PATCH_NOTES_2026-03-10.md

Patch Notes: Experiment Pipeline Fixes

Date: 2026-03-10

Scope

This patch fixes experiment orchestration bugs found during review across:

• src/negbiodb/export.py
• scripts/train_baseline.py
• scripts/prepare_exp_data.py
• scripts/collect_results.py
• tests/test_pipeline_scripts.py

Bugs Fixed

1. Split export row fanout with multiple split versions

Problem:

• export_negative_dataset() joined split columns by split_strategy.
• If random_v2 or another later split definition existed for the same strategy, the export query could duplicate rows.

Fix:

• Export now resolves a single latest split_id per strategy before joining split_assignments.
• Cold split integrity checks were aligned to use the latest split definition too.
• Split selection now prefers explicit version semantics (version column and _vN suffixes) rather than raw insertion order.

Impact:

• Prevents silent corruption of negbiodb_dti_pairs.parquet when new split versions are added.

2. Incomplete graph cache silently degrading GraphDTA/DrugBAN

Problem:

• Existing graph_cache.pt files could be incomplete.
• Missing SMILES fell through to placeholder graphs during batching.
• When no cache existed, train fold could save a train-only cache that val/test later reused.

Fix:

• Added _prepare_graph_cache() in train_baseline.py.
• The cache is now built or backfilled against the full dataset parquet before folds are created.
• DTIDataset._load_graphs() also backfills missing SMILES if a partial cache path is still passed in.

Impact:

• Prevents valid molecules from silently training/evaluating as dummy graphs.

3. Training run outputs overwriting each other

Problem:

• Output directories were keyed only by model/split/negative.
• Different datasets or seeds could overwrite prior runs.

Fix:

• Run names now include dataset and seed.
• Added _build_run_name() helper to keep naming deterministic and testable.

Impact:

• Balanced vs. realistic and multi-seed runs can now coexist safely.

3b. Unsupported realistic Exp 1 controls now fail fast

Problem:

• uniform_random and degree_matched controls did not have realistic dataset variants.
• The CLI still accepted --dataset realistic for those negatives, which mislabeled balanced-control runs as realistic.

Fix:

• Removed those unsupported mappings from train_baseline.py.
• The CLI now rejects realistic random-control combinations explicitly.

Impact:

• Prevents mislabeled Exp 1 results from being written to disk and summarized later.

4. Invalid DDB CLI combinations

Problem:

• ddb was exposed as a negative source even though it is a split mode.
• Commands like --split random --negative ddb were accepted.

Fix:

• Removed ddb from --negative choices.
• Added explicit validation so DDB is only valid for split=ddb, negative=negbiodb, and dataset=balanced.

Impact:

• Reduces mislabeled experiment runs and ambiguous result directories.

5. prepare_exp_data.py --skip-exp4 still required the pairs parquet

Problem:

• Input validation always required exports/negbiodb_dti_pairs.parquet, even when Exp 4 was being skipped.

Fix:

• Conditionalized required inputs so the large pairs parquet is only required when Exp 4 runs.
• Added defensive deduplication and many_to_one merge validation in prepare_exp4_ddb().

Impact:

• Exp 1 preparation can now run independently.

6. Result collection mixed datasets and hid seed information

Problem:

• collect_results.py dropped dataset and seed from Table 1 output.
• Exp 1 summary selected the first matching row instead of grouping safely.

Fix:

• Table output now preserves dataset and seed.
• Exp 1 summary is now grouped by dataset and averages only across seeds present in all compared negative conditions.

Impact:

• Prevents balanced/realistic runs from being conflated in downstream summaries.

6b. Result collection now supports explicit dataset/seed filters

Problem:

• collect_results.py previously aggregated every run under results/baselines/.
• As more experiments accumulate, paper tables can accidentally mix exploratory and final runs.

Fix:

• Added --dataset and repeatable --seed filters to collect_results.py.
• Added repeatable --model, --split, and --negative filters too.
• The command now fails fast if filters remove all rows.
• Added optional --aggregate-seeds output that writes table1_aggregated.csv with mean/std over seeds.
• Added table1_aggregated.md with human-readable mean +/- std formatting.

