SirStone
f58d65d2e8
Adds a per-sample intrinsic-confidence field (GunPrediction.confidence, threaded through FeedbackEvent/VirtualBullet, populated by Pattern, DecayGF, KNN, GuessFactor, Tsetlin, TMHorizon) and an offline recorder + analyzer that reproduce the paper's Figure 2 per gun and its Eq-8 composite. Measured on 3 held-out tr-bridge DrussGT battles (33k ticks, ~133k samples/gun): - FAITHFUL: DecayGF (rho +0.133), KNN (+0.090), Pattern (+0.064, weak). - GuessFactor is ANTI-faithful (rho -0.067); Tsetlin c_max is useless (0.001). - No pair of guns specialises complementarily: the same gun dominates both high-confidence slices in every pair. - Eq-8 alpha-normalised confidence-weighted composite: 18.41% vs Pattern 20.45% (McNemar p=3.1e-126). Faithful-only variant 18.68%, still loses. Shuffle control passes weakly (composite > shuffle, p=4e-14) so ~0.7pp of competence is real but ~2pp short. Offline veto: design is dead. See docs/tmcomposites_gate.md.
439 lines
16 KiB
Python
439 lines
16 KiB
Python
#!/usr/bin/env python3
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"""TMComposites GATE — analyse the per-sample confidence dump.
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Reads the JSONL produced by ``common_libs/tests/measure_tmcomposites.nim``
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(one row per resolved virtual bullet: fixture, split, gun, tick, bin, conf,
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hit, relDeg, range, missPx) and answers the three questions of
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``docs/tmcomposites_gate.md``:
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A. Is each gun's intrinsic confidence FAITHFUL?
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Rank samples by the gun's own confidence; report the accuracy-vs-confidence
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curve, Spearman(confidence, hit), and top-half vs bottom-half accuracy with
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a two-proportion p-value.
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B. Are the faithful guns COMPLEMENTARY SPECIALISTS?
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For each pair, on the samples where A's alpha-normalised confidence beats
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B's, is A the more accurate one? Report the two slices and the win rates.
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C. Does the Eq-8 alpha-normalised confidence-weighted composite beat the best
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single gun on held-out battles, and is the gain attributable to competence?
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The recorder already ran ``Composite`` and its within-sample
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confidence-shuffle control ``CompositeShuf`` through the SAME virtual-bullet
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geometry, so the comparison is paired (McNemar).
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Pure stdlib (no numpy/scipy on this machine). MEASURED = every number printed;
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INFERRED = the causal reading in the doc.
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"""
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from __future__ import annotations
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import argparse
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import collections
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import json
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import math
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import random
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import sys
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DETERMINISTIC = ["HeadOn", "Linear", "Circular", "WallBounce", "Accel",
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"StopShot", "Displace", "AvgLead"]
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# ───────────────────────────── stats (stdlib) ──────────────────────────────
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def mean(xs):
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return sum(xs) / len(xs) if xs else float("nan")
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def spearman(xs, ys):
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"""Spearman rho with average ranks for ties, and a normal-approx p."""
