def prep(df):
mystem = Mystem()
df['text'] = df['text'].apply(lambda post: pr.preprocess_post(
post, to_upos=False, mystem=mystem))
print(df.head())
mystem.close()
return df
def preprocess_df(df):
print(df.head())
print(len(df))
mystem = Mystem() # Most important to set it here!
df['text'] = df['text'].apply(
lambda x: pr.preprocess_post(x, False, mystem))
mystem.close()
return df
def predict(user_id, wv_path, kmeans_path, regr_path):
# Collect user's info from vk.com
session = tokens.Session()
user_data, log = parser.parse_user(session,
user_id=user_id,
running_mins=15,
outburst_time=30,
outburst_likes_count=10,
post_likes_threshold=1000,
time_period=4 * 365,
skipping_amount=400,
group_posts_count=3000,
liked_posts_threshold=20)
print(log)
user_info = parse_user_info(user_data) # Merge it with clusters vector
text_posts = []
post_idx = -1
for post in user_data['user_posts']['items'] + [
like for group in user_data['groups']
for like in group['liked_posts']
]:
post_idx += 1
post_content = parse_post(post, user_data['id'])
text = post_content[0]
if text != '':
text_posts.append([user_data['id'], post_idx, text])
# Add user data to dataframes
df = pd.DataFrame.from_records(text_posts)
df.columns = text_header[:3] # [:3]
df.dropna()
df = df.drop(columns=['post_id'])
# Preprocess posts
mystem = Mystem()
df['text'] = df['text'].apply(
lambda x: pr.preprocess_post(x, False, mystem))
mystem.close()
text_n = len(df['text']) # Save text posts number
if text_n < 100:
print(
'Для пользователя собрано не достаточно много информации. Предсказание может быть неточным'
)
print('Кол-во текстовых постов: {}'.format(text_n))
# Turn posts to vectors and predict vectors' clusters
wv = KeyedVectors.load(wv_path, mmap='r')
df['text'] = df['text'].apply(
lambda x: pr.to_vector_tfidf(x, wv, one_vector=True))
df = df.dropna()
kmeans = load(kmeans_path)
li = df.groupby('user_id')['text'].apply(list).to_numpy()
df['cluster'] = kmeans.predict(li[0])
df = kt.clusters_to_vector(df, kmeans.n_clusters).set_index('user_id')
# Create info df and merge it with clusters df
df_info = pd.DataFrame.from_records([user_info])
df_info.columns = info_header
df_info.set_index('user_id', inplace=True)
df_info.drop(columns=[
'sex', 'status', 'smoking', 'alcohol', 'parth_id', 'country', 'city'
],
inplace=True)
df = df_info.merge(df, on='user_id',
how='inner') # Merging user's info and clusters
df.drop(columns=['bdate', 'posts_n'], inplace=True)
# Predicting age
regr = load(regr_path)
age = regr.predict(df)
print('\n\nПользователю {} лет'.format(datetime.today().year -
datetime.fromtimestamp(age).year))