def jump(args):
try:
search_history_id = args.get('search_history_id')
paper_id = args.get('paper_id')
search_history = SearchHistory.objects(id=search_history_id).get()
search_item = search_history.item
paper = Paper.objects(id=paper_id).get()
click_history = ClickHistory(
search_item=search_item,
search_history=search_history,
paper=paper,
user=User.objects(id=flask_login.current_user.id).get() if flask_login.current_user.is_authenticated else None
)
click_history.save()
if ClickCount.objects(search_item=search_item, paper=paper).count() > 0:
click_count = ClickCount.objects(search_item=search_item, paper=paper).get()
else:
click_count = ClickCount(
search_item=search_item, paper=paper
)
click_count.count = click_count.count + 1
click_count.save()
return redirect(paper.url)
except Exception as e:
logging.warning(e)
abort(401)
def train_model(item):
x, y = [], []
if len(item.papers) > 0 and ClickCount.objects(search_item=item).count() > 0:
try:
click_counts = ClickCount.objects(search_item=item)
h = {}
for click_count in click_counts:
h[str(click_count.paper.id)] = click_count.count
for paper in item.papers:
if str(paper.id) in h:
count = h[str(paper.id)]
else:
count = 0
x.append(vectorize_paper(paper))
y.append(count)
regressor = tree.DecisionTreeRegressor()
regressor.fit(x, y)
return regressor
except:
print(x)
print(y)
else:
return None
