def get_features(self, text, model_type='00'):
try:
return self.get_lexical_features(text)
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error get_features: {0}'.format(e))
return None
def dependency_child(self, text):
result = []
try:
doc = self.analysis_pipe(text.lower())
for token in doc:
item = {
'chunk': token.text,
'text': token.text,
'pos_': token.pos_,
'dep_': token.dep_,
'tag_': token.tag_,
'head_text': token.head.text,
'head_pos': token.head.pos_,
'children': None
}
if len(list(token.children)) > 0:
item['children'] = [{
'child': child,
'pos_': child.pos_,
'dep_': child.dep_,
'tag_': child.tag_,
'head.text': child.head.text,
'head.pos_': child.head.pos_
} for child in token.children]
result.append(item)
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error dependency_child: {0}'.format(e))
return result
def transform(self, text):
try:
return self.get_features(text)
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error transform: {0}'.format(e))
return None
def __init__(self, lang):
try:
dict_lang = {'es': 'spanish', 'en': 'english'}
self.stemmer = SnowballStemmer(dict_lang[lang])
Token.set_extension('stem', default='', force=True)
except Exception as e:
Util.standard_error(sys.exc_info())
def tagger(self, text):
result = None
try:
list_tagger = []
doc = self.analysis_pipe(text.lower())
for token in doc:
item = {
'text': token.text,
'lemma': token.lemma_,
'stem': token._.stem,
'pos': token.pos_,
'tag': token.tag_,
'dep': token.dep_,
'shape': token.shape_,
'is_alpha': token.is_alpha,
'is_stop': token.is_stop,
'is_digit': token.is_digit,
'is_punct': token.is_punct
}
list_tagger.append(item)
result = list_tagger
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error tagger: {0}'.format(e))
return result
def train(self, file_output='predictive_sentiment', iteration=10, fold=10):
try:
result = {}
best_model = None
best_classifier = None
best_f1 = 0.0
date_file = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
file_report = '{0}_Fold{1}_Iteration{2}_report_{3}.csv'.format(file_output, fold, iteration, date_file)
output = DIR_OUTPUT + file_report
label = preprocessing.LabelEncoder()
x_train, x_test, y_train, y_test = Util.import_dataset()
with open(output, 'w') as out_csv:
writer = csv.DictWriter(out_csv, fieldnames=fieldnames, delimiter=';', lineterminator='\n')
headers = dict((n, n) for n in fieldnames)
writer.writerow(headers)
for model_name, value in list_model.items():
print('{0}| Start Model: {1}|{0}'.format("#" * 15, model_name))
# data train
print('Get train features')
x_train = [self.fex.get_features(text=text, model_type=value) for text in tqdm(x_train)]
x_train = preprocessing.normalize(x_train)
y_train = label.fit_transform(y_train)
# data test
print('Get test features')
x_test = [self.fex.get_features(text=text, model_type=value) for text in tqdm(x_test)]
x_test = preprocessing.normalize(x_test)
y_test = label.fit_transform(y_test)
# crear una función que reciba por parametro el modelo(algoritmo de clasificación)
# x_train, y_train, x_test, y_test
data_result = {}
[writer.writerow(model_i) for model_i in data_result]
out_csv.flush()
print('Model {0} save successful!'.format(model_name))
for row in data_result:
f1_j = float(row['f1'])
classifier = row['classifier']
if f1_j > best_f1:
best_f1 = f1_j
best_model = row['model_name']
best_classifier = row['classifier_name']
# save model
file_model = '{0}{1}_model.sav'.format(DIR_MODELS, file_output)
outfile = open(file_model, 'wb')
pickle.dump(classifier, outfile)
outfile.close()
print('Model exported in {0}'.format(file_model))
out_csv.close()
print('{0}| End Model: {1}|{0}'.format("#" * 15, model_name))
print('The best model is {0}, {1} with F1 score = {2}'.format(best_model, best_classifier, best_f1))
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error train: {0}'.format(e))
return None
def get_chunks(self, text):
try:
doc = self.analysis_pipe(text)
return [chunk.text for chunk in doc.noun_chunks]
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error get_chunks: {0}'.format(e))
return None
def __call__(self, doc):
try:
for token in doc:
if not token.is_punct and not token.is_stop and not token.is_digit:
token._.set('stem', self.stemmer.stem(token.text))
return doc
except Exception as e:
Util.standard_error(sys.exc_info())
def analysis_pipe(self, text):
result = None
try:
result = self.nlp(text)
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error analysis_pipe: {0}'.format(e))
return result
def sentence_detection(self, text):
result = []
try:
doc = self.analysis_pipe(text)
result = [sent.string.strip() for sent in doc.sents]
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error sentence_detection: {0}'.format(e))
return result
def proper_encoding(text):
result = None
try:
text = unicodedata.normalize('NFD', text)
text = text.encode('ascii', 'ignore')
result = text.decode("utf-8")
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error proper_encoding: {0}'.format(e))
return result
def __init__(self):
"""
:rtype: object
:return: Machine learning object
"""
try:
print('Load Machine Learning....')
