File size: 2,036 Bytes
672ecc3 | 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 | import hashlib
import math
import json
from dataclasses import asdict
from public_context import feedback_text
def choose(predictions):
if len(predictions) != 5:
raise ValueError('Expected four candidates and one current program')
utility = [row['utility'] for row in predictions]
if not all((math.isfinite(v) and 0 <= v <= 1.25 for v in utility)):
raise ValueError('Invalid utility')
best = max(range(4), key=lambda i: utility[i])
return best if utility[best] > utility[4] else 4
class Predictor:
def __init__(self, checkpoint, backbone):
from semantic_world import SemanticExecutionWorld
self.model = SemanticExecutionWorld.load(checkpoint, backbone, device='cuda').eval()
self.identity = self.model.identity
def predict(self, problem, code, feedback, tests, candidates):
public = [asdict(test) for test in tests if test.test_id.startswith('public_')]
observed = asdict(feedback)
observed['results'] = [row for row in observed['results'] if row['test_id'].startswith('public_')]
contexts = [f'Whole-training-suite execution forecast. Only public observations are supplied.\nProblem:\n{problem}\nCurrent program:\n{code}\nTrusted previous public execution:\n{feedback_text(observed)}\nCandidate action:\n{candidate.code}\nOfficial public test suite:\n' + json.dumps(public, ensure_ascii=False) for candidate in candidates]
values = self.model.score_suite(contexts)
return [dict(candidate_id=c.candidate_id, utility=float(values['utility'][i]), full_suite_pass_probability=float(values['full_suite_pass_probability'][i]), predicted_pass_fraction=float(values['predicted_pass_fraction'][i])) for (i, c) in enumerate(candidates)]
def state_key(code, feedback, step):
outcomes = [{k: getattr(row, k) for k in ('test_id', 'status', 'actual', 'expected', 'stderr')} for row in feedback.results]
return hashlib.sha256(json.dumps(dict(code=code, feedback=outcomes, step=step), sort_keys=True).encode()).hexdigest()
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