def edit():
events = request.form['events']
#print(events)
events = [event.split() for event in events.split(',')]
#print(events)
processedEvents = [EditExample([[event[0]], [event[1]], event[2:]],['?']) for event in tqdm(events)]
valid_eval = ret_model.ret_and_make_ex(processedEvents, new_lsh, examples.train, 0, train_mode=False)
beam_list, edit_traces = edit_model.edit(valid_eval)
# base retriever.
import gtd.retrieval_func as rf
lsh, dict = rf.make_hash(examples.train)
output_index = rf.grab_nbs(processedEvents, lsh, dict)
ret_pred = rf.generate_predictions(examples.train, output_index)
####
# eval code
gen_out = []
for i in tqdm(range(len(edit_traces))):
gen = beam_list[i][0]
gen_out.append(gen)
dist = []
prob = []
for i in tqdm(range(len(edit_traces))):
dist.append(str(valid_eval[i].dist))
prob.append(str(edit_traces[i].decoder_trace.candidates[0].prob))
output = [gen_out, dist, prob]
return str(json.dumps(output))
def edit(events):
x = "with enraged yells <PRP> repeatedly throws Synset('entity.n.01') at <ORGANIZATION>8 Synset('natural_phenomenon.n.01') that seals the Synset('action.n.01') startling <PERSON>14 the Synset('defender.n.01') on Synset('group_action.n.01')".split(
)
processedEvents = [
EditExample([[event[0]], [event[1]], event[2:]], x) for event in events
]
print(processedEvents[0])
valid_eval = ret_model.ret_and_make_ex(processedEvents,
new_lsh,
examples.train,
0,
train_mode=False)
beam_list, edit_traces = edit_model.edit(valid_eval)
# base retriever.
import gtd.retrieval_func as rf
lsh, dict = rf.make_hash(examples.train)
output_index = rf.grab_nbs(processedEvents, lsh, dict)
ret_pred = rf.generate_predictions(examples.train, output_index)
####
# eval code
gen_out = []
for i in range(len(edit_traces)):
gen = beam_list[i][0]
gen_out.append(gen)
dist = []
prob = []
for i in range(len(edit_traces)):
dist.append(str(valid_eval[i].dist))
prob.append(str(edit_traces[i].decoder_trace.candidates[0].prob))
output = {'output': gen_out, 'distances': dist, 'beamProb': prob}
print(output)
return output
if target_mask[i] == 1.0:
all_ranks_noret[i].append(gold_rank)
position += 1
del token_list
del vocab_probs
return all_ranks_noret
all_ranks_noret = []
for chunk in tqdm(chunks(examples.test[0:eval_num], 16), total=eval_num / 16):
all_ranks_noret.extend(eval_batch_noret(chunk))
###
# base retriever.
import gtd.retrieval_func as rf
lsh, dict = rf.make_hash(examples.train)
output_index = rf.grab_nbs(examples.test[0:eval_num], lsh, dict)
ret_pred = rf.generate_predictions(examples.train, output_index)
def agree_vec(ref, targ):
rank_vec = []
for i in range(max(len(ref), len(targ))):
if i < len(targ) and i < len(ref):
agree_ind = ref[i] == targ[i]
rank_vec.append((1.0 - agree_ind) * 100.0)
else:
rank_vec.append(100.0)
return rank_vec