.npyio.NpzFile, key: str | None) -> np.ndarray: +def select_array_key(data: np.lib.npyio.NpzFile, key: str | None) -> str: + """Return the name of the array to read from an NPZ archive. + + With ``key=None`` the first name in :data:`DEFAULT_ARRAY_KEYS` that is present is used; + otherwise the first numeric array (so that string side-arrays such as FMG's ``smiles`` + are never selected). + """ if key is not None: - return data[key] - if "arr_0" in data.files: - return data["arr_0"] - return data[data.files[0]] + if key not in data.files: + raise KeyError(f"Key {key!r} not found in NPZ; available: {list(data.files)}") + return key + for name in DEFAULT_ARRAY_KEYS: + if name in data.files: + return name + for name in data.files: + arr = data[name] + if np.issubdtype(arr.dtype, np.number) or arr.dtype == bool: + return name + return data.files[0] + + +def _select_npz_key(data: np.lib.npyio.NpzFile, key: str | None) -> np.ndarray: + return data[select_array_key(data, key)] + + +def resolve_embedding_file(path: Path) -> Path: + """Resolve a directory holding exactly one embedding file to that file.""" + path = Path(path) + if not path.is_dir(): + return path + candidates = sorted(p for p in path.iterdir() if p.suffix.lower() in EMBEDDING_SUFFIXES) + if len(candidates) != 1: + raise ValueError( + f"{path} is a directory with {len(candidates)} embedding files; pass a file path " + "(or use the by-shard / per-molecule directory options of the benchmark)." + ) + return candidates[0] def load_embeddings(path: Path, *, key: str | None = None) -> EmbeddingArray: """Load embeddings from NPZ/NPY/PKL containers. - Returns an EmbeddingArray with kind inferred from the object type. + ``path`` may also be a directory that contains exactly one embedding file (e.g. + ``data/embeddings/chirality/gemnet``). Returns an EmbeddingArray with kind inferred from + the object type. """ + path = resolve_embedding_file(Path(path)) suffix = path.suffix.lower() if suffix == ".npz": with np.load(path) as data: @@ -45,7 +88,14 @@ def load_embeddings(path: Path, *, key: str | None = None) -> EmbeddingArray: elif len(payload) == 1: arr = next(iter(payload.values())) else: - raise ValueError("Pickle contains multiple entries; specify --embedding-key.") + chosen = next((k for k in DEFAULT_PICKLE_KEYS if k in payload), None) + if chosen is None: + raise ValueError("Pickle contains multiple entries; specify --embedding-key.") + warnings.warn( + f"{path.name}: no embedding key given; using entry {chosen!r} of {sorted(payload)}", + stacklevel=2, + ) + arr = payload[chosen] else: arr = payload else: diff --git a/src/three_dbench/embeddings/published.py b/src/three_dbench/embeddings/published.py new file mode 100644 index 0000000..9036cb6 --- /dev/null +++ b/src/three_dbench/embeddings/published.py @@ -0,0 +1,122 @@ +"""Layout of the published baseline embeddings (Hugging Face ``EscheWang/3dcs-embeddings``). + +Files are stored with their original bytes and array keys; only directory names are +normalised. ``python -m three_dbench download embeddings`` places them under +``/`` (default ``data/embeddings``), so the paths below are relative to that root. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path + +EMBEDDINGS_REPO_ID = "EscheWang/3dcs-embeddings" +DATASET_REPO_ID = "EscheWang/3dcs" +TASKS = ("chirality", "traj", "rotation") +MODELS = ("e3fp", "gemnet", "molae", "molspectra", "unimol", "fmg", "mace") +RMD17_MOLECULES = ( + "aspirin", + "azobenzene", + "benzene", + "ethanol", + "malonaldehyde", + "naphthalene", + "paracetamol", + "salicylic", + "toluene", + "uracil", +) + + +@dataclass(frozen=True) +class PublishedEmbedding: + """Where a published embedding lives and how to read it.""" + + task: str + model: str + path: str # file (chirality), directory (traj / rotation) + key: str | None # array key (npz) or dict entry (pkl); None for pickled lists + layout: str # "flat" | "per-molecule" | "by-shard" + file_pattern: str | None = None + shards: tuple[int, ...] | None = None + notes: str = "" + + def local_path(self, root: str | Path = "data/embeddings") -> Path: + return Path(root) / self.path + + +_ALL_SHARDS = tuple(range(16)) + +PUBLISHED_EMBEDDINGS: dict[tuple[str, str], PublishedEmbedding] = { + # Table 2 set: 52,391 conformers in the row order of the HF chirality config. + ("chirality", "e3fp"): PublishedEmbedding("chirality", "e3fp", "chirality/e3fp/sampled_chi.pkl", "e3fp", "flat"), + ("chirality", "gemnet"): PublishedEmbedding( + "chirality", "gemnet", "chirality/gemnet/sampled_feature.npz", "gemnet", "flat" + ), + ("chirality", "molae"): PublishedEmbedding("chirality", "molae", "chirality/molae/1.npz", "arr_0", "flat"), + ("chirality", "molspectra"): PublishedEmbedding( + "chirality", "molspectra", "chirality/molspectra/sampled_mol_feature.npz", "arr_0", "flat" + ), + ("chirality", "unimol"): PublishedEmbedding("chirality", "unimol", "chirality/unimol/1.npz", "arr_0", "flat"), + ("chirality", "fmg"): PublishedEmbedding( + "chirality", + "fmg", + "chirality/fmg/chirality_bench_conformers_noised_only_aslist_embed.npz", + "embeddings", + "flat", + ), + ("chirality", "mace"): PublishedEmbedding("chirality", "mace", "chirality/mace/chirality.npz", "arr_0", "flat"), + # rMD17: one file per molecule, rmd17_.{npz,pkl}. + ("traj", "e3fp"): PublishedEmbedding( + "traj", "e3fp", "traj/e3fp", None, "per-molecule", "rmd17_{molecule}.pkl", notes="pickled list of bit vectors" + ), + ("traj", "gemnet"): PublishedEmbedding( + "traj", "gemnet", "traj/gemnet", "gemnet", "per-molecule", "rmd17_{molecule}.npz" + ), + ("traj", "molae"): PublishedEmbedding( + "traj", "molae", "traj/molae", "arr_0", "per-molecule", "rmd17_{molecule}.npz" + ), + ("traj", "molspectra"): PublishedEmbedding( + "traj", "molspectra", "traj/molspectra", "arr_0", "per-molecule", "rmd17_{molecule}.npz" + ), + ("traj", "unimol"): PublishedEmbedding( + "traj", "unimol", "traj/unimol", "arr_0", "per-molecule", "rmd17_{molecule}.npz" + ), + ("traj", "fmg"): PublishedEmbedding( + "traj", "fmg", "traj/fmg", "embeddings", "per-molecule", "rmd17_{molecule}.npz" + ), + ("traj", "mace"): PublishedEmbedding("traj", "mace", "traj/mace", "arr_0", "per-molecule", "rmd17_{molecule}.npz"), + # Rotation: file index = shard id, rows ordered by the per-shard offset of the HF rotation config. + ("rotation", "gemnet"): PublishedEmbedding( + "rotation", "gemnet", "rotation/gemnet", "gemnet", "by-shard", "rotation_conformers_{shard}.npz", _ALL_SHARDS + ), + ("rotation", "fmg"): PublishedEmbedding( + "rotation", + "fmg", + "rotation/fmg", + "embeddings", + "by-shard", + "rot_mol_list_{shard}_embed.npz", + (0,), + notes="shard 0 only; not used in the paper", + ), + ("rotation", "mace"): PublishedEmbedding( + "rotation", + "mace", + "rotation/mace", + "arr_0", + "by-shard", + "rot{shard}.npz", + (0,), + notes="shard 0 only; not used in the paper", + ), +} + + +def published_embedding(task: str, model: str) -> PublishedEmbedding: + """Look up a published embedding; raises KeyError with the available combinations.""" + try: + return PUBLISHED_EMBEDDINGS[(task, model)] + except KeyError: + avail = sorted(f"{t}/{m}" for t, m in PUBLISHED_EMBEDDINGS) + raise KeyError(f"No published embedding for task={task!r}, model={model!r}. Available: {avail}") from None diff --git a/src/three_dbench/rotation/evaluation.py b/src/three_dbench/rotation/evaluation.py index c8819b7..195508e 100644 --- a/src/three_dbench/rotation/evaluation.py +++ b/src/three_dbench/rotation/evaluation.py @@ -1,10 +1,14 @@ -"""Rotation geometry evaluation with optional shard sampling and batching. +"""Rotation geometry evaluation helpers and the legacy LMDB-based driver. -Highlights: -- Sample-level control via SAMPLE_RATIO_SAMPLES / SAMPLE_MAX_SAMPLES / SAMPLE_SEED. -- Non-sampled entries only advance offsets without cutting embeddings. -- Ensures cosine distances are handled vector-wise and fixes the MOLAE directory path. -- Aggregates shard outputs into gzipped JSON summaries. +The CLI / Python API entry point is :func:`three_dbench.benchmarks.rotation.evaluate_rotation_embeddings`, +which uses the versioned metric definitions in :mod:`three_dbench.rotation.metrics`. + +This module keeps: +- RMSD and distance helpers (``rmsd_matrix_from_one_mol``, ``pairwise_distances_from_*``); +- ``compute_all_geometry_metrics``: the metric set of the initial release (``metric_version="legacy"``); +- ``main`` / ``process_shard``: the legacy driver that reads the authors' LMDB layout. Its defaults + (10 % sample with seed ``12345 + shard``) are **not** the settings of the published runs; see + ``docs/metrics/geometry.md``. """ import gzip @@ -15,7 +19,6 @@ from concurrent.futures import ProcessPoolExecutor, as_completed from typing import Optional -import lmdb import numpy as np from numpy.linalg import norm from rdkit import Chem, DataStructs @@ -238,8 +241,7 @@ def compute_all_geometry_metrics( UNIMOL_DIR = SOURCE_ROOT / "unimol" MOLAE_DIR = SOURCE_ROOT / "molae" MOLSPEC_DIR = SOURCE_ROOT / "molspectra" -OUT_DIR = RESULT_ROOT / "metrics_dict" -OUT_DIR.mkdir(parents=True, exist_ok=True) +OUT_DIR = RESULT_ROOT / "metrics_dict" # created by main(), not at import time REPORT_SEC = 5.0 METRICS = ("cosine", "euclidean") # Use ("cosine",) to reduce runtime if needed @@ -282,6 +284,7 @@ def process_shard( sample_seed: int = SAMPLE_SEED, ): _pin_threads_single() + import lmdb # imported lazily: only the legacy LMDB driver needs it lmdb_fp_with_source_path = LMDB_FP_WITH_SOURCE_DIR / f"rot_{i}.lmdb" lmdb_deg_path = LMDB_DEG_DIR / f"rot_conf_deg_{i}.lmdb" @@ -431,6 +434,7 @@ def main(max_workers: Optional[int] = None): done += 1 print(f"[main] shard {done}/{len(shards)} merged | elapsed {time.time() - t0:.1f}s", flush=True) + OUT_DIR.mkdir(parents=True, exist_ok=True) out_path = OUT_DIR / "metrics_all_shards.json.gz" with gzip.open(out_path, "wt", encoding="utf-8") as f: json.dump(combined, f, ensure_ascii=False, separators=(",", ":")) diff --git a/src/three_dbench/rotation/metrics.py b/src/three_dbench/rotation/metrics.py new file mode 100644 index 0000000..58f350a --- /dev/null +++ b/src/three_dbench/rotation/metrics.py @@ -0,0 +1,390 @@ +"""Geometry (rotation) metric definitions and versioned presets. + +Three presets are provided (see ``docs/metrics/geometry.md`` for the full rationale): + +``paper`` + Reproduces the published Table 1 numbers. The definitions were recovered by matching + per-molecule values in the backup outputs of the original runs, because the original + evaluation scripts (``eval_geo_single.py`` / ``eval_geo_single_sup.py``) are not available. +``v2`` + Definitions as written in the paper's Table 1 caption and Appendix C.3/C.4 + (LIE with k=3 nearest neighbours excluding the conformer itself, AS as the median + ``||z_{i+1} - z_i|| / |phi_{i+1} - phi_i|`` in radians, isotonic fit of D on Delta, + RBF-CKA with median-heuristic bandwidths). +``legacy`` + The behaviour of ``compute_all_geometry_metrics`` in the initial public release (06dee1c). +""" + +from __future__ import annotations + +import math +from dataclasses import asdict, dataclass, replace + +import numpy as np +from sklearn.isotonic import IsotonicRegression + +from three_dbench.common.metrics import ( + cka_rbf, + distance_correlation, + isotonic_r2, + kendall_correlation, + kruskal_stress, + mantel_test, + spearman_correlation, + to_condensed, + torsion_embedding_spearman, + triplet_order_preservation, +) + +METRIC_VERSIONS = ("paper", "v2", "legacy") + +LIE_VARIANTS = ("released", "exclude_self") +AS_VARIANTS = ( + "mean_delta_circular", # legacy release: mean of Delta per radian, sorted torsions incl. wrap-around step + "median_delta_circular", # published 10 % run (not used in Table 1) + "median_halfdelta_circular", # published full "sup" run (Table 1 AS row) + "median_dz_linear", # v2 / Appendix C.4: median ||z_{i+1} - z_i|| / |dphi|, no wrap-around step +) +CKA_VARIANTS = ("median_2sigma2", "paper_run") +ISOR2_VARIANTS = ("model_on_reference", "reference_on_model", "reference_on_model_noguard") +LIE_DELTA_SCALES = ("raw", "unit_range") + + +@dataclass(frozen=True) +class GeometryMetricSpec: + """Configuration of the per-molecule geometry metrics.""" + + name: str = "custom" + lie_k: int = 10 + lie_include_self: bool = True + lie_delta_scale: str = "raw" + lie_min_conformers: int = 2 + as_variant: str = "mean_delta_circular" + cka_variant: str = "median_2sigma2" + isor2_variant: str = "model_on_reference" + kendall_min_conformers: int = 0 + kendall_max_pairs: int | None = 100_000 + extra_metrics: bool = False + + def to_dict(self) -> dict: + return asdict(self) + + +PRESETS: dict[str, GeometryMetricSpec] = { + "legacy": GeometryMetricSpec( + name="legacy", + lie_k=10, + lie_include_self=True, + lie_delta_scale="raw", + lie_min_conformers=2, + as_variant="mean_delta_circular", + cka_variant="median_2sigma2", + isor2_variant="model_on_reference", + kendall_min_conformers=0, + extra_metrics=True, + ), + "paper": GeometryMetricSpec( + name="paper", + lie_k=10, + lie_include_self=True, + lie_delta_scale="unit_range", + lie_min_conformers=2, + as_variant="median_halfdelta_circular", + cka_variant="paper_run", + isor2_variant="reference_on_model_noguard", + kendall_min_conformers=11, + extra_metrics=False, + ), + "v2": GeometryMetricSpec( + name="v2", + lie_k=3, + lie_include_self=False, + lie_delta_scale="raw", + lie_min_conformers=3, + as_variant="median_dz_linear", + cka_variant="median_2sigma2", + isor2_variant="reference_on_model", + kendall_min_conformers=0, + extra_metrics=False, + ), +} + + +def resolve_metric_spec( + metric_version: str = "paper", + *, + lie_k: int | None = None, + lie_include_self: bool | None = None, + as_variant: str | None = None, + extra_metrics: bool | None = None, +) -> GeometryMetricSpec: + """Return the preset for ``metric_version`` with optional explicit overrides.""" + if metric_version not in PRESETS: + raise ValueError(f"metric_version must be one of {sorted(PRESETS)}, got {metric_version!r}") + spec = PRESETS[metric_version] + overrides = {} + if lie_k is not None: + overrides["lie_k"] = int(lie_k) + if lie_include_self is not None: + overrides["lie_include_self"] = bool(lie_include_self) + if as_variant is not None: + if as_variant not in AS_VARIANTS: + raise ValueError(f"as_variant