self.fex = FeatureExtraction(text_analysis=None, lang='es')
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error constructor: {0}'.format(e))
def delete_special_patterns(self, text):
result = None
try:
text = re.sub(r'\©|\×|\⇔|\_|\»|\«|\~|\#|\$|\€|\Â|\�|\¬', ' ', text)# Elimina caracteres especilaes
text = re.sub(r'\,|\;|\:|\!|\¡|\’|\‘|\”|\“|\"|\'|\`', ' ', text)# Elimina puntuaciones
text = re.sub(r'\}|\{|\[|\]|\(|\)|\<|\>|\?|\¿|\°|\|', ' ', text) # Elimina parentesis
text = re.sub(r'\/|\-|\+|\*|\=|\^|\%|\&|\$', ' ', text) # Elimina operadores
result = text.lower()
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error delete_special_patterns: {0}'.format(e))
return result
def language_detector(self, text):
result = None
try:
doc = self.analysis_pipe(text.lower())
for sent in doc.sents:
if sent._.language['score'] > 0.8:
result = sent._.language['language']
break
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error language_detector: {0}'.format(e))
return result
def dependency(self, text):
result = []
try:
doc = self.analysis_pipe(text.lower())
doc_chunks = list(doc.noun_chunks)
for chunk in doc_chunks:
item = {'chunk': chunk, 'text': chunk.text,
'root_text': chunk.root.text, 'root_dep': chunk.root.dep_}
result.append(item)
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error dependency: {0}'.format(e))
return result
def load_sapcy(self, lang):
result = None
try:
stemmer_text = Steaming(lang) # initialise component
result = spacy.load('es_core_news_md') if lang == 'es' else spacy.load('en_core_web_md')
emoji = Emoji(result)
result.add_pipe(emoji, first=True)
result.add_pipe(stemmer_text, after='parser', name='stemmer')
print('Language: {0}\nText Analysis: {1}'.format(lang, result.pipe_names))
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error load_sapcy: {0}'.format(e))
return result
def weighted_position(tokens_text):
result = None
try:
size = len(tokens_text)
weighted_words = 0.0
weighted_normalized = 0.0
for w in tokens_text:
weighted_words += 1 / (1 + tokens_text.index(w))
weighted_normalized += (1 + tokens_text.index(w)) / size
result = (weighted_words, weighted_normalized)
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error weighted_position: {0}'.format(e))
return result
def clean_text(self, text, **kwargs):
result = None
try:
labels = ['EMAIL', 'EMOJI', 'MENTION', 'HASHTAG', 'URL']
url = kwargs.get('url') if type(
kwargs.get('url')) is bool else False
mention = kwargs.get('mention') if type(
kwargs.get('mention')) is bool else False
emoji = kwargs.get('emoji') if type(
kwargs.get('emoji')) is bool else False
hashtag = kwargs.get('hashtag') if type(
kwargs.get('hashtag')) is bool else False
relabel = kwargs.get('relabel') if type(
kwargs.get('relabel')) is bool else False
stopwords = kwargs.get('stopwords') if type(
kwargs.get('stopwords')) is bool else False
text_out = str(text).lower()
text_out = re.sub(
r'(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)',
'[EMAIL]', text_out)
text_out = re.sub("[\U0001f000-\U000e007f]", '[EMOJI]',
text_out) if emoji else text_out
text_out = re.sub(
r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',
'[URL]', text_out) if url else text_out
text_out = re.sub("@([A-Za-z0-9_]{1,40})", '[MENTION]',
text_out) if mention else text_out
text_out = re.sub("#([A-Za-z0-9_]{1,40})", '[HASHTAG]',
text_out) if hashtag else text_out
text_out = re.sub("[0-9]", '', text_out)
if not relabel:
for label in labels:
text_out = re.sub(r'\[' + label + r'\]', ' ',
text_out) if mention else text_out
text_out = self.delete_special_patterns(text_out)
