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Bug fix for analysis module of text classification #3246

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merged 4 commits into from
Sep 14, 2022
Merged

Bug fix for analysis module of text classification #3246

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e100767
analysis_module_bug_fix
lugimzzz Sep 13, 2022
1fe4dbb
Merge branch 'develop' into bug_fix
lugimzzz Sep 14, 2022
f533c65
Merge branch 'develop' into bug_fix
lugimzzz Sep 14, 2022
316efb6
Merge branch 'develop' into bug_fix
lugimzzz Sep 14, 2022
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69 changes: 42 additions & 27 deletions applications/text_classification/multi_class/analysis/evaluate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,14 +52,14 @@ def preprocess_function(examples, tokenizer, max_seq_length, is_test=False):
return result


def read_local_dataset(path, label_list):
def read_local_dataset(path, label_map):
"""
Read dataset file
"""
with open(path, 'r', encoding='utf-8') as f:
for line in f:
sentence, label = line.strip().split('\t')
yield {'text': sentence, 'label': label_list[label]}
yield {'text': sentence, 'label': label_map[label]}


@paddle.no_grad()
Expand All @@ -85,20 +85,20 @@ def evaluate():
train_path = os.path.join(args.dataset_dir, args.train_file)
dev_path = os.path.join(args.dataset_dir, args.dev_file)

label_list = {}
label_map = {}
label_list = []
with open(label_path, 'r', encoding='utf-8') as f:
for i, line in enumerate(f):
l = line.strip()
label_list[l] = i
label_map[i] = l
label_map[l] = i
label_list.append(l)
train_ds = load_dataset(read_local_dataset,
path=train_path,
label_list=label_list,
label_map=label_map,
lazy=False)
dev_ds = load_dataset(read_local_dataset,
path=dev_path,
label_list=label_list,
label_map=label_map,
lazy=False)
trans_func = functools.partial(preprocess_function,
tokenizer=tokenizer,
Expand Down Expand Up @@ -157,33 +157,48 @@ def evaluate():
logger.info("Accuracy in dev dataset: {:.2f}%".format(report['accuracy'] *
100))
logger.info("Top-2 accuracy in dev dataset: {:.2f}%".format(
top_k_accuracy_score(labels, probs, k=2) * 100))
top_k_accuracy_score(y_true=labels,
y_score=probs,
k=2,
labels=list(range(len(label_list)))) * 100))
logger.info("Top-3 accuracy in dev dataset: {:.2f}%".format(
top_k_accuracy_score(labels, probs, k=3) * 100))
for i in label_map:
logger.info("Class name: {}".format(label_map[i]))
logger.info(
"Evaluation examples in train dataset: {}({:.1f}%) | precision: {:.2f} | recall: {:.2f} | F1 score {:.2f}"
.format(report_train[str(i)]['support'],
100 * report_train[str(i)]['support'] / len(train_ds),
report_train[str(i)]['precision'] * 100,
report_train[str(i)]['recall'] * 100,
report_train[str(i)]['f1-score'] * 100))
logger.info(
"Evaluation examples in dev dataset: {}({:.1f}%) | precision: {:.2f} | recall: {:.2f} | F1 score {:.2f}"
.format(report[str(i)]['support'],
100 * report[str(i)]['support'] / len(dev_ds),
report[str(i)]['precision'] * 100,
report[str(i)]['recall'] * 100,
report[str(i)]['f1-score'] * 100))
top_k_accuracy_score(y_true=labels,
y_score=probs,
k=3,
labels=list(range(len(label_list)))) * 100))

for i, l in enumerate(label_list):
logger.info("Class name: {}".format(l))
i = str(i)
if i in report_train:
logger.info(
"Evaluation examples in train dataset: {}({:.1f}%) | precision: {:.2f} | recall: {:.2f} | F1 score {:.2f}"
.format(report_train[i]['support'],
100 * report_train[i]['support'] / len(train_ds),
report_train[i]['precision'] * 100,
report_train[i]['recall'] * 100,
report_train[i]['f1-score'] * 100))
else:
logger.info("Evaluation examples in train dataset: 0 (0%)")

if i in report:
logger.info(
"Evaluation examples in dev dataset: {}({:.1f}%) | precision: {:.2f} | recall: {:.2f} | F1 score {:.2f}"
.format(report[i]['support'],
100 * report[i]['support'] / len(dev_ds),
report[i]['precision'] * 100, report[i]['recall'] * 100,
report[i]['f1-score'] * 100))
else:
logger.info("Evaluation examples in dev dataset: 0 (0%)")

logger.info("----------------------------")
with open(args.bad_case_path, 'w', encoding="utf-8") as f:
f.write("Confidence\tPrediction\tLabel\tText\n")
for i, (p, l) in enumerate(zip(preds, labels)):
p, l = int(p), int(l)
if p != l:
f.write("{:.2f}".format(probs[i][p]) + "\t" + label_map[p] +
"\t" + label_map[l] + "\t" + dev_ds.data[i]["text"] +
f.write("{:.2f}".format(probs[i][p]) + "\t" + label_list[p] +
"\t" + label_list[l] + "\t" + dev_ds.data[i]["text"] +
"\n")
f.close()
logger.info("Bad case in dev dataset saved in {}".format(
Expand Down