pred_y = []
current_index = 0
for sent_y in test_y:
pred_y.append(flat_pred_y[current_index:current_index + len(sent_y)])
current_index += len(sent_y)
assert len(pred_y) == len(test_x_features)
sents = [[word['word[0]'] for word in words] for words in test_x_features]
for words, features,\
true_labels, pred_labels in izip(sents,
test_x_features, test_y, pred_y):
print_label_error(words,
features,
true_labels,
pred_labels,
target_true_label='IC',
target_pred_label='AL',
print_features=True,
model=model,
dict_vect=dict_vect,
label_encoder=label_encoder)
print "Confusion matrix:"
table = pds.DataFrame(confusion_matrix(list(chain.from_iterable(test_y)),
list(chain.from_iterable(pred_y)),
labels=labels),
index=map(lambda s: '{}_true'.format(s), labels),
columns=map(lambda s: '{}_pred'.format(s), labels))
print table
def eval_rule_based(output_path, okform_dir,
accepted_labels=set(['AL', 'IC']),
print_errors=False):
"""
Return:
numpy.ndarray: (#label, 3)
count of #match, #mode, #ref for each label
First word of sentence is ignored
"""
ret_stat = np.zeros((len(accepted_labels), 3),
dtype=np.float64)
n_finished = 0
n_errorless = 0
with Path(output_path).open('r', encoding='utf8') as prediction_file:
while True:
if n_finished % 1000 == 0:
logger.info('Finished {}/{}'.format(n_errorless, n_finished))
line1 = prediction_file.readline()
line2 = prediction_file.readline()
if not line2:
break
try:
id_ = line1.strip()
pred_json = json.loads(line2.strip())
if pred_json['resultingHeadline'] is None:
continue
pred_tokens = pred_json['resultingHeadline']
auxil_path = str(Path(okform_dir) /
Path(id_).with_suffix('.auxil'))
paf_path = str(Path(okform_dir) /
Path(id_).with_suffix('.paf'))
title_sents, _ = separate_title_from_body(auxil_path, paf_path)
true_tokens = [item['token']
for item in title_sents[0]['features']]
if is_consistent_prediction(pred_tokens, true_tokens):
stat = eval_stat(pred_tokens, true_tokens,
accepted_labels)
if print_errors:
print_label_error(true_tokens,
# we don't have features here
features=None,
instance_id=id_,
excluded_indices=set([0]),
correct_labels=map(get_label,
true_tokens),
predicted_labels=map(get_label,
pred_tokens),
target_true_label='IC',
target_pred_label='AL',
print_features=False)
ret_stat += stat
n_errorless += 1
else:
logger.debug(
'Predicted and true tokens inconsisent:\n{}\n{}\n'.format(
pred_tokens, true_tokens)
)
except:
logger.error(traceback.format_exc())
continue
finally:
n_finished += 1
return ret_stat
word_counter = Counter()
feature_counter = Counter()
for words, s in izip(load_sents(content_path),
load_test_data(test_data_path)):
correct_labels = [l for _, l in s]
features = [f for f, _ in s]
predicted_labels = tagger.tag(features)
# all the predicted/true labels
# used for confusion matrix
pred_y += predicted_labels
true_y += correct_labels
print_label_error(words, features, correct_labels, predicted_labels,
args.true_label, args.pred_label,
args.print_features)
cm = confusion_matrix(true_y, pred_y, labels=labels)
table = df(cm,
index=map(lambda s: '{}_true'.format(s), labels),
columns=map(lambda s: '{}_pred'.format(s), labels))
print table
print word_counter.most_common(10)
print feature_counter.most_common(10)
# import sys
# sys.stderr.write("Confusion matrix:\n")
# sys.stderr.write("{}".format(cm))
def eval_rule_based(output_path,
okform_dir,
accepted_labels=set(['AL', 'IC']),
print_errors=False):
"""
Return:
numpy.ndarray: (#label, 3)
count of #match, #mode, #ref for each label
First word of sentence is ignored
"""
ret_stat = np.zeros((len(accepted_labels), 3), dtype=np.float64)
n_finished = 0
n_errorless = 0
with Path(output_path).open('r', encoding='utf8') as prediction_file:
while True:
if n_finished % 1000 == 0:
logger.info('Finished {}/{}'.format(n_errorless, n_finished))
line1 = prediction_file.readline()
line2 = prediction_file.readline()
if not line2:
break
try:
id_ = line1.strip()
pred_json = json.loads(line2.strip())
if pred_json['resultingHeadline'] is None:
continue
pred_tokens = pred_json['resultingHeadline']
auxil_path = str(
Path(okform_dir) / Path(id_).with_suffix('.auxil'))
paf_path = str(
Path(okform_dir) / Path(id_).with_suffix('.paf'))
title_sents, _ = separate_title_from_body(auxil_path, paf_path)
true_tokens = [
item['token'] for item in title_sents[0]['features']
]
if is_consistent_prediction(pred_tokens, true_tokens):
stat = eval_stat(pred_tokens, true_tokens, accepted_labels)
if print_errors:
print_label_error(
true_tokens,
# we don't have features here
features=None,
instance_id=id_,
excluded_indices=set([0]),
correct_labels=map(get_label, true_tokens),
predicted_labels=map(get_label, pred_tokens),
target_true_label='IC',
target_pred_label='AL',
print_features=False)
ret_stat += stat
n_errorless += 1
else:
logger.debug(
'Predicted and true tokens inconsisent:\n{}\n{}\n'.
format(pred_tokens, true_tokens))
except:
logger.error(traceback.format_exc())
continue
finally:
n_finished += 1
return ret_stat
for words, s in izip(
load_sents(content_path),
load_test_data(test_data_path)):
correct_labels = [l for _, l in s]
features = [f for f, _ in s]
predicted_labels = tagger.tag(features)
# all the predicted/true labels
# used for confusion matrix
pred_y += predicted_labels
true_y += correct_labels
print_label_error(words, features,
correct_labels, predicted_labels,
args.true_label, args.pred_label,
args.print_features)
cm = confusion_matrix(true_y, pred_y,
labels=labels)
table = df(cm,
index=map(lambda s: '{}_true'.format(s), labels),
columns=map(lambda s: '{}_pred'.format(s), labels))
print table
print word_counter.most_common(10)
print feature_counter.most_common(10)
# import sys
# sys.stderr.write("Confusion matrix:\n")
# sys.stderr.write("{}".format(cm))
