def evaluate(self, args, data, cnn):
res = []
for idts, idbs, labels in data:
xt = self.embedding.forward(idts.ravel())
xt = xt.reshape((idts.shape[0], idts.shape[1], self.embedding.n_d))
xb = self.embedding.forward(idbs.ravel())
xb = xb.reshape((idbs.shape[0], idbs.shape[1], self.embedding.n_d))
titles = Variable(torch.from_numpy(xt)).float()
bodies = Variable(torch.from_numpy(xb)).float()
if args.cuda:
titles = titles.cuda()
bodies = bodies.cuda()
outputs = cnn(titles, bodies)
pos = outputs[0].view(1, outputs[0].size(0))
scores = torch.mm(pos, outputs[1:].transpose(1, 0)).squeeze()
if args.cuda:
scores = scores.data.cpu().numpy()
else:
scores = scores.data.numpy()
assert len(scores) == len(labels)
ranks = (-scores).argsort()
ranked_labels = labels[ranks]
res.append(ranked_labels)
e = Evaluation(res)
MAP = e.MAP() * 100
MRR = e.MRR() * 100
P1 = e.Precision(1) * 100
P5 = e.Precision(5) * 100
return MAP, MRR, P1, P5
def evaluate(self, data, session):
# return for each query the labels, ranked results, and scores
eval_func = self.score_func
all_ranked_labels = []
all_ranked_ids = []
all_ranked_scores = []
query_ids = []
all_MAP, all_MRR, all_Pat1, all_Pat5 = [], [], [], []
for idts, idbs, labels, pid, qids in data:
scores = eval_func(idts, idbs, session)
assert len(scores) == len(labels)
ranks = (-scores).argsort()
ranked_scores = np.array(scores)[ranks]
ranked_labels = labels[ranks]
ranked_ids = np.array(qids)[ranks]
query_ids.append(pid)
all_ranked_labels.append(ranked_labels)
all_ranked_ids.append(ranked_ids)
all_ranked_scores.append(ranked_scores)
this_ev = Evaluation([ranked_labels])
all_MAP.append(this_ev.MAP())
all_MRR.append(this_ev.MRR())
all_Pat1.append(this_ev.Precision(1))
all_Pat5.append(this_ev.Precision(5))
print 'average all ... ', sum(all_MAP) / len(all_MAP), sum(
all_MRR) / len(all_MRR), sum(all_Pat1) / len(all_Pat1), sum(
all_Pat5) / len(all_Pat5)
return all_MAP, all_MRR, all_Pat1, all_Pat5, all_ranked_labels, all_ranked_ids, query_ids, all_ranked_scores
def evaluate(self, data, sess):
res = []
all_labels = []
all_scores = []
sample = 0
for idts, idbs, id_labels in data:
sample += 1
cur_scores = self.eval_batch(idts, idbs, sess)
assert len(id_labels) == len(cur_scores) # equal to 20
all_labels.append(id_labels)
all_scores.append(cur_scores)
ranks = (-cur_scores).argsort()
ranked_labels = id_labels[ranks]
res.append(ranked_labels)
e = Evaluation(res)
MAP = e.MAP()
MRR = e.MRR()
P1 = e.Precision(1)
P5 = e.Precision(5)
if 'mlp_dim' in self.args and self.args.mlp_dim != 0:
loss1 = dev_entropy_loss(all_labels, all_scores)
else:
loss1 = devloss1(all_labels, all_scores)
loss0 = devloss0(all_labels, all_scores)
loss2 = devloss2(all_labels, all_scores)
return MAP, MRR, P1, P5, loss0, loss1, loss2
def evaluate(all_ranked_labels):
evaluator = Evaluation(all_ranked_labels)
MAP = evaluator.MAP()*100
MRR = evaluator.MRR()*100
P1 = evaluator.Precision(1)*100
P5 = evaluator.Precision(5)*100
return MAP, MRR, P1, P5
def evaluate(data, labels, model):
res = [ ]
model.eval()
res = compute_scores(data, labels, model)
evaluation = Evaluation(res)
MAP = evaluation.MAP()*100
MRR = evaluation.MRR()*100
P1 = evaluation.Precision(1)*100
P5 = evaluation.Precision(5)*100
print MAP, MRR, P1, P5
return MAP, MRR, P1, P5
def evaluate(self, data, eval_func):
res = []
for idts, idbs, labels in data:
scores = eval_func(idts, idbs)
assert len(scores) == len(labels)
ranks = (-scores).argsort()
ranked_labels = labels[ranks]
res.append(ranked_labels)
e = Evaluation(res)
MAP = e.MAP() * 100
MRR = e.MRR() * 100
P1 = e.Precision(1) * 100
P5 = e.Precision(5) * 100
return MAP, MRR, P1, P5
def evaluate(self, data, eval_func):
res = []
for idts, idbs, labels, weights in data:
qq_query_weights = weights[0]
individual_scores = []
individual_scores_weights = []
