def file_chunks(filepath: Path2, chunk_size: int):
with filepath.open(mode='r') as f:
while True:
chunk = list(itertools.islice(f, chunk_size))
if len(chunk) == 0:
return
yield chunk
def resume(resume_file: Path2, weights: Path2,
q_estimator: QEstimator) -> \
Tuple[List[LabeledTransition],
List[Job],
List[Tuple[str, ReinforceGraph]]]:
eprint("Looks like there was a session in progress for these weights! "
"Resuming")
q_estimator_name, *saved = \
torch.load(str(weights))
q_estimator.load_saved_state(*saved)
replay_memory = []
with resume_file.open('r') as f:
num_samples = sum(1 for _ in f)
if num_samples > args.buffer_max_size:
samples_to_use = random.sample(range(num_samples),
args.buffer_max_size)
else:
samples_to_use = None
with resume_file.open('r') as f:
for (idx, line) in enumerate(f, start=1):
if num_samples > args.buffer_max_size and \
idx not in samples_to_use:
continue
try:
replay_memory.append(LabeledTransition.from_dict(
json.loads(line)))
except json.decoder.JSONDecodeError:
eprint(f"Problem loading line {idx}: {line}")
raise
already_done = []
graphs_done = []
with weights.with_suffix('.done').open('r') as f:
for line in f:
next_done = json.loads(line)
already_done.append((Path2(next_done[0]), next_done[1],
next_done[2]))
graphpath = (args.graphs_dir / next_done[1])\
.with_suffix(".png")
graph = ReinforceGraph.load(graphpath + ".json")
graphs_done.append((graphpath, graph))
return replay_memory, already_done, graphs_done
def file_chunks(filepath : Path2, chunk_size : int):
with filepath.open(mode='r') as f:
while True:
chunk = list(itertools.islice(f, chunk_size))
if len(chunk) == chunk_size:
while chunk[-1] != "-----\n":
nextline = f.readline()
if not nextline:
break
chunk += [nextline]
assert len(chunk) < chunk_size * 2
elif len(chunk) == 0:
return
yield chunk
def save_weights(self, filename: Path2, args: argparse.Namespace) -> None:
with cast(BinaryIO, filename.open('wb')) as f:
torch.save(
("features evaluator", args, sys.argv,
(self.tactic_map, self.token_map), self.model.state_dict()),
f)
def save_weights(self, filename: Path2, args: argparse.Namespace) -> None:
with cast(BinaryIO, filename.open('wb')) as f:
torch.save(("polyarg evaluator", args, sys.argv, True,
self.model.state_dict()), f)
def write_summary_html(filename : Path2,
options : Sequence[Tuple[str, str]],
unparsed_args : List[str],
cur_commit : str, cur_date : datetime.datetime,
weights_hash: str,
individual_stats : List[ReportStats],
combined_stats : ReportStats) -> None:
def report_header(tag : Any, doc : Doc, text : Text) -> None:
html_header(tag, doc, text,index_css, index_js,
"Proverbot Report")
doc, tag, text, line = Doc().ttl()
with tag('html'):
report_header(tag, doc, text)
with tag('body'):
with tag('h4'):
text("{} files processed".format(len(individual_stats)))
with tag('h5'):
text("Commit: {}".format(cur_commit))
with tag('h5'):
text("Run on {}".format(cur_date.strftime("%Y-%m-%d %H:%M:%S.%f")))
with tag('img',
('src', 'logo.png'),
('id', 'logo')):
pass
with tag('h2'):
text("Proofs Completed: {}% ({}/{})"
.format(stringified_percent(combined_stats.num_proofs_completed,
combined_stats.num_proofs),
combined_stats.num_proofs_completed,
combined_stats.num_proofs))
with tag('ul'):
for k, v in options:
if k == 'filenames':
continue
elif not v:
continue
with tag('li'):
text("{}: {}".format(k, v))
with tag('table'):
with tag('tr', klass="header"):
line('th', 'Filename')
line('th', 'Number of Proofs in File')
line('th', '% Proofs Completed')
line('th', '% Proofs Incomplete')
line('th', '% Proofs Failed')
line('th', 'Details')
sorted_rows = sorted(individual_stats,
key=lambda fresult:fresult.num_proofs,
reverse=True)
for fresult in sorted_rows:
if fresult.num_proofs == 0:
continue
with tag('tr'):
line('td', fresult.filename)
line('td', str(fresult.num_proofs))
line('td', stringified_percent(fresult.num_proofs_completed,
fresult.num_proofs))
line('td', stringified_percent(fresult.num_proofs -
(fresult.num_proofs_completed +
fresult.num_proofs_failed),
fresult.num_proofs))
line('td', stringified_percent(fresult.num_proofs_failed,
fresult.num_proofs))
with tag('td'):
with tag('a',
href=escape_filename(fresult.filename) + ".html"):
text("Details")
with tag('tr'):
line('td', "Total")
line('td', str(combined_stats.num_proofs))
line('td', stringified_percent(combined_stats.num_proofs_completed,
combined_stats.num_proofs))
line('td', stringified_percent(combined_stats.num_proofs -
(combined_stats.num_proofs_completed +
combined_stats.num_proofs_failed),
combined_stats.num_proofs))
line('td', stringified_percent(combined_stats.num_proofs_failed,
combined_stats.num_proofs))
text(f'Trained as: {unparsed_args}')
doc.stag('br')
text(f"Reported as: {sys.argv}")
doc.stag('br')
text(f"Weights hash: {weights_hash}")
with filename.open("w") as fout:
fout.write(doc.getvalue())