text_out = self.stopwords(text_out) if stopwords else text_out
# removing any single letter on a string
text_out = re.sub(r'((?<=^)|(?<= )).((?=$)|(?= ))', ' ',
text_out).strip()
# condense multiple spaces with a single space
text_out = re.sub(r'\s+', ' ', text_out).strip()
text_out = text_out.rstrip()
result = text_out if text_out != ' ' else None
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error clean_text: {0}'.format(e))
return result
def lexical_diversity(text):
result = None
try:
text_out = re.sub(r"[\U00010000-\U0010ffff]", '', text)
text_out = re.sub(
r'(?i)\b((?:https?://|www\d{0,3}[.]|[a-z0-9.\-]+[.][a-z]{2,4}/)(?:[^\s()<>]+|\(([^\s()<>]+'
r'|(\([^\s()<>]+\)))*\))+(?:\(([^\s()<>]+|(\([^\s()<>]+\)))*\)|[^\s`!()\[\]{};:\'".,<>?«»“”‘’]))',
'', text_out)
text_out = text_out.lower()
result = round((len(set(text_out)) / len(text_out)), 4)
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error lexical_diversity: {0}'.format(e))
return result
def stopwords(self, text):
try:
nlp = Spanish() if self.lang == 'es' else English()
doc = nlp(text)
token_list = [token.text for token in doc]
sentence = []
for word in token_list:
lexeme = nlp.vocab[word]
if not lexeme.is_stop:
sentence.append(word)
return ' '.join(sentence)
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error stopwords: {0}'.format(e))
return None
def dependency_all(self, text):
result = []
try:
doc = self.analysis_pipe(text.lower())
for chunk in doc.noun_chunks:
item = {'chunk': chunk, 'text': chunk.root.text, 'pos_': chunk.root.pos_, 'dep_': chunk.root.dep_,
'tag_': chunk.root.tag_, 'lemma_': chunk.root.lemma_, 'is_stop': chunk.root.is_stop,
'is_punct': chunk.root.is_punct, 'head_text': chunk.root.head.text,
'head_pos': chunk.root.head.pos_,
'children': [{'child': child, 'pos_': child.pos_, 'dep_': child.dep_,
'tag_': child.tag_, 'lemma_': child.lemma_, 'is_stop': child.is_stop,
'is_punct': child.is_punct, 'head.text': child.head.text,
'head.pos_': child.head.pos_} for child in chunk.root.children]}
result.append(item)
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error dependency_all: {0}'.format(e))
return result
def pos_frequency(self, text):
dict_token = {'NOUN': 0, 'VERB': 0, 'ADJ': 0, 'ANOTHER': 0}
try:
doc = self.ta.tagger(text)
for token in doc:
if token['pos'] == 'NOUN':
value = dict_token['NOUN']
dict_token['NOUN'] = value + 1
elif token['pos'] == 'VERB':
value = dict_token['VERB']
dict_token['VERB'] = value + 1
elif token['pos'] == 'ADJ':
value = dict_token['ADJ']
dict_token['ADJ'] = value + 1
else:
value = dict_token['ANOTHER']
dict_token['ANOTHER'] = value + 1
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error pos_frequency: {0}'.format(e))
return dict_token
class TestMessage(unittest.TestCase):
def setUp(self):
self.util = Util()
@parameterized.expand([
[{'A1': ActivityDiagramElement(name='A1', element_type=Util().START_NODE),
'A2': ActivityDiagramElement(name='A2', element_type=Util().TRANSITION_NODE)}],
[{'B1':ActivityDiagramElement(name='B1', element_type=Util().START_NODE), \
'B2': ActivityDiagramElement(name='B2', element_type=Util().DECISION_NODE), \
'B3': ActivityDiagramElement(name='B3', element_type=Util().MERGE_NODE)}],
[{'C1': ActivityDiagramElement(name='C1', element_type=Util().START_NODE)}],
])
def test_check_start_node_existence_true(self, nodes):
response = self.util.check_start_node_existence(nodes, None)
self.assertTrue(response)
@parameterized.expand([
[{'A1': ActivityDiagramElement(name='A1', element_type=Util().END_NODE), \
'A2': ActivityDiagramElement(name='A2', element_type=Util().TRANSITION_NODE)}],
