Kemal Yaylali
e76ae847a1
A review of the ranking's arithmetic found four things wrong, all of which made the score look better informed than it was. Measurements below are from this repo, not estimates. **Components now abstain instead of inventing a number.** A run without a VEP cache returns no allele frequencies, and rarity_score(None) read that as "absent from gnomAD, therefore maximally rare" and awarded every variant a free 0.25. jobs.has_frequencies / has_effect_scores record what the run actually produced, absent components are dropped from the weighted mean, and the remaining weights are renormalised so the score keeps its meaning. The UI shows "not looked up" rather than a bar, and the funnel stops calling a step "rare" when nothing was filtered. **Allele frequency is no longer a model feature.** It dominated: the same missense variant scored 0.887 at AF 0 and 0.0003 at AF 0.01. That double- counted, because the ranking already scores frequency explicitly, putting ~45% of every rank on one measurement; and it was circular, because ACMG assigns ClinVar's benign labels using frequency (BA1/BS1). Retraining without it moves missense AUROC from 0.872 to 0.500 — exactly random. The old figure was allele frequency, not variant-effect knowledge. The model therefore abstains unless CADD or AlphaMissense is present, since otherwise it only restates the consequence class. **Phenotype matching is weighted by information content** and HPO annotations are propagated up the ontology. Counting terms alike let "global developmental delay" (IC 0.93) count as much as "dilated left subclavian artery" (IC 7.88). **A real bug in the propagation, found by checking it.** The ancestor walk read a pre-order DFS backwards, which on a DAG lets a term resolve before one of its parents and inherit that parent alone instead of its lineage. It dropped 399 terms out of the phenotype branch, Camptodactyly and Chiari malformation among them. Now a true post-order, tested against a reference transitive closure. The ontology arithmetic moved to rarelens_ml.hpo so it is covered by tests, and rarelens_ml.benchmark measures the whole thing: across 10,178 published cases the causal gene ranks first 45.9-81.0% of the time against 5,269 genes, versus 0.02% for chance. docs/data.md reports that with its contamination (HPO's annotations come from these same case reports), and includes the measurement showing information-content weighting earns its place while propagation does not - kept anyway, for a reason the docs argue rather than assume.
35 lines
1.9 KiB
Python
35 lines
1.9 KiB
Python
"""Feature engineering: the only copy.
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Training imports it, and train.log_and_register ships this package inside the logged pyfunc
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(code_paths), so serving runs exactly this code on the raw columns below.
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**Allele frequency is deliberately not a feature.** It used to be, and it dominated everything:
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the same missense variant scored 0.887 at AF 0 and 0.0003 at AF 0.01, so the model was largely a
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frequency lookup. That caused two problems. It double-counted, because the ranking already scores
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frequency explicitly and auditably in `triage.rarity_score`, putting ~45% of the rank on one
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measurement. And it was circular, because ClinVar's labels are assigned with ACMG criteria that
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call a variant benign *on frequency* (BA1/BS1), so the model was rediscovering the rule used to
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label its own training data — which is most of why the headline AUROC looked so good.
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What is left is the variant's predicted effect: what it does to the protein, and how damaging two
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independent predictors think that is. That is evidence the rest of the ranking does not already
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have, which is the only reason to give the model a weight at all.
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"""
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import pandas as pd
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# What serving must send: raw values as stored in the variants table / its annotations.
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RAW_COLUMNS = ["impact", "consequence", "cadd_phred", "am_pathogenicity"]
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IMPACT_ORDER = {"MODIFIER": 0, "LOW": 1, "MODERATE": 2, "HIGH": 3}
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def build(df: pd.DataFrame) -> pd.DataFrame:
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out = pd.DataFrame(index=df.index)
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out["impact_rank"] = df["impact"].map(IMPACT_ORDER).fillna(0).astype(int)
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# Left as NaN on purpose: LightGBM handles missing natively, and imputing a number here would
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# assert a score nobody computed.
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out["cadd_phred"] = pd.to_numeric(df["cadd_phred"], errors="coerce")
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out["am_pathogenicity"] = pd.to_numeric(df["am_pathogenicity"], errors="coerce")
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out["consequence"] = df["consequence"].astype("category")
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return out
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