def api(label_tags, test_y, y_scores, all_ids):
eval_samples = []
for sample in range(test_y.shape[0]):
if (test_y[sample, :] == np.ones(test_y.shape[1])).any():
eval_samples.append(sample)
test_y, y_scores = test_y[eval_samples, :], y_scores[eval_samples, :]
ev = Evaluation(y_scores, None, test_y)
all_rankedat10_tags = []
query_ids = []
for sample_id, sample_output in zip(eval_samples, y_scores):
q_id = all_ids[sample_id]
query_ids.append(q_id)
cols = np.argsort(sample_output)[-10:]
rankedat10_tags = []
for col in cols[::-1]:
label_name = label_tags[col]
rankedat10_tags.append(label_name)
all_rankedat10_tags.append(rankedat10_tags)
all_Pat5, all_Pat10, all_Rat5, all_Rat10 = \
ev.Precision(5, True), ev.Precision(10, True), ev.Recall(5, True), ev.Recall(10, True)
upper_bounds_pat5 = ev.upper_bound(5, True)
upper_bounds_pat10 = ev.upper_bound(10, True)
all_MAP = ev.MeanAveragePrecision(True)
assert len(all_Pat5) == len(all_rankedat10_tags)
R = (query_ids, all_rankedat10_tags, list(all_Pat5), list(all_Pat10),
list(all_Rat5), list(all_Rat10), upper_bounds_pat5,
upper_bounds_pat10, all_MAP)
raw_corpus = myio.read_corpus(args.corpus_w_tags, with_tags=True)
with open(args.results_file, 'w') as f:
for i in range(len(R[0])):
query_id, rankedat10_tags, Pat5, Pat10, Rat5, Rat10, UB5, UB10, MAP = \
R[0][i], R[1][i], R[2][i], R[3][i], R[4][i], R[5][i], R[6][i], R[7][i], R[8][i]
real_tags = raw_corpus[str(query_id)][2]
real_tags = list(set(real_tags) & set(label_tags))
real_tags = " ".join([str(x) for x in real_tags])
rankedat10_tags = " ".join([str(x) for x in rankedat10_tags])
f.write("{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format(
query_id, real_tags, rankedat10_tags, Pat5, Pat10, Rat5, Rat10,
UB5, UB10, MAP))
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(test_y, y_scores, verbose=0, tag_names=None):
"""------------------------------------------remove ill evaluation-------------------------------------------"""
# eval_labels = []
# for label in range(test_y.shape[1]):
# if (test_y[:, label] == np.ones(test_y.shape[0])).any():
# eval_labels.append(label)
eval_samples = []
for sample in range(test_y.shape[0]):
if (test_y[sample, :] == np.ones(test_y.shape[1])).any():
eval_samples.append(sample)
test_y, y_scores = test_y[eval_samples, :], y_scores[eval_samples, :]
# test_y, y_scores = test_y[:, eval_labels], y_scores[:, eval_labels]
ev = Evaluation(y_scores, None, test_y)
EVAL_LABELS = set()
for sample_id, sample_scores in zip(eval_samples, y_scores):
cols = np.argsort(sample_scores)[-10:]
for col in cols[::-1]:
label_name = tag_names[col]
EVAL_LABELS.add(label_name)
mat = ev.ConfusionMatrix(5)
eval_labels = list(EVAL_LABELS & set(TOP50LABELS))
print_matrix(
mat,
tag_names,
'Confusion:True Tag on x-axis, False Tag on y-axis',
some_labels=eval_labels,
)
if verbose:
print 'P@1: {}\tP@3: {}\tP@5: {}\tP@10: {}\tR@1: {}\tR@3: {}\tR@5: {}\tR@10: {}\tUBP@5: {}\tUBP@10: {}\tMAP: {}\n'.format(
ev.Precision(1), ev.Precision(3), ev.Precision(5),
ev.Precision(10), ev.Recall(1), ev.Recall(3), ev.Recall(5),
ev.Recall(10), ev.upper_bound(5), ev.upper_bound(10),
ev.MeanAveragePrecision())
return ev.Recall(10)
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(test_x, test_y, model):
""""""
"""------------------------------------------remove ill evaluation-------------------------------------------"""
eval_samples = []
for sample in range(test_y.shape[0]):
if (test_y[sample, :] == np.ones(test_y.shape[1])).any():
eval_samples.append(sample)
print '\n{} samples ouf of {} will be evaluated (zero-labeled-samples removed).'.format(len(eval_samples), test_y.shape[0])
print type(test_y), test_y.shape
test_x = test_x[eval_samples, :]
test_y = test_y[eval_samples, :]
# test_y = test_y[:, eval_labels]
print test_x.shape, test_x.dtype, type(test_x), test_y.shape, test_y.dtype, type(test_y)
"""------------------------------------------remove ill evaluation-------------------------------------------"""
y_scores = model.predict_proba(test_x) # probability for each class
predictions = model.predict(test_x) # 1 or 0 for each class
ev = Evaluation(y_scores, predictions, test_y)
print 'P@1: {}\tP@3: {}\tP@5: {}\tP@10: {}\tR@1: {}\tR@3: {}\tR@5: {}\tR@10: {}\tUBP@5: {}\tUBP@10: {}\tMAP: {}\n'.format(
ev.Precision(1), ev.Precision(3), ev.Precision(5), ev.Precision(10),