[{'B1':ActivityDiagramElement(name='B1', element_type=Util().MERGE_NODE), \
'B2': ActivityDiagramElement(name='B2', element_type=Util().DECISION_NODE), \
'B3': ActivityDiagramElement(name='B3', element_type=Util().MERGE_NODE)}],
[{'C1': ActivityDiagramElement(name='C1', element_type=Util().ACTIVITY_NODE)}],
])
def test_check_start_node_existence_false(self, nodes):
with self.assertRaises(OrderError):
response = self.util.check_start_node_existence(nodes, None)
@parameterized.expand([
[{'A1': ActivityDiagramElement(name='A1', element_type=Util().START_NODE), \
'A2': ActivityDiagramElement(name='A2', element_type=Util().DECISION_NODE)}],
[{'B1':ActivityDiagramElement(name='B1', element_type=Util().START_NODE), \
'B2': ActivityDiagramElement(name='B2', element_type=Util().DECISION_NODE), \
'B3': ActivityDiagramElement(name='B3', element_type=Util().DECISION_NODE)}],
[{'C1': ActivityDiagramElement(name='C1', element_type=Util().DECISION_NODE)}],
])
def test_check_join_possibility(self, nodes):
response = self.util.check_join_possibility(nodes)
self.assertTrue(response)
@parameterized.expand([
[{'A1': ActivityDiagramElement(name='A1', element_type=Util().START_NODE), \
'A2': ActivityDiagramElement(name='A2', element_type=Util().MERGE_NODE)}],
[{'B1':ActivityDiagramElement(name='B1', element_type=Util().START_NODE), \
'B2': ActivityDiagramElement(name='B2', element_type=Util().MERGE_NODE), \
'B3': ActivityDiagramElement(name='B3', element_type=Util().MERGE_NODE)}],
[{'C1': ActivityDiagramElement(name='C1', element_type=Util().START_NODE)}],
])
def test_check_join_possibility_false(self, nodes):
with self.assertRaises(OrderError):
response = self.util.check_join_possibility(nodes)
@parameterized.expand([
[False, False, False, ('<ActivityDiagram name="ActivityDiagram1">\n'
' <ActivityDiagramElements>\n'
' <StartNode name="StartNode1"/>\n'
' <DecisionNode name="DecisionNode1"/>\n'
' <MergeNode name="MergeNode1"/>\n'
' <EndNode name="EndNode1"/>\n'
' </ActivityDiagramElements>\n'
' <ActivityDiagramTransitions>\n'
' <Transition name="FirstTr" prob="0.5"/>\n'
' <Transition name="Tr2" prob="0.5"/>\n'
' <Transition name="LastTr" prob="0.5"/>\n'
' </ActivityDiagramTransitions>\n'
'</ActivityDiagram>\n')],
[True, False, False, ('<ActivityDiagram name="ActivityDiagram1">\n'
' <ActivityDiagramElements>\n'
' <StartNode name="StartNode1"/>\n'
' <DecisionNode name="DecisionNode1"/>\n'
' <MergeNode name="MergeNode1"/>\n'
' <Activity name="ActivityNode1"/>\n'
' <EndNode name="EndNode1"/>\n'
' </ActivityDiagramElements>\n'
' <ActivityDiagramTransitions>\n'
' <Transition name="FirstTr" prob="0.5"/>\n'
' <Transition name="Tr2" prob="0.5"/>\n'
' <Transition name="Tr3" prob="0.5"/>\n'
' <Transition name="LastTr" prob="0.5"/>\n'
' </ActivityDiagramTransitions>\n'
'</ActivityDiagram>\n'
'<SequenceDiagrams>\n'
' <Lifelines>\n'
' <Lifeline name="LifeLineX"/>\n'
' </Lifelines>\n'
'</SequenceDiagrams>\n')],
[True, True, False, ('<ActivityDiagram name="ActivityDiagram1">\n'
' <ActivityDiagramElements>\n'
' <StartNode name="StartNode1"/>\n'
' <DecisionNode name="DecisionNode1"/>\n'
' <MergeNode name="MergeNode1"/>\n'
' <Activity name="ActivityNode1"/>\n'
' <EndNode name="EndNode1"/>\n'
' </ActivityDiagramElements>\n'
' <ActivityDiagramTransitions>\n'
' <Transition name="FirstTr" prob="0.5"/>\n'
' <Transition name="Tr2" prob="0.5"/>\n'
' <Transition name="Tr3" prob="0.5"/>\n'
' <Transition name="LastTr" prob="0.5"/>\n'
' </ActivityDiagramTransitions>\n'
'</ActivityDiagram>\n'
'<SequenceDiagrams>\n'
' <Lifelines>\n'
' <Lifeline name="LifeLineX"/>\n'
' </Lifelines>\n'
' <Fragments>\n'