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n = len(xs)
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if n < 3:
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return float("nan"), float("nan")
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def ranks(v):
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order = sorted(range(n), key=lambda i: v[i])
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r = [0.0] * n
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i = 0
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while i < n:
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j = i
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while j + 1 < n and v[order[j + 1]] == v[order[i]]:
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j += 1
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avg = (i + j) / 2.0 + 1.0
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for k in range(i, j + 1):
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r[order[k]] = avg
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i = j + 1
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return r
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rx, ry = ranks(xs), ranks(ys)
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mx, my = mean(rx), mean(ry)
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num = sum((a - mx) * (b - my) for a, b in zip(rx, ry))
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den = math.sqrt(sum((a - mx) ** 2 for a in rx) * sum((b - my) ** 2 for b in ry))
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if den == 0:
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return 0.0, 1.0
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rho = num / den
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rho = max(-1.0, min(1.0, rho))
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z = rho * math.sqrt(n - 1)
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p = math.erfc(abs(z) / math.sqrt(2.0))
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return rho, p
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def norm_two_prop(z):
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return math.erfc(abs(z) / math.sqrt(2.0))
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def two_prop_p(h1, n1, h2, n2):
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if n1 == 0 or n2 == 0:
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return float("nan"), float("nan")
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p1, p2 = h1 / n1, h2 / n2
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p = (h1 + h2) / (n1 + n2)
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se = math.sqrt(p * (1 - p) * (1 / n1 + 1 / n2))
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if se == 0:
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return float("nan"), float("nan")
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z = (p1 - p2) / se
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return z, norm_two_prop(z)
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def binom_two_sided(k, n, p=0.5):
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"""Exact two-sided binomial p (used for McNemar's discordant pairs)."""
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if n == 0:
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return 1.0
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def pmf(i):
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return math.comb(n, i) * p ** i * (1 - p) ** (n - i)
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obs = pmf(k)
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tot = 0.0
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for i in range(n + 1):
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if pmf(i) <= obs + 1e-12:
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tot += pmf(i)
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return min(1.0, tot)
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def mcnemar_p(a_hit_b_miss, a_miss_b_hit):
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"""McNemar: exact binomial for small discordant counts, normal approx for
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large (the exact branch overflows math.comb for n in the hundred-thousands)."""
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b, c = a_hit_b_miss, a_miss_b_hit
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n = b + c
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if n == 0:
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return 1.0
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if n < 500:
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return binom_two_sided(b, n)
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z = (b - c) / math.sqrt(n)
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return math.erfc(abs(z) / math.sqrt(2.0))
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# ───────────────────────────── data loading ────────────────────────────────
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class Dump:
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def __init__(self, path):
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self.rows = [] # list of dicts
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self.by_gun = collections.defaultdict(list)
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# key -> {gun: (conf, hit)}
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self.by_sample = collections.defaultdict(dict)
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with open(path) as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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o = json.loads(line)
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self.rows.append(o)
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self.by_gun[o["gun"]].append(o)
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self.by_sample[(o["fixture"], o["tick"], o["bin"])][o["gun"]] = (
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o["conf"], bool(o["hit"]))
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def guns(self):
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return sorted(self.by_gun.keys())
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def split(self, gun, split):
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return [r for r in self.by_gun[gun] if r["split"] == split]
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def faithful_curve(rows, bins=10):
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rows = sorted(rows, key=lambda r: r["conf"])
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n = len(rows)
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if n == 0:
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return []
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out = []
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for b in range(bins):
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lo = b * n // bins
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hi = (b + 1) * n // bins
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chunk = rows[lo:hi]
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if not chunk:
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continue
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out.append(dict(
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lo=chunk[0]["conf"], hi=chunk[-1]["conf"], n=len(chunk),
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hit=mean([1.0 if r["hit"] else 0.0 for r in chunk]),
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))
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return out
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def faithfulness_report(dump):
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"""Question A: per-gun faithfulness on the pooled test samples."""
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out = {}
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for gun in dump.guns():
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rows = dump.split(gun, "test")
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if not rows:
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continue
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confs = [r["conf"] for r in rows]
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hits = [1.0 if r["hit"] else 0.0 for r in rows]
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nz = sum(1 for c in confs if c > 1e-12)
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rho, p = spearman(confs, hits)
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order = sorted(range(len(rows)), key=lambda i: confs[i])
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half = len(order) // 2
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lo_idx, hi_idx = order[:half], order[half:]
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h_lo = sum(hits[i] for i in lo_idx)
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h_hi = sum(hits[i] for i in hi_idx)
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z, phalf = two_prop_p(h_hi, len(hi_idx), h_lo, len(lo_idx))
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out[gun] = dict(
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n=len(rows), nonzero_conf=nz,
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base=mean(hits), rho=rho, rho_p=p,
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bottom_half_acc=(h_lo / len(lo_idx)) if lo_idx else float("nan"),
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top_half_acc=(h_hi / len(hi_idx)) if hi_idx else float("nan"),
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half_z=z, half_p=phalf,
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curve=faithful_curve(rows),
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)
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return out
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def alphas(dump):
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"""alpha_t = max-min of the gun's confidence over the TRAIN samples (Eq 7)."""