ev.Recall(1), ev.Recall(3), ev.Recall(5), ev.Recall(10), ev.upper_bound(5), ev.upper_bound(10),
ev.MeanAveragePrecision()
)
"""------------------------------------------remove ill evaluation-------------------------------------------"""
print 'outputs before ', y_scores.shape
eval_labels = []
for label in range(test_y.shape[1]):
if (test_y[:, label] == np.ones(test_y.shape[0])).any():
eval_labels.append(label)
print '\n{} labels out of {} will be evaluated (zero-sampled-labels removed).'.format(len(eval_labels), test_y.shape[1])
y_scores, predictions, targets = y_scores[:, eval_labels], predictions[:, eval_labels], test_y[:, eval_labels]
print 'outputs after ', y_scores.shape
ev = Evaluation(y_scores, predictions, targets)
print 'precision recall f1 macro: {}'.format(ev.precision_recall_fscore('macro'))
print 'precision recall f1 micro: {}'.format(ev.precision_recall_fscore('micro'))
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(self, data, tag_names, folder, session):
all_ids = []
eval_func = self.predict_func
outputs, targets = [], []
for ids, idts, idbs, tags in data:
all_ids += ids
output = eval_func(idts, idbs, session)
outputs.append(output)
targets.append(tags)
outputs = np.vstack(outputs)
targets = np.vstack(targets).astype(np.int32) # it was dtype object
"""------------------------------------------remove ill evaluation-------------------------------------------"""
eval_samples = []
for sample in range(targets.shape[0]):
if (targets[sample, :] == np.ones(targets.shape[1])).any():
eval_samples.append(sample)
print '\n{} samples ouf of {} will be evaluated (zero-labeled-samples removed).'.format(
len(eval_samples), outputs.shape[0])
outputs, targets = outputs[eval_samples, :], targets[eval_samples, :]
"""------------------------------------------remove ill evaluation-------------------------------------------"""
ev = Evaluation(outputs, None, targets)
all_rankedat10_tags = []
query_ids = []
# EVAL_LABELS = set()
for sample_id, sample_output in zip(eval_samples, outputs):
q_id = all_ids[sample_id]
query_ids.append(q_id)
cols = np.argsort(sample_output)[-10:]
rankedat10_tags = []
for col in cols[::-1]:
# label_id = eval_labels[col]
# label_name = tag_names[label_id]
label_name = tag_names[col]
# EVAL_LABELS.add(label_name)
rankedat10_tags.append(label_name)
all_rankedat10_tags.append(rankedat10_tags)
# eval_labels = list(EVAL_LABELS & set(TOP50LABELS))
all_Pat5, all_Pat10, all_Rat5, all_Rat10 = \
ev.Precision(5, True), ev.Precision(10, True), ev.Recall(5, True), ev.Recall(10, True)
upper_bounds_pat5 = ev.upper_bound(5, True)
upper_bounds_pat10 = ev.upper_bound(10, True)
all_MAP = ev.MeanAveragePrecision(True)
assert len(all_Pat5) == len(all_rankedat10_tags)
# mat = ev.ConfusionMatrix(5)
# print_matrix(
# mat,
# tag_names,
# 'Confusion:True Tag on x-axis, False Tag on y-axis',
# folder,
# some_labels=eval_labels,
# )
# mat = ev.CorrelationMatrix()
# print_matrix(
# mat,
# tag_names,
# 'Correlation: True Tag on both axis',
# folder,
# some_labels=eval_labels
# )
print 'average: P@5: {} P@10: {} R@5: {} R@10: {} UBP@5: {} UBP@10: {} MAP: {}'.format(
ev.Precision(5), ev.Precision(10), ev.Recall(5), ev.Recall(10),
ev.upper_bound(5), ev.upper_bound(10), ev.MeanAveragePrecision())
"""------------------------------------------remove ill evaluation-------------------------------------------"""
print 'outputs before ', outputs.shape
eval_labels = []
for label in range(targets.shape[1]):
if (targets[:, label] == np.ones(targets.shape[0])).any():
eval_labels.append(label)
print '\n{} labels out of {} will be evaluated (zero-sampled-labels removed).'.format(
len(eval_labels), targets.shape[1])
outputs, targets = outputs[:, eval_labels], targets[:, eval_labels]
print 'outputs after ', outputs.shape
predictions = np.where(outputs > 0.5, np.ones_like(outputs),
np.zeros_like(outputs))
ev = Evaluation(outputs, predictions, targets)
print 'precision recall f1 macro: {}'.format(
ev.precision_recall_fscore('macro'))
print 'precision recall f1 micro: {}'.format(
ev.precision_recall_fscore('micro'))
return query_ids, all_rankedat10_tags, list(all_Pat5), list(all_Pat10), list(all_Rat5), list(all_Rat10), \
upper_bounds_pat5, upper_bounds_pat10, all_MAP
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)
