| # Offline acquisition and matrix export |
|
|
| This directory packages the six-policy acquisition implementation from |
| `MolAS_AL_restart/AL_MolAS/mean_ucb_cost_5fold_paper`. It queries existing docking |
| tables in simulation; it does not execute docking engines or train the downstream |
| MolAS selector. The numerical scoring, completion, runtime-surrogate, and |
| acquisition functions are preserved from the inspected source. Publication |
| packaging did not rerun acquisition or docking experiments or replace the |
| historical feature cache. |
|
|
| The original source files available during packaging are preserved separately in |
| [`../../provenance/historical_acquisition_source`](../../provenance/historical_acquisition_source/). |
| Its manifest records hashes for all twelve recovered historical core text files; |
| no auxiliary source file remains unavailable. This is a snapshot of inspected source, not a claim that its exact |
| revision generated every paper figure. The archived outputs provide additional |
| lineage evidence described in the repository's provenance documentation. |
|
|
| ## Files and dependency boundary |
|
|
| | File | Purpose | |
| | --- | --- | |
| | `run_experiment.py` | Six-policy acquisition, completion, and checkpoint exports | |
| | `release_support.py` | Portable paths, run identity, and resume safeguards | |
| | `launch_all.py` | Bounded parallel launcher; invoking it starts experiments | |
| | `audit_inputs.py` | Read-only schema and feature-order validation | |
| | `finalize_audit.py` | Read-only output completeness and optional incremental-log audit | |
| | `summarize_results.py` | Aggregate existing acquisition diagnostics; not downstream analysis | |
| | `formula_tests.py` | Preserved legacy finiteness checks | |
| | `tests/test_release.py` | Fabricated-data regression checks; no AL loop or model fitting | |
|
|
| Python 3.10 or newer is required. Runtime dependencies are NumPy, Pandas, and |
| scikit-learn; their normal transitive dependencies are also required. Install in |
| an isolated environment: |
|
|
| ```sh |
| python -m venv .venv |
| .venv/bin/python -m pip install -r code/acquisition/requirements.txt |
| ``` |
|
|
| `requirements.txt` preserves the original minimum-version specification. It is |
| not a recovered historical lockfile and does not promise identical floating-point |
| results on every platform. The release's validation record identifies the |
| environment used for its fabricated checks. Every new run records its actual |
| Python/package versions and source hashes. |
|
|
| The acquisition runner imports no other `AL_MolAS` source package. PyTorch, |
| torch-geometric, pretrained feature models, and DAL-toolbox are not needed when |
| the prepared feature array is supplied. The original feature-building functions |
| are preserved as a **non-executable provenance excerpt**, with dependency notes. |
| Data validation compares the existing cache with its source transformation; the |
| archived cache itself is retained unchanged. |
|
|
| The preserved feature builder normalizes over the supplied complex IDs before |
| random projection. The archived cache therefore must not be described as having |
| fold-local feature preprocessing. This is separate from the runtime surrogate's |
| `StandardScaler`, which is fitted only on already observed cells. The runner |
| consumes the prepared cache unchanged. The data provenance directory records the |
| independent alignment check of that cache against the existing molecular inputs. |
|
|
| ## Required inputs |
|
|
| Paths are relative to the repository by default and can be overridden explicitly. |
|
|
| | Option | Default | Contract | |
| | --- | --- | --- | |
| | `--data-dir` | `data/raw` | Three CSV matrices listed below | |
| | `--features` | `data/features/complex_features_ligand_protein_d256_seed0.npy` | Finite `N × 256` numeric array, no pickle | |
| | `--feature-ids` | `data/features/complex_feature_ids.csv` | One-column CSV with producer-verified complex IDs in feature-row order | |
| | `--descriptors` | `data/descriptors/moad_physical_descriptors_filtered.csv` | Identifier plus exactly four numeric descriptors | |
| | `--fold-split` / `--fold-file` | `data/splits/moad_5fold_split_seed42.csv` | Identifier plus one integer fold column, encoded 0–4 or 1–5 | |
| | `--output-dir` | `outputs/acquisition` | New outputs; keep historical archives separate | |
|
|
| The three raw matrix names are `moad_rmsd_trimmed.csv`, |
| `moad_pb_ratio_trimmed.csv`, and `moad_runtime_trimmed.csv`. Their first column |
| contains complex IDs. Algorithm columns use suffixes `_rmsd`, `_pb_ratio`, and |
| `_runtime`, respectively, for `surfdock`, `unimol`, `diffdock`, `diffdock_l`, |
| `gnina`, `autodock`, and `smina`. Runtime values are finite nonnegative seconds. |
| The three matrices must have identical ordered, unique identifiers. |
|
|
| Descriptors and folds are aligned by identifier. Molecular features require an |
| **independently established ordered-ID mapping**, not merely the correct number |
| of rows. The runner fails before acquisition if the feature ID file is missing |
| or mismatched. Do not manufacture this file from raw-matrix order when the |
| feature-cache order is unverified. The repository's raw-matrix ID list does not |
| by itself prove cache alignment. All required acquisition inputs and the independently verified feature-ID mapping |
| are included in this package; no synthetic input is substituted. |
|
|
| If using the documented recovered held-out membership file, supply it explicitly: |
| `--fold-split data/splits/recovered_heldout_folds_from_downstream.csv`. This is not |
| represented as recovery of the original seed-42 splitting procedure. |
|
|
| ## Validation without experiments |
|
|
| From the repository root, these commands do not execute an acquisition loop: |
|
|
| ```sh |
| python -m unittest discover -s code/acquisition/tests -v |
| python code/acquisition/run_experiment.py --help |
| python code/acquisition/audit_inputs.py --help |
| ``` |
|
|
| Once all authentic inputs and their ordered feature mapping are available: |
