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21c7db9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | """Placeholder hetero graph encoder."""
from __future__ import annotations
import numpy as np
from app.knowledge.ddi_knowledge import top_risky_pairs
from app.knowledge.drug_catalog import DRUG_CLASSES
from app.knowledge.side_effect_ontology import SIDE_EFFECT_TAGS
def encode_regimen(drugs: list[str], dim: int = 24) -> np.ndarray:
vec = np.zeros(dim, dtype=float)
ordered = sorted(drugs)
for idx, drug in enumerate(ordered[:12]):
vec[idx] = (hash(drug) % 1000) / 1000.0
class_counts: dict[str, int] = {}
for drug in ordered:
cls = DRUG_CLASSES.get(drug, "unknown")
class_counts[cls] = class_counts.get(cls, 0) + 1
class_values = sorted(class_counts.values(), reverse=True)
for i, value in enumerate(class_values[:5], start=12):
vec[i] = min(1.0, value / 4.0)
side_effect_count = sum(len(SIDE_EFFECT_TAGS.get(drug, [])) for drug in ordered)
vec[17] = min(1.0, side_effect_count / 20.0)
vec[18] = min(1.0, len(ordered) / 12.0)
vec[19] = min(1.0, len(top_risky_pairs(ordered)) / 4.0)
vec[20] = float(any("sedative" == DRUG_CLASSES.get(drug) for drug in ordered))
vec[21] = float(any("anticoagulant" == DRUG_CLASSES.get(drug) for drug in ordered))
vec[22] = float(any("glucose_lowering" == DRUG_CLASSES.get(drug) for drug in ordered))
vec[23] = min(1.0, sum(ord(ch) for ch in "".join(ordered)) % 1000 / 1000.0)
return vec
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