def success():
if request.method == 'POST':
username = request.form['username']
if not username:
return render_template("failure.html",
error='Please enter a username')
# Check if user is in db
user = User.from_name(username)
if not user:
try:
user_data = get_user_data(username)
comments_data = get_comment_data(username)
except HTTPError as err:
if err.response.status_code == 404:
return render_template(
"failure.html",
error=f'User \'{username}\' does not exist')
return render_template("failure.html",
error='External API error')
user = User(**user_data)
user.comments = [
Comment(**comment_data) for comment_data in comments_data
]
user.prediction = pred_lean(user)
db.session.add(user)
user.searches += 1
if not user.prediction:
user.prediction = pred_lean(user)
db.session.commit()
return render_template(
"success.html",
stance_name=user.prediction.stance_name(),
user=user.name,
img=user.prediction.img(),
h_fullstance=user.prediction.stance_name(axis='h_binary'),
v_fullstance=user.prediction.stance_name(axis='v_binary'),
h_confidence=f'{abs(user.prediction.h_pos):.0%}',
v_confidence=f'{abs(user.prediction.v_pos):.0%}')
elif request.method == 'GET':
return redirect(url_for('index'))
nonexist_file = 'nonexist.csv'
with open(nonexist_file) as f:
reader = csv.reader(f)
nonexist = {row[0] for row in reader}
with open(data_file) as f:
reader = csv.reader(f)
usernames = {row[2]: row[3] for row in reader if row[2] not in nonexist}
total = len(usernames)
nonexist_new = set() # Buffer-Like construction that we use to write non-existent users to csv
for i, (username, stance_name) in enumerate(usernames.items()):
if stance_name == 'libright2':
stance_name = 'libright'
if stance_name != 'None' and not User.from_name(username):
try:
user_data = get_user_data(username)
comments_data = get_comment_data(username)
except HTTPError as e:
print(f'{username:<24} | HTTPError {e.response.status_code} thrown')
if e.response.status_code in [404, 403]:
nonexist_new.add(username)
continue
user = User(**user_data)
user.comments = [Comment(**comment_data) for comment_data in comments_data]
user.stance = Stance.from_str(username, stance_name)
db.session.add(user)
db.session.commit()
if __name__ == '__main__':
from tables import Comment, User, db
from collections import defaultdict
comment_groups = Comment.query.with_entities(Comment.author, Comment.subreddit, db.func.count(Comment.subreddit))\
.group_by(Comment.author, Comment.subreddit)\
.all()
subreddit_counts = defaultdict(dict)
for author, subreddit, count in comment_groups:
subreddit_counts[author][subreddit] = count
features, labels = [], []
for author, subs in subreddit_counts.items():
user = User.from_name(author)
if user and user.stance:
features.append(subs)
labels.append((user.stance.v_pos, user.stance.h_pos))
labels = np.array(labels)
features = pd.Series(features)
splitter = StratifiedShuffleSplit(n_splits=1,
test_size=0.2,
random_state=42)
for train_index, test_index in splitter.split(features, labels):
X_train, y_train = features[train_index], labels[train_index]
X_test, y_test = features[test_index], labels[test_index]
if not args.noval:
