def get_dict(features, labels, percentage_words=0.1, iterations=50):
real_features = features.copy()
words_label = USES_MULTI.words_label(features, labels)
number_words = len(words_label)
number_selected_words = int(round(number_words * percentage_words))
distinct_labels = list(set(labels))
number_selected_words_label = int(number_selected_words /
len(distinct_labels))
features_score = USES_MULTI.feature_score(words_label, labels)
candidates = {}
for label in features_score:
positive_candidates = features_score[
label][:number_selected_words_label]
candidates.update(positive_candidates)
negative_candidates = features_score[label][
-number_selected_words_label:]
candidates.update(negative_candidates)
results = {}
actual_iteration = 0
while (actual_iteration < iterations):
actual_candidates = USES_MULTI.random_items(
candidates, number_selected_words)
filtered_features = NLP.filter_features(features,
actual_candidates)
try:
#pprint(actual_iteration)
FMeasure = USES_MULTI.classifier(filtered_features, labels)
#pprint(FMeasure)
except Exception as e:
#pprint(e)
FMeasure = 0
results.update({FMeasure: actual_candidates})
actual_iteration = actual_iteration + 1
results = sorted(results.items(), key=lambda x: x[0], reverse=True)
best_FM = results[0][0]
dict_words = results[0][1]
#print('Best: ', best_FM)
return dict_words
def get_dict(features, labels, max_words=5):
real_features = features.copy()
real_labels = labels.copy()
words_label = USES_MULTI.words_label(features, labels)
number_words = len(words_label)
if (number_words < max_words):
max_words = number_words
features_score = ALTER_USES.feature_score(words_label, labels)
results = {}
number_words = 1
while (number_words <= max_words):
dict_words = ALTER_USES.build_dict(features, labels,
features_score, number_words)
filtered_features = NLP.filter_features(features, dict_words)
try:
#pprint(number_words)
FMeasure = USES_MULTI.classifier(filtered_features, labels)
#pprint(FMeasure)
except Exception as e:
#pprint(e)
FMeasure = 0
results.update({FMeasure: dict_words})
number_words = number_words + 1
results = sorted(results.items(), key=lambda x: x[0], reverse=True)
best_FM = results[0][0]
dict_words = results[0][1]
#print('Best FM: ', best_FM)
return dict_words
defect = ' '.join(lines)
defect = defect.lower().strip().replace("\n", "")
features = NLP.tokenizer([defect])
features = NLP.remove_numbers(features)
features = NLP.remove_small_words(features)
features = NLP.remove_stop_words(features, 'portuguese')
features = NLP.lemmatizer(features, 'portuguese')
features = NLP.remove_punctuation(features)
dict_words = load('files/dict.joblib')
dict_words = dict_words[0]
features = NLP.filter_features(features, dict_words)
features = NLP.text_to_numeric(features, [dict_words])
model = load('files/model.joblib')
labels = model.predict(features)
if (labels[0] == '1'):
print('High Severity')
else:
print('Low Severity')
fim = time.time()
#print ("Tempo decorrido: ", fim-ini)