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out = {}
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for gun in dump.guns():
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rows = dump.split(gun, "train")
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if not rows:
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continue
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cs = [r["conf"] for r in rows]
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out[gun] = max(1e-12, max(cs) - min(min(cs), 0.0))
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return out
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def normalised(conf, gun, alpha):
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return conf / alpha.get(gun, 1.0)
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def complementarity(dump, alphas_, faithful):
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"""Question B: pairwise complementary slices over the test samples.
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For each pair we split the samples by which gun has the higher
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alpha-normalised confidence and, ON EACH SLICE, measure BOTH guns' accuracy.
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A pair is complementary when each gun is the more accurate one on its own
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winning slice, and each slice is a substantial (>=10%) share.
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"""
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tfs = test_fixtures(dump)
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pairs = []
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names = [g for g in faithful
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if faithful[g]["nonzero_conf"] > 0
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and not g.startswith("Composite")]
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for i in range(len(names)):
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for j in range(i + 1, len(names)):
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A, B = names[i], names[j]
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a_win = b_win = 0
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# hits ON the A-winning slice, and ON the B-winning slice
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aA = bA = aB = bB = 0
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aA_only = bA_only = aB_only = bB_only = 0
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for key, guns in dump.by_sample.items():
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if key[0] not in tfs:
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continue
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if A not in guns or B not in guns:
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continue
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ca = normalised(guns[A][0], A, alphas_)
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cb = normalised(guns[B][0], B, alphas_)
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if ca <= 0 and cb <= 0:
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continue
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ha, hb = int(guns[A][1]), int(guns[B][1])
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if ca > cb:
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a_win += 1; aA += ha; bA += hb
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if ha and not hb: aA_only += 1
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elif hb and not ha: bA_only += 1
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elif cb > ca:
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b_win += 1; aB += ha; bB += hb
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if ha and not hb: aB_only += 1
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elif hb and not ha: bB_only += 1
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both = a_win + b_win
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def frac(h, n):
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return h / n if n else float("nan")
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accA_on_A, accB_on_A = frac(aA, a_win), frac(bA, a_win)
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accA_on_B, accB_on_B = frac(aB, b_win), frac(bB, b_win)
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comp = (both > 0 and a_win >= 0.10 * both and b_win >= 0.10 * both
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and accA_on_A > accB_on_A and accB_on_B > accA_on_B)
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pairs.append(dict(
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A=A, B=B, a_win=a_win, b_win=b_win, both=both,
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A_acc_on_A_slice=accA_on_A, B_acc_on_A_slice=accB_on_A,
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A_acc_on_B_slice=accA_on_B, B_acc_on_B_slice=accB_on_B,
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p_on_A_slice=mcnemar_p(aA_only, bA_only),
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p_on_B_slice=mcnemar_p(bB_only, aB_only),
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complementary=comp))
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return pairs
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def compl_ok(a_win, b_win, both, aa, ba):
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"""Retained for backwards compatibility; superseded by the per-slice test
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in `complementarity`."""
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if both == 0 or a_win == 0 or b_win == 0:
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return False
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return a_win >= 0.10 * both and b_win >= 0.10 * both
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# ───────────────────────── composite comparison ────────────────────────────
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def paired(dump, gun_a, gun_b, tfs):
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"""Return (a_hit_b_miss, a_miss_b_hit, a_hits, b_hits) over the test fixtures."""