Impact:

• Makes paper/report generation reproducible from an explicit run subset.
• Adds a paper-friendly seed-aggregated summary without removing access to raw runs.

7. SLURM submission metadata aligned with current run naming

Problem:

• SLURM job names and log file names did not include dataset or seed.
• Submission wrappers relied on implicit defaults instead of exporting DATASET and SEED explicitly.

Fix:

• Updated SLURM wrapper scripts to include dataset and seed in job/log naming.
• Submission wrappers now export DATASET and SEED explicitly to the training job.
• Training job logging now prints seed together with model/split/negative/dataset.

Impact:

• Makes cluster logs line up with the run directories and result-collection filters.

7b. SLURM submission scripts now support seed sweeps

Problem:

• Multi-seed experiments required manual resubmission or manual editing of shell scripts.

Fix:

• Added SEEDS="42 43 44" style support to SLURM submission wrappers.
• Added optional MODELS, SPLITS, and NEGATIVES filters to submit_all.sh.
• Default behavior remains SEEDS=42, preserving the prior single-seed workflow.

Impact:

• Makes it straightforward to launch reproducible seed sweeps that match collect_results.py --seed ....
• Makes selective experiment submission possible without editing shell scripts.

7c. Added Cayuga SSH helper wrappers for remote submission and monitoring

Problem:

• The documented Cayuga workflow relies on SSH ControlMaster and non-interactive remote commands.
• Reconstructing the exact remote submit/monitor commands by hand is error-prone.

Fix:

• Added slurm/remote_submit_cayuga.sh to call submit_all.sh remotely via ssh ${HPC_LOGIN:-cayuga-login1}.
• Added slurm/remote_monitor_cayuga.sh to inspect squeue and recent log files remotely.
• Made SLURM binary paths overridable via SBATCH_BIN / SQUEUE_BIN.

Impact:

• Makes the repository align directly with the Cayuga SSH workflow documented in the lab notes.

Regression Tests Added

Added tests/test_pipeline_scripts.py covering:

• export uses latest split version without row duplication
• prepare_exp_data.py --skip-exp4 no longer requires the pairs parquet
• run names include dataset and seed
• invalid DDB dataset resolution is rejected
• unsupported realistic random-control runs are rejected
• train_baseline.py rejects split=ddb with dataset=realistic
• train_baseline.py writes results into a dataset+seed-qualified run directory
• graph cache backfills missing SMILES
• result collection preserves dataset/seed and groups Exp 1 summaries by dataset using matched seeds only
• result collection can filter by dataset and seed before building tables
• result collection can also filter by model, split, and negative source
• result collection can optionally write seed-aggregated tables
• result collection writes both aggregated CSV and aggregated Markdown summaries
• SLURM job/log names now include dataset and seed, matching run output naming
• SLURM submission wrappers support multi-seed sweeps via the SEEDS environment variable
• submit_all.sh can filter submitted experiments by model, split, and negative source
• Added remote Cayuga SSH helper scripts for submission and monitoring

Verification

Commands run:

uv run pytest tests/test_export.py -q
uv run pytest tests/test_pipeline_scripts.py -q

Observed results:

• tests/test_export.py: 52 passed
• tests/test_pipeline_scripts.py: 17 passed, 1 skipped

Skip reason:

• the graph cache backfill test is skipped when torch is unavailable in the environment

Follow-up

Recommended next step:

• add one end-to-end script test that exercises train_baseline.py CLI with heavy training functions mocked out, to lock down argument validation and results writing behavior together

Lessons Learned (2026-03-12)

1. Exp 4 must be defined as a full-task split, not a negative-only transform

What we learned:

• Treating DDB as "only reassign negatives while positives keep split_random" weakens the claim.
• That setup mixes split bias with class-specific handling and no longer measures the benchmark-level effect of node-degree bias.

Decision:

• Exp 4 is now defined as a full-task degree-balanced split on merged M1 balanced data.
• Positives and negatives must be reassigned together under the same split_degree_balanced policy.

