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f1adc24 | 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 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | #!/usr/bin/env python3
"""
Step 3: Extract training examples for a specific filter condition.
Takes a single filtered_candidates.json and the full examples_grouped.json
(from prove_and_extract.py on the 'none' filter), looks up proof results
for each candidate, and writes examples.json.
Optionally subsample N candidates (randomly) from the filtered set.
Usage:
python split_examples.py \
--grouped .../none/examples_grouped.json \
--candidates .../novel-1.0/filtered_candidates.json \
--output_dir .../novel-1.0
# Keep only 50 randomly sampled candidates:
python split_examples.py \
--grouped .../none/examples_grouped.json \
--candidates .../novel-1.0/filtered_candidates.json \
--output_dir .../novel-1.0-N50 \
-N 50 --seed 42
"""
import argparse
import json
import random
from pathlib import Path
def main():
parser = argparse.ArgumentParser(
description='Extract training examples for a filtered candidate set')
parser.add_argument('--grouped', required=True,
help='Path to examples_grouped.json (from prove_and_extract.py on none)')
parser.add_argument('--candidates', required=True,
help='Path to filtered_candidates.json')
parser.add_argument('--output_dir', required=True,
help='Output directory for examples')
parser.add_argument('-N', type=int, default=None,
help='Randomly sample N candidates (default: keep all)')
parser.add_argument('--seed', type=int, default=42,
help='Random seed for subsampling (default: 42)')
args = parser.parse_args()
# Load grouped examples (keyed by candidate statement)
print(f"Loading grouped examples from {args.grouped}")
with open(args.grouped) as f:
grouped = json.load(f)
stmt_to_entry = {}
for entry in grouped['problems']:
stmt_to_entry[entry['candidate']] = entry
print(f" {len(stmt_to_entry)} candidates with proof results")
# Load filtered candidates
print(f"\nLoading candidates from {args.candidates}")
with open(args.candidates) as f:
cand_data = json.load(f)
filter_name = cand_data['filter']
candidates = cand_data['candidates']
print(f" Filter: {filter_name}")
print(f" Candidates: {len(candidates)}")
# Subsample if requested
if args.N is not None and args.N < len(candidates):
rng = random.Random(args.seed)
candidates = rng.sample(candidates, args.N)
print(f" Subsampled to N={args.N} (seed={args.seed})")
# Look up examples
problems = []
flat_examples = []
missing = 0
for stmt in candidates:
entry = stmt_to_entry.get(stmt)
if entry is None:
missing += 1
continue
problems.append(entry)
flat_examples.extend(entry['examples'])
n_proved = sum(1 for p in problems if p['success'])
n_examples = len(flat_examples)
n_per_proved = [len(p['examples']) for p in problems if p['success']]
avg_ex = sum(n_per_proved) / len(n_per_proved) if n_per_proved else 0
print(f"\n Matched: {len(problems)}, Missing: {missing}")
print(f" Proved: {n_proved}, Examples: {n_examples}, Avg/proved: {avg_ex:.1f}")
# Save
out_dir = Path(args.output_dir)
out_dir.mkdir(parents=True, exist_ok=True)
with open(out_dir / 'examples_grouped.json', 'w') as f:
json.dump({
'filter': filter_name,
'source_grouped': str(args.grouped),
'source_candidates': str(args.candidates),
'n_candidates': len(candidates),
'n_sampled': args.N,
'sample_seed': args.seed if args.N else None,
'n_proved': n_proved,
'n_examples': n_examples,
'problems': problems,
}, f, indent=2)
with open(out_dir / 'examples.json', 'w') as f:
json.dump(flat_examples, f)
with open(out_dir / 'candidates.txt', 'w') as f:
for stmt in candidates:
f.write(stmt + '\n')
summary = {
'filter': filter_name,
'source_grouped': str(args.grouped),
'source_candidates': str(args.candidates),
'n_candidates': len(candidates),
'n_sampled': args.N,
'n_proved': n_proved,
'n_examples': n_examples,
'avg_examples_per_problem': avg_ex,
}
with open(out_dir / 'summary.json', 'w') as f:
json.dump(summary, f, indent=2)
print(f"\nSaved to {out_dir}/")
print(f" examples.json ({n_examples} examples)")
print(f" examples_grouped.json ({len(problems)} problems)")
print(f" candidates.txt ({len(candidates)} statements)")
print(f" summary.json")
if __name__ == '__main__':
main()
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