|
|
| ```sh |
| python code/acquisition/audit_inputs.py --fold-split PATH_TO_VERIFIED_FOLDS |
| python code/acquisition/run_experiment.py --validate-only --fold-split PATH_TO_VERIFIED_FOLDS |
| ``` |
|
|
| The unit suite uses temporary fabricated arrays/records. Runtime-predictor tests |
| replace fitting with a fake predictor. No docking, SVD fitting, MLP fitting, real |
| benchmark acquisition, or downstream selector training occurs in these checks. |
|
|
| ## Explicit experiment commands |
|
|
| The following commands **would run new offline acquisition experiments**. They |
| are provided for future use and were not run during publication packaging. |
| They require all authentic inputs described above; add path overrides as needed. |
|
|
| ```sh |
| # One method/fold/seed, in a fresh output directory: |
| python code/acquisition/run_experiment.py --methods plain_mean_ucb --folds 0 --seeds 0 --output-dir outputs/new_acquisition |
| |
| # All six methods, five folds, three acquisition seeds (90 runs): |
| python code/acquisition/launch_all.py --workers 3 --output-dir outputs/new_acquisition |
| ``` |
|
|
| `--smoke-test` is also a real acquisition run on the first 120 training complexes, |
| not a data-free unit test. It writes under `smoke/` rather than `runs/` and is |
| explicitly identified in metadata. The launcher retains the historical |
| single-thread numerical-library limits in each worker. It no longer suppresses |
| all warnings. Completed runs are skipped only after matching input/code/ |
| environment/job provenance; an existing legacy output directory is never adopted |
| as a validated release run. |
|
|
| For existing release outputs, use: |
|
|
| ```sh |
| python code/acquisition/finalize_audit.py --output-dir outputs/new_acquisition --check-incremental |
| python code/acquisition/summarize_results.py --output-dir outputs/new_acquisition |
| ``` |
|
|
| Both accept explicit `--methods`, `--folds`, and `--seeds`. The summary retains the |
| historical descriptive fold-seed means/standard deviations and requires the |
| plain Mean-UCB baseline. These are not confidence intervals over independent |
| replicates. The completeness audit reports file-count scope unless |
| `--check-incremental` is supplied; that option additionally reads incremental logs |
| and detects repeated prior blocks across checkpoints. Neither audit certifies |
| historical figure lineage or downstream training. |
|
|
| ## Numerical definitions retained |
|
|
| - Strict score is the clipped product of the normalized RMSD sigmoid with |
| `pb_ratio == 1.0`; lambda is 3. Partial PB validity is not accepted as valid. |
| - Acquisition uses SVD ranks 1, 2, 3, and 5, each with four completion iterations; |
| dispersion is population standard deviation. Checkpoint matrices in this |
| inspected implementation use rank 3 and six iterations. These code facts are |
| not an unsupported attribution of the exact historical figure-generating run. |
| - Mean-UCB uses beta 1. Plain and direct cost-utility use raw utility; the other |
| four variants use ascending average-tie percentile ranks, floored at 0.001. |
| - Candidate costs are min–max scaled to `[1, 5]` where used; numerically equal |
| extrema map to 1. Five-tier boundaries are candidate-cost quintiles, with ties |
| assigned to the lower tier. Direct division adds `1e-6` to predicted seconds. |
| - Runtime prediction fits only revealed runtimes. Each prediction is transformed |
| from log seconds and clipped to observed 5th/95th percentile bounds. The |
| uncertainty policy uses three models; their mean plus population SD is clipped |
| again before cost scaling. Other policies use one model. |
| - The runtime MLP retains widths `(32, 16)` and at most ten iterations per refit. |
| Acquisition re-ranks after batches of at most 50 cells, truncated at integer |
| percentage checkpoint boundaries. Cumulative cost includes the warm start. |
|
|
| ## Publication changes and provenance limits |
|
|
| 1. **Portable inputs and output location.** The inherited sibling-folder paths |
| became explicit CLI options. Ordered feature IDs, finite feature values, |
| integer fold labels, unique matrix IDs, and valid runtimes are checked. Valid |
| scientific computations are unchanged. |
| 2. **Correct incremental export after resume.** The next export starts after the |
| last completed checkpoint, rather than the checkpoint before it. The historical |
| defect was observed in cost-tiers/fold-0/seed-0 checkpoint 17: its incremental |
| CSV repeated 178 earlier records. Its cumulative records and matrices remained |
| correct. The release does not alter that archived CSV or assert that the defect |
| changed any published figure. |
| 3. **Keep pending audit records on resume.** Round diagnostics for a partly |
| completed checkpoint are included in saved state, so resuming does not discard |
| the earlier rounds of that checkpoint's diagnostic JSON. |
| 4. **Actual scope and run identity.** Each invocation gets a separate manifest |
| containing its selected methods/folds/seeds and smoke/resume mode. Per-run |
| manifests bind input hashes, source hashes, actual environment, and job |
| settings. A completed marker alone cannot silently skip a changed run. A |
| legacy directory without release provenance is rejected; use a fresh output |
| location. `--no-resume` also refuses to overwrite an existing run. |
| 5. **Focused verification.** Fabricated checks cover exact formula contracts, |
| clipping order, data identity, resume-export uniqueness, and metadata. An AST |
| comparison verifies the scientific functions against the preserved snapshot. |
|
|
| The historical runtime-feature-ablation script is not the paper's engine-specific |
| ridge-probe implementation. Neither it nor the downstream MolAS/figure pipeline |
| is fabricated here. The exact historical training environment, source revision, |
| and every source-to-figure link have not been recovered. Consult the repository's |
| data and downstream documentation for available artifacts and remaining inputs. |
|
|