# for every (original and generated) query question
for i, qq_query_weight in enumerate(qq_query_weights):
idts_t = idts.transpose()
# score all other question titles and generated questions
idts_individual = np.array(
[idts_t[i]] + idts_t[len(qq_query_weights):].tolist(),
dtype=np.int32).transpose()
# now we will add all scores
scores_for_qq = eval_func(idts_individual)
for j, cq_weights in enumerate(weights[1:]):
if len(individual_scores) == j:
individual_scores.append([])
individual_scores_weights.append([])
individual_scores[j] += scores_for_qq[:len(cq_weights
)].tolist()
individual_scores_weights[j] += [
cq_weight * qq_query_weight for cq_weight in cq_weights
]
scores_for_qq = scores_for_qq[len(cq_weights):]
# now we determine the weights
scores = []
for individual_scores_item, individual_scores_weights_item in zip(
individual_scores, individual_scores_weights):
scores.append(
np.average(individual_scores_item,
weights=individual_scores_weights_item))
assert len(scores) == len(labels)
scores = np.array(scores)
ranks = (-scores).argsort()
ranked_labels = labels[ranks]
res.append(ranked_labels)
e = Evaluation(res)
MAP = e.MAP() * 100
MRR = e.MRR() * 100
P1 = e.Precision(1) * 100
P5 = e.Precision(5) * 100
return MAP, MRR, P1, P5
def evaluate(self, data, eval_func):
res = []
for t, b, labels in data:
idts, idbs = myio.create_one_batch(t, b, self.padding_id)
scores = eval_func(idts)
#assert len(scores) == len(labels)
ranks = (-scores).argsort()
ranked_labels = labels[ranks]
res.append(ranked_labels)
e = Evaluation(res)
MAP = e.MAP() * 100
MRR = e.MRR() * 100
P1 = e.Precision(1) * 100
P5 = e.Precision(5) * 100
return MAP, MRR, P1, P5
def on_test_epoch_end(self):
print("Calculating test accuracy...")
vacc = self.testaccuracy.compute()
e = Evaluation(self.eval_res)
MAP = e.MAP() * 100
MRR = e.MRR() * 100
P1 = e.Precision(1) * 100
P5 = e.Precision(5) * 100
# print(e)
print("Test accuracy:", vacc),
print(MAP, MRR, P1, P5)
self.log('test_acc_epoch', vacc)
self.log('t_MAP', MAP)
self.log('t_Mrr', MRR)
self.log('t_p1', P1)
self.log('t_p5', P5)
return vacc, MAP, MRR, P1, P5
def evaluate_z(self,
data,
data_raw,
ids_corpus,
zeval_func,
dump_path=None):
args = self.args
padding_id = self.padding_id
tot_p1 = 0.0
portion_title = 0.0
tot_selected = 0.0
res = []
output_data = []
for i in range(len(data)):
idts, labels = data[i]
pid, qids, _ = data_raw[i]
scores, p1, z = zeval_func(idts)
assert len(scores) == len(labels)
ranks = (-scores).argsort()
ranked_labels = labels[ranks]
res.append(ranked_labels)
tot_p1 += p1
for wids_i, z_i, question_id in zip(idts.T, z.T, [pid] + qids):
z2_i = [
zv for wid, zv in zip(wids_i, z_i) if wid != padding_id
]
title, body = ids_corpus[question_id]
#portion_title += sum(z2_i[:len(title)])
if args.merge == 1 or question_id % 2 == 0:
portion_title += sum(z2_i[:len(title)])
else:
portion_title += sum(z2_i[-len(title):])
tot_selected += sum(z2_i)
if dump_path is not None:
output_data.append(("Query: ", idts[:, 0], z[:, 0], pid))
for id in ranks[:3]:
output_data.append(("Retrieved: {} label={}".format(
scores[id],
labels[id]), idts[:, id + 1], z[:, id + 1], qids[id]))
if dump_path is not None:
embedding_layer = self.embedding_layer
padding = "<padding>"
filter_func = lambda w: w != padding
with open(dump_path, "w") as fout:
for heading, wordids, z, question_id in output_data:
words = embedding_layer.map_to_words(wordids)
fout.write(heading + "\tID: {}\n".format(question_id))
fout.write(" " + " ".join(filter(filter_func, words)) +
"\n")
fout.write("------------\n")
fout.write("Rationale:\n")
fout.write(" " + " ".join(w if zv == 1 else "__"
for w, zv in zip(words, z)
if w != padding) + "\n")
fout.write("\n\n")
e = Evaluation(res)
MAP = e.MAP() * 100
MRR = e.MRR() * 100
P1 = e.Precision(1) * 100
P5 = e.Precision(5) * 100
return MAP, MRR, P1, P5, tot_p1 / len(data), portion_title / (
tot_selected + 1e-8)