must be one of {AS_VARIANTS}, got {as_variant!r}") + overrides["as_variant"] = as_variant + if extra_metrics is not None: + overrides["extra_metrics"] = bool(extra_metrics) + if overrides: + spec = replace(spec, name=f"{spec.name}+custom", **overrides) + return spec + + +# --------------------------------------------------------------------------- +# Individual metric implementations +# --------------------------------------------------------------------------- + + +def local_isometry_error_knn( + D: np.ndarray, + Delta: np.ndarray, + *, + k: int, + include_self: bool, + eps: float = 1e-12, +) -> float: + """LIE@k: mean over conformers of the RMS difference of locally normalised distances. + + ``include_self=True`` reproduces the released implementation, whose neighbour set + (``argpartition(D)[:, :k]``) contains the conformer itself (distance 0). + ``include_self=False`` uses the k nearest *other* conformers, as in Appendix C.4. + ``k`` is capped at ``n - 1``. + """ + D = np.asarray(D, dtype=np.float64) + Delta = np.asarray(Delta, dtype=np.float64) + n = D.shape[0] + if n <= 1: + return float("nan") + k = int(min(max(1, k), n - 1)) + if include_self: + nbrs = np.argpartition(D, kth=k, axis=1)[:, :k] + else: + Dm = D.copy() + np.fill_diagonal(Dm, np.inf) + if k < n - 1: + nbrs = np.argpartition(Dm, kth=k - 1, axis=1)[:, :k] + else: + nbrs = np.argsort(Dm, axis=1, kind="stable")[:, :k] + lies = np.empty(n, dtype=np.float64) + for i in range(n): + Di = D[i, nbrs[i]] + Xi = Delta[i, nbrs[i]] + diff = Di / (Di.mean() + eps) - Xi / (Xi.mean() + eps) + lies[i] = math.sqrt(float(np.mean(diff * diff))) + return float(np.mean(lies)) + + +def _sorted_steps(torsion_deg: np.ndarray, circular: bool) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Consecutive conformer pairs along the torsion scan and their angular increments (radians).""" + phi = np.deg2rad(np.asarray(torsion_deg, dtype=np.float64).reshape(-1)) + order = np.argsort(phi, kind="stable") + n = order.size + if circular: + i_idx = order + j_idx = np.roll(order, -1) + dphi = np.abs(phi[j_idx] - phi[i_idx]) + dphi = np.minimum(dphi, 2.0 * np.pi - dphi) + else: + i_idx = order[: n - 1] + j_idx = order[1:] + dphi = np.abs(phi[j_idx] - phi[i_idx]) + return i_idx, j_idx, dphi + + +def angular_smoothness_variant( + torsion_deg: np.ndarray, + *, + variant: str, + Delta: np.ndarray | None = None, + Z: np.ndarray | None = None, +) -> float: + """Angular smoothness (AS) under one of :data:`AS_VARIANTS`. + + Steps with an angular increment below 1e-9 rad are ignored. + For ``median_dz_linear`` the displacement is ``||z_{i+1} - z_i||`` (Euclidean norm of the raw + embedding difference); when ``Z`` is not a numeric array (e.g. fingerprints) the representation + distance ``Delta`` is used instead. + """ + torsion_deg = np.asarray(torsion_deg, dtype=np.float64).reshape(-1) + n = torsion_deg.size + if n < 2: + return float("nan") + if variant == "mean_delta_circular": + i_idx, j_idx, dphi = _sorted_steps(torsion_deg, circular=True) + steps = np.asarray(Delta, dtype=np.float64)[i_idx, j_idx] + agg = np.mean + elif variant == "median_delta_circular": + i_idx, j_idx, dphi = _sorted_steps(torsion_deg, circular=True) + steps = np.asarray(Delta, dtype=np.float64)[i_idx, j_idx] + agg = np.median + elif variant == "median_halfdelta_circular": + i_idx, j_idx, dphi = _sorted_steps(torsion_deg, circular=True) + steps = 0.5 * np.asarray(Delta, dtype=np.float64)[i_idx, j_idx] + agg = np.median + elif variant == "median_dz_linear": + i_idx, j_idx, dphi = _sorted_steps(torsion_deg, circular=False) + if Z is not None and isinstance(Z, np.ndarray) and Z.ndim == 2 and np.issubdtype(Z.dtype, np.number): + Zd = np.asarray(Z, dtype=np.float64) + steps = np.linalg.norm(Zd[j_idx] - Zd[i_idx], axis=1) + else: + steps = np.asarray(Delta, dtype=np.float64)[i_idx, j_idx] + agg = np.median + else: + raise ValueError(f"Unknown AS variant {variant!r}; expected one of {AS_VARIANTS}") + mask = dphi > 1e-9 + if not mask.any(): + return float("nan") + return float(agg(steps[mask] / dphi[mask])) + + +def cka_rbf_paper_run(D: np.ndarray, Delta: np.ndarray) -> float: + """RBF-CKA as computed in the published geometry runs. + + ``K = exp(-d^2 / (median(d) + 1e-12)^2)`` with the median over the upper triangle, + computed separately for D and Delta, followed by HKH centring. (The appendix and + :func:`three_dbench.common.metrics.cka_rbf` use ``exp(-d^2 / (2 sigma^2))``.) + """ + D = np.asarray(D, dtype=np.float64) + Delta = np.asarray(Delta, dtype=np.float64) + n = D.shape[0] + if n < 2: + return float("nan") + iu = np.triu_indices(n, 1) + + def _kernel(M: np.ndarray) -> np.ndarray: + sigma = float(np.median(M[iu])) + 1e-12 + return np.exp(-(M**2) / sigma**2) + + H = np.eye(n) - 1.0 / n + A = H @ _kernel(D) @ H + B = H @ _kernel(Delta) @ H + den = float(np.linalg.norm(A) * np.linalg.norm(B)) + if den == 0.0: + return float("nan") + return float(np.sum(A * B) / (den + 1e-20)) + + +def isotonic_r2_reference_on_model(D: np.ndarray, Delta: np.ndarray, *, guard: bool = True) -> float: + """Isotonic R^2 of the fit ``D_hat = f_iso(Delta)`` (Appendix C.3 direction). + + With ``guard=True`` the value is NaN for fewer than two pairs or a constant Delta + (same guard as :func:`three_dbench.common.metrics.isotonic_r2`). With ``guard=False`` no + guard is applied, which reproduces the published runs (a single pair gives 1.0). + """ + if guard: + return isotonic_r2(Delta, D) + x = to_condensed(Delta) + y = to_condensed(D) + mask = np.isfinite(x) & np.isfinite(y) + x, y = x[mask], y[mask] + if x.size == 0: + return float("nan") + order = np.argsort(x) + xs, ys = x[order], y[order] + yhat = IsotonicRegression(increasing=True, out_of_bounds="clip").fit_transform(xs, ys) + ss_res = float(np.sum((ys - yhat) ** 2)) + ss_tot = float(np.sum((ys - ys.mean()) ** 2)) + 1e-12 + return float(1.0 - ss_res / ss_tot) + + +def _unit_range(Delta: np.ndarray, delta_metric: str) -> np.ndarray: + """Map a distance matrix to [0, 1] as described in Appendix C.2.""" + if delta_metric == "cosine": + return 0.5 * Delta + return Delta + + +def compute_geometry_metrics( + D: np.ndarray, + Delta: np.ndarray, + *, + torsion_deg: np.ndarray | None, + spec: GeometryMetricSpec, + Z: np.ndarray | None = None, + delta_metric: str = "cosine", + random_state: int | None = 0, +) -> dict[str, float]: + """Compute per-molecule geometry metrics for one representation distance matrix. + + Keys follow the naming of the original outputs: ``A1_spearman``, ``A2_kendall``, + ``G_cka_rbf``, ``H_LIE@k``, ``J_isotonic_R2``, ``torsion_sp``, ``AS`` (+ ``B_dcor``, + ``C_mantel_r``, ``C_mantel_p``, ``D_stress1``, ``D_fit_a``, ``D_fit_b``, ``E_triplet_OP`` + when ``spec.extra_metrics``). + """ + D = np.asarray(D, dtype=np.float64) + Delta = np.asarray(Delta, dtype=np.float64) + if D.ndim != 2 or D.shape != Delta.shape: + raise ValueError(f"D and Delta must be square matrices of equal shape, got {D.shape} and {Delta.shape}") + n = D.shape[0] + out: dict[str, float] = {} + out["A1_spearman"] = spearman_correlation(D, Delta) + if n >= max(2, spec.kendall_min_conformers): + out["A2_kendall"] = kendall_correlation(D, Delta, max_pairs=spec.kendall_max_pairs, random_state=random_state) + else: + out["A2_kendall"] = float("nan") + if spec.cka_variant == "paper_run": + out["G_cka_rbf"] = cka_rbf_paper_run(D, Delta) + elif spec.cka_variant == "median_2sigma2": + out["G_cka_rbf"] = cka_rbf(D, Delta) if n >= 2 else float("nan") + else: + raise ValueError(f"Unknown CKA variant {spec.cka_variant!r}") + + if n >= max(2, spec.lie_min_conformers): + X_lie = _unit_range(Delta, delta_metric) if spec.lie_delta_scale == "unit_range" else Delta + out["H_LIE@k"] = local_isometry_error_knn(D, X_lie, k=spec.lie_k, include_self=spec.lie_include_self) + else: + out["H_LIE@k"] = float("nan") + + if spec.isor2_variant == "model_on_reference": + out["J_isotonic_R2"] = isotonic_r2(D, Delta) + elif spec.isor2_variant == "reference_on_model": + out["J_isotonic_R2"] = isotonic_r2_reference_on_model(D, Delta, guard=True) + elif spec.isor2_variant == "reference_on_model_noguard": + out["J_isotonic_R2"] = isotonic_r2_reference_on_model(D, Delta, guard=False) + else: + raise ValueError(f"Unknown isoR2 variant {spec.isor2_variant!r}") + + if torsion_deg is not None and len(torsion_deg) == n: + deg = np.asarray(torsion_deg, dtype=np.float64) + out["torsion_sp"] = torsion_embedding_spearman(Delta, deg) + out["AS"] = angular_smoothness_variant(deg, variant=spec.as_variant, Delta=Delta, Z=Z) + else: + out["torsion_sp"] = float("nan") + out["AS"] = float("nan") + + if spec.extra_metrics: + out["B_dcor"] = distance_correlation(D, Delta) + if n >= 3: + rM, pM = mantel_test(D, Delta, n_permutations=199, method="spearman", random_state=random_state) + else: + rM, pM = float("nan"), float("nan") + out["C_mantel_r"] = rM + out["C_mantel_p"] = pM + s1, a_fit, b_fit = kruskal_stress(D, Delta) + out["D_stress1"] = s1 + out["D_fit_a"] = a_fit + out["D_fit_b"] = b_fit + out["E_triplet_OP"] = triplet_order_preservation(D, Delta, n_triplets=100_000, random_state=random_state) + return out + + +__all__ = [ + "AS_VARIANTS", + "METRIC_VERSIONS", + "PRESETS", + "GeometryMetricSpec", + "angular_smoothness_variant", + "cka_rbf_paper_run", + "compute_geometry_metrics", + "isotonic_r2_reference_on_model", + "local_isometry_error_knn", + "resolve_metric_spec", +] diff --git a/src/three_dbench/traj/evaluation.py b/src/three_dbench/traj/evaluation.py index 59b36e1..b75de00 100644 --- a/src/three_dbench/traj/evaluation.py +++ b/src/three_dbench/traj/evaluation.py @@ -59,12 +59,11 @@ def condensed_index(i: int, j: int, n: int) -> int: def _pair_indices_from_n(n: int) -> tuple[np.ndarray, np.ndarray]: - ii, jj = [], [] - for i in range(n - 1): - j0 = i + 1 - ii.extend([i] * (n - j0)) - jj.extend(range(j0, n)) - return np.asarray(ii, int), np.asarray(jj, int) + """Row-major upper-triangle indices ``(i, j)`` with ``i < j`` (condensed order).""" + if n < 2: + return np.zeros(0, dtype=int), np.zeros(0, dtype=int) + ii, jj = np.triu_indices(n, 1) + return ii.astype(int, copy=False), jj.astype(int, copy=False) def expand_condensed_to_square( @@ -403,29 +402,14 @@ def vector_to_absdiff_condensed(x: np.ndarray, dtype=np.float32) -> np.ndarray: n = x.size if n < 2: return np.array([], dtype=dtype) - i_idx = [] - j_idx = [] - for i in range(n - 1): - j0 = i + 1 - cnt = n - j0 - i_idx.extend([i] * cnt) - j_idx.extend(range(j0, n)) - i_idx = np.asarray(i_idx, dtype=np.int64) - j_idx = np.asarray(j_idx, dtype=np.int64) + i_idx, j_idx = _pair_indices_from_n(n) v = np.abs(x[i_idx] - x[j_idx]).astype(dtype, copy=False) return v def energy_diff_condensed(E: np.ndarray) -> np.ndarray: E = np.asarray(E, float).reshape(-1) - n = E.size - ii, jj = [], [] - for i in range(n - 1): - j0 = i + 1 - ii.extend([i] * (n - j0)) - jj.extend(range(j0, n)) - ii = np.asarray(ii, int) - jj = np.asarray(jj, int) + ii, jj = _pair_indices_from_n(E.size) return np.abs(E[ii] - E[jj]) @@ -662,18 +646,21 @@ def ejs_auc_halfnormal( def _condensed_fetch_pairs(Delta_cond: ArrayLike, pairs: tuple[np.ndarray, np.ndarray]) -> np.ndarray: + """Fetch ``Delta[i, j]`` for index pairs from a condensed vector (0 on the diagonal).""" Delta_cond = np.asarray(Delta_cond, float) n = n_from_condensed_len(Delta_cond.shape[0]) - ii, jj = pairs - vals = np.empty(ii.shape[0], dtype=np.float64) - for k, (i, j) in enumerate(zip(ii, jj)): - if i == j: - vals[k] = 0.0 - else: - if i > j: - i, j = j, i - idx = condensed_index(i, j, n) - vals[k] = Delta_cond[idx] + ii = np.asarray(pairs[0], dtype=np.int64).reshape(-1) + jj = np.asarray(pairs[1], dtype=np.int64).reshape(-1) + lo = np.minimum(ii, jj) + hi = np.maximum(ii, jj) + off = lo != hi + vals = np.zeros(ii.shape[0], dtype=np.float64) + if np.any(off): + lo_o, hi_o = lo[off], hi[off] + if np.any(lo_o < 0) or np.any(hi_o >= n): + raise IndexError(f"Pair index out of range for n={n}") + idx = n * lo_o - (lo_o * (lo_o + 1)) // 2 + (hi_o - lo_o - 1) + vals[off] = Delta_cond[idx] return vals diff --git a/src/three_dbench/traj/io.py b/src/three_dbench/traj/io.py new file mode 100644 index 0000000..7ae05db --- /dev/null +++ b/src/three_dbench/traj/io.py @@ -0,0 +1,144 @@ +"""Loading per-molecule trajectory embeddings (continuous vectors or RDKit fingerprints). + +Supported layouts +----------------- +* A directory with one file per molecule, ``rmd17_.