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ab = ba = na = nb = 0
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for key, guns in dump.by_sample.items():
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if key[0] not in tfs:
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continue
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if gun_a in guns and gun_b in guns:
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a = guns[gun_a][1]
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b = guns[gun_b][1]
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if a and not b:
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ab += 1
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elif b and not a:
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ba += 1
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if a:
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na += 1
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if b:
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nb += 1
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return ab, ba, na, nb
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def test_fixtures(dump):
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out = set()
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for r in dump.rows:
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if r["split"] == "test":
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out.add(r["fixture"])
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return out
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def composite_report(dump):
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"""Question C: composite vs best single vs shuffle control on test."""
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tfs = test_fixtures(dump)
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acc = {}
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n = {}
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for gun in dump.guns():
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rows = [r for r in dump.by_gun[gun] if r["split"] == "test"]
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if not rows:
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continue
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acc[gun] = mean([1.0 if r["hit"] else 0.0 for r in rows])
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n[gun] = len(rows)
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members = [g for g in dump.guns()
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if not g.startswith("Composite") and g not in DETERMINISTIC]
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best_member = max(members, key=lambda g: acc[g]) if members else None
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res = dict(acc=acc, n=n, best_member=best_member)
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# Oracle ceiling: if a perfect per-sample selector could pick ANY member,
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# how often would it hit? This bounds what a member-selection composite
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# could ever reach (the vote can do worse but not better than this).
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oracle = 0
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oracle_n = 0
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for key, guns in dump.by_sample.items():
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if key[0] not in tfs:
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continue
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hits = [guns[m][1] for m in members if m in guns]
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if hits:
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oracle += int(any(hits))
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oracle_n += 1
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res["member_oracle"] = (oracle / oracle_n) if oracle_n else float("nan")
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res["member_oracle_n"] = oracle_n
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comps = [g for g in dump.guns() if g.startswith("Composite") and not g.endswith("Shuf")]
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res["composites"] = {}
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for comp in comps:
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entry = {}
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if best_member:
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ab, ba, na, nb = paired(dump, comp, best_member, tfs)
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entry["vs_best"] = dict(best=best_member, comp_hit=na, best_hit=nb,
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total=n[comp], mcnemar_ab=ab, mcnemar_ba=ba,
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p=mcnemar_p(ab, ba))
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if "Pattern" in acc:
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ab, ba, na, nb = paired(dump, comp, "Pattern", tfs)
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entry["vs_pattern"] = dict(comp_hit=na, pattern_hit=nb, total=n[comp],
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mcnemar_ab=ab, mcnemar_ba=ba, p=mcnemar_p(ab, ba))
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shuf = comp + "Shuf"
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if shuf in acc:
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ab, ba, na, nb = paired(dump, comp, shuf, tfs)
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entry["vs_shuffle"] = dict(comp_hit=na, shuffle_hit=nb, total=n[comp],
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mcnemar_ab=ab, mcnemar_ba=ba, p=mcnemar_p(ab, ba))
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res["composites"][comp] = entry
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# per-fixture composite vs best single vs shuffle
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per = {}
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for fx in sorted(tfs):
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row = {}