' <Optional name="Fragment1" representedBy="SequenceDiagram"/>\n'
' </Fragments>\n'
' <SequenceDiagram name="SequenceDiagram">\n'
' <Message name="MensagemX" prob="0.5" source="LifeLineX" target="LifeLineX"/>\n'
' <Fragment name="Fragment1"/>\n'
' </SequenceDiagram>\n'
'</SequenceDiagrams>\n')],
[True, True, True, ('<ActivityDiagram name="ActivityDiagram1">\n'
' <ActivityDiagramElements>\n'
' <StartNode name="StartNode1"/>\n'
' <DecisionNode name="DecisionNode1"/>\n'
' <MergeNode name="MergeNode1"/>\n'
' <Activity name="ActivityNode1"/>\n'
' <EndNode name="EndNode1"/>\n'
' </ActivityDiagramElements>\n'
' <ActivityDiagramTransitions>\n'
' <Transition name="FirstTr" prob="0.5"/>\n'
' <Transition name="Tr2" prob="0.5"/>\n'
' <Transition name="Tr3" prob="0.5"/>\n'
' <Transition name="LastTr" prob="0.5"/>\n'
' </ActivityDiagramTransitions>\n'
'</ActivityDiagram>\n'
'<SequenceDiagrams>\n'
' <Lifelines>\n'
' <Lifeline name="LifeLineX"/>\n'
' </Lifelines>\n'
' <Fragments>\n'
' <Optional name="Fragment1" representedBy="SequenceDiagram"/>\n'
' <Optional name="Fragment2" representedBy="SequenceDiagram"/>\n'
' </Fragments>\n'
' <SequenceDiagram name="SequenceDiagram">\n'
' <Message name="MensagemX" prob="0.5" source="LifeLineX" target="LifeLineX"/>\n'
' <Fragment name="Fragment1"/>\n'
' </SequenceDiagram>\n'
' <SequenceDiagram name="SequenceDiagram">\n'
' <Message name="MensagemX" prob="0.5" source="LifeLineX" target="LifeLineX"/>\n'
' <Fragment name="Fragment2"/>\n'
' </SequenceDiagram>\n'
'</SequenceDiagrams>\n')],
])
def test_generate_diagram(self, has_activity, has_fragment,
has_multiple_fragments, result_diagram):
activity_diagram = self.mock_activity_diagram(has_activity, has_fragment,
has_multiple_fragments)
self.util.generate_diagram(activity_diagram)
diagram_file = open(f'xmls/{activity_diagram.name}.xml', 'r')
diagram_from_file = diagram_file.read()
self.assertEqual(diagram_from_file, result_diagram)
diagram_file.close()
os.remove(f'xmls/{activity_diagram.name}.xml')
def mock_activity_diagram(self, has_activity=False, has_fragment=False, has_multiple_fragments=False):
activity_diagram = ActivityDiagram(name='ActivityDiagram1')
# Start Node
start_node = ActivityDiagramElement(name='StartNode1', element_type=self.util.START_NODE)
activity_diagram.set_elements(start_node)
activity_diagram.set_start_node(start_node)
# Decision Node
decision_node = ActivityDiagramElement(name='DecisionNode1',
element_type=self.util.DECISION_NODE)
activity_diagram.set_elements(decision_node)
activity_diagram.set_transitions(self.create_transition(name='FirstTr', source_node=start_node,
target_node=decision_node))
# Merge Node
merge_node = ActivityDiagramElement(name='MergeNode1', element_type=self.util.MERGE_NODE)
activity_diagram.set_elements(merge_node)
activity_diagram.set_transitions(self.create_transition(name='Tr2', source_node=decision_node,
target_node=merge_node))
last_node = merge_node
if has_activity:
activity_node = ActivityDiagramElement(name='ActivityNode1', element_type=self.util.ACTIVITY_NODE)
activity_diagram.set_elements(activity_node)
activity_diagram.set_transitions(self.create_transition(name='Tr3',
source_node=merge_node,
target_node=activity_node))
last_node = activity_node
sequence_diagram = self.mock_sequence_diagram(has_fragment, has_multiple_fragments)
activity_diagram.set_sequence_diagrams(sequence_diagram)
# EndNode
end_node = ActivityDiagramElement(name='EndNode1', element_type=self.util.END_NODE)
activity_diagram.set_elements(end_node)
activity_diagram.set_transitions(self.create_transition(name='LastTr', source_node=last_node,
