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allaun
862992ec98
- generate_predictions.py: reads 278 equations from classifier receipt, builds 8×8 strand adjacency matrices, deduplicates to 250 by equation_id - build_pist_matrices_250.py: emit each matrix as a separate Lean def (match-based findMatrix) to avoid kernel term-size blowup - rrc_pist_predictions_250_v1.json regenerated in Research Stack shared-data/ - Shakeout finding: text-derived matrices have spectral radius < 2.0, so classifyExact returns none (expected — 250 non-zero matrices, weak features)
128 lines
4.3 KiB
Python
128 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""Regenerate rrc_pist_predictions_250_v1.json from the classifier receipt.
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Reads all 278 compiled equations, builds 8×8 strand adjacency matrices,
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deduplicates by equation_id per the AGENTS.md merge rules, and writes
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the predictions artifact.
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Usage:
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python3 python/generate_predictions.py
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python3 python/generate_predictions.py \
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--receipt /path/to/rrc_equation_classifier_receipt.json \
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--output /path/to/rrc_pist_predictions_250_v1.json
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import re
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import sys
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from pathlib import Path
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from datetime import datetime, timezone
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from collections import OrderedDict
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def tokenize(text: str) -> list[str]:
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text = text.lower()
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return re.findall(r"[a-z0-9]+|[+\-*/=^(){}\[\]]", text)
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def build_matrix(tokens: list[str]) -> list[list[int]]:
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vocab = sorted(set(tokens))
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matrix = [[0 for _ in range(8)] for _ in range(8)]
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for t_i, t_j in zip(tokens, tokens[1:]):
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matrix[vocab.index(t_i) % 8][vocab.index(t_j) % 8] += 1
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return matrix
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def canonical_json_matrix(matrix: list[list[int]]) -> str:
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return json.dumps(matrix, separators=(",", ":"))
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def build_record(text: str, equation_id: str, name: str) -> dict:
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tokens = tokenize(text)
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vocab = sorted(set(tokens))
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matrix = build_matrix(tokens)
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matrix_json = canonical_json_matrix(matrix)
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return {
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"equation_id": equation_id,
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"name": name,
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"equation": text,
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"schema": "rrc_pist_predictions_250_v1",
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"claim_boundary": "matrix-only;no-classifier;no-lean-spectral",
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"proxy_pred": None,
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"exact_pred": None,
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"matrix_schema": "token_strand_adjacency_8x8_v1",
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"matrix_hash": hashlib.sha256(matrix_json.encode("utf-8")).hexdigest(),
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"matrix_8x8": matrix,
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"global_vocab_hash": hashlib.sha256(
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json.dumps(vocab, separators=(",", ":")).encode("utf-8")
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).hexdigest(),
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"source_records": [],
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}
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def main():
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parser = argparse.ArgumentParser(description="Regenerate PIST predictions")
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parser.add_argument("--receipt", type=Path,
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default=Path("/home/allaun/Research Stack/archive/experimental-shim-probes/rrc_equation_classifier_receipt.json"))
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parser.add_argument("--output", type=Path,
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default=Path("/home/allaun/Research Stack/shared-data/rrc_pist_predictions_250_v1.json"))
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args = parser.parse_args()
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receipt = json.loads(args.receipt.read_text())
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equations = receipt["compiled_equations"]
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print(f"Loaded {len(equations)} source equations", file=sys.stderr)
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# Dedup by equation_id: deterministic representative selection per AGENTS.md
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# representative = min(records, key = equation_record.equation_id)
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merged: dict[str, dict] = OrderedDict()
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for eq in equations:
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rec = eq.get("equation_record", eq)
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eid = rec["equation_id"]
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name = rec.get("name", eid)
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text = rec.get("equation", "")
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if eid not in merged:
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merged[eid] = build_record(text, eid, name)
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merged[eid]["source_records"] = []
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merged[eid]["source_records"].append({
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"equation_record_id": eid,
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"name": name,
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})
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predictions = list(merged.values())
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# Build global vocab
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all_tokens = set()
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for p in predictions:
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all_tokens.update(tokenize(p.get("equation", "")))
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global_vocab = sorted(all_tokens)
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output = {
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"schema": "rrc_pist_predictions_250_v1",
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"generated_at": datetime.now(timezone.utc).isoformat(),
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"claim_boundary": "matrix-only;no-classifier;no-lean-spectral",
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"global_vocab_hash": hashlib.sha256(
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json.dumps(global_vocab, separators=(",", ":")).encode("utf-8")
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).hexdigest(),
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"global_vocab": global_vocab,
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"matrix_schema": "token_strand_adjacency_8x8_v1",
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"summary": {
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"total_source_records": len(equations),
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"unique_equation_ids": len(predictions),
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},
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"predictions": predictions,
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}
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args.output.parent.mkdir(parents=True, exist_ok=True)
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args.output.write_text(json.dumps(output, indent=2) + "\n")
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print(f"Wrote {args.output} ({len(predictions)} unique predictions from {len(equations)} source records)", file=sys.stderr)
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if __name__ == "__main__":
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main()
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