kemal
5463f489a3
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End-to-end variant interpretation platform for rare genetic disease research: SvelteKit UI, FastAPI + PostgreSQL API, Nextflow/Ensembl VEP pipeline, LightGBM pathogenicity scoring with MLflow, K8s/ArgoCD/GCP infrastructure. Public test data only; no clinical claims.
17 lines
757 B
Python
17 lines
757 B
Python
"""Feature engineering shared by training and serving. Keep this identical to api/app/services/scoring.py."""
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import pandas as pd
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IMPACT_ORDER = {"MODIFIER": 0, "LOW": 1, "MODERATE": 2, "HIGH": 3}
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CATEGORICAL = ["consequence"]
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NUMERIC = ["impact_rank", "gnomad_af", "cadd_phred", "am_pathogenicity"]
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def build(df: pd.DataFrame) -> pd.DataFrame:
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out = pd.DataFrame()
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out["impact_rank"] = df["impact"].map(IMPACT_ORDER).fillna(0)
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out["gnomad_af"] = pd.to_numeric(df["gnomad_af"], errors="coerce").fillna(0.0)
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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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