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"""
FunGO — Smart Tier Filtering
==============================
Removes generic/root GO terms and assigns evidence tiers
to remaining predictions.
Changes from original:
1. Tier names updated:
GOLD → STRONG (Strong Evidence)
GOOD → MODERATE (Moderate Evidence)
SILVER → INDICATIVE
2. Combined score = ia_weight × confidence
Used for ranking — more scientifically sound.
3. filter_predictions() returns a dict with two keys:
"display" — top 20 by combined score (for UI screen)
"all" — full filtered list (for CSV download)
4. summarise() updated to use new tier keys.
5. Blacklist + IA/confidence thresholds → completely unchanged.
"""
import logging
from config import (
BLACKLIST_TERMS,
TIER_GOLD_IA, TIER_GOLD_CONF,
TIER_GOOD_IA, TIER_GOOD_CONF,
TIER_SILVER_IA, TIER_SILVER_CONF,
)
logger = logging.getLogger(__name__)
ONT_LABELS = {
"MFO": "Molecular Function",
"BPO": "Biological Process",
"CCO": "Cellular Component",
}
TIER_LABELS = {
"STRONG": "Strong Evidence",
"MODERATE": "Moderate Evidence",
"INDICATIVE": "Indicative",
}
TIER_RANK = {"STRONG": 0, "MODERATE": 1, "INDICATIVE": 2}
# Max predictions shown on screen per protein
TOP_N_DISPLAY = 20
def assign_tier(go_term: str, ia: float, confidence: float) -> str:
"""
Assign evidence tier. Thresholds unchanged from original.
Returns: "STRONG" | "MODERATE" | "INDICATIVE" | "NOISE"
"""
if go_term in BLACKLIST_TERMS:
return "NOISE"
if ia > TIER_GOLD_IA and confidence >= TIER_GOLD_CONF:
return "STRONG"
if ia > TIER_GOOD_IA and confidence >= TIER_GOOD_CONF:
return "MODERATE"
if ia > TIER_SILVER_IA and confidence >= TIER_SILVER_CONF:
return "INDICATIVE"
return "NOISE"
def combined_score(ia: float, confidence: float) -> float:
"""
Ranking score = ia_weight × confidence.
Balances specificity (IA) and model certainty (confidence).
"""
return round(ia * confidence, 6)
def filter_predictions(raw_predictions: list, ia_weights: dict) -> dict:
"""
Filter raw predictions and return display + full sets.
Returns
-------
{
"display": top-20 predictions (sorted by combined_score desc),
"all": all filtered predictions (for CSV)
}
Each prediction dict contains:
go_term, ontology, ontology_label, confidence, threshold,
ia_weight, combined_score, tier, tier_rank, tier_label
"""
filtered = []
for pred in raw_predictions:
go_term = pred["go_term"]
confidence = pred["confidence"]
ia = float(ia_weights.get(go_term, 0.0))
tier = assign_tier(go_term, ia, confidence)
if tier == "NOISE":
continue
if tier not in TIER_RANK:
logger.warning("Unknown tier %r for %s — skipping", tier, go_term)
continue
score = combined_score(ia, confidence)
filtered.append({
**pred,
"ia_weight": round(ia, 4),
"combined_score": score,
"tier": tier,
"tier_rank": TIER_RANK[tier],
"tier_label": TIER_LABELS[tier],
"ontology_label": ONT_LABELS.get(pred["ontology"], pred["ontology"]),
})
# Sort by combined score descending, tier_rank as tiebreaker
filtered.sort(key=lambda x: (-x["combined_score"], x["tier_rank"]))
return {
"display": filtered[:TOP_N_DISPLAY],
"all": filtered,
}
def summarise(filtered_display: list, all_filtered: list, protein_id: str) -> dict:
"""
Per-protein summary. Counts are over ALL filtered (not just top-20).
"""
ont_counts = {"MFO": 0, "BPO": 0, "CCO": 0}
tier_counts = {"STRONG": 0, "MODERATE": 0, "INDICATIVE": 0}
for p in all_filtered:
ont = p.get("ontology", "")
if ont in ont_counts:
ont_counts[ont] += 1
t = p.get("tier", "")
if t in tier_counts:
tier_counts[t] += 1
n = len(all_filtered)
return {
"protein_id": protein_id,
"total_filtered": n,
"displayed": len(filtered_display),
"by_ontology": ont_counts,
"by_tier": tier_counts,
"has_strong_evidence": tier_counts["STRONG"] > 0,
"avg_confidence": round(sum(p["confidence"] for p in all_filtered) / n, 4) if n else 0.0,
"avg_ia": round(sum(p["ia_weight"] for p in all_filtered) / n, 4) if n else 0.0,
"avg_combined_score": round(sum(p["combined_score"] for p in all_filtered) / n, 4) if n else 0.0,
}
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