target_node=end_node))
return activity_diagram
def mock_sequence_diagram(self, has_fragment, has_multiple_fragments):
sequence_diagram = SequenceDiagram(name='SequenceDiagram', guard_condition=True)
# LifeLines
lifelines = {0 : Lifeline(id=0, name='LifeLineX')}
sequence_diagram.set_life_lines(lifelines)
# Message
message = Message(name='MensagemX', source=lifelines[0],
target=lifelines[0], prob=0.5,
message_type='Synchronous')
sequence_diagram.set_messages(message)
if has_fragment:
fragment = Fragment(name='Fragment1',
represented_by=sequence_diagram.name,
sequence_diagram=sequence_diagram)
sequence_diagram.set_fragments(fragment)
if has_multiple_fragments:
fragment = Fragment(name='Fragment2',
represented_by=sequence_diagram.name,
sequence_diagram=self.mock_sequence_diagram(False, False))
sequence_diagram.set_fragments(fragment)
if has_fragment:
sequence_diagram.get_fragments()[0].sequence_diagram = sequence_diagram
return sequence_diagram
def create_transition(self, name, source_node, target_node):
return Transition(name=name,
prob=0.5,
source=source_node,
target=target_node,
element_type=self.util.TRANSITION_NODE)
def setUp(self):
self.util = Util()
from utils.utils import Util
util = Util()
class SequenceDiagramElement():
def __init__(self, nome=''):
self.nome = nome
def __eq__(self, sequence_diagram_element): # pragma: no cover
return self.nome == sequence_diagram_element.nome
def __str__(self): # pragma: no cover
return 'nome: {}\n'.format(self.nome)
def set_nome(self, nome):
self.nome = nome
def get_nome(self):
return self.nome
def dispose(self):
self.nome = ""
class Fragment(SequenceDiagramElement):
def __init__(self, nome='', represented_by=None):
super().__init__(nome)
self.represented_by = represented_by
def __eq__(self, fragment): # pragma: no cover
return self.nome == fragment.nome and \
def syntax_patterns(self, text):
result = None
try:
doc = self.nlp(text)
dict_noun = {}
dict_verb = {}
dict_adv = {}
dict_adj = {}
for span in doc.sents:
result_dependency = self.dependency_all(str(span))
for item in result_dependency:
if item['is_stop'] is not True and item['is_punct'] is not True and item['pos_'] not in 'PRON':
if item['pos_'] == 'NOUN':
# NOUN
chunk = str(item['chunk']).lower()
chunk_value = [chunk, item['pos_']]
dict_noun[chunk] = chunk_value
# Chinking
for child in item['children']:
if child['pos_'] == 'ADJ':
# ADJ + NOUN
chunk = str(child['child']).lower() + ' ' + str(item['chunk']).lower()
chunk_value = [[str(child['child']).lower(), child['pos_']],
[str(item['chunk']).lower(), item['pos_']]]
dict_noun[chunk] = chunk_value
dict_adj[chunk] = chunk_value
elif child['pos_'] == 'ADP':
# ADP + NOUN
chunk = str(child['child']).lower() + ' ' + str(item['chunk']).lower()
chunk_value = [[str(child['child']).lower(), child['pos_']],
[str(item['chunk']).lower(), item['pos_']]]
dict_noun[chunk] = chunk_value
elif item['pos_'] in ['PRON', 'PROPN']:
for child in item['children']:
if child['pos_'] == 'NOUN':
# PRON | PROPN + NOUN
chunk = str(item['chunk']).lower() + ' ' + str(child['child']).lower()
chunk_value = [[str(item['chunk']).lower(), item['pos_']],
[str(child['child']).lower(), child['pos_']]]
dict_noun[chunk] = chunk_value
elif item['pos_'] == 'ADJ':
# ADJ
chunk = str(item['chunk']).lower()
chunk_value = [chunk, item['pos_']]
dict_adj[chunk] = chunk_value
for child in item['children']:
if child['pos_'] == 'NOUN':
# ADJ + NOUN
chunk = str(item['chunk']).lower() + ' ' + str(child['child']).lower()
chunk_value = [[str(item['chunk']).lower(), item['pos_']],
[str(child['child']).lower(), child['pos_']]]
dict_adj[chunk] = chunk_value