{npz,npy,pkl}`` (the layout of + ``EscheWang/3dcs-embeddings`` under ``traj//``). NPZ files are read with ``key`` (default + ``arr_0``, or the only numeric array). Pickles may hold a list of RDKit bit vectors (E3FP), an + array, or a dict (read with ``key``). +* A single ``.npz`` or ``.pkl`` whose keys/entries are molecule names (``rmd17_``). + +Values are loaded lazily: the returned mapping holds zero-argument loaders, so only one molecule's +embeddings need to be in memory at a time. +""" + +from __future__ import annotations + +import pickle +from functools import partial +from pathlib import Path +from typing import Any, Callable, Union + +import numpy as np + +TrajEmbedding = Union[np.ndarray, list] +TrajEmbeddingLoader = Callable[[], TrajEmbedding] + +_FILE_SUFFIXES = (".npz", ".npy", ".pkl") + + +def is_fingerprint_sequence(obj: Any) -> bool: + """True for a list/tuple of RDKit bit vectors (objects exposing ``GetNumBits``).""" + if not isinstance(obj, (list, tuple)): + return False + return len(obj) > 0 and hasattr(obj[0], "GetNumBits") + + +def _as_embedding(obj: Any, *, source: str) -> TrajEmbedding: + if is_fingerprint_sequence(obj): + return list(obj) + arr = np.asarray(obj) + if arr.ndim != 2 or not np.issubdtype(arr.dtype, np.number): + raise ValueError(f"Expected a 2-D numeric array or a list of RDKit bit vectors in {source}, got {arr.shape}") + return arr + + +def _select_npz_array(data: Any, key: str | None, *, source: str) -> np.ndarray: + files = list(data.files) + if key is not None: + if key not in files: + raise KeyError(f"Key {key!r} not found in {source} (available: {files})") + return np.asarray(data[key]) + if "arr_0" in files: + return np.asarray(data["arr_0"]) + numeric = [k for k in files if np.issubdtype(data[k].dtype, np.number) and data[k].ndim == 2] + if len(numeric) == 1: + return np.asarray(data[numeric[0]]) + raise ValueError(f"Cannot choose an array in {source} (keys {files}); pass --embedding-key") + + +def load_traj_embedding_file(path: Path, key: str | None = None) -> TrajEmbedding: + """Load one molecule's embeddings from ``.npz``, ``.npy`` or ``.pkl``.""" + path = Path(path) + suffix = path.suffix.lower() + if suffix == ".npz": + with np.load(path, allow_pickle=False) as data: + return _as_embedding(_select_npz_array(data, key, source=str(path)), source=str(path)) + if suffix == ".npy": + return _as_embedding(np.load(path, allow_pickle=False), source=str(path)) + if suffix == ".pkl": + with path.open("rb") as f: + payload = pickle.load(f) + if isinstance(payload, dict): + if key is not None: + payload = payload[key] + elif len(payload) == 1: + payload = next(iter(payload.values())) + else: + raise ValueError(f"Pickle {path} holds several entries {list(payload)[:5]}; pass --embedding-key") + return _as_embedding(payload, source=str(path)) + raise ValueError(f"Unsupported embedding file type: {path}") + + +def _load_npz_member(path: Path, member: str) -> TrajEmbedding: + with np.load(path, allow_pickle=False) as data: + return _as_embedding(np.asarray(data[member]), source=f"{path}[{member}]") + + +def _load_pickle_member(path: Path, member: str) -> TrajEmbedding: + with path.open("rb") as f: + payload = pickle.load(f) + return _as_embedding(payload[member], source=f"{path}[{member}]") + + +def load_traj_embeddings( + path: Path, + *, + key: str | None = None, + file_prefix: str = "rmd17_", +) -> dict[str, TrajEmbeddingLoader]: + """Return ``{mol_type: loader}`` for a directory or a single multi-molecule file.""" + path = Path(path) + if path.is_dir(): + found: dict[str, Path] = {} + for candidate in sorted(path.iterdir()): + if not candidate.is_file() or candidate.suffix.lower() not in _FILE_SUFFIXES: + continue + if not candidate.name.startswith(file_prefix): + continue + stem = candidate.stem + if stem in found: + raise ValueError(f"Several embedding files for {stem} in {path}: {found[stem].name}, {candidate.name}") + found[stem] = candidate + if not found: + raise FileNotFoundError(f"No {file_prefix}*{{.npz,.npy,.pkl}} files in {path}") + return {stem: partial(load_traj_embedding_file, p, key) for stem, p in found.items()} + + suffix = path.suffix.lower() + if suffix == ".npz": + with np.load(path, allow_pickle=False) as data: + members = list(data.files) + if key is not None: + members = [key] + return {m: partial(_load_npz_member, path, m) for m in members} + if suffix == ".pkl": + with path.open("rb") as f: + payload = pickle.load(f) + if not isinstance(payload, dict): + raise ValueError(f"Expected a dict of molecule -> embeddings in {path}") + members = [key] if key is not None else list(payload) + return {m: partial(_load_pickle_member, path, m) for m in members} + raise ValueError(f"Unsupported embeddings path: {path}") + + +def resolve_embedding(value: TrajEmbedding | TrajEmbeddingLoader) -> TrajEmbedding: + """Materialise a loader, or pass arrays / fingerprint lists through.""" + if callable(value): + value = value() + if isinstance(value, (list, tuple)): + return list(value) + return np.asarray(value) + + +def embedding_length(value: TrajEmbedding) -> int: + return len(value) if isinstance(value, (list, tuple)) else int(np.asarray(value).shape[0]) diff --git a/src/three_dbench/traj/metrics_v2.py b/src/three_dbench/traj/metrics_v2.py new file mode 100644 index 0000000..29fc1ac --- /dev/null +++ b/src/three_dbench/traj/metrics_v2.py @@ -0,0 +1,162 @@ +"""Corrected ("v2") energy metrics for the trajectory benchmark. + +The published tables (paper Tables 3, 6, 7) were computed with the definitions in +:func:`three_dbench.traj.evaluation.compute_energy_metrics_from_condensed`, +:func:`~three_dbench.traj.evaluation.thresholded_smoothness` and +:func:`~three_dbench.traj.evaluation.ks_wasserstein_against_energy_diff` (``--metric-version paper``). +Those implementations differ from the definitions written in the paper appendix (C.3, C.6) in +several places. ``--metric-version v2`` implements the appendix definitions, with the choices the +appendix leaves open made explicit. See ``docs/metrics/energy.md`` for formulas and rationale. + +Differences from ``paper`` (per window of ``n`` frames, ``dE_ij = |E_i - E_j|``, ``dZ_ij = Delta_ij``): + +* Distances are kept as float32 (``paper`` stores them as float16) and ``dE`` as float64 (``paper`` + casts ``dE`` to float32 for the rank/CKA/isotonic metrics). +* ``cka_rbf``: separate median-heuristic RBF bandwidths for ``dE`` and ``dZ`` (appendix C.3). The + ``paper`` code uses one bandwidth from the concatenation of both, which is dominated by the + representation distances and makes the energy kernel nearly an indicator of tied energies. +* ``iso_R2``: isotonic fit ``dE ~ f(dZ)`` and R^2 of ``dE`` (appendix C.3). ``paper`` fits the + reverse direction (a constant representation then scores 1.0); v2 returns NaN for constant ``dZ``. +* ``EJS_*`` and ``EJS_num_jumps_*``: ``sigma_d = sqrt(mean(dE^2))`` (appendix C.6). ``paper`` uses the + robust ``median(dE) / 0.6745``. ``ROC_AUC``/``PR_AUC`` already used ``sqrt(mean(dE^2))`` in ``paper``. +* ``KS``/``W1``: computed between commensurate, dimensionless quantities ``dZ / Q90(dZ)`` and + ``dE / Q90(dE)``. ``paper`` compares ``dZ`` in [0, 1] with ``dE`` in kcal/mol, so KS is ~1 for every + continuous representation. Smaller is better. +* ``TS`` and ``Smoothness`` are defined on consecutive frames and are only meaningful when the frames + are in simulation-time order. They are NaN unless ``time_ordered=True``. rMD17 frames are **not** + time-ordered (the ``old_indices`` are shuffled; consecutive-frame |dE| equals random-pair |dE|), so + these two metrics are not reported for rMD17 in v2. +""" + +from __future__ import annotations + +import numpy as np +from scipy.stats import ks_2samp, wasserstein_distance + +from three_dbench.common.metrics import ( + cka_rbf, + distance_correlation, + isotonic_r2, + kendall_correlation, + spearman_correlation, +) + +from .evaluation import ( + _pair_indices_from_n, + ejs_auc_halfnormal, + energy_jump_sensitivity_auto, + n_from_condensed_len, +) + +LAMBDA_GRID = (0.1, 0.5, 1.0, 2.0, 3.0) +TS_GAMMA = 0.2 + + +def _q90(values: np.ndarray, eps: float) -> float: + v = values[np.isfinite(values)] + if v.size == 0: + return float("nan") + return float(np.quantile(v, 0.90)) + eps + + +def consecutive_segments(Delta_cond: np.ndarray, E: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Return ``(dZ_k, dE_k)`` for consecutive frames ``k -> k+1`` (``k = 0..n-2``).""" + n = E.size + k = np.arange(n - 1, dtype=np.int64) + idx = n * k - (k * (k + 1)) // 2 # condensed index of (k, k+1) + return np.asarray(Delta_cond, dtype=np.float64)[idx], np.abs(np.diff(E)) + + +def isotonic_r2_reference_from_representation(dE: np.ndarray, dZ: np.ndarray) -> float: + """Appendix C.3 isoR^2: fit ``dE_hat = f_iso(dZ)`` (non-decreasing) and return R^2 of ``dE``.""" + return isotonic_r2(dZ, dE) + + +def compute_energy_metrics_v2( + Delta_cond: np.ndarray, + E: np.ndarray, + *, + time_ordered: bool = False, + ts_gamma: float = TS_GAMMA, + eps: float = 1e-12, + compute_ejs_auc: bool = True, +) -> dict[str, float]: + """Compute the v2 energy metrics for one window (see module docstring).""" + D = np.asarray(Delta_cond, dtype=np.float64).reshape(-1) + n = n_from_condensed_len(D.size) + E = np.asarray(E, dtype=np.float64).reshape(-1) + if E.size != n: + raise ValueError(f"Energy length {E.size} != n inferred from Delta_cond ({n}).") + + ii, jj = _pair_indices_from_n(n) + dE = np.abs(E[ii] - E[jj]) + + out: dict[str, float] = {} + out["spearman"] = spearman_correlation(dE, D) + out["kendall"] = kendall_correlation(dE, D) + out["iso_R2"] = isotonic_r2_reference_from_representation(dE, D) + try: + out["dCor"] = distance_correlation(dE, D) + except Exception: + out["dCor"] = np.nan + try: + out["cka_rbf"] = cka_rbf(dE, D, share_sigma=False) + except Exception: + out["cka_rbf"] = np.nan + + ejs = energy_jump_sensitivity_auto( + E, + D, + lam_grid=LAMBDA_GRID, + lam_rep=2.0, + robust_sigma=False, + dist_quantile=0.75, + use_global_quantile=True, + ) + for key, value in ejs.items(): + if key.startswith("EJS") and isinstance(value, (int, float, np.floating, np.integer)): + out[key] = float(value) + out["EJS"] = float(ejs.get("EJS", np.nan)) + out["EJS_num_jumps"] = float(ejs.get("num_jumps", np.nan)) + out["EJS_tau"] = float(ejs.get("tau", np.nan)) + + if compute_ejs_auc: + try: + auc = ejs_auc_halfnormal(D, E, lam=2.0, robust_sigma=False) + out["ROC_AUC"] = float(auc.get("ROC_AUC", np.nan)) + out["PR_AUC"] = float(auc.get("PR_AUC", np.nan)) + except Exception: + out["ROC_AUC"] = np.nan + out["PR_AUC"] = np.nan + + qZ = _q90(D, eps) + qE = _q90(dE, eps) + out["qZ90"] = qZ + out["qE90"] = qE + mask = np.isfinite(D) & np.isfinite(dE) + if mask.sum() >= 10 and np.isfinite(qZ) and np.isfinite(qE): + x = D[mask] / qZ + y = dE[mask] / qE + out["KS"] = float(ks_2samp(x, y, alternative="two-sided", mode="auto").statistic) + out["W1"] = float(wasserstein_distance(x, y)) + else: + out["KS"] = np.nan + out["W1"] = np.nan + + out["time_ordered"] = 1.0 if time_ordered else 0.0 + out["T_E"] = float(ts_gamma * qE) if np.isfinite(qE) else np.nan + if time_ordered and n >= 2: + dZk, dEk = consecutive_segments(D, E) + ok = np.isfinite(dZk) & np.isfinite(dEk) + ratio = np.exp(-(dZk / qZ) / (dEk / qE + eps)) + out["Smoothness"] = float(np.mean(ratio[ok])) if ok.any() else np.nan + out["Smoothness_segments"] = float(ok.sum()) + jump = ok & (dEk > out["T_E"]) + out["TS"] = float(np.mean(ratio[jump])) if jump.any() else np.nan + out["used"] = float(jump.sum()) + else: + out["Smoothness"] = np.nan + out["Smoothness_segments"] = 0.0 + out["TS"] = np.nan + out["used"] = 0.0 + return out diff --git a/src/three_dbench/traj/protocol.py b/src/three_dbench/traj/protocol.py new file mode 100644 index 0000000..dacfe2c --- /dev/null +++ b/src/three_dbench/traj/protocol.py @@ -0,0 +1,201 @@ +"""Sampling protocol and per-window computation for the trajectory (energy) benchmark. + +Window schemes +-------------- +``legacy`` (default; reproduces the published Tables 3, 6 and 7) + For every molecule a fresh ``numpy.random.default_rng(seed)`` draws ``n_samples`` starts with + ``integers(0, legacy_traj_len - window)``; ``legacy_traj_len`` is 100,000 (the nominal rMD17 + length, also used for azobenzene with 99,988 frames). Every molecule therefore gets the same + starts, and a molecule's windows do not depend on which other molecules are evaluated. This is + exactly :func:`three_dbench.traj.evaluation.run_once_for_molecule`, the runner behind the paper. + Molecules are processed in the legacy order (``DEFAULT_MOL_TYPES``), which fixes the row order of + the details and the floating-point summation order of the summary. +``shared`` + The scheme of the 0.1.0 CLI: one generator shared across molecules in dataset row order, with + ``integers(0, n_frames - window, size=min(n_samples, n_frames - window))``. + +Metric versions +--------------- +``paper`` (default): the implementation used for the published numbers (float16 distances, robust +EJS sigma, shared CKA bandwidth, ...). ``v2``: definitions from the paper appendix, see +:mod:`three_dbench.traj.metrics_v2` and ``docs/metrics/energy.md``. +""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence +from contextlib import contextmanager +from typing import Any + +import numpy as np + +from .evaluation import ( + DEFAULT_MOL_TYPES, + compute_energy_metrics_from_condensed, + ks_wasserstein_against_energy_diff, + pairwise_distances_from_embeddings_large, + pairwise_tanimoto_fps_large, + thresholded_smoothness, +) +from .metrics_v2 import compute_energy_metrics_v2 + +WINDOW_SCHEMES = ("legacy", "shared") +METRIC_VERSIONS = ("paper", "v2") +EMBED_METRICS = ("cosine", "euclidean", "tanimoto") +LEGACY_TRAJ_LEN = 100_000 +LEGACY_MOL_ORDER = tuple(DEFAULT_MOL_TYPES) + +DISTANCE_DTYPE = {"paper": np.float16, "v2": np.float32} + + +def resolve_molecules(requested: Sequence[str] | None, available: Sequence[str]) -> list[str]: + """Match requested molecule names (``aspirin`` or ``rmd17_aspirin``) against dataset names.""" + available = list(available) + if not requested: + return available + out = [] + for name in requested: + name = str(name).strip() + if name in available: + match = name + elif f"rmd17_{name}" in available: + match = f"rmd17_{name}" + else: + raise KeyError(f"Molecule {name!r} not in dataset (available: {available})") + if match not in out: + out.append(match) + return out + + +def order_molecules(molecules: Sequence[str], window_scheme: str) -> list[str]: + """Evaluation order: legacy order for the legacy scheme, otherwise the given (dataset) order.""" + molecules = list(molecules) + if window_scheme != "legacy": + return molecules + rank = {m: i for i, m in enumerate(LEGACY_MOL_ORDER)} + return sorted(molecules, key=lambda m: (rank.get(m, len(rank)), molecules.index(m))) + + +def draw_window_starts( + n_frames_by_mol: Mapping[str, int], + *, + window_scheme: str = "legacy", + n_samples: int = 100, + window: int = 2000, + random_seed: int = 2025, + legacy_traj_len: int = LEGACY_TRAJ_LEN, +) -> dict[str, np.ndarray]: + """Draw window start indices for every molecule of ``n_frames_by_mol`` (in dataset order). + + For the ``shared`` scheme the mapping must contain *all* dataset molecules in dataset order, + because each molecule's draw depends on the draws before it. + """ + if window_scheme not in WINDOW_SCHEMES: + raise ValueError(f"window_scheme must be one of {WINDOW_SCHEMES}, got {window_scheme!r}") + if window < 2: + raise ValueError("window must be >= 2") + starts: dict[str, np.ndarray] = {} + if window_scheme == "legacy": + max_start = int(legacy_traj_len) - int(window) + if max_start <= 0: + raise ValueError("legacy_traj_len must be greater than window to draw samples.") + for mol_type, n_frames in n_frames_by_mol.items(): + rng = np.random.default_rng(random_seed) + s = rng.integers(0, max_start, size=n_samples, endpoint=False) + if s.size and int(s.max()) + window > int(n_frames): + raise ValueError( + f"Legacy window [{int(s.max())}, {int(s.max()) + window}) exceeds the {n_frames} frames of " + f"{mol_type}; use --window-scheme shared