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.
91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
import math
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import uuid
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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 httpx import AsyncClient
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from sqlalchemy import select
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from app.db import SessionLocal
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from app.models import Job, JobStatus, Prediction, Sample, Variant
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from app.services import scoring
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def variant(**kw: object) -> Variant:
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fields: dict = {"chrom": "22", "pos": 1, "ref": "A", "alt": "G", "annotations": {}}
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fields.update(kw)
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return Variant(**fields)
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def test_raw_frame_sends_the_model_contract_columns() -> None:
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frame = scoring.raw_frame([
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variant(impact="HIGH", consequence="stop_gained", gnomad_af=None,
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annotations={"CADD_PHRED": "35", "am_pathogenicity": "0.98"}),
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variant(impact="LOW", consequence="synonymous_variant", gnomad_af=0.2, annotations={}),
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])
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assert list(frame.columns) == scoring.RAW_COLUMNS
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assert frame["impact"].tolist() == ["HIGH", "LOW"]
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assert frame["cadd_phred"].iloc[0] == "35"
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assert pd.isna(frame["cadd_phred"].iloc[1])
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assert math.isnan(frame["gnomad_af"].iloc[0])
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class FakeModel:
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def __init__(self, score: float) -> None:
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self.score = score
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def predict(self, frame: pd.DataFrame) -> np.ndarray:
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assert list(frame.columns) == scoring.RAW_COLUMNS
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return np.full(len(frame), self.score)
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async def make_job(status: JobStatus, n_variants: int) -> uuid.UUID:
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async with SessionLocal() as s:
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sample = Sample(name=f"s-{uuid.uuid4()}", vcf_uri="gs://b/x.vcf.gz", assembly="GRCh38")
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job = Job(sample=sample, status=status)
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s.add_all([sample, job, *(variant(job=job, pos=i + 1) for i in range(n_variants))])
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await s.commit()
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return job.id
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async def predictions(job_id: uuid.UUID) -> list[Prediction]:
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async with SessionLocal() as s:
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rows = await s.scalars(
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select(Prediction).join(Variant).where(Variant.job_id == job_id)
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)
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return list(rows)
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@pytest.mark.usefixtures("db")
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async def test_scoring_twice_updates_instead_of_failing(
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client: AsyncClient, monkeypatch: pytest.MonkeyPatch
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) -> None:
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job_id = await make_job(JobStatus.succeeded, n_variants=3)
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monkeypatch.setattr(scoring, "load_model", lambda: (FakeModel(0.9), "7"))
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r = await client.post(f"/api/predictions/score/{job_id}")
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assert r.status_code == 200, r.text
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assert r.json() == {"job_id": str(job_id), "scored": 3, "model_version": "7"}
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monkeypatch.setattr(scoring, "load_model", lambda: (FakeModel(0.2), "8"))
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r = await client.post(f"/api/predictions/score/{job_id}")
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assert r.status_code == 200, r.text
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preds = await predictions(job_id)
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assert len(preds) == 3
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assert {(p.score, p.model_version) for p in preds} == {(0.2, "8")}
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@pytest.mark.usefixtures("db")
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async def test_scoring_unknown_job_is_404(client: AsyncClient) -> None:
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r = await client.post(f"/api/predictions/score/{uuid.uuid4()}")
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assert r.status_code == 404
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@pytest.mark.usefixtures("db")
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async def test_scoring_unfinished_job_is_409(client: AsyncClient) -> None:
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job_id = await make_job(JobStatus.running, n_variants=1)
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r = await client.post(f"/api/predictions/score/{job_id}")
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assert r.status_code == 409
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