if item['dep_'] is not ['ROOT']:
if item['head_pos'] == 'NOUN':
for child in item['children']:
if child['pos_'] == 'ADP':
# NOUN + ADP + NOUN
chunk = str(item['head_text']).lower() + ' ' + \
str(child['child']).lower() + ' ' + \
str(item['chunk']).lower()
chunk_value = [[str(item['head_text']).lower(), item['head_pos']],
[str(child['child']).lower(), child['pos_']],
[str(item['chunk']).lower(), item['pos_']]]
dict_noun[chunk] = chunk_value
elif item['head_pos'] == 'ADJ':
for child in item['children']:
if child['pos_'] == 'ADJ':
# ADJ + ADJ + NOUN
chunk = str(item['head_text']).lower() + ' ' + \
str(child['child']).lower() + ' ' + \
str(item['chunk']).lower()
chunk_value = [[str(item['head_text']).lower(), item['head_pos']],
[str(child['child']).lower(), child['pos_']],
[str(item['chunk']).lower(), item['pos_']]]
dict_noun[chunk] = chunk_value
dict_adj[chunk] = chunk_value
elif item['head_pos'] == 'VERB':
for child in item['children']:
if child['pos_'] == 'ADJ':
# VERB + NOUN + ADJ
chunk = str(item['head_text']).lower() + ' ' + \
str(item['chunk']).lower() + ' ' + \
str(child['child']).lower()
chunk_value = [[str(item['head_text']).lower(), item['head_pos']],
[str(item['chunk']).lower(), item['pos_']],
[str(child['child']).lower(), child['pos_']]]
dict_verb[chunk] = chunk_value
elif child['pos_'] == 'ADP':
# VERB + ADP + NOUN
chunk = str(item['head_text']).lower() + ' ' + \
str(child['child']).lower() + ' ' + \
str(item['chunk']).lower()
chunk_value = [[str(item['head_text']).lower(), item['head_pos']],
[str(child['child']).lower(), child['pos_']],
[str(item['chunk']).lower(), item['pos_']]]
dict_verb[chunk] = chunk_value
elif str(item['head_pos']) == 'ADV':
for child in item['children']:
if child['pos_'] == 'ADV':
# ADV + ADV + NOUN
chunk = str(item['head_text']).lower() + ' ' + \
str(child['child']).lower() + ' ' + \
str(item['chunk']).lower()
chunk_value = [[str(item['head_text']).lower(), item['head_pos']],
[str(child['child']).lower(), child['pos_']],
[str(item['chunk']).lower(), item['pos_']]]
dict_adv[chunk] = chunk_value
elif child['pos_'] == 'ADJ':
# ADV + NOUN + ADV
chunk = str(item['head_text']).lower() + ' ' + \
str(item['chunk']).lower() + ' ' + \
str(child['child']).lower()
chunk_value = [[str(item['head_text']).lower(), item['head_pos']],
[str(item['chunk']).lower(), item['pos_']],
[str(child['child']).lower(), child['pos_']]]
dict_adv[chunk] = chunk_value
dict_chunk = {'NOUN': dict_noun, 'VERB': dict_verb, 'ADV': dict_adv, 'ADJ': dict_adj}
result = dict_chunk
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error syntax_patterns: {0}'.format(e))
return result
def get_lexical_features(self, text):
try:
setting = {'url': True, 'mention': True, 'emoji': True,
'hashtag': True, 'stopwords': False, 'relabel': True}
text_tokenizer = TweetTokenizer()
tags = ('mention', 'url', 'hashtag', 'emoji', 'rt', 'numero', 'nombre', 'apellido')
vector = dict()
vector['lexical_diversity'] = self.lexical_diversity(text)
text = self.ta.clean_text(text, **setting)
tokens_text = text_tokenizer.tokenize(text)
vector['weighted_position'], vector['weighted_normalized'] = self.weighted_position(tokens_text)
vector['label_mention'] = float(sum(1 for word in tokens_text if word == 'mention'))
vector['label_url'] = float(sum(1 for word in tokens_text if word == 'url'))
vector['label_hashtag'] = float(sum(1 for word in tokens_text if word == 'hashtag'))
vector['label_emoji'] = float(sum(1 for word in tokens_text if word == 'emoji'))
vector['label_retweets'] = float(sum(1 for word in tokens_text if word == 'rt'))