or a smaller --legacy-traj-len." + ) + starts[mol_type] = s + return starts + + rng = np.random.default_rng(random_seed) + for mol_type, n_frames in n_frames_by_mol.items(): + max_start = int(n_frames) - int(window) + if max_start <= 0: + raise ValueError(f"Window size {window} exceeds trajectory length for {mol_type}.") + starts[mol_type] = rng.integers(0, max_start, size=min(n_samples, max_start), endpoint=False) + return starts + + +def _is_fingerprints(X: Any) -> bool: + return isinstance(X, (list, tuple)) + + +def resolve_embed_metric(metric_embed: str | None, X: Any) -> str: + """``None`` selects Tanimoto for fingerprint lists and cosine for vectors.""" + if metric_embed is None: + return "tanimoto" if _is_fingerprints(X) else "cosine" + if metric_embed not in EMBED_METRICS: + raise ValueError(f"metric_embed must be one of {EMBED_METRICS}, got {metric_embed!r}") + if _is_fingerprints(X) and metric_embed != "tanimoto": + raise ValueError("Fingerprint (bit-vector) embeddings support only the tanimoto distance.") + return metric_embed + + +def tanimoto_distances_dense(X: np.ndarray, *, dtype_out=np.float16) -> np.ndarray: + """Condensed ``1 - Tanimoto`` distances for a dense binary matrix (non-zero entries are on-bits). + + Matches RDKit ``BulkTanimotoSimilarity`` on the equivalent bit vectors (similarity 0 when both + vectors are empty), including the float32 intermediate used by the fingerprint path. + """ + B = (np.asarray(X) != 0).astype(np.float64) + n = B.shape[0] + counts = B.sum(axis=1) + inter = B @ B.T + union = counts[:, None] + counts[None, :] - inter + with np.errstate(invalid="ignore", divide="ignore"): + sim = np.where(union > 0, inter / np.where(union > 0, union, 1.0), 0.0) + iu = np.triu_indices(n, 1) + return (1.0 - sim.astype(np.float32)[iu]).astype(dtype_out, copy=False) + + +def window_distances( + X: Any, + *, + metric: str, + metric_version: str = "paper", + block_size: int = 4096, +) -> np.ndarray: + """Condensed representation distances for one window (``X`` already sliced).""" + dtype_out = DISTANCE_DTYPE[metric_version] + if metric == "tanimoto": + if _is_fingerprints(X): + return pairwise_tanimoto_fps_large( + list(X), block_size=block_size, out_mode="condensed", dtype_out=dtype_out, progress=False + ) + return tanimoto_distances_dense(X, dtype_out=dtype_out) + return pairwise_distances_from_embeddings_large( + X, metric=metric, block_size=block_size, out_mode="condensed", dtype_out=dtype_out, progress=False + ) + + +def compute_window_metrics( + D: np.ndarray, + E: np.ndarray, + *, + metric_version: str = "paper", + time_ordered: bool = False, +) -> dict[str, float]: + """All energy metrics for one window.""" + if metric_version == "paper": + m1 = compute_energy_metrics_from_condensed(D, E) + m2 = thresholded_smoothness(D, E, eps=1e-12) + m3 = ks_wasserstein_against_energy_diff(D, E) + return {**m1, **m2, **m3} + if metric_version == "v2": + return compute_energy_metrics_v2(D, E, time_ordered=time_ordered) + raise ValueError(f"metric_version must be one of {METRIC_VERSIONS}, got {metric_version!r}") + + +@contextmanager +def single_threaded_blas(): + """Limit BLAS/OpenMP pools to one thread (reproducible float32 BLAS results, no oversubscription).""" + try: + from threadpoolctl import threadpool_limits + except ImportError: # pragma: no cover - threadpoolctl ships with scikit-learn + yield + return + with threadpool_limits(limits=1): + yield diff --git a/src/three_dbench/utils/paths.py b/src/three_dbench/utils/paths.py index 8dfd7d1..1818180 100644 --- a/src/three_dbench/utils/paths.py +++ b/src/three_dbench/utils/paths.py @@ -1,14 +1,36 @@ -"""Shared path helpers for locating data and result directories.""" +"""Shared path helpers for locating data and result directories. + +Default data and result directories are resolved relative to ``$THREE_DBENCH_HOME`` when it is +set, otherwise relative to the current working directory (not relative to the installed package). +""" from __future__ import annotations +import os from pathlib import Path -PROJECT_ROOT = Path(__file__).resolve().parents[3] -SRC_ROOT = PROJECT_ROOT / "src" + +def get_project_root() -> Path: + """Return ``$THREE_DBENCH_HOME`` if set, else the current working directory.""" + env = os.environ.get("THREE_DBENCH_HOME") + return Path(env).expanduser().resolve() if env else Path.cwd() + + +def get_data_root() -> Path: + return get_project_root() / "data" + + +def get_results_root() -> Path: + return get_project_root() / "results" + + +# Module-level constants, evaluated at import time (kept for backwards compatibility). +PROJECT_ROOT = get_project_root() +SRC_ROOT = Path(__file__).resolve().parents[2] DATA_ROOT = PROJECT_ROOT / "data" RESULTS_ROOT = PROJECT_ROOT / "results" HF_DATA_ROOT = DATA_ROOT / "hf" +EMBEDDINGS_ROOT = DATA_ROOT / "embeddings" def ensure_dir(path: Path) -> Path: diff --git a/tests/conftest.py b/tests/conftest.py index e4fbedf..33d84dc 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -2,9 +2,18 @@ from __future__ import annotations +import sys +from pathlib import Path + import numpy as np import pytest +# `tests/test_demo.py` imports `examples.demo`, which lives in the checkout, not in the installed +# package. Make it importable regardless of the working directory pytest is started from. +_REPO_ROOT = Path(__file__).resolve().parent.parent +if str(_REPO_ROOT) not in sys.path: + sys.path.insert(0, str(_REPO_ROOT)) + @pytest.fixture def rng(): diff --git a/tests/test_chirality_distance.py b/tests/test_chirality_distance.py new file mode 100644 index 0000000..7547865 --- /dev/null +++ b/tests/test_chirality_distance.py @@ -0,0 +1,269 @@ +"""Chirality: distance switch, best-k silhouette kmax, CLI options and dataset/embedding validation.""" + +from __future__ import annotations + +import json +import pickle +import subprocess +import sys + +import numpy as np +import pytest + +from three_dbench.chirality.evaluation import ( + distance_matrix_for_subset, + evaluate_en_separation_from_counts, + resolve_unsup_kmax, +) + + +def _scale_only_embeddings(rng, n_per_class=20, dim=6): + """Both classes share one direction and differ only in norm (x1): separable under Euclidean only.""" + u = rng.standard_normal(dim) + u /= np.linalg.norm(u) + x0 = u + 0.05 * rng.standard_normal((n_per_class, dim)) + x1 = 10.0 * (u + 0.05 * rng.standard_normal((n_per_class, dim))) + return np.vstack([x0, x1]).astype(np.float32) + + +def _tiny_dataset(tmp_path, counts): + datasets = pytest.importorskip("datasets") + rows, offset = [], 0 + for key, n in counts.items(): + mol_id, en = key.split("::en") + rows.append( + {"key": key, "mol_id": mol_id, "en_id": en, "n_conformers": n, "offset": offset, "mol_blocks": [""] * n} + ) + offset += n + ds = datasets.Dataset.from_list(rows) + path = tmp_path / "hf_chirality" + ds.save_to_disk(str(path)) + return path + + +# ----------------------------- distance switch ----------------------------- +def test_distance_matrices(rng): + X = rng.standard_normal((5, 4)).astype(np.float32) + idx = np.arange(5) + De = distance_matrix_for_subset(X, idx, "continuous", distance="euclidean") + ref = np.linalg.norm(X[:, None, :].astype(np.float64) - X[None, :, :], axis=2) + np.testing.assert_allclose(De, ref, atol=1e-5) + + Dc = distance_matrix_for_subset(X, idx, "continuous", distance="cosine") + Xn = X / np.linalg.norm(X, axis=1, keepdims=True) + ref_c = 1.0 - Xn @ Xn.T + np.fill_diagonal(ref_c, 0.0) + np.testing.assert_allclose(Dc, ref_c, atol=1e-6) + assert np.all(np.diag(Dc) == 0.0) + + +def test_default_distance_is_euclidean(rng): + counts = {"MOL::en0": 20, "MOL::en1": 20} + emb = _scale_only_embeddings(rng) + _, s_default = evaluate_en_separation_from_counts(counts, emb, unsup_kmax=4) + _, s_euc = evaluate_en_separation_from_counts(counts, emb, unsup_kmax=4, distance="euclidean") + assert s_default == s_euc + + +def test_distance_changes_supervised_metrics(rng): + counts = {"MOL::en0": 20, "MOL::en1": 20} + emb = _scale_only_embeddings(rng) + _, s_euc = evaluate_en_separation_from_counts(counts, emb, unsup_kmax=4, distance="euclidean") + _, s_cos = evaluate_en_separation_from_counts(counts, emb, unsup_kmax=4, distance="cosine") + assert s_euc["NN1_acc_mean"] > 0.95 + assert s_euc["ESA_AUC_mean"] > 0.95 + # Directions are identically distributed in both classes, so cosine cannot separate them. + assert s_cos["NN1_acc_mean"] < 0.75 + assert s_cos["ESA_AUC_mean"] < 0.65 + + +def test_invalid_distance_raises(rng): + with pytest.raises(ValueError, match="distance"): + evaluate_en_separation_from_counts({"M::en0": 3, "M::en1": 3}, rng.standard_normal((6, 3)), distance="l1") + + +def test_fingerprints_ignore_distance(): + DataStructs = pytest.importorskip("rdkit.DataStructs") + rng = np.random.default_rng(1) + fps = [] + for cls in range(2): + for _ in range(6): + bv = DataStructs.ExplicitBitVect(64) + on = rng.choice(32, size=20, replace=False) + 32 * cls + for b in on: + bv.SetBit(int(b)) + fps.append(bv) + counts = {"FP::en0": 6, "FP::en1": 6} + r_e, s_e = evaluate_en_separation_from_counts(counts, fps, distance="euclidean") + r_c, s_c = evaluate_en_separation_from_counts(counts, fps, distance="cosine") + assert r_e[0]["embedding_mode"] == "fingerprint" + assert s_e == s_c + assert s_e["ESA_AUC_mean"] == pytest.approx(1.0) + + +# ----------------------------- best-k silhouette kmax ----------------------------- +def test_resolve_unsup_kmax(): + assert resolve_unsup_kmax(40, None) == 39 + assert resolve_unsup_kmax(40, 10) == 10 + assert resolve_unsup_kmax(5, 10) == 4 + with pytest.raises(ValueError): + resolve_unsup_kmax(5, 1) + + +@pytest.mark.filterwarnings("ignore:Number of distinct clusters") +def test_unsup_kmax_default_is_unbounded(): + rng = np.random.default_rng(0) + centers = rng.standard_normal((12, 5)) * 50.0 + X = np.vstack([c + 0.01 * rng.standard_normal((3, 5)) for c in centers]).astype(np.float32) + counts = {"MOL::en0": 18, "MOL::en1": 18} # 12 tight clusters, labels unrelated to them + rows_default, _ = evaluate_en_separation_from_counts(counts, X) + rows_capped, _ = evaluate_en_separation_from_counts(counts, X, unsup_kmax=10) + assert rows_default[0]["k_unsup"] == 12 + assert rows_capped[0]["k_unsup"] <= 10 + assert rows_default[0]["sil_unsup"] > rows_capped[0]["sil_unsup"] + + +def test_n_jobs_does_not_change_results(rng): + counts = {} + for m in range(5): + counts[f"M{m}::en0"] = 4 + m + counts[f"M{m}::en1"] = 3 + m + total = sum(counts.values()) + X = rng.standard_normal((total, 6)).astype(np.float32) + r1, s1 = evaluate_en_separation_from_counts(counts, X, n_jobs=1) + r2, s2 = evaluate_en_separation_from_counts(counts, X, n_jobs=2) + assert json.dumps(r1, sort_keys=True) == json.dumps(r2, sort_keys=True) + assert json.dumps(s1, sort_keys=True) == json.dumps(s2, sort_keys=True) + + +# ----------------------------- validation ----------------------------- +def test_row_count_mismatch_message(rng): + counts = {"M::en0": 3, "M::en1": 3} + with pytest.raises(ValueError, match=r"Embedding rows \(5\) != total conformers in the dataset \(6"): + evaluate_en_separation_from_counts(counts, rng.standard_normal((5, 4))) + + +def test_non_finite_and_bad_shape_raise(rng): + counts = {"M::en0": 3, "M::en1": 3} + X = rng.standard_normal((6, 4)) + X[2, 1] = np.nan + with pytest.raises(ValueError, match="NaN/Inf"): + evaluate_en_separation_from_counts(counts, X) + with pytest.raises(ValueError, match="2-D"): + evaluate_en_separation_from_counts(counts, np.zeros(6)) + with pytest.raises(ValueError, match="Unrecognised embeddings"): + evaluate_en_separation_from_counts(counts, [[0.0]] * 6) + + +def test_dataset_offset_and_duplicate_checks(): + from three_dbench.benchmarks.chirality import _key_counts_from_dataset + + ok = [ + {"key": "A::en0", "n_conformers": 2, "offset": 0}, + {"key": "A::en1", "n_conformers": 3, "offset": 2}, + ] + assert dict(_key_counts_from_dataset(ok)) == {"A::en0": 2, "A::en1": 3} + bad_offset = [dict(ok[0]), dict(ok[1], offset=0)] + with pytest.raises(ValueError, match="offset"): + _key_counts_from_dataset(bad_offset) + dup = [dict(ok[0]), dict(ok[0], offset=2)] + with pytest.raises(ValueError, match="Duplicate key"): + _key_counts_from_dataset(dup) + + +def test_load_chirality_embeddings_pickle_dict(tmp_path, rng): + from three_dbench.benchmarks.chirality import load_chirality_embeddings + + path = tmp_path / "fp.pkl" + with path.open("wb") as f: + pickle.dump({"fp_a": [1, 2, 3], "fp_b": [4, 5, 6]}, f) + with pytest.raises(ValueError, match="fp_b"): + load_chirality_embeddings(path) + assert load_chirality_embeddings(path, key="fp_a").array == [1, 2, 3] + + npz = tmp_path / "e.npz" + np.savez(npz, gemnet=rng.standard_normal((3, 2))) + with pytest.raises(KeyError, match="gemnet"): + load_chirality_embeddings(npz, key="arr_0") + + +# ----------------------------- benchmark entry point + CLI ----------------------------- +def test_benchmark_writes_config(tmp_path, rng): + from three_dbench.benchmarks.chirality import evaluate_chirality_embeddings + + counts = {"MOLA::en0": 5, "MOLA::en1": 5, "MOLB::en0": 4, "MOLB::en1": 3} + ds_dir = _tiny_dataset(tmp_path, counts) + X = rng.standard_normal((17, 4)).astype(np.float32) + out = tmp_path / "out" + _, summary = evaluate_chirality_embeddings( + dataset_dir=ds_dir, + embeddings=X, + output_dir=out, + model_name="toy", + distance="cosine", + metric_version="v2", + unsup_kmax=3, + ) + cfg = json.loads((out / "config.json").read_text()) + assert cfg["distance"] == "cosine" + assert cfg["metric_version"] == "v2" + assert cfg["unsup_kmax"] == 3 + assert cfg["n_conformers"] == 17 + assert cfg["coverage"]["n_molecules"] == 2 + assert (out / "toy_per_molecule.json").exists() + assert (out / "summary.csv").exists() + assert summary["n_molecules"] == 2 + + +def test_cli_chirality_options(tmp_path, rng): + counts = {"MOLA::en0": 5, "MOLA::en1": 5} + ds_dir = _tiny_dataset(tmp_path, counts) + emb = tmp_path / "emb.npz" + np.savez(emb, arr_0=rng.standard_normal((10, 4)).astype(np.float32)) + out = tmp_path / "cli_out" + cmd = [ + sys.executable, + "-m", + "three_dbench", + "evaluate", + "chirality", + "--dataset-dir", + str(ds_dir), + "--embeddings", + str(emb), + "--model-name", + "toy", + "--output-dir", + str(out), + "--distance", + "cosine", + "--metric-version", + "v2", + "--unsup-kmax", + "n-1", + ] + res = subprocess.run(cmd, capture_output=True, text=True) + assert res.returncode == 0, res.stderr + cfg = json.loads((out / "config.json").read_text()) + assert (cfg["distance"], cfg["metric_version"], cfg["unsup_kmax"]) == ("cosine", "v2", "n-1") + + bad = subprocess.run(cmd[:-1] + ["1"], capture_output=True, text=True) + assert bad.returncode != 0 + assert "--unsup-kmax" in bad.stderr + + +def test_cli_help_lists_chirality_options(): + res = subprocess.run( + [sys.executable, "-m", "three_dbench", "evaluate", "--help"], capture_output=True, text=True, check=True + ) + for flag in ("--distance", "--metric-version", "--unsup-kmax", "--n-jobs"): + assert flag in res.stdout + + +def test_import_has_no_side_effects(): + code = ( + "import sys; import three_dbench.chirality.evaluation as e; " + "print(int('three_dbench.utils.paths' in sys.modules), int('sklearn_extra' in sys.modules))" + ) + res = subprocess.run([sys.executable, "-c", code], capture_output=True, text=True, check=True) + assert res.stdout.split() == ["0", "0"] diff --git a/tests/test_chirality_metric_version.py b/tests/test_chirality_metric_version.py new file mode 100644 index 0000000..8a58d0c --- /dev/null +++ b/tests/test_chirality_metric_version.py @@ -0,0 +1,129 @@ +"""Chirality: ``metric_version`` paper vs v2 definitions (synthetic, fast).""" + +from __future__ import annotations + +import numpy as np +import pytest + +from three_dbench.chirality.evaluation import ( + ChiralitySettings, + davies_bouldin_centroid, + davies_bouldin_from_D, + evaluate_en_separation_from_counts, + evaluate_molecule, + nn1_leave_one_out_from_D, + nn1_leave_one_out_v2, +) + + +def test_invalid_metric_version_raises(rng): + with pytest.raises(ValueError, match="metric_version"): + evaluate_en_separation_from_counts({"M::en0": 3, "M::en1": 3}, rng.standard_normal((6, 3)), metric_version="v3") + + +def test_default_metric_version_is_paper(chirality_key_counts, rng): + X = rng.standard_normal((sum(chirality_key_counts.values()), 5)).astype(np.float32) + r_default, s_default = evaluate_en_separation_from_counts(chirality_key_counts, X) + r_paper, s_paper = evaluate_en_separation_from_counts(chirality_key_counts, X, metric_version="paper") + assert s_default == s_paper + + +def test_nn1_ties_paper_vs_v2(): + # Point 0 has two exactly tied nearest neighbours: point 1 (same class) and point 2 (other class). + D = np.array( + [ + [0.0, 1.0, 1.0, 5.0], + [1.0, 0.0, 3.0, 5.0], + [1.0, 3.0, 0.0, 2.0], + [5.0, 5.0, 2.0, 0.0], + ] + ) + y = np.array(["a", "a", "b", "b"], dtype=object) + # paper: np.argmin breaks the tie towards the lowest index (point 1, correct) -> 3/4 + assert nn1_leave_one_out_from_D(D, y) == pytest.approx(0.75) + # v2: point 0 scores 1/2 (one of its two tied neighbours shares its label) + assert nn1_leave_one_out_v2(D, y) == pytest.approx((0.5 + 1.0 + 0.0 + 1.0) / 4) + # swapping rows 1 and 2 flips the paper tie-break (-> 2/4) but leaves v2 unchanged + p = np.array([0, 2, 1, 3]) + Dp, yp = D[np.ix_(p, p)], y[p] + assert nn1_leave_one_out_from_D(Dp, yp) == pytest.approx(0.5) + assert nn1_leave_one_out_v2(Dp, yp) == pytest.approx(nn1_leave_one_out_v2(D, y)) + + +def test_nn1_v2_excludes_points_without_same_class_partner(): + D = np.array([[0.0, 1.0, 4.0], [1.0, 0.0, 4.0], [4.0, 4.0, 0.0]]) + y = np.array(["a", "a", "b"], dtype=object) + assert nn1_leave_one_out_from_D(D, y) == pytest.approx(2 / 3) # point 2 can never be right + assert nn1_leave_one_out_v2(D, y) == pytest.approx(1.0) + y_all_single = np.array(["a", "b", "c"], dtype=object) + assert nn1_leave_one_out_from_D(D, y_all_single) == 0.0 + assert np.isnan(nn1_leave_one_out_v2(D, y_all_single)) + + +def test_dbi_centroid_matches_sklearn(rng): + sklearn_metrics = pytest.importorskip("sklearn.metrics") + X = np.vstack([rng.standard_normal((6, 3)) + 4, rng.standard_normal((5, 3)) - 4, rng.standard_normal((7, 3))]) + y = np.array(["0"] * 6 + ["1"] * 5 + ["2"] * 7, dtype=object) + ref = sklearn_metrics.davies_bouldin_score(X, y) + assert davies_bouldin_centroid(X, y) == pytest.approx(ref, rel=1e-9) + # the paper (medoid) approximation differs in general + from three_dbench.chirality.evaluation import euclidean_distances + + D = euclidean_distances(X) + assert davies_bouldin_from_D(D, y, "continuous") != pytest.approx(ref, rel=1e-6) + + +def test_v2_all_singleton_molecule_is_nan_not_zero(rng): + counts = {f"M::en{i}": 1 for i in range(4)} # four stereoisomers, one conformer each + X = rng.standard_normal((4, 3)) + rows_p, _ = evaluate_en_separation_from_counts(counts, X, metric_version="paper") + rows_v, _ = evaluate_en_separation_from_counts(counts, X, metric_version="v2") + assert rows_p[0]["NN1_acc"] == 0.0 + assert np.isnan(rows_v[0]["NN1_acc"]) + assert np.isnan(rows_v[0]["DBI"]) + assert np.isnan(rows_p[0]["ESA_AUC"]) and np.isnan(rows_v[0]["ESA_AUC"]) + + +def test_hopkins_population_single_stereoisomer(rng): + X = rng.standard_normal((12, 4)) + y = np.array(["0"] * 12, dtype=object) + paper = evaluate_molecule("M", X, y, "continuous", ChiralitySettings(metric_version="paper")) + v2 = evaluate_molecule("M", X, y, "continuous", ChiralitySettings(metric_version="v2")) + assert paper["mode"] == v2["mode"] == "skip_single_en" + assert np.isfinite(paper["hopkins"]) + assert np.isnan(v2["hopkins"]) + opt_in = ChiralitySettings(metric_version="v2", do_unsup_when_single_en=True, unsup_kmax=4) + v2_unsup = evaluate_molecule("M", X, y, "continuous", opt_in) + assert v2_unsup["mode"] == "unsupervised_only" + assert np.isnan(v2_unsup["hopkins"]) and np.isfinite(v2_unsup["sil_unsup"]) + small = evaluate_molecule( + "S", X[:9], np.array(["0"] * 5 + ["1"] * 4, dtype=object), "continuous", ChiralitySettings() + ) + assert np.isnan(small["hopkins"]) # n < 10 in both versions + + +def test_v2_euclidean_matches_paper_on_shared_definitions(rng): + counts = {"A::en0": 7, "A::en1": 6, "B::en0": 5, "B::en1": 8} + X = rng.standard_normal((26, 5)).astype(np.float32) + rp, _ = evaluate_en_separation_from_counts(counts, X, metric_version="paper") + rv, _ = evaluate_en_separation_from_counts(counts, X, metric_version="v2") + for a, b in zip(rp, rv): + for k in ("ESA_AUC", "sil_sup", "hopkins", "clarity"): + assert a[k] == b[k] + # best-k silhouette on precomputed Euclidean D equals the silhouette on X up to rounding + assert b["sil_unsup"] == pytest.approx(a["sil_unsup"], abs=1e-5) + assert b["k_unsup"] == a["k_unsup"] + + +def test_v2_cosine_uses_unit_sphere_geometry(rng): + counts = {"A::en0": 12, "A::en1": 12} + X = rng.standard_normal((24, 5)).astype(np.float32) + scales = rng.uniform(0.5, 20.0, size=(24, 1)).astype(np.float32) + # v2 + cosine is invariant to per-row rescaling; paper + cosine is not (Hopkins/KMeans on raw X) + r1, s1 = evaluate_en_separation_from_counts(counts, X, distance="cosine", metric_version="v2") + r2, s2 = evaluate_en_separation_from_counts(counts, X * scales, distance="cosine", metric_version="v2") + for k in ("ESA_AUC_mean", "NN1_acc_mean", "sil_sup_mean", "hopkins_mean", "DBI_mean", "sil_unsup_mean"): + assert s1[k] == pytest.approx(s2[k], abs=1e-4) + _, p1 = evaluate_en_separation_from_counts(counts, X, distance="cosine", metric_version="paper") + _, p2 = evaluate_en_separation_from_counts(counts, X * scales, distance="cosine", metric_version="paper") + assert p1["hopkins_mean"] != pytest.approx(p2["hopkins_mean"], abs=1e-4) diff --git a/tests/test_compare.py b/tests/test_compare.py new file mode 100644 index 0000000..4850f1f --- /dev/null +++ b/tests/test_compare.py @@ -0,0 +1,65 @@ +"""Tests for reproduce/compare.py.""" + +from __future__ import annotations + +import importlib.util +import subprocess +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +SCRIPT = ROOT / "reproduce" / "compare.py" + + +def _load(): + spec = importlib.util.spec_from_file_location("compare", SCRIPT) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + + +EXPECTED = """table,model,metric,variant,paper_value,expected_value,tolerance,notes +1,gemnet,spearman,paper,0.560,0.559754,0.001, +1,gemnet,kendall,paper,0.336,0.335809,,default tolerance +1,unimol,spearman,paper,0.697,0.697176,0.001,embeddings not available +1,gemnet,lie,v2,,,0.001,pending +""" + + +def _write(tmp_path, results: str): + exp = tmp_path / "expected.csv" + res = tmp_path / "results.csv" + exp.write_text(EXPECTED) + res.write_text(results) + return exp, res + + +def test_pass_missing_noexpected(tmp_path): + exp, res = _write( + tmp_path, "table,model,metric,variant,value\n1,gemnet,spearman,paper,0.5601\n1,gemnet,kendall,paper,0.3350\n" + ) + mod = _load() + code = mod.main(["--expected", str(exp), "--results", str(res)]) + assert code == 0 + report, _ = mod.compare(mod._read(exp, mod.KEY), mod._read(res, mod.KEY)) + assert [r["status"] for r in report] == ["PASS", "PASS", "MISSING", "NO_EXPECTED"] + assert mod.main(["--expected", str(exp), "--results", str(res), "--strict"]) == 1 + + +def test_fail_exit_code_cli(tmp_path): + exp, res = _write(tmp_path, "table,model,metric,variant,value\n1,gemnet,spearman,paper,0.57\n") + proc = subprocess.run( + [sys.executable, str(SCRIPT), "--expected", str(exp), "--results", str(res)], capture_output=True, text=True + ) + assert proc.returncode == 1 + assert "FAIL" in proc.stdout + + +def test_nothing_compared(tmp_path): + exp, res = _write(tmp_path, "table,model,metric,variant,value\n9,x,y,z,1.0\n") + assert _load().main(["--expected", str(exp), "--results", str(res)]) == 2 + + +def test_nan_value_fails(tmp_path): + exp, res = _write(tmp_path, "table,model,metric,variant,value\n1,gemnet,spearman,paper,nan\n") + assert _load().main(["--expected", str(exp), "--results", str(res)]) == 1 diff --git a/tests/test_download.py b/tests/test_download.py new file mode 100644 index 0000000..a98f1af --- /dev/null +++ b/tests/test_download.py @@ -0,0 +1,133 @@ +"""Offline tests of the download helper (Hugging Face calls are mocked).""" + +from __future__ import annotations + +import hashlib +import shutil +from pathlib import Path + +import numpy as np +import pytest + +from three_dbench import download + + +def _sha(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +@pytest.fixture +def fake_repo(tmp_path): + """A local directory that mimics EscheWang/3dcs-embeddings.""" + repo = tmp_path / "repo" + files = { + "chirality/gemnet/sampled_feature.npz": lambda p: np.savez(p, gemnet=np.ones((3, 2), np.float32)), + "chirality/molae/1.npz": lambda p: np.savez(p, arr_0=np.zeros((3, 4), np.float32)), + "rotation/gemnet/rotation_conformers_0.npz": lambda p: np.savez(p, gemnet=np.ones((5, 2), np.float32)), + "rotation/gemnet/rotation_conformers_1.npz": lambda p: np.savez(p, gemnet=np.ones((4, 2), np.float32)), + "results/rotation/all_metric.csv": lambda p: p.write_text("a,b\n1,2\n"), + "results/chirality/chirality_metrics_summary.csv": lambda p: p.write_text("x\n1\n"), + } + rows = ["path,task,model,size_bytes,sha256,format,keys,shape,dtype"] + for rel, make in files.items(): + p = repo / rel + p.parent.mkdir(parents=True, exist_ok=True) + make(p) + parts = rel.split("/") + task = parts[0] if parts[0] != "results" else "results" + model = parts[1] if parts[0] != "results" else "" + rows.append(f"{rel},{task},{model},{p.stat().st_size},{_sha(p)},npz,,,") + (repo / "manifest.csv").write_text("\n".join(rows) + "\n") + return repo + + +def _fake_hf_hub_download(repo: Path, calls: list): + def _dl(*, repo_id, filename, repo_type, revision, local_dir): + assert repo_type == "dataset" + calls.append(filename) + target = Path(local_dir) / filename + target.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(repo / filename, target) + return str(target) + + return _dl + + +def test_download_embeddings_selects_and_verifies(fake_repo, tmp_path): + calls: list = [] + out = tmp_path / "emb" + paths = download.download_embeddings( + "rotation", out, models=["gemnet"], hf_hub_download_fn=_fake_hf_hub_download(fake_repo, calls) + ) + assert sorted(p.relative_to(out).as_posix() for p in paths) == [ + "rotation/gemnet/rotation_conformers_0.npz", + "rotation/gemnet/rotation_conformers_1.npz", + ] + assert calls[0] == "manifest.csv" + # second call: files are cached and verified, only the manifest is fetched again + calls.clear() + download.download_embeddings( + "rotation", out, models=["gemnet"], hf_hub_download_fn=_fake_hf_hub_download(fake_repo, calls) + ) + assert calls == ["manifest.csv"] + + +def test_download_embeddings_results_and_models(fake_repo, tmp_path): + calls: list = [] + paths = download.download_embeddings( + "chirality", + tmp_path / "emb", + models=["molae"], + include_results=True, + hf_hub_download_fn=_fake_hf_hub_download(fake_repo, calls), + ) + rels = sorted(p.relative_to(tmp_path / "emb").as_posix() for p in paths) + assert rels == ["chirality/molae/1.npz", "results/chirality/chirality_metrics_summary.csv"] + + +def test_download_embeddings_checksum_mismatch(fake_repo, tmp_path): + manifest = (fake_repo / "manifest.csv").read_text().splitlines() + manifest = [manifest[0]] + [ + line.replace(line.split(",")[4], "0" * 64) if line.startswith("chirality/molae") else line + for line in manifest[1:] + ] + (fake_repo / "manifest.csv").write_text("\n".join(manifest) + "\n") + with pytest.raises(download.ChecksumError): + download.download_embeddings( + "chirality", tmp_path / "emb", models=["molae"], hf_hub_download_fn=_fake_hf_hub_download(fake_repo, []) + ) + + +def test_download_embeddings_unknown_selection(fake_repo, tmp_path): + with pytest.raises(ValueError, match="No manifest entries"): + download.download_embeddings("traj", tmp_path / "emb", hf_hub_download_fn=_fake_hf_hub_download(fake_repo, [])) + with pytest.raises(ValueError): + download.download_embeddings("nope", tmp_path / "emb", hf_hub_download_fn=_fake_hf_hub_download(fake_repo, [])) + + +def test_download_dataset_paths(tmp_path): + datasets = pytest.importorskip("datasets") + calls = [] + + def fake_load_dataset(repo_id, name, split, **kwargs): + calls.append((repo_id, name, split)) + return datasets.Dataset.from_dict({"config": [name]}) + + saved = download.download_dataset("traj", tmp_path / "hf", load_dataset_fn=fake_load_dataset) + assert calls == [("EscheWang/3dcs", "traj_energies", "train"), ("EscheWang/3dcs", "traj_frames", "train")] + assert saved["traj_energies"] == tmp_path / "hf" / "traj" / "energies" + assert datasets.load_from_disk(str(saved["traj_frames"]))["config"] == ["traj_frames"] + # existing directories are kept + calls.clear() + download.download_dataset("traj", tmp_path / "hf", load_dataset_fn=fake_load_dataset) + assert calls == [] + with pytest.raises(ValueError): + download.download_dataset("nope", tmp_path / "hf", load_dataset_fn=fake_load_dataset) + + +def test_parse_manifest_infers_task_model(): + text = "path,sha256\nchirality/fmg/x.npz,abc\nresults/traj/traj_e.csv,def\n" + entries = download.parse_manifest(text) + assert (entries[0].task, entries[0].model) == ("chirality", "fmg") + sel = download.select_manifest_entries(entries, task="traj", include_results=True) + assert [e.path for e in sel] == ["results/traj/traj_e.csv"] diff --git a/tests/test_infra.py b/tests/test_infra.py new file mode 100644 index 0000000..2b6b263 --- /dev/null +++ b/tests/test_infra.py @@ -0,0 +1,104 @@ +"""CLI help, import side effects, path resolution and embedding IO helpers.""" + +from __future__ import annotations + +import os +import subprocess +import sys +from pathlib import Path + +import numpy as np +import pytest + +import three_dbench +from three_dbench.embeddings import load_embeddings, published_embedding, select_array_key + +# Subprocesses run from a temporary directory: make them import the same package as this process. +_PKG_PARENT = str(Path(three_dbench.