JesseMarkowitzandClaude Opus 5
fa3769d0fe
Research toward building a content pack from a story corpus, kept on its own branch and independent of the game. Records the selection experiments against blind labels, and settles selection as gate G2 followed by a human review: review.py writes REVIEW.md and a review.json form, apply_review.py checks the filled form and writes situations.json for the next stage. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C6UDQ9o6L6Ey173U7XVou6
41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
"""G3: S1 floor, percentile against the same-size null, mandatory member floor (MMIN), optional W.
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Builds on gate2 (similarities, word test, k-means). On first import it samples the same-size null
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distribution of S1 and caches the sorted samples in null-samples.pkl.
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"""
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import bisect, pathlib, pickle, random
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import gate2 as g
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HERE = pathlib.Path(__file__).resolve().parent
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_path = HERE / 'null-samples.pkl'
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if _path.exists():
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NULLS = pickle.loads(_path.read_bytes())
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else:
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rnd, NULLS = random.Random(3033), {}
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for m in range(5, 151):
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need, samples = (1000 if m <= 60 else 200), []
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while len(samples) < need:
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members = rnd.sample(range(g.n), m)
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if g.eligible(members):
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samples.append(g.cohesion(members)[0])
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NULLS[m] = sorted(samples)
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_path.write_bytes(pickle.dumps(NULLS))
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def percentile(s1, size):
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samples = NULLS[min(max(size, 5), 150)]
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return bisect.bisect_left(samples, s1) / len(samples)
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def signals(m):
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sig = g.signals(m) # size, stories, dominant, S1, Z1, MMIN, W, W_word, S6_old
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sig['PCT'] = percentile(sig['S1'], sig['size'])
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return sig
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def passes(sig, gate):
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if not (sig['size'] >= g.MIN_SIZE and sig['stories'] >= g.MIN_STORIES and sig['dominant'] <= g.MAX_DOMINANT):
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return False
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if sig['S1'] < gate['S1'] or sig['PCT'] < gate['PCT'] or sig['MMIN'] < gate['MMIN']:
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return False
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if gate.get('W') is not None and sig['W'] >= gate['W']:
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return False
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return True
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