File size: 5,511 Bytes
797eb32 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | import csv
import json
import math
from collections import defaultdict
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
SCENARIOS = ROOT / "scenarios"
RESULTS = ROOT / "results"
IN_PATH = RESULTS / "ollama_stepwise_evidence.csv"
TRAJ_OUT = RESULTS / "trajectories_stepwise_ollama.csv"
SUMMARY_OUT = RESULTS / "stepwise_ollama_scenario_summary.csv"
LEARNING_RATE = 0.4
def sigmoid(x: float) -> float:
return 1.0 / (1.0 + math.exp(-x))
def verdict_from_p(p: float) -> str:
if p > 0.65:
return "forward"
if p < 0.35:
return "backward"
return "ambiguous"
def parse_bool(value):
s = str(value).strip().lower()
if s == "true":
return True
if s == "false":
return False
return None
def load_source_weights():
weights = {}
for path in sorted(SCENARIOS.glob("sc*.json")):
scenario = json.loads(path.read_text(encoding="utf-8"))
sid = scenario["id"]
for i, step in enumerate(scenario["steps"], start=1):
weights[(sid, i)] = float(step.get("source_weight", 1.0))
return weights
def main():
if not IN_PATH.exists():
raise FileNotFoundError(f"Missing input file: {IN_PATH}")
source_weights = load_source_weights()
by_scenario = defaultdict(list)
with IN_PATH.open("r", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
try:
row["step_int"] = int(row["step"])
except (TypeError, ValueError):
continue
by_scenario[row["scenario_id"]].append(row)
trajectory_rows = []
summary_rows = []
for sid in sorted(by_scenario.keys()):
rows = sorted(by_scenario[sid], key=lambda r: r["step_int"])
p = 0.5
prev_delta = None
flips = 0
parsed_steps = 0
skipped_steps = 0
trajectory_rows.append({
"scenario_id": sid,
"step": 0,
"p_forward": round(p, 6),
"delta": 0.0,
"direction_flips": 0,
"supports_forward": "",
"confidence": "",
"source_weight": "",
"skipped": False,
})
for row in rows:
step = row["step_int"]
parse_ok = str(row.get("parse_ok", "")).lower() == "true"
supports_forward = parse_bool(row.get("supports_forward", ""))
confidence = float(row["confidence"]) if row.get("confidence") else 0.5
source_weight = source_weights.get((sid, step), 1.0)
if not parse_ok or supports_forward is None:
skipped_steps += 1
trajectory_rows.append({
"scenario_id": sid,
"step": step,
"p_forward": round(p, 6),
"delta": 0.0,
"direction_flips": flips,
"supports_forward": "",
"confidence": confidence,
"source_weight": source_weight,
"skipped": True,
})
continue
parsed_steps += 1
direction = 1 if supports_forward else -1
old_p = p
old_logit = math.log(p / (1 - p))
new_logit = old_logit + direction * confidence * source_weight * LEARNING_RATE
p = max(0.001, min(0.999, sigmoid(new_logit)))
delta = p - old_p
if prev_delta is not None and delta * prev_delta < 0:
flips += 1
if delta != 0:
prev_delta = delta
trajectory_rows.append({
"scenario_id": sid,
"step": step,
"p_forward": round(p, 6),
"delta": round(delta, 6),
"direction_flips": flips,
"supports_forward": supports_forward,
"confidence": confidence,
"source_weight": source_weight,
"skipped": False,
})
total_steps = parsed_steps + skipped_steps
summary_rows.append({
"scenario_id": sid,
"final_p": round(p, 6),
"verdict": verdict_from_p(p),
"direction_flips": flips,
"parsed_steps": parsed_steps,
"skipped_steps": skipped_steps,
"total_steps": total_steps,
"parse_success_rate": round(parsed_steps / total_steps, 6) if total_steps else 0.0,
})
with TRAJ_OUT.open("w", encoding="utf-8", newline="") as f:
fieldnames = [
"scenario_id",
"step",
"p_forward",
"delta",
"direction_flips",
"supports_forward",
"confidence",
"source_weight",
"skipped",
]
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(trajectory_rows)
with SUMMARY_OUT.open("w", encoding="utf-8", newline="") as f:
fieldnames = [
"scenario_id",
"final_p",
"verdict",
"direction_flips",
"parsed_steps",
"skipped_steps",
"total_steps",
"parse_success_rate",
]
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(summary_rows)
print(f"Saved: {TRAJ_OUT}")
print(f"Saved: {SUMMARY_OUT}")
if __name__ == "__main__":
main()
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