__file__).resolve().parents[1]) + + +def _env(**extra): + env = {k: v for k, v in os.environ.items() if k != "THREE_DBENCH_HOME"} + env["PYTHONPATH"] = os.pathsep.join([_PKG_PARENT, env.get("PYTHONPATH", "")]).rstrip(os.pathsep) + env.update(extra) + return env + + +def _run(args, **kw): + return subprocess.run([sys.executable, "-m", "three_dbench", *args], capture_output=True, text=True, **kw) + + +def test_cli_download_help(): + res = _run(["download", "--help"]) + assert res.returncode == 0 + for token in ("dataset", "embeddings", "--task", "--models", "--out", "--revision"): + assert token in res.stdout + + +def test_cli_evaluate_rotation_flags(): + res = _run(["evaluate", "--help"]) + assert res.returncode == 0 + for token in ( + "by-shard", + "--molecule-list", + "--sample-ratio", + "--sample-seed", + "--metric-version", + "--lie-k", + "--as-variant", + ): + assert token in res.stdout + + +def test_import_has_no_side_effects(tmp_path): + env = _env(THREE_DBENCH_HOME=str(tmp_path / "home")) + code = ( + "import three_dbench.benchmarks, three_dbench.rotation.evaluation as r; " + "from three_dbench.utils import paths; print(paths.DATA_ROOT)" + ) + res = subprocess.run([sys.executable, "-c", code], capture_output=True, text=True, env=env, cwd=tmp_path) + assert res.returncode == 0, res.stderr + assert res.stdout.strip() == str((tmp_path / "home" / "data").resolve()) + assert not (tmp_path / "home").exists() + assert not (tmp_path / "results").exists() + + +def test_paths_default_to_cwd(tmp_path): + env = _env() + res = subprocess.run( + [sys.executable, "-c", "from three_dbench.utils.paths import RESULTS_ROOT; print(RESULTS_ROOT)"], + capture_output=True, + text=True, + env=env, + cwd=tmp_path, + ) + assert Path(res.stdout.strip()) == Path(tmp_path).resolve() / "results" + + +def test_select_array_key_skips_strings(tmp_path): + p = tmp_path / "fmg.npz" + np.savez(p, smiles=np.array(["C", "CC"]), embeddings=np.ones((2, 3), np.float32)) + with np.load(p) as data: + assert select_array_key(data, None) == "embeddings" + p2 = tmp_path / "other.npz" + np.savez(p2, names=np.array(["a", "b"]), vectors=np.ones((2, 3))) + with np.load(p2) as data: + assert select_array_key(data, None) == "vectors" + with pytest.raises(KeyError): + select_array_key(data, "missing") + + +def test_load_embeddings_from_directory(tmp_path): + d = tmp_path / "chirality" / "gemnet" + d.mkdir(parents=True) + np.savez(d / "sampled_feature.npz", gemnet=np.arange(6, dtype=np.float32).reshape(3, 2)) + emb = load_embeddings(d) + assert emb.kind == "vector" and emb.array.shape == (3, 2) + + +def test_published_registry(): + spec = published_embedding("rotation", "gemnet") + assert spec.layout == "by-shard" and spec.key == "gemnet" and len(spec.shards) == 16 + assert published_embedding("chirality", "fmg").key == "embeddings" + with pytest.raises(KeyError): + published_embedding("rotation", "unimol") diff --git a/tests/test_rotation_metrics.py b/tests/test_rotation_metrics.py new file mode 100644 index 0000000..2a8e62a --- /dev/null +++ b/tests/test_rotation_metrics.py @@ -0,0 +1,109 @@ +"""Geometry metric definitions (paper / v2 / legacy presets).""" + +from __future__ import annotations + +import numpy as np +import pytest + +from three_dbench.common.metrics import cka_rbf, isotonic_r2, local_isometry_error +from three_dbench.rotation.metrics import ( + PRESETS, + angular_smoothness_variant, + cka_rbf_paper_run, + compute_geometry_metrics, + isotonic_r2_reference_on_model, + local_isometry_error_knn, + resolve_metric_spec, +) + + +def _dist(X): + X = np.asarray(X, dtype=float) + return np.sqrt(((X[:, None, :] - X[None, :, :]) ** 2).sum(-1)) + + +@pytest.fixture +def toy(rng): + Z = rng.standard_normal((9, 4)) + D = _dist(rng.standard_normal((9, 3))) + return D, _dist(Z), Z + + +def test_lie_include_self_matches_released(toy): + D, Delta, _ = toy + assert local_isometry_error_knn(D, Delta, k=10, include_self=True) == pytest.approx( + local_isometry_error(D, Delta, k=10) + ) + + +def test_lie_exclude_self_uses_other_conformers(): + # 1D points: nearest other neighbours are well defined + x = np.array([[0.0], [1.0], [3.0], [6.0]]) + D = _dist(x) + # identical geometry -> zero error for any k when self is excluded + assert local_isometry_error_knn(D, D.copy(), k=2, include_self=False) == pytest.approx(0.0, abs=1e-9) + # a distortion of one far pair changes the exclude-self score + Delta = D.copy() + Delta[0, 1] = Delta[1, 0] = 2.0 + assert local_isometry_error_knn(D, Delta, k=2, include_self=False) > 0.0 + + +def test_as_variants(): + deg = np.array([170.0, -170.0, 0.0, 90.0]) # sorted: -170, 0, 90, 170 ; wrap step 170 -> -170 = 20 deg + Z = np.array([[0.0, 0.0], [1.0, 0.0], [3.0, 0.0], [6.0, 0.0]]) + Delta = _dist(Z) + lin = angular_smoothness_variant(deg, variant="median_dz_linear", Z=Z, Delta=Delta) + order = np.argsort(np.deg2rad(deg)) + steps = [ + np.linalg.norm(Z[order[i + 1]] - Z[order[i]]) / abs(np.deg2rad(deg[order[i + 1]] - deg[order[i]])) + for i in range(3) + ] + assert lin == pytest.approx(np.median(steps)) + circ = angular_smoothness_variant(deg, variant="median_delta_circular", Delta=Delta) + half = angular_smoothness_variant(deg, variant="median_halfdelta_circular", Delta=Delta) + assert half == pytest.approx(circ / 2) + mean_circ = angular_smoothness_variant(deg, variant="mean_delta_circular", Delta=Delta) + assert np.isfinite(mean_circ) + with pytest.raises(ValueError): + angular_smoothness_variant(deg, variant="nope", Delta=Delta) + + +def test_isotonic_direction(): + D = _dist(np.arange(5, dtype=float)[:, None]) + const = np.ones_like(D) + np.fill_diagonal(const, 0.0) + # released direction (Delta = f(D)) rewards a constant representation + assert isotonic_r2(D, const) == pytest.approx(1.0) + # appendix direction (D = f(Delta)) does not + assert np.isnan(isotonic_r2_reference_on_model(D, const, guard=True)) + assert isotonic_r2_reference_on_model(D, const, guard=False) == pytest.approx(0.0, abs=1e-6) + two = np.array([[0.0, 1.0], [1.0, 0.0]]) + assert isotonic_r2_reference_on_model(two, two, guard=False) == pytest.approx(1.0) + + +def test_cka_variants_identical_inputs(toy): + D, _, _ = toy + assert cka_rbf_paper_run(D, D) == pytest.approx(1.0) + assert cka_rbf(D, D) == pytest.approx(1.0) + + +def test_presets_and_overrides(): + assert PRESETS["v2"].lie_k == 3 and PRESETS["v2"].lie_include_self is False + assert PRESETS["paper"].kendall_min_conformers == 11 + spec = resolve_metric_spec("paper", lie_k=3, lie_include_self=False, as_variant="median_dz_linear") + assert (spec.lie_k, spec.lie_include_self, spec.as_variant) == (3, False, "median_dz_linear") + assert spec.name == "paper+custom" + with pytest.raises(ValueError): + resolve_metric_spec("v3") + + +def test_compute_geometry_metrics_keys(toy, rng): + D, Delta, Z = toy + deg = rng.uniform(-180, 180, size=D.shape[0]) + paper = compute_geometry_metrics(D, Delta, torsion_deg=deg, spec=PRESETS["paper"], Z=Z) + assert set(paper) == {"A1_spearman", "A2_kendall", "G_cka_rbf", "H_LIE@k", "J_isotonic_R2", "torsion_sp", "AS"} + assert np.isnan(paper["A2_kendall"]) # paper runs: Kendall only for >= 11 conformers + v2 = compute_geometry_metrics(D, Delta, torsion_deg=deg, spec=PRESETS["v2"], Z=Z) + assert np.isfinite(v2["A2_kendall"]) + legacy = compute_geometry_metrics(D, Delta, torsion_deg=deg, spec=PRESETS["legacy"], Z=Z) + assert "C_mantel_r" in legacy and "E_triplet_OP" in legacy diff --git a/tests/test_rotation_sharding.py b/tests/test_rotation_sharding.py new file mode 100644 index 0000000..451b492 --- /dev/null +++ b/tests/test_rotation_sharding.py @@ -0,0 +1,222 @@ +"""Shard-aware slicing for the rotation benchmark (synthetic data in the published HF layout).""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +pytest.importorskip("rdkit") +datasets = pytest.importorskip("datasets") + +from rdkit import Chem # noqa: E402 +from rdkit.Chem import AllChem, rdMolTransforms # noqa: E402 + +from three_dbench.benchmarks.rotation import ( # noqa: E402 + ShardedEmbeddings, + build_rotation_index, + evaluate_rotation_embeddings, + find_shard_files, + select_rows, + write_flat_embeddings, +) +from three_dbench.embeddings import EmbeddingArray # noqa: E402 + +# Shards appear in string order, as in EscheWang/3dcs (rotation): 0, 1, 10, 2. +SHARD_ROWS = {0: [3, 4], 1: [5, 1, 3], 10: [4, 3], 2: [3, 2]} +SMILES = ["CCCC", "CCCO", "CCCN", "OCCO"] + + +def _conformer_blocks(smiles: str, n: int, seed: int) -> tuple[list[str], list[float]]: + mol = Chem.AddHs(Chem.MolFromSmiles(smiles)) + AllChem.EmbedMolecule(mol, randomSeed=seed) + rng = np.random.default_rng(seed) + angles = sorted(rng.uniform(-180.0, 180.0, size=n).tolist()) + blocks = [] + for ang in angles: + m = Chem.Mol(mol) + rdMolTransforms.SetDihedralDeg(m.GetConformer(), 0, 1, 2, 3, float(ang)) + blocks.append(Chem.MolToMolBlock(m)) + return blocks, angles + + +@pytest.fixture(scope="module") +def synthetic(): + rng = np.random.default_rng(0) + rows = [] + shard_arrays = {} + truth = {} + seed = 0 + for shard in (0, 1, 10, 2): + local = 0 + chunks = [] + for i, n in enumerate(SHARD_ROWS[shard]): + key = f"s{shard}_m{i}" + blocks, degs = _conformer_blocks(SMILES[seed % len(SMILES)], n, seed) + seed += 1 + Z = rng.standard_normal((n, 8)).astype(np.float32) + chunks.append(Z) + truth[key] = Z + rows.append( + { + "key": key, + "shard": shard, + "n_conformers": n, + "offset": local, + "mol_blocks": blocks, + "torsion_deg": degs, + } + ) + local += n + shard_arrays[shard] = np.concatenate(chunks) + ds = datasets.Dataset.from_list(rows) + return ds, shard_arrays, truth + + +def _per_key(result): + return result["per_key"].sort_values(["key", "space"]).reset_index(drop=True) + + +def test_index_detects_per_shard_offsets(synthetic): + ds, _, _ = synthetic + idx = build_rotation_index(ds) + assert idx.attrs["offset_mode"] == "per-shard" + assert idx["flat_offset"].tolist() == [0, 3, 7, 12, 13, 16, 20, 23, 26] + assert idx["local_offset"].tolist() == [0, 3, 0, 5, 6, 0, 4, 0, 3] + + +def test_by_shard_matches_by_key(synthetic, tmp_path): + ds, shard_arrays, truth = synthetic + for shard, arr in shard_arrays.items(): + np.savez(tmp_path / f"rotation_conformers_{shard}.npz", gemnet=arr) + assert sorted(find_shard_files(tmp_path)) == [0, 1, 2, 10] + sharded = ShardedEmbeddings.from_directory(tmp_path) + kw = dict(dataset=ds, metrics=["cosine", "euclidean"], metric_version="paper", min_conformers=2) + by_shard = evaluate_rotation_embeddings(embeddings_by_shard=sharded, **kw) + by_key = evaluate_rotation_embeddings(embeddings_by_key=truth, **kw) + pd.testing.assert_frame_equal(_per_key(by_shard), _per_key(by_key)) + # the 1-conformer molecule is skipped + assert "s1_m1" not in set(by_shard["per_key"]["key"]) + assert by_shard["config"]["selection"]["skipped_min_conformers"] == 1 + + +def test_flat_dataset_order_matches_by_key(synthetic, tmp_path): + ds, shard_arrays, truth = synthetic + idx = build_rotation_index(ds) + flat = write_flat_embeddings(idx, ShardedEmbeddings.from_arrays(shard_arrays), tmp_path / "flat.npy") + manual = np.concatenate([truth[k] for k in ds["key"]]) + np.testing.assert_array_equal(np.asarray(flat), manual) + kw = dict(dataset=ds, metrics=["cosine"], metric_version="v2") + res_flat = evaluate_rotation_embeddings(embeddings=EmbeddingArray(array=flat, kind="vector"), **kw) + res_key = evaluate_rotation_embeddings(embeddings_by_key=truth, **kw) + pd.testing.assert_frame_equal(_per_key(res_flat), _per_key(res_key)) + + +def test_naive_global_offsets_would_misalign(synthetic): + """The released code sliced a flat array with the stored per-shard offset; that picks wrong rows.""" + ds, shard_arrays, truth = synthetic + flat = np.concatenate([shard_arrays[s] for s in sorted(shard_arrays)]) # numeric shard order + wrong = 0 + for row in ds: + if not np.array_equal(flat[row["offset"] : row["offset"] + row["n_conformers"]], truth[row["key"]]): + wrong += 1 + assert wrong > 0 + + +def test_global_offsets_with_by_shard_embeddings(synthetic): + """Datasets written by the converter store global offsets in numeric shard order.""" + ds, shard_arrays, truth = synthetic + rows = sorted(ds.to_list(), key=lambda r: (r["shard"], r["offset"])) + cursor = 0 + for r in rows: + r["offset"] = cursor + cursor += r["n_conformers"] + ds_global = datasets.Dataset.from_list(rows) + idx = build_rotation_index(ds_global) + assert idx.attrs["offset_mode"] == "global" + kw = dict(dataset=ds_global, metrics=["cosine"], metric_version="paper") + res_shard = evaluate_rotation_embeddings(embeddings_by_shard=shard_arrays, **kw) + res_key = evaluate_rotation_embeddings(embeddings_by_key=truth, **kw) + pd.testing.assert_frame_equal(_per_key(res_shard), _per_key(res_key)) + + +def test_selection_options(synthetic, tmp_path): + ds, _, _ = synthetic + idx = build_rotation_index(ds) + keys_file = tmp_path / "keys.txt" + keys_file.write_text("# comment\ns10_m1\ns2_m0\nmissing_key\n") + sel, stats = select_rows(idx, molecule_list=keys_file) + assert sel["key"].tolist() == ["s10_m1", "s2_m0"] + assert stats["molecule_list_missing"] == 1 + sel, _ = select_rows(idx, shards=[10]) + assert set(sel["shard"]) == {10} + a, _ = select_rows(idx, sample_ratio=0.5, sample_seed=7) + b, _ = select_rows(idx, sample_ratio=0.5, sample_seed=7) + assert a["key"].tolist() == b["key"].tolist() + sel, _ = select_rows(idx, max_keys=3) + assert len(sel) == 3 + + +def test_missing_shard_file_raises(synthetic): + ds, shard_arrays, _ = synthetic + partial = {0: shard_arrays[0]} + with pytest.raises(ValueError, match="No embedding file for shards"): + evaluate_rotation_embeddings(dataset=ds, embeddings_by_shard=partial, metrics=["cosine"]) + res = evaluate_rotation_embeddings(dataset=ds, embeddings_by_shard=partial, metrics=["cosine"], shards=[0]) + assert set(res["per_key"]["shard"]) == {0} + + +def test_outputs_written(synthetic, tmp_path): + ds, shard_arrays, _ = synthetic + out = tmp_path / "out" + res = evaluate_rotation_embeddings( + dataset=ds, embeddings_by_shard=shard_arrays, metrics=["cosine"], output_dir=out, model_name="toy" + ) + assert (out / "toy_per_key.parquet").exists() + assert (out / "config.json").exists() + summary = pd.read_csv(out / "summary.csv") + assert {"model", "metric_version", "space", "metric", "mean", "median", "n", "n_finite"} <= set(summary.columns) + assert res["config"]["layout"] == "by-shard" + + +def test_replicate_offset_drift(synthetic): + """A failing molecule shifts later embeddings of its shard only in the __offset_drift columns.""" + ds, shard_arrays, truth = synthetic + rows = ds.to_list() + # make s1_m0 (shard 1, local offset 0, 5 conformers) fail: mix molecules with different heavy-atom counts + bad = next(r for r in rows if r["key"] == "s1_m0") + other, _ = _conformer_blocks("CCCCC", 1, 99) + bad["mol_blocks"] = bad["mol_blocks"][:-1] + other + ds_bad = datasets.Dataset.from_list(rows) + res = evaluate_rotation_embeddings( + dataset=ds_bad, embeddings_by_shard=shard_arrays, metrics=["cosine"], replicate_offset_drift=True + ) + cfg = res["config"]["selection"] + assert list(cfg["failed_keys"]) == ["s1_m0"] + assert cfg["offset_drift"]["1"]["n_affected_molecules"] == 1 # s1_m2 (s1_m1 has 1 conformer, skipped) + pk = res["per_key"].set_index("key") + # regular columns: aligned embeddings + aligned = evaluate_rotation_embeddings(dataset=ds_bad, embeddings_by_key=truth, metrics=["cosine"]) + al = aligned["per_key"].set_index("key") + assert np.allclose(pk.loc["s1_m2", "H_LIE@k"], al.loc["s1_m2", "H_LIE@k"], equal_nan=True) + # drift columns: s1_m2 (local offset 6) uses rows 1..3 of shard 1; other shards unchanged + shifted = {"s1_m2": shard_arrays[1][6 - 5 : 6 - 5 + 3]} + ds_one = datasets.Dataset.from_list([r for r in rows if r["key"] == "s1_m2"]) + drift = evaluate_rotation_embeddings(dataset=ds_one, embeddings_by_key=shifted, metrics=["cosine"])["per_key"] + assert pk.loc["s1_m2", "AS__offset_drift"] == pytest.approx(float(drift["AS"].iloc[0])) + assert pk.loc["s1_m2", "H_LIE@k__offset_drift"] == pytest.approx(float(drift["H_LIE@k"].iloc[0])) + assert pk.loc["s10_m0", "AS__offset_drift"] == pytest.approx(pk.loc["s10_m0", "AS"]) + with pytest.raises(ValueError, match="by-shard"): + evaluate_rotation_embeddings(dataset=ds_bad, embeddings_by_key=truth, replicate_offset_drift=True) + + +def test_subset_of_published_layout_uses_per_shard_offsets(synthetic): + """A filtered subset (first rows of shards removed) still slices with the per-shard offsets.""" + ds, shard_arrays, truth = synthetic + subset = ds.filter(lambda k: k not in {"s0_m0", "s1_m0", "s10_m0", "s2_m0"}, input_columns=["key"]) + idx = build_rotation_index(subset) + assert idx.attrs["offset_mode"] == "per-shard" + kw = dict(dataset=subset, metrics=["cosine"], metric_version="v2") + res_shard = evaluate_rotation_embeddings(embeddings_by_shard=shard_arrays, **kw) + res_key = evaluate_rotation_embeddings(embeddings_by_key=truth, **kw) + pd.testing.assert_frame_equal(_per_key(res_shard), _per_key(res_key)) diff --git a/tests/test_traj_legacy_equivalence.py b/tests/test_traj_legacy_equivalence.py new file mode 100644 index 0000000..e141924 --- /dev/null +++ b/tests/test_traj_legacy_equivalence.py @@ -0,0 +1,135 @@ +"""The CLI protocol reproduces the legacy runner behind the published tables bit for bit.""" + +from __future__ import annotations + +import pickle + +import numpy as np +import pandas as pd +import pytest +from traj_helpers import random_fingerprints, save_energy_dataset, synthetic_embeddings, synthetic_energies + +import three_dbench.traj.evaluation as ev +from three_dbench.benchmarks import evaluate_trajectory_embeddings + +# ---- reference loop implementations of the index helpers (0.1.0 code) ---- + + +def _pairs_loop(n): + ii, jj = [], [] + for i in range(n - 1): + ii.extend([i] * (n - i - 1)) + jj.extend(range(i + 1, n)) + return np.asarray(ii, int), np.asarray(jj, int) + + +def _fetch_loop(Delta_cond, pairs): + Delta_cond = np.asarray(Delta_cond, float) + n = ev.n_from_condensed_len(Delta_cond.shape[0]) + vals = np.empty(len(pairs[0]), dtype=np.float64) + for k, (i, j) in enumerate(zip(*pairs)): + if i == j: + vals[k] = 0.0 + else: + i, j = min(i, j), max(i, j) + vals[k] = Delta_cond[ev.condensed_index(i, j, n)] + return vals + + +@pytest.mark.parametrize("n", [0, 1, 2, 3, 57]) +def test_vectorised_index_helpers_match_loops(n): + ii, jj = ev._pair_indices_from_n(n) + ri, rj = _pairs_loop(n) + np.testing.assert_array_equal(ii, ri) + np.testing.assert_array_equal(jj, rj) + assert ii.dtype == ri.dtype + x = synthetic_energies(n, seed=n) if n else np.zeros(0) + ref = np.abs(x[ri] - x[rj]) + np.testing.assert_array_equal(ev.energy_diff_condensed(x), ref) + np.testing.assert_array_equal(ev.vector_to_absdiff_condensed(x), ref.astype(np.float32)) + if n >= 2: + D = np.random.default_rng(n).random(n * (n - 1) // 2).astype(np.float16) + rng = np.random.default_rng(0) + pairs = (rng.integers(0, n, 200), rng.integers(0, n, 200)) + np.testing.assert_array_equal(ev._condensed_fetch_pairs(D, pairs), _fetch_loop(D, pairs)) + consecutive = (np.arange(n - 1), np.arange(1, n)) + np.testing.assert_array_equal(ev._condensed_fetch_pairs(D, consecutive), _fetch_loop(D, consecutive)) + + +@pytest.fixture +def legacy_layout(tmp_path): + """Two molecules in the legacy on-disk layout plus the equivalent HF-style dataset.""" + n = 400 + mols = ["rmd17_toluene", "rmd17_naphthalene"] # dataset order differs from the legacy order + energies = {m: synthetic_energies(n, seed=i) for i, m in enumerate(mols)} + root = tmp_path / "results" + (root / "gemnet").mkdir(parents=True) + (root / "e3fp").mkdir() + energy_dir = tmp_path / "npz_data" + energy_dir.mkdir() + gem, fps = {}, {} + for i, m in enumerate(mols): + np.savez(energy_dir / f"{m}.npz", energies=energies[m]) + gem[m] = synthetic_embeddings(energies[m], 12, seed=100 + i) + np.savez(root / "gemnet" / f"{m}.npz", gemnet=gem[m]) + fps[m] = random_fingerprints(n, 128, seed=200 + i, density=0.2) + with (root / "e3fp" / f"{m}.pkl").open("wb") as f: + pickle.dump(fps[m], f) + dataset_dir = save_energy_dataset(tmp_path / "hf", energies) + return { + "n": n, + "mols": mols, + "root": root, + "energy_dir": energy_dir, + "dataset_dir": dataset_dir, + "gem": gem, + "fps": fps, + "out": tmp_path, + } + + +def _legacy_details(layout, n_samples, window): + cfg = ev.Config( + mol_types=list(ev.DEFAULT_MOL_TYPES), + root_dir=str(layout["root"]), + energy_dir=str(layout["energy_dir"]), + n_samples=n_samples, + window=window, + traj_len=layout["n"], + random_seed=2025, + n_jobs=1, + out_dir=str(layout["out"] / "legacy"), + save_intermediate=False, + ) + frames = [ev.run_once_for_molecule(cfg, m) for m in ev.DEFAULT_MOL_TYPES if m in layout["mols"]] + df = pd.concat(frames, ignore_index=True) + return df[df["metric"] != "ERROR"] + + +@pytest.mark.parametrize("n_jobs", [1, 2]) +def test_cli_protocol_equals_legacy_runner(legacy_layout, n_jobs): + n_samples, window = 3, 60 + legacy = _legacy_details(legacy_layout, n_samples, window) + legacy_summary = ev.aggregate_overall(legacy) + for model, embeddings in (("GemNet", legacy_layout["gem"]), ("E3FP", legacy_layout["fps"])): + details, summary = evaluate_trajectory_embeddings( + dataset_dir=legacy_layout["dataset_dir"], + embeddings_by_mol=embeddings, + model_name=model, + n_samples=n_samples, + window=window, + n_jobs=n_jobs, + legacy_traj_len=legacy_layout["n"], + verbose=False, + ) + ref = legacy[legacy["model"] == model].reset_index(drop=True) + new = details.reset_index(drop=True) + cols = ["mol_type", "sample_start", "sample_end", "model", "metric"] + pd.testing.assert_frame_equal(new[cols], ref[cols], check_dtype=False) + np.testing.assert_array_equal(new["value"].to_numpy(float), ref["value"].to_numpy(float)) + for _, row in summary.iterrows(): + assert row["mean"] == legacy_summary.loc[model, f"{row['metric']}_mean"] or ( + np.isnan(row["mean"]) and np.isnan(legacy_summary.loc[model, f"{row['metric']}_mean"]) + ) + ci_ref = legacy_summary.loc[model, f"{row['metric']}_ci95"] + assert row["ci95"] == ci_ref or (np.isnan(row["ci95"]) and np.isnan(ci_ref)) diff --git a/tests/test_traj_metric_version.py b/tests/test_traj_metric_version.py new file mode 100644 index 0000000..6d332b4 --- /dev/null +++ b/tests/test_traj_metric_version.py @@ -0,0 +1,152 @@ +"""paper vs v2 energy metric definitions.""" + +from __future__ import annotations + +import json +import subprocess +import sys + +import numpy as np +import pytest +from scipy.special import erfinv +from traj_helpers import save_energy_dataset, synthetic_embeddings, synthetic_energies + +from three_dbench.common.metrics import cka_rbf +from three_dbench.traj.evaluation import energy_diff_condensed +from three_dbench.traj.protocol import compute_window_metrics, window_distances + + +@pytest.fixture +def window(): + E = synthetic_energies(120, seed=0) + Z = synthetic_embeddings(E, 16, seed=1) + return E, Z + + +def test_ejs_sigma_paper_robust_v2_rms(window): + E, Z = window + dE = energy_diff_condensed(E) + robust = np.median(dE) / (np.sqrt(2.0) * erfinv(0.5) + 1e-12) + rms = np.sqrt(np.mean(dE**2) + 1e-12) + paper = compute_window_metrics(window_distances(Z, metric="cosine", metric_version="paper"), E) + v2 = compute_window_metrics(window_distances(Z, metric="cosine", metric_version="v2"), E, metric_version="v2") + for lam, tag in [(0.5, "0p5"), (2.0, "2")]: + assert paper[f"EJS_theta_lam{tag}"] == pytest.approx(lam * robust) + assert paper[f"EJS_num_jumps_lam{tag}"] == float((dE > lam * robust).sum()) + assert v2[f"EJS_theta_lam{tag}"] == pytest.approx(lam * rms) + assert v2[f"EJS_num_jumps_lam{tag}"] == float((dE > lam * rms).sum()) + + +def test_distance_precision_per_version(window): + _, Z = window + assert window_distances(Z, metric="cosine", metric_version="paper").dtype == np.float16 + assert window_distances(Z, metric="cosine", metric_version="v2").dtype == np.float32 + + +def test_cka_bandwidth(window): + E, Z = window + D = window_distances(Z, metric="cosine", metric_version="v2") + v2 = compute_window_metrics(D, E, metric_version="v2") + dE = energy_diff_condensed(E) + assert v2["cka_rbf"] == pytest.approx(cka_rbf(dE, D.astype(np.float64), share_sigma=False)) + assert v2["cka_rbf"] != pytest.approx(cka_rbf(dE, D.astype(np.float64), share_sigma=True)) + + +@pytest.mark.filterwarnings("ignore:An input array is constant") +def test_isotonic_direction_constant_representation(): + E = synthetic_energies(80, seed=2) + D = np.zeros(80 * 79 // 2, dtype=np.float32) # constant representation + paper = compute_window_metrics(D.astype(np.float16), E) + v2 = compute_window_metrics(D, E, metric_version="v2") + assert paper["iso_R2"] == pytest.approx(1.0) # published implementation fits Delta ~ f(dE) + assert np.isnan(v2["iso_R2"]) # appendix: dE ~ f(Delta), undefined for constant Delta + + +def test_v2_ks_is_scale_free(window): + E, Z = window + D = window_distances(Z, metric="cosine", metric_version="v2") + E_scaled = E.mean() + 50.0 * (E - E.mean()) + v2 = compute_window_metrics(D, E, metric_version="v2") + v2_scaled = compute_window_metrics(D, E_scaled, metric_version="v2") + assert v2["KS"] == pytest.approx(v2_scaled["KS"], abs=1e-12) + D16 = D.astype(np.float16) + paper = compute_window_metrics(D16, E) + paper_scaled = compute_window_metrics(D16, E_scaled) + assert paper_scaled["KS"] > paper["KS"] + 0.05 # Delta in [0, 1] vs |dE| in energy units + + +def test_v2_ts_requires_time_order(window): + E, Z = window + D = window_distances(Z, metric="cosine", metric_version="v2") + unordered = compute_window_metrics(D, E, metric_version="v2") + assert np.isnan(unordered["TS"]) and np.isnan(unordered["Smoothness"]) + ordered = compute_window_metrics(D, E, metric_version="v2", time_ordered=True) + assert 0.0 <= ordered["TS"] <= 1.0 + assert 0.0 <= ordered["Smoothness"] <= 1.0 + assert ordered["Smoothness_segments"] == E.size - 1 + paper = compute_window_metrics(D.astype(np.float16), E) + assert np.isfinite(paper["TS"]) and np.isfinite(paper["Smoothness"]) + + +def test_unknown_metric_version(window): + E, Z = window + with pytest.raises(ValueError): + compute_window_metrics(window_distances(Z, metric="cosine"), E, metric_version="v3") + + +def test_cli_metric_version_switch(tmp_path): + n = 160 + E = synthetic_energies(n, seed=3) + dataset_dir = save_energy_dataset(tmp_path / "ds", {"rmd17_aspirin": E, "rmd17_ethanol": E[::-1].copy()}) + emb_dir = tmp_path / "emb" + emb_dir.mkdir() + np.savez(emb_dir / "rmd17_aspirin.npz", arr_0=synthetic_embeddings(E, 8, seed=4)) + np.savez(emb_dir / "rmd17_ethanol.npz", arr_0=synthetic_embeddings(E[::-1], 8, seed=5)) + summaries = {} + for version in ("paper", "v2"): + out = tmp_path / version + cmd = [ + sys.executable, + "-m", + "three_dbench", + "evaluate", + "traj", + "--dataset-dir", + str(dataset_dir), + "--embeddings", + str(emb_dir), + "--output-dir", + str(out), + "--n-samples", + "2", + "--window", + "60", + "--legacy-traj-len", + str(n), + "--molecules", + "ethanol", + "--metric-version", + version, + "--n-jobs", + "2", + ] + result = subprocess.run(cmd, capture_output=True, text=True) + assert