def add_to_db(adder, comments, verified, verified_user_id, is_rude):
for parsed_args in comments:
params = CSVDataUploader.make_site_comment_params(
parsed_args, verified, verified_user_id, is_rude)
if SiteComment.is_exist(adder, params.get('comment_id')):
continue
adder.add(SiteComment(params))
def play():
rude_comments = SiteComment.rude_comments()
normal_comments = SiteComment.normal_comments()
rude_words_wiki = WiktionaryOrg.humiliation_words()
total_rude = 0
total_normal = 0
for comment in rude_comments:
if len(comment.processed_body.split(' ')) == 0:
continue
words = [
word for word in comment.processed_body.split(u' ')
if word in rude_words_wiki
]
if len(words) > 0:
total_rude += 1
# print("[Words: %s] ([%s]) || %s\r\n" % ( str(u' '.join(words)), str(u' '.join(comment.processed_body.split(u' '))), str(comment.body) ))
for comment in normal_comments:
if len(comment.processed_body.split(' ')) == 0:
continue
words = [
word for word in comment.processed_body.split(u' ')
if word in rude_words_wiki
]
if len(words) > 0:
total_normal += 1
# print("[Words: %s] || %s\r\n" % ( str(words), str(comment.body) ))
print("Total rude %s, total normal %s, comment [%s]" %
(str(total_rude), str(total_normal), str(comment.body)))
def api_features():
if g.user is None:
abort(404)
x = int(request.args.get("x", -1))
if x < 0:
abort(404)
if CURRENT_MODEL != MODEL_LOGISITIC_REGRESSION:
abort(404)
rude_comments = SiteComment.rude_comments()
normal_comments = SiteComment.normal_comments()
def get_data(comments, feature, label):
data = list()
for comment in comments:
feature_value = SiteCommentFeatures.manual_feature_value(
comment, feature)
data.append({"x": feature_value, "label": label})
return data
positive_data = get_data(rude_comments, x, SiteCommentFeatures.RUDE_CLASS)
negative_data = get_data(normal_comments, x,
SiteCommentFeatures.NORMAL_CLASS)
return jsonify(
**{
"x_name": SiteCommentFeatures.feature_desc(x),
"positive": positive_data,
"negative": negative_data
})
def analyse_with_bayes_classifier():
rude_comments = SiteComment.rude_comments()
normal_comments = SiteComment.normal_comments()
classifier = BinaryNaiveBayesClassifier(True)
classifier.train(rude_comments, normal_comments)
classifier.print_params()
return classifier
def actions_verify(comment_id):
if g.user is None or g.user.role != "moderator":
abort(404)
if request.args.get("is_rude", None) is None:
abort(404)
is_rude = json.loads(request.args.get("is_rude").lower())
comment = SiteComment.by_comment_id(comment_id)
if comment is None:
abort(404)
adder = DBModelAdder()
adder.start()
comment.verified = datetime.datetime.now()
comment.is_rude = is_rude
comment.verified_user_id = g.user.user_id
comment.skipped = None
adder.add(comment)
adder.done()
resp = {
"status": True,
"msg": "OK",
"comment_id": comment_id,
"is_rude": is_rude,
"verified_user_id": g.user.user_id
}
return jsonify(**resp)
def analyse_comments(analysed_at=None):
classifier = None
if CURRENT_MODEL == MODEL_LOGISITIC_REGRESSION:
classifier = analyse_with_logistic_regression()
if classifier is None:
print("Classifier is not set up. Set up classifier first.")
return
print("Model is ready. Starting analysis...")
suspected = 0
adder = DBModelAdder()
adder.start()
comments_for_analysis = SiteComment.comments_for_analysis(analysed_at)
for comment in comments_for_analysis:
comment.analysed = datetime.datetime.now()
comment.looks_rude = classifier.classify_rude(comment)
adder.add(comment)
if comment.looks_rude:
suspected += 1
adder.done()
print("Analysis was done for %s comments, %s suspected to be rude." %
(str(len(comments_for_analysis)), str(suspected)))
def load_comments_from_se_to_db():
def make_site_comment_params(comment, info):
comment_id, post_id, body, creation_date, author_id, author_name = comment
question_id, answer_id, post_author_id, post_author_name, score, title, post_creation_date = info
return {
"comment_id": comment_id,
"question_id": question_id,
"answer_id": answer_id,
"post_author_id": post_author_id,
"post_score": score,
"title": title,
"body": body,
"processed_body": process_text(body),
"creation_date": creation_date,
"author_id": author_id,
"author_name": author_name,
"verified": None,
"is_rude": False,
"diff_with_post":
(creation_date - post_creation_date).total_seconds()
}
last_one = SiteComment.last_comment()
comments = get_recent_comments(last_one.creation_date)
infos = dict()
ids = [comment[1] for comment in comments]
page_size = 20
counter = 0
while counter < len(ids):
req_ids = ids[counter:counter + page_size]
info = get_post_infos(req_ids)
infos.update(info)
counter += page_size
adder = DBModelAdder()
adder.start()
for comment in comments:
if SiteComment.is_exist(adder, comment[0]):
continue
adder.add(
SiteComment(
make_site_comment_params(comment, infos.get(comment[1]))))
adder.done()
def create_model():
if CURRENT_MODEL == MODEL_LOGISITIC_REGRESSION:
feature_list = SiteCommentFeatureList(SiteComment.rude_comments(),
SiteComment.normal_comments())
feature_maker = feature_list.maker()
classifier = LogisticRegaression(feature_list, feature_maker, True)
classifier.train()
rude_total, rude_right, normal_total, normal_right = classifier.test(
True)
tpr = float(rude_right) / float(rude_total)
tnr = float(normal_right) / float(normal_total)
total_objects = float(rude_total + normal_total)
acc = (rude_right / total_objects) * tpr + (normal_right /
total_objects) * tnr
print("Accuracy: %s, rude: %s (%s), normal: %s (%s) " %
(str(acc), str(rude_right), str(rude_total), str(normal_right),
str(normal_total)))
adder = DBModelAdder()
adder.start()
feature_data = feature_maker.store()
json_fd = JSONObjectData(JSONObjectData.FEATURE_TYPE_ID,
json.dumps(feature_data))
adder.add(json_fd)
classifier_data = classifier.store()
classifier_extra = {
"acc": acc,
"rude_right": rude_right,
"rude_total": rude_total,
"normal_right": normal_right,
"normal_total": normal_total
}
json_cd = JSONObjectData(JSONObjectData.LOGREG_TYPE_ID,
json.dumps(classifier_data),
json.dumps(classifier_extra))
adder.add(json_cd)
adder.done()
print("A new logistic regression classifier was added to the DB.")
