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  1. README.md +2 -2
  2. predict.py +2 -2
README.md CHANGED
@@ -4,7 +4,7 @@ tags:
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  - enzyme
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  - leaderboard
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  ---
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- # Enzyme classification: DIAMOND homology + learned EC re-ranker (v5)
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  Submission for the Leak CURBER enzyme classification task (`leak-curber-enzyme-classification`). Only the provided `train.parquet` is used. No external labelled data.
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@@ -40,7 +40,7 @@ These numbers cover queries that have at least one hit. About 5% of held-out que
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  - Tested under `ulimit -f 16384 -n 256 -u 128 -s 10240`: the output is identical to an unrestricted run, in about 3 minutes.
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  - `predict.py` logs environment diagnostics: CPU flags, memory and process limits, and mounts.
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  - Each DIAMOND binary is smoke-tested before use: first the official release binary, then a generic, fully static build of DIAMOND v2.1.10 (`bin/diamond-generic`, compiled with `-march=x86-64`). A crash is logged with its signal.
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- - A watchdog stops DIAMOND at 420 s.
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  - If DIAMOND cannot run, a pure numpy/scipy 3-mer TF-IDF nearest-neighbour fallback (`kmer_knn.py`) predicts every input. It scores about 0.39 L4 locally.
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  ## Runtime
 
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  - enzyme
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  - leaderboard
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  ---
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+ # Enzyme classification: DIAMOND homology + learned EC re-ranker (v6)
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  Submission for the Leak CURBER enzyme classification task (`leak-curber-enzyme-classification`). Only the provided `train.parquet` is used. No external labelled data.
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  - Tested under `ulimit -f 16384 -n 256 -u 128 -s 10240`: the output is identical to an unrestricted run, in about 3 minutes.
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  - `predict.py` logs environment diagnostics: CPU flags, memory and process limits, and mounts.
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  - Each DIAMOND binary is smoke-tested before use: first the official release binary, then a generic, fully static build of DIAMOND v2.1.10 (`bin/diamond-generic`, compiled with `-march=x86-64`). A crash is logged with its signal.
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+ - A watchdog stops DIAMOND at 420 s. In v6 the ultra-sensitive second pass runs when the first pass finishes within 215 s. Under the sandbox limits the whole run takes about 300 s.
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  - If DIAMOND cannot run, a pure numpy/scipy 3-mer TF-IDF nearest-neighbour fallback (`kmer_knn.py`) predicts every input. It scores about 0.39 L4 locally.
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  ## Runtime
predict.py CHANGED
@@ -1,4 +1,4 @@
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- """Offline CPU inference: DIAMOND homology search + learned EC re-ranker (v5).
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  1. Search every unique input against the training set (diamond blastp
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  --very-sensitive, 50 hits).
@@ -253,7 +253,7 @@ def homology_predictions(seqs, labels, freq):
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  weak = [i for i in range(n) if i not in hits or hits[i][0][1] <= WEAK_PID]
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  log(f'main search ({mode}, {cfg[0][0]}) done: {len(hits)}/{n} with hits, {len(weak)} weak')
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  # The second pass costs about as much as the first, so run it only with a wide margin.
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- if weak and len(weak) <= 3000 and t1 < 150 and cfg[0][0] == 'official':
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  try:
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  extra = run_diamond(write_chunks(((i, seqs[i]) for i in weak), workdir, 'b'),
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  'ultra-sensitive', cfg, workdir)
 
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+ """Offline CPU inference: DIAMOND homology search + learned EC re-ranker (v6).
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  1. Search every unique input against the training set (diamond blastp
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  --very-sensitive, 50 hits).
 
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  weak = [i for i in range(n) if i not in hits or hits[i][0][1] <= WEAK_PID]
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  log(f'main search ({mode}, {cfg[0][0]}) done: {len(hits)}/{n} with hits, {len(weak)} weak')
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  # The second pass costs about as much as the first, so run it only with a wide margin.
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+ if weak and len(weak) <= 3000 and t1 < 215 and cfg[0][0] == 'official':
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  try:
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  extra = run_diamond(write_chunks(((i, seqs[i]) for i in weak), workdir, 'b'),
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  'ultra-sensitive', cfg, workdir)