def get_class_incident_by_text(self, inc_text):
logging.info('Зашли в функцию')
inc_lemmatization = (ml.lemmatization(inc_text))
logging.info('Сделали лемматизацию')
inc_tokenize = ml.w2v_tokenize_text(inc_lemmatization)
logging.info('Сделали токенизацию')
inc_average_list = ml.word_averaging(self.kv_inc, inc_tokenize)
logging.info('Вычислили вектор инцидента')
predicted = self.logreg.predict([inc_average_list])
return (predicted[0])
def get_closed_similar_incident_by_text(self, inc_text):
df_result = self.df_result
X_full_word_average = self.X_full_word_average #ml.word_averaging_list(self.kv_inc,df_result['full_tokenized'])
inc_lemmatization = (ml.lemmatization(inc_text))
inc_tokenize = ml.w2v_tokenize_text(inc_lemmatization)
inc_average_list = ml.word_averaging(self.kv_inc, inc_tokenize)
df_result['similar_index'] = ml.find_similar_index(
inc_average_list, X_full_word_average.T)
df_result = df_result.sort_values('similar_index')
df_order = df_result[df_result['similar_index'] < 0.3].head(1000)
return (list(
zip(df_order['Number'], df_order['FixedDescription'],
df_order['similar_index'], df_order['DateCreate'].astype(str),
df_order['Status_New'])))
def get_similar_incident(self, number):
df_result = ServerJson.prepare_vector_matrix(self,
number) #type DataFrame
X_full_word_average = ml.word_averaging_list(
self.kv_inc, df_result['full_tokenized'])
inc_text = ServerJson.get_text_incident('', int(number))
inc_lemmatization = (ml.lemmatization(inc_text))
inc_tokenize = ml.w2v_tokenize_text(inc_lemmatization)
inc_average_list = ml.word_averaging(self.kv_inc, inc_tokenize)
df_result['similar_index'] = ml.find_similar_index(
inc_average_list, X_full_word_average.T)
df_order = df_result[df_result['similar_index'] < 0.3].head(1000)
return (list(
zip(df_order['Number'], df_order['FixedDescription'],
df_order['similar_index'], df_order['DateCreate'].astype(str),
df_order['Status_New'])))
def prepare_vector_matrix(self, number):
cnxn = pyodbc.connect(
'DRIVER={SQL Server};SERVER=MyServer;DATABASE=MyDataBase;UID=MyUser;PWD=MyPassword'
)
cursor = cnxn.cursor()
sql = '''
SELECT Number, Description, ShortDescription, DateCreate, Status_New
FROM [SMP].[dbo].[OP_Request] (nolock)
where RoleAssigned in ('IT Инж 2 ур 1С', 'IT Инж 2 ур 1С эксперт')
and ShortDescription like '1C Retail%'
and Status_New in ('Устранение инцидента', 'Ожидание')'''
df_result = pd.read_sql_query(sql, cnxn)
df_result['FixedDescription'] = df_result['Description']
for index, row in df_result.iterrows():
text = row['Description']
text_new = ml.lemmatization(text)
df_result.iat[index, 1] = text_new
full_tokenized = df_result.apply(
lambda r: ml.w2v_tokenize_text(r['Description']), axis=1).values
df_result['full_tokenized'] = full_tokenized
return (df_result)