Operational rule:

• Any future regeneration of exports/negbiodb_m1_balanced_ddb.parquet must be done from the merged M1 benchmark, not by patching only the negative subset.

2. Eval-only rewrites can make stale checkpoints look fresh

What we learned:

• Using results.json modification time alone is not a reliable freshness signal.
• eval_checkpoint.py can rewrite results for an old checkpoint and make a stale run appear newly generated.

Decision:

• Freshness checks must use training artifacts first (best.pt, last.pt, training_log.csv) and only fall back to results.json if no training artifacts exist.

Operational rule:

• Any future result filtering or release packaging should treat checkpoint/log timestamps as the source of truth for run freshness.

3. DDB benchmark regeneration invalidates prior DDB model results

What we learned:

• Once exports/negbiodb_m1_balanced_ddb.parquet changes, all prior *_ddb_* model results become semantically stale even if files still parse.
• Silent reuse of those results is more dangerous than having missing rows.

Decision:

• collect_results.py now excludes stale DDB runs by default when they are older than the current DDB parquet.

Operational rule:

• After any future DDB regeneration, immediately retrain the DDB model trio before producing paper tables.

4. Remote HPC execution can drift from local fixes

What we learned:

• The Cayuga working directory is effectively a deployed copy, not a git-controlled checkout.
• Local fixes do not automatically exist on the remote side, so "submitted successfully" does not imply "submitted the corrected code."

Decision:

• Before remote submission, explicitly sync changed code or regenerate artifacts in place on Cayuga.
• For large derived files, remote regeneration is usually safer than bulk transfer.

Operational rule:

• Prefer:
  1. sync small code/script changes
  2. regenerate derived artifacts on Cayuga
  3. then submit jobs

5. Submission wrappers need shell syntax coverage, not just static review

What we learned:

• slurm/submit_all.sh still contained loop-closing bugs after functional edits.
• These did not surface until the exact submission path was exercised on the remote cluster.

Decision:

• Shell wrappers should be syntax-checked with bash -n whenever edited.

Operational rule:

• For any future SLURM wrapper change:
  • run bash -n <script>
  • then run one narrow submission command before treating the wrapper as good

Future Reference

Step 4 current ground truth

• Exp 4 = split=ddb, negative=negbiodb, dataset=balanced
• DDB parquet = exports/negbiodb_m1_balanced_ddb.parquet
• DDB parquet is generated from merged M1 balanced data with positives and negatives reassigned together
• Stale DDB results are excluded by default in scripts/collect_results.py
• Override flag if absolutely needed: --allow-stale-ddb

Safe workflow for future DDB reruns

1. Sync changed code to Cayuga.
2. Regenerate DDB parquet on Cayuga:
   • source ${CONDA_PREFIX:-/path/to/conda}/miniconda3/etc/profile.d/conda.sh
   • conda activate negbiodb-ml
   • cd ${SCRATCH_DIR:-/path/to/scratch}/negbiodb
   • python scripts/prepare_exp_data.py --skip-exp1
3. Submit only DDB jobs:
   • SEEDS="42" MODELS="deepdta graphdta drugban" SPLITS="ddb" NEGATIVES="negbiodb" DATASETS="balanced" bash slurm/submit_all.sh
4. Verify queue state:
   • /opt/ohpc/pub/software/slurm/24.05.2/bin/squeue -u ${USER}
5. Re-collect tables after those runs finish.

Current known-good DDB submission example

Successful Cayuga submission on 2026-03-12:

• negbio_deepdta_balanced_ddb_negbiodb_seed42 → job 2702356
• negbio_graphdta_balanced_ddb_negbiodb_seed42 → job 2702357
• negbio_drugban_balanced_ddb_negbiodb_seed42 → job 2702358

Files to inspect first next time

• scripts/prepare_exp_data.py
• src/negbiodb/export.py
• scripts/collect_results.py
• scripts/train_baseline.py
• scripts/eval_checkpoint.py
• slurm/submit_all.sh
• tests/test_pipeline_scripts.py