label_word = vector['label_mention'] + vector['label_url'] + vector['label_hashtag']
label_word = label_word + vector['label_emoji'] + vector['label_retweets']
vector['label_word'] = float(len(tokens_text) - label_word)
vector['first_person_singular'] = float(
sum(1 for word in tokens_text if word in lexical['first_person_singular']))
vector['second_person_singular'] = float(
sum(1 for word in tokens_text if word in lexical['second_person_singular']))
vector['third_person_singular'] = float(
sum(1 for word in tokens_text if word in lexical['third_person_singular']))
vector['first_person_plurar'] = float(
sum(1 for word in tokens_text if word in lexical['first_person_plurar']))
vector['second_person_plurar'] = float(
sum(1 for word in tokens_text if word in lexical['second_person_plurar']))
vector['third_person_plurar'] = float(
sum(1 for word in tokens_text if word in lexical['third_person_plurar']))
vector['avg_word'] = np.nanmean([len(word) for word in tokens_text if word not in tags])
vector['avg_word'] = vector['avg_word'] if not np.isnan(vector['avg_word']) else 0.0
vector['avg_word'] = round(vector['avg_word'], 4)
vector['kur_word'] = kurtosis([len(word) for word in tokens_text if word not in tags])
vector['kur_word'] = vector['kur_word'] if not np.isnan(vector['kur_word']) else 0.0
vector['kur_word'] = round(vector['kur_word'], 4)
vector['skew_word'] = skew(np.array([len(word) for word in tokens_text if word not in tags]))
vector['skew_word'] = vector['skew_word'] if not np.isnan(vector['skew_word']) else 0.0
vector['skew_word'] = round(vector['skew_word'], 4)
# adverbios
vector['adverb_neg'] = sum(1 for word in tokens_text if word in lexical['adverb_neg'])
vector['adverb_neg'] = float(vector['adverb_neg'])
vector['adverb_time'] = sum(1 for word in tokens_text if word in lexical['adverb_time'])
vector['adverb_time'] = float(vector['adverb_time'])
vector['adverb_place'] = sum(1 for word in tokens_text if word in lexical['adverb_place'])
vector['adverb_place'] = float(vector['adverb_place'])
vector['adverb_mode'] = sum(1 for word in tokens_text if word in lexical['adverb_mode'])
vector['adverb_mode'] = float(vector['adverb_mode'])
vector['adverb_cant'] = sum(1 for word in tokens_text if word in lexical['adverb_cant'])
vector['adverb_cant'] = float(vector['adverb_cant'])
vector['adverb_all'] = float(vector['adverb_neg'] + vector['adverb_time'] + vector['adverb_place'])
vector['adverb_all'] = float(vector['adverb_all'] + vector['adverb_mode'] + vector['adverb_cant'])
vector['adjetives_neg'] = sum(1 for word in tokens_text if word in lexical['adjetives_neg'])
vector['adjetives_neg'] = float(vector['adjetives_neg'])
vector['adjetives_pos'] = sum(1 for word in tokens_text if word in lexical['adjetives_pos'])
vector['adjetives_pos'] = float(vector['adjetives_pos'])
vector['who_general'] = sum(1 for word in tokens_text if word in lexical['who_general'])
vector['who_general'] = float(vector['who_general'])
vector['who_male'] = sum(1 for word in tokens_text if word in lexical['who_male'])
vector['who_male'] = float(vector['who_male'])
vector['who_female'] = sum(1 for word in tokens_text if word in lexical['who_female'])
vector['who_female'] = float(vector['who_female'])
vector['noun'] = self.pos_frequency(text)['NOUN'] * 0.8
vector['verb'] = self.pos_frequency(text)['VERB'] * 0.5
vector['adj'] = self.pos_frequency(text)['ADJ'] * 0.4
vector['pos_others'] = self.pos_frequency(text)['ANOTHER'] * 0.1
return np.array(list(vector.values()))
except Exception as e:
Util.standard_error(sys.exc_info())
print('Error get_lexical_features: {0}'.format(e))
return None
