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Running on Zero
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66ee87e | 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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | """Normalisation of model answers into one comparable record per question.
Pure Python (math only): no torch, no model code, so it can be tested anywhere.
Moved verbatim from app/models.py in 1.2.0.
"""
from __future__ import annotations
import math
def option_keys(q: dict) -> list[str]:
qtype = q.get("type", "choice")
crit = q.get("criteria", q.get("options"))
if qtype == "noul":
return ["true", "false"]
if qtype == "score":
return [str(i) for i in range(len(crit or []))]
if isinstance(crit, dict):
return [str(k) for k in crit]
return [str(c) for c in (crit or [])]
def _lookup(raw: dict, key: str):
"""Tolerant key lookup: exact, str(), lower-case, and bool spellings."""
if not isinstance(raw, dict):
return None
candidates = [key, key.lower(), key.capitalize()]
if key == "true":
candidates += [True, "True", "yes", "Yes", 1, "1"]
if key == "false":
candidates += [False, "False", "no", "No", 0, "0"]
if key.isdigit():
candidates.append(int(key))
for c in candidates:
if c in raw:
return raw[c]
return None
def normalise(q: dict, probs_raw: dict | None, choice=None, p_true=None, level=None) -> dict:
"""One comparable record per question, whatever the model returned."""
qtype = q.get("type", "choice")
keys = option_keys(q)
probs = {}
if qtype == "noul" and p_true is not None:
p = float(p_true)
probs = {"true": p, "false": 1.0 - p}
elif probs_raw:
for k in keys:
v = _lookup(probs_raw, k)
probs[k] = float(v) if v is not None else 0.0
if not probs or sum(probs.values()) <= 0:
# The model gave only its answer: represent it as a point mass so the UI still works.
probs = {k: 0.0 for k in keys}
if qtype == "score" and level is not None:
probs[str(int(round(float(level))))] = 1.0
elif choice is not None and str(choice).lower() in {k.lower() for k in keys}:
probs[next(k for k in keys if k.lower() == str(choice).lower())] = 1.0
s = sum(probs.values()) or 1.0
probs = {k: v / s for k, v in probs.items()}
top = max(probs, key=probs.get)
n = len(probs)
ent = -sum(p * math.log(p) for p in probs.values() if p > 0)
ent_conf = 1.0 - ent / math.log(n) if n > 1 else 1.0
ent_conf = max(0.0, min(1.0, ent_conf))
# Laya reports 1 - normalised entropy for choice/score, and max(p, 1 - p) for yes/no questions.
laya_conf = probs[top] if qtype == "noul" else ent_conf
rec = dict(type=qtype, choice=top, probs=probs, top_prob=probs[top], entropy_conf=ent_conf, laya_conf=laya_conf)
if qtype == "score":
rec["expected_level"] = sum(int(k) * p for k, p in probs.items())
rec["levels"] = len(keys)
if qtype == "noul":
rec["p_true"] = probs["true"]
return rec
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