Kemal Yaylali
11fb6b3d73
An end-to-end audit found the repo could not build, test or run as shipped. This fixes every finding, then adds a Cloud Run track so the demo costs about £1/month idle instead of ~£150. CI (red on its first run) - api: setuptools could not build the package (flat layout with app/ and alembic/) - web: missing @types/node; `vitest run` exited 1 with no test files - pipeline: the stub run needed a gitignored VCF, and no process had a stub block - ruff pinned, mypy configured, DB tests on real Postgres (pgserver locally, service in CI) ML serving (scores were meaningless) - the registered model now carries its own feature engineering and returns predict_proba, so serving sends raw columns and cannot drift from training - resolve by registry alias (stages are deprecated in MLflow 3) and record the real version; re-scoring upserts instead of failing on the unique constraint - ClinVar labels parsed from VEP's lowercase terms Pipeline - exact ref/alt recovered from a CHROM_POS_REF_ALT VCF ID; loading is idempotent - job status reaches running/failed/succeeded, so the UI stops polling dead jobs - DATABASE_URL travels in the environment or a Nextflow secret, never on a command line - VEP cache and plugins staged as inputs; the gcp profile runs tasks on Google Batch Deployment - the API serves /api (matching the ingress); the web app reads its API URL at runtime - migrations run in an init container under a Postgres advisory lock - terraform: custom VPC shared with Batch, private Cloud SQL, API enablement, Workload Identity bindings, Secret Manager, deletion protection - serverless track, now the default: Cloud Run services scaling to zero, a Cloud Run job for the Nextflow driver, and Neon or Cloud SQL behind one DATABASE_URL secret. GKE and Argo remain, behind -var deploy_kubernetes=true. See docs/cloud.md. Correctness and security - 409 on duplicate sample names, 422 on bad paging, natural chromosome ordering, wider VEP text columns, enum dropped on downgrade, the sample's assembly actually used - vcf_uri restricted to gs:// objects or files under the data root, blocking option injection - CORS restricted to configured origins; `make down` no longer deletes volumes Data - docs/data.md records the peer-reviewed, openly licensed sources (GIAB HG002, ClinVar, gnomAD) with citations and an honest evaluation plan; `make data` fetches a chr22 slice Verified: api 50 tests, ml 18, loader 16, web 12; ruff, mypy, svelte-check, terraform validate and both kustomize overlays clean.
105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
from pathlib import Path
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import numpy as np
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import pandas as pd
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import pytest
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from rarelens_ml.train import label, read_vep_tab
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HEADER = [
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"Uploaded_variation", "Location", "Allele", "Consequence", "IMPACT", "SYMBOL",
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"gnomADe_AF", "CLIN_SIG", "CADD_PHRED", "am_pathogenicity",
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]
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def write_vep_tab(path: Path, rows: list[list[str]]) -> Path:
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lines = [
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"## ENSEMBL VARIANT EFFECT PREDICTOR v113.0",
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"## Column descriptions:",
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"#" + "\t".join(HEADER),
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*("\t".join(r) for r in rows),
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]
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path.write_text("\n".join(lines) + "\n")
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return path
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@pytest.mark.parametrize(
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("clin_sig", "expected"),
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[
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# VEP writes lowercase, comma-separated terms from co-located ClinVar records.
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("pathogenic", 1),
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("pathogenic,likely_pathogenic", 1),
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("likely_benign", 0),
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("benign,likely_benign", 0),
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# ClinVar VCF CLNSIG spelling must keep working too.
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("Pathogenic/Likely_pathogenic", 1),
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("Benign", 0),
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("uncertain_significance", None),
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("pathogenic,benign", None), # conflicting evidence is not a label
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("-", None),
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("", None),
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(np.nan, None),
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],
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)
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def test_label(clin_sig: object, expected: int | None) -> None:
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assert label(clin_sig) == expected
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def test_read_vep_tab_uses_the_hash_header_and_keeps_dashes(tmp_path: Path) -> None:
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tsv = write_vep_tab(
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tmp_path / "x.vep.tsv",
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[["22_1_A_G", "22:1", "G", "missense_variant", "MODERATE", "TBX1", "-", "pathogenic", "28", "0.9"]],
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)
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df = read_vep_tab(tsv)
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assert list(df.columns) == HEADER
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assert df.loc[0, "gnomADe_AF"] == "-"
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assert df.loc[0, "CLIN_SIG"] == "pathogenic"
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def test_load_returns_raw_serving_columns_and_labels(tmp_path: Path) -> None:
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from rarelens_ml.features import RAW_COLUMNS
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from rarelens_ml.train import load
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tsv = write_vep_tab(
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tmp_path / "x.vep.tsv",
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[
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["a", "22:1", "G", "missense_variant", "MODERATE", "TBX1", "0.0001", "pathogenic", "28", "0.9"],
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["b", "22:2", "A", "synonymous_variant", "LOW", "CHEK2", "0.12", "benign", "3", "-"],
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["c", "22:3", "T", "intron_variant", "MODIFIER", "CHEK2", "0.3", "uncertain_significance", "1", "-"],
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],
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)
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X, y = load(str(tsv))
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assert list(X.columns) == RAW_COLUMNS
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assert y.tolist() == [1, 0] # the VUS row is dropped
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def test_logged_model_returns_probabilities_from_raw_columns(tmp_path: Path) -> None:
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"""The registered model must take the raw columns serving sends and return P(pathogenic)."""
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import mlflow
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from rarelens_ml.train import fit, log_and_register
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rng = np.random.default_rng(0)
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n = 400
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impact = rng.choice(["HIGH", "MODERATE", "LOW", "MODIFIER"], n)
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y = pd.Series(((impact == "HIGH") | (rng.random(n) < 0.1)).astype(int))
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X = pd.DataFrame({
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"impact": impact,
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"consequence": rng.choice(["stop_gained", "missense_variant", "intron_variant"], n),
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"gnomad_af": rng.random(n).round(4).astype(str), # strings, as read from the DB
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"cadd_phred": (rng.random(n) * 40).round(1).astype(str),
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"am_pathogenicity": "-",
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})
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mlflow.set_tracking_uri(f"sqlite:///{tmp_path}/mlflow.db")
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mlflow.set_experiment("test")
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clf = fit(X, y)
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version = log_and_register(clf, model_name="rarelens-test", alias="production")
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model = mlflow.pyfunc.load_model("models:/rarelens-test@production")
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scores = np.asarray(model.predict(X.head(50)))
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assert version == "1"
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assert scores.shape == (50,)
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assert ((scores >= 0) & (scores <= 1)).all()
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assert not set(np.unique(scores)) <= {0.0, 1.0}, "got class labels, expected probabilities"
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