result.returncode == 0, result.stderr + config = json.loads((out / "config.json").read_text()) + assert config["metric_version"] == version + assert config["window_scheme"] == "legacy" + assert config["molecules"] == ["rmd17_ethanol"] + import pandas as pd + + summaries[version] = pd.read_csv(out / "summary.csv").set_index("metric")["mean"] + assert np.isfinite(summaries["paper"]["TS"]) and np.isnan(summaries["v2"]["TS"]) + assert summaries["paper"]["EJS_num_jumps_lam2"] != summaries["v2"]["EJS_num_jumps_lam2"] + + +def test_cli_help_lists_trajectory_options(): + result = subprocess.run( + [sys.executable, "-m", "three_dbench", "evaluate", "--help"], capture_output=True, text=True + ) + assert result.returncode == 0 + for flag in ("--window-scheme", "--metric-version", "--n-jobs", "--molecules", "--energy-precision-check"): + assert flag in result.stdout diff --git a/tests/test_traj_precision.py b/tests/test_traj_precision.py new file mode 100644 index 0000000..55b6a34 --- /dev/null +++ b/tests/test_traj_precision.py @@ -0,0 +1,93 @@ +"""float64 energy handling and the quantization detector.""" + +from __future__ import annotations + +import numpy as np +import pytest +from traj_helpers import save_energy_dataset, synthetic_embeddings, synthetic_energies + +from three_dbench.datasets.traj import ( + QuantizedEnergiesError, + check_energy_precision, + convert_traj_energy_npz_to_hf, + detect_quantized_energies, + load_traj_energy_dataset, +) + + +def test_float64_energies_not_flagged(): + report = detect_quantized_energies(synthetic_energies(5000, seed=0), mol_type="rmd17_x") + assert not report.quantized + assert report.unique_fraction == 1.0 + assert not report.float32_representable + + +def test_float32_cast_of_absolute_energies_flagged(): + energies = synthetic_energies(5000, seed=1).astype(np.float32).astype(np.float64) + report = detect_quantized_energies(energies, mol_type="rmd17_x") + assert report.quantized + assert report.float32_representable + assert "float32" in report.reason + + +def test_few_distinct_values_flagged(): + energies = np.repeat(np.arange(40, dtype=np.float64) * 0.123456789, 100) + report = detect_quantized_energies(energies) + assert report.quantized + assert "distinct" in report.reason + + +def test_relative_float32_energies_not_flagged(): + rng = np.random.default_rng(3) + energies = (5.0 * rng.standard_normal(5000)).astype(np.float32).astype(np.float64) + assert not detect_quantized_energies(energies).quantized + + +def test_check_energy_precision_policies(): + bad = detect_quantized_energies(synthetic_energies(2000, seed=2).astype(np.float32), mol_type="rmd17_bad") + good = detect_quantized_energies(synthetic_energies(2000, seed=2), mol_type="rmd17_good") + with pytest.raises(QuantizedEnergiesError, match="rmd17_bad"): + check_energy_precision([good, bad], policy="error") + with pytest.warns(UserWarning, match="quantized"): + flagged = check_energy_precision([good, bad], policy="warn") + assert [r.mol_type for r in flagged] == ["rmd17_bad"] + assert check_energy_precision([good], policy="error") == [] + assert len(check_energy_precision([bad], policy="ignore")) == 1 + with pytest.raises(ValueError): + check_energy_precision([good], policy="strict") + + +def test_converter_keeps_float64_exactly(tmp_path): + src = tmp_path / "npz" + src.mkdir() + energies = {"rmd17_a": synthetic_energies(300, seed=4), "rmd17_b": synthetic_energies(250, seed=5)} + for mol, e in energies.items(): + np.savez(src / f"{mol}.npz", energies=e, coords=np.zeros((e.size, 3, 3))) + out = tmp_path / "hf" + convert_traj_energy_npz_to_hf(src, out) + ds = load_traj_energy_dataset(out) + for row in ds: + loaded = np.asarray(row["energies"], dtype=np.float64) + assert row["n_frames"] == loaded.size + np.testing.assert_array_equal(loaded, energies[row["mol_type"]]) + assert not np.array_equal(loaded, loaded.astype(np.float32)) + + +def test_evaluate_rejects_quantized_energies(tmp_path): + from three_dbench.benchmarks import evaluate_trajectory_embeddings + + e64 = synthetic_energies(200, seed=6) + dataset_dir = save_energy_dataset(tmp_path / "ds", {"rmd17_a": e64.astype(np.float32)}) + emb = {"rmd17_a": synthetic_embeddings(e64, 8, seed=7)} + kwargs = dict( + dataset_dir=dataset_dir, + embeddings_by_mol=emb, + n_samples=1, + window=40, + legacy_traj_len=200, + verbose=False, + ) + with pytest.raises(QuantizedEnergiesError): + evaluate_trajectory_embeddings(**kwargs) + _, summary = evaluate_trajectory_embeddings(**kwargs, energy_precision_check="ignore") + assert not summary.empty diff --git a/tests/test_traj_protocol.py b/tests/test_traj_protocol.py new file mode 100644 index 0000000..f1f1f3e --- /dev/null +++ b/tests/test_traj_protocol.py @@ -0,0 +1,88 @@ +"""Window schemes and molecule selection for the trajectory benchmark.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from three_dbench.traj.evaluation import DEFAULT_MOL_TYPES +from three_dbench.traj.protocol import ( + LEGACY_TRAJ_LEN, + draw_window_starts, + order_molecules, + resolve_molecules, +) + +RMD17_FRAMES = { + "rmd17_aspirin": 100_000, + "rmd17_azobenzene": 99_988, + "rmd17_benzene": 100_000, + "rmd17_ethanol": 100_000, + "rmd17_malonaldehyde": 100_000, + "rmd17_naphthalene": 100_000, + "rmd17_paracetamol": 100_000, + "rmd17_salicylic": 100_000, + "rmd17_toluene": 100_000, + "rmd17_uracil": 100_000, +} + + +def test_legacy_starts_are_published_windows(): + starts = draw_window_starts(RMD17_FRAMES, window_scheme="legacy", n_samples=100, window=2000, random_seed=2025) + reference = np.random.default_rng(2025).integers(0, LEGACY_TRAJ_LEN - 2000, size=100, endpoint=False) + assert list(reference[:3]) == [43852, 97456, 97273] # backed-up energy_metrics_out windows + for mol, s in starts.items(): + np.testing.assert_array_equal(s, reference, err_msg=mol) + # max start 97,636: no window is truncated for azobenzene (99,988 frames) + assert int(reference.max()) + 2000 <= RMD17_FRAMES["rmd17_azobenzene"] + + +def test_legacy_starts_are_deterministic_and_subset_independent(): + full = draw_window_starts(RMD17_FRAMES, window_scheme="legacy", n_samples=7, window=50, random_seed=11) + again = draw_window_starts(RMD17_FRAMES, window_scheme="legacy", n_samples=7, window=50, random_seed=11) + subset = draw_window_starts( + {"rmd17_uracil": 100_000}, window_scheme="legacy", n_samples=7, window=50, random_seed=11 + ) + for mol in full: + np.testing.assert_array_equal(full[mol], again[mol]) + np.testing.assert_array_equal(full["rmd17_uracil"], subset["rmd17_uracil"]) + + +def test_shared_scheme_matches_0_1_0_cli(): + n_samples, window, seed = 5, 2000, 2025 + starts = draw_window_starts( + RMD17_FRAMES, window_scheme="shared", n_samples=n_samples, window=window, random_seed=seed + ) + rng = np.random.default_rng(seed) # the 0.1.0 CLI loop (benchmarks/trajectory.py) + for mol, n_frames in RMD17_FRAMES.items(): + max_start = n_frames - window + expected = rng.integers(0, max_start, size=min(n_samples, max_start), endpoint=False) + np.testing.assert_array_equal(starts[mol], expected) + assert not np.array_equal(starts["rmd17_aspirin"], starts["rmd17_azobenzene"]) + + +def test_legacy_window_beyond_trajectory_raises(): + with pytest.raises(ValueError, match="exceeds"): + draw_window_starts({"rmd17_x": 500}, window_scheme="legacy", n_samples=10, window=100, random_seed=0) + starts = draw_window_starts( + {"rmd17_x": 500}, window_scheme="legacy", n_samples=10, window=100, random_seed=0, legacy_traj_len=500 + ) + assert int(starts["rmd17_x"].max()) + 100 <= 500 + + +def test_unknown_scheme_raises(): + with pytest.raises(ValueError): + draw_window_starts(RMD17_FRAMES, window_scheme="random") + + +def test_order_and_resolve_molecules(): + dataset_order = sorted(DEFAULT_MOL_TYPES) + assert order_molecules(dataset_order, "legacy") == list(DEFAULT_MOL_TYPES) + assert order_molecules(dataset_order, "shared") == dataset_order + assert resolve_molecules(["aspirin", "rmd17_uracil", "aspirin"], dataset_order) == [ + "rmd17_aspirin", + "rmd17_uracil", + ] + assert resolve_molecules(None, dataset_order) == dataset_order + with pytest.raises(KeyError): + resolve_molecules(["caffeine"], dataset_order) diff --git a/tests/test_traj_tanimoto.py b/tests/test_traj_tanimoto.py new file mode 100644 index 0000000..0e2fe22 --- /dev/null +++ b/tests/test_traj_tanimoto.py @@ -0,0 +1,102 @@ +"""Fingerprint (E3FP) inputs with Tanimoto distance in the trajectory benchmark.""" + +from __future__ import annotations + +import pickle + +import numpy as np +import pytest +from traj_helpers import random_fingerprints, save_energy_dataset, synthetic_embeddings, synthetic_energies + +pytest.importorskip("rdkit") + +from three_dbench.traj.evaluation import pairwise_tanimoto_fps_large # noqa: E402 +from three_dbench.traj.io import load_traj_embeddings, resolve_embedding # noqa: E402 +from three_dbench.traj.protocol import resolve_embed_metric, tanimoto_distances_dense, window_distances # noqa: E402 + + +def _dense(fps, n_bits): + X = np.zeros((len(fps), n_bits), dtype=np.uint8) + for i, fp in enumerate(fps): + X[i, list(fp.GetOnBits())] = 1 + return X + + +@pytest.mark.parametrize("dtype", [np.float16, np.float32]) +def test_dense_tanimoto_matches_rdkit(dtype): + fps = random_fingerprints(60, 128, seed=0) + ref = pairwise_tanimoto_fps_large(fps, block_size=16, out_mode="condensed", dtype_out=dtype, progress=False) + dense = tanimoto_distances_dense(_dense(fps, 128), dtype_out=dtype) + assert dense.dtype == ref.dtype + # Pairs of two all-zero fingerprints are excluded: RDKit's similarity for them changed from 1.0 + # (<= 2025.09) to 0.0 (>= 2026.03). The published fingerprints contain no all-zero vector, so the + # reproduction is unaffected; see test_dense_tanimoto_empty_pairs for the dense convention. + on_bits = np.asarray([fp.GetNumOnBits() for fp in fps]) + i, j = np.triu_indices(len(fps), 1) + comparable = (on_bits[i] > 0) | (on_bits[j] > 0) + np.testing.assert_array_equal(dense[comparable], ref[comparable]) + + +def test_dense_tanimoto_empty_pairs(): + """Two all-zero fingerprints have similarity 0 (distance 1) in the dense implementation.""" + X = np.zeros((3, 8), dtype=np.uint8) + X[2, [1, 4]] = 1 + D = tanimoto_distances_dense(X, dtype_out=np.float32) + assert D[0] == pytest.approx(1.0) # (0, 1): both empty + assert D[1] == pytest.approx(1.0) # (0, 2): empty vs non-empty + + +def test_fingerprints_default_to_tanimoto(): + fps = random_fingerprints(5, 32, seed=1) + assert resolve_embed_metric(None, fps) == "tanimoto" + assert resolve_embed_metric(None, np.zeros((5, 4))) == "cosine" + with pytest.raises(ValueError): + resolve_embed_metric("cosine", fps) + D = window_distances(fps, metric="tanimoto", metric_version="paper") + assert D.dtype == np.float16 and D.size == 10 + + +def test_trajectory_directory_with_fingerprint_pickles(tmp_path): + from three_dbench.benchmarks import evaluate_trajectory_embeddings + + n = 150 + energies = {"rmd17_a": synthetic_energies(n, seed=2), "rmd17_b": synthetic_energies(n, seed=3)} + dataset_dir = save_energy_dataset(tmp_path / "ds", energies) + emb_dir = tmp_path / "e3fp" + emb_dir.mkdir() + for k, mol in enumerate(energies): + with (emb_dir / f"{mol}.pkl").open("wb") as f: + pickle.dump(random_fingerprints(n, 256, seed=10 + k, density=0.3), f) + + loaders = load_traj_embeddings(emb_dir) + assert sorted(loaders) == ["rmd17_a", "rmd17_b"] + assert isinstance(resolve_embedding(loaders["rmd17_a"]), list) + + details, summary = evaluate_trajectory_embeddings( + dataset_dir=dataset_dir, + embeddings_by_mol=loaders, + output_dir=tmp_path / "out", + model_name="E3FP", + n_samples=2, + window=50, + legacy_traj_len=n, + verbose=False, + ) + assert set(details["mol_type"]) == {"rmd17_a", "rmd17_b"} + ks = summary.set_index("metric").loc["KS", "mean"] + assert np.isfinite(ks) + import json + + config = json.loads((tmp_path / "out" / "config.json").read_text()) + assert config["metric_embed_resolved"] == {"rmd17_a": "tanimoto", "rmd17_b": "tanimoto"} + + +def test_directory_loader_handles_npz_keys(tmp_path): + e = synthetic_energies(30, seed=4) + np.savez(tmp_path / "rmd17_a.npz", embeddings=synthetic_embeddings(e, 4, seed=5), smiles=np.array(["C"] * 30)) + np.savez(tmp_path / "rmd17_b.npz", gemnet=synthetic_embeddings(e, 4, seed=6), other=np.zeros((30, 2))) + loaders = load_traj_embeddings(tmp_path) + assert resolve_embedding(loaders["rmd17_a"]).shape == (30, 4) # only numeric 2-D array + with pytest.raises(ValueError, match="embedding-key"): + resolve_embedding(loaders["rmd17_b"]) + assert resolve_embedding(load_traj_embeddings(tmp_path, key="gemnet")["rmd17_b"]).shape == (30, 4) diff --git a/tests/traj_helpers.py b/tests/traj_helpers.py new file mode 100644 index 0000000..865af1e --- /dev/null +++ b/tests/traj_helpers.py @@ -0,0 +1,46 @@ +"""Synthetic trajectory data shared by the trajectory tests.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np + + +def synthetic_energies(n_frames: int, seed: int, offset: float = -4.06e5) -> np.ndarray: + """Absolute float64 energies with a few kcal/mol spread (not float32-representable).""" + rng = np.random.default_rng(seed) + return offset + 4.0 * rng.standard_normal(n_frames) + rng.random(n_frames) * 1e-6 + + +def synthetic_embeddings(energies: np.ndarray, dim: int, seed: int) -> np.ndarray: + """float32 vectors weakly coupled to the energies.""" + rng = np.random.default_rng(seed) + z = rng.standard_normal((energies.size, dim)) + z[:, 0] += 0.3 * (energies - energies.mean()) + return z.astype(np.float32) + + +def save_energy_dataset(path: Path, energies_by_mol: dict[str, np.ndarray]) -> Path: + import datasets + + rows = [ + {"mol_type": mol, "n_frames": int(e.size), "energies": np.asarray(e, dtype=np.float64).tolist()} + for mol, e in sorted(energies_by_mol.items()) + ] + datasets.Dataset.from_list(rows).save_to_disk(str(path)) + return path + + +def random_fingerprints(n: int, n_bits: int, seed: int, density: float = 0.1): + from rdkit.DataStructs import ExplicitBitVect + + rng = np.random.default_rng(seed) + fps = [] + for i in range(n): + bv = ExplicitBitVect(n_bits) + if i % 17 != 3: # keep a few empty fingerprints + on = np.flatnonzero(rng.random(n_bits) < density) + bv.SetBitsFromList([int(b) for b in on]) + fps.append(bv) + return fps