def run(args):
check_present(args, ["input", "parser", "output"])
assert os.path.exists(args.input), args.input
assert os.path.exists(args.parser), args.parser
# Read parser flow.
flow = Flow()
flow.load(args.parser)
# Initialize the spec from the flow.
spec = Spec()
spec.from_flow(flow)
# Initialize the model from the flow.
caspar = Caspar(spec)
caspar.from_flow(flow)
corpus = Corpora(args.input, caspar.spec.commons)
writer = sling.RecordWriter(args.output)
count = 0
for document in corpus:
state, _, _, trace = caspar.forward(document,
train=False,
debug=args.trace)
state.write()
if trace:
trace.write()
writer.write(str(count), state.encoded())
count += 1
if count % 100 == 0:
print "Annotated", count, "documents", now(), mem()
writer.close()
print "Annotated", count, "documents", now(), mem()
print "Wrote annotated documents to", args.output
if args.evaluate:
f = tempfile.NamedTemporaryFile(delete=False)
fname = f.name
caspar.spec.commons.save(fname, binary=True)
f.close()
eval_result = frame_evaluation(gold_corpus_path=args.input, \
test_corpus_path=args.output, commons=caspar.spec.commons)
os.unlink(fname)
return eval_result