else:
print("Please specify a model to create first.")
def verifying():
if g.user is None or g.user.role != "moderator":
return redirect(url_for('index'))
page = max(int(request.args.get("page", 1)), 1)
paginator = SiteComment.paginate_unverified(page)
return render_template('index.html',
paginator=paginator,
base_url=url_for("verifying"),
so_url=SO_URL,
active_tab="verifying")
def analyse_with_cosine():
stats = DocsStats()
rude_comments = SiteComment.rude_comments()
rude_docs = list()
for comment in rude_comments:
rude_docs.append(
Document(stats, comment.id, comment.body, comment.processed_body))
unverified_comments = SiteComment.comments_for_analysis()
unverified_docs = list()
for comment in unverified_comments:
unverified_docs.append(
Document(stats, comment.id, comment.body, comment.processed_body))
stats.calculate_idfs()
stats.vectorise_documents()
cosine = CosineSimilarity(rude_docs)
rude_cluster = cosine.biggest_cluster()
for item in rude_cluster:
print("- ", item.body, "\r\n")
def comment_feed():
limit = min(int(session.get("limit", 30)), 1000)
comments = SiteComment.analysed_as_rude(limit)
last_update = datetime.datetime.now()
if len(comments) > 0:
last_update = comments[0].analysed
resp = make_response(
render_template('feed_proto.xml',
app_url=APP_URL,
app_title=FEED_APP_TITLE,
so_url=SO_URL,
last_update=last_update,
entries=comments))
resp.headers['Content-type'] = 'application/atom+xml; charset=utf-8'
return resp
def dump_verified_comments():
directory = "./dump"
if os.path.exists(directory):
shutil.rmtree(directory)
os.makedirs(directory)
def dump_to(filename, comments):
def date_or_none(field):
return field.strftime(
"%Y-%m-%d %H:%M:%S") if field is not None else None
with open(filename, 'w', encoding="utf8") as csvfile:
writer = csv.writer(csvfile, lineterminator='\n', delimiter=',')
for comment in comments:
writer.writerow([
comment.comment_id, comment.question_id, comment.answer_id,
comment.post_author_id, comment.post_score, comment.title,
comment.body,
date_or_none(comment.creation_date), comment.author_id,
comment.author_name, comment.diff_with_post,
date_or_none(comment.verified), comment.is_rude,
comment.verified_user_id,
date_or_none(comment.added),
date_or_none(comment.analysed), comment.looks_rude,
date_or_none(comment.skipped)
])
rude_comments = SiteComment.rude_comments()
dump_to(directory + "/rude_comments.csv", rude_comments)
normal_comments = SiteComment.normal_comments()
dump_to(directory + "/normal_comments.csv", normal_comments)
skipped_comments = SiteComment.skipped_comments()
dump_to(directory + "/skipped_comments.csv", skipped_comments)
def actions_skipp(comment_id):
if g.user is None or g.user.role != "moderator":
abort(404)
comment = SiteComment.by_comment_id(comment_id)
if comment is None:
abort(404)
adder = DBModelAdder()
adder.start()
comment.skipped = datetime.datetime.now()
comment.verified = None
comment.verified_user_id = -1
adder.add(comment)
adder.done()
resp = {"status": True, "msg": "OK", "comment_id": comment_id}
return jsonify(**resp)
def check_to_rebuild():
saved_data = JSONObjectData.last(JSONObjectData.LOGREG_TYPE_ID)
feature_saved_data = JSONObjectData.last(JSONObjectData.FEATURE_TYPE_ID)
if saved_data is None or feature_saved_data is None:
print("There are no saved data. Starting rebuilding...")
create_model()
print("Now, do analysis for previous comments with the new model...")
analyse_comments(datetime.datetime.now())
return
unseen_for_model = SiteComment.verified_after(saved_data.added)
print(
"There are currently %s comments which the model has not seen. The threshold is %s"
% (str(unseen_for_model), str(REBUILD_MODEL_THRESHOLD)))
if unseen_for_model >= REBUILD_MODEL_THRESHOLD:
print("We are above the threshold. Starting rebuilding...")
create_model()
print("Now, do analysis for previous comments with the new model...")
analyse_comments(datetime.datetime.now())
return
print("No reason to rebuild. We will wait a bit more.")
def add(adder, comments):
for comment in comments:
if SiteComment.is_exist(adder, comment.get("comment_id")):
continue
adder.add(SiteComment(comment))