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for gun in ([best_member] if best_member else []) + comps:
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rows = [r for r in dump.by_gun[gun] if r["split"] == "test" and r["fixture"] == fx]
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if rows:
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row[gun] = dict(n=len(rows), acc=mean([1.0 if r["hit"] else 0.0 for r in rows]))
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per[fx] = row
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res["per_fixture"] = per
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return res
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# ───────────────────────────────── main ────────────────────────────────────
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--input", default="/tmp/tmc_full.jsonl")
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ap.add_argument("--json", default=None)
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args = ap.parse_args()
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dump = Dump(args.input)
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print(f"rows={len(dump.rows)} guns={len(dump.guns())} "
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f"test fixtures={sorted(test_fixtures(dump))}")
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a = faithfulness_report(dump)
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print("\n=== A. FAITHFULNESS (test samples; rank by own confidence) ===")
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print(f"{'gun':<14}{'n':>7}{'nonzero':>8}{'base%':>7}{'rho':>8}{'rho_p':>9}"
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f"{'bot%':>7}{'top%':>7}{'z':>7}{'p':>9} verdict")
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verdicts = {}
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for gun in sorted(a, key=lambda g: -a[g]["base"]):
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r = a[gun]
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if r["nonzero_conf"] == 0:
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v = "NO SIGNAL"
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elif r["rho_p"] < 0.01 and r["rho"] > 0.05:
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v = "FAITHFUL"
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elif r["rho_p"] < 0.01 and r["rho"] < -0.05:
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v = "ANTI-FAITHFUL"
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else:
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v = "USELESS"
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verdicts[gun] = v
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print(f"{gun:<14}{r['n']:>7}{r['nonzero_conf']:>8}{100*r['base']:>7.2f}"
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f"{r['rho']:>8.3f}{r['rho_p']:>9.2g}{100*r['bottom_half_acc']:>7.2f}"
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f"{100*r['top_half_acc']:>7.2f}{r['half_z']:>7.2f}{r['half_p']:>9.2g} {v}")
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print("\ncurves (deciles, low->high confidence):")
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for gun in sorted(a, key=lambda g: -a[g]["base"]):
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if verdicts[gun] == "NO SIGNAL":
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continue
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cur = " ".join(f"{100*c['hit']:.0f}%" for c in a[gun]["curve"])
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print(f" {gun:<14} {cur}")
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al = alphas(dump)
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pairs = complementarity(dump, al, a)
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print("\n=== B. PAIRWISE COMPLEMENTARITY (test; alpha-normalised confidence) ===")
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print(f"{'A':<14}{'B':<14}{'A_wins':>8}{'A|A':>7}{'B|A':>7}{'p_A':>9}| {'B_wins':>8}{'A|B':>7}{'B|B':>7}{'p_B':>9} comp")
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for p in pairs:
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print(f"{p['A']:<14}{p['B']:<14}{p['a_win']:>8}"
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f"{100*p['A_acc_on_A_slice']:>7.1f}{100*p['B_acc_on_A_slice']:>7.1f}{p['p_on_A_slice']:>9.2g}| "
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f"{p['b_win']:>8}{100*p['A_acc_on_B_slice']:>7.1f}"
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f"{100*p['B_acc_on_B_slice']:>7.1f}{p['p_on_B_slice']:>9.2g} {p['complementary']}")
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print(" (A|A = A's accuracy on the slice A wins; B|A = B's accuracy on that same slice; etc.)")
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|
|
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c = composite_report(dump)
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print("\n=== C. COMPOSITE vs BEST SINGLE vs SHUFFLE CONTROL (test) ===")
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for gun in sorted(c["acc"], key=lambda g: -c["acc"][g]):
|
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print(f" {gun:<14} {100*c['acc'][gun]:>6.2f}% n={c['n'][gun]}")
|
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if "member_oracle" in c:
|
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print(f" member_oracle (any member hits, per sample) : {100*c['member_oracle']:.2f}%")
|
|
for comp, entry in c.get("composites", {}).items():
|
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print(f" {comp}:")
|
|
for k, r in entry.items():
|
|
print(f" {k}: {r}")
|
|
print("\nper-fixture:")
|
|
for fx, row in c["per_fixture"].items():
|
|
parts = " ".join(f"{g}={100*v['acc']:.1f}%({v['n']})" for g, v in row.items())
|
|
print(f" {fx:<32} {parts}")
|
|
|
|
if args.json:
|
|
blob = dict(
|
|
faithfulness=a, alphas=al, verdicts=verdicts,
|
|
complementarity=pairs, composite=c,
|
|
test_fixtures=sorted(test_fixtures(dump)),
|
|
)
|
|
with open(args.json, "w") as f:
|
|
json.dump(blob, f, indent=2)
|
|
print(f"\n[json] wrote {args.json}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|