class Main(QMainWindow):
def __init__(self, parent=None):
super().__init__()
uic.loadUi('main.ui', self)
self.initUI()
def initUI(self):
self.btn_recommend.setStyleSheet(
'QPushButton {background-color: #A3C1DA}')
self.btn_recommend.clicked.connect(self.recommend)
self.btn_complain.setStyleSheet(
'QPushButton {background-color: #A3C1DA}')
self.btn_complain.clicked.connect(self.complain)
self.btn_profil.setStyleSheet(
'QPushButton {background-color: #A3C1DA}')
self.btn_profil.clicked.connect(self.profil)
self.btn_backToAutentification.setStyleSheet(
'QPushButton {background-color: #A3C1DA}')
self.btn_backToAutentification.clicked.connect(
self.backToAutentification)
def recommend(self): # Переход в рубрику "Рекомендую"
self.m1 = Recommend(self)
self.m1.show()
def complain(self): # Переход в рубрику "Пожаловаться"
self.m2 = Complain(self)
self.m2.show()
def profil(self): # Переход во вкладку "Профиль"
self.m3 = Profil(self)
self.m3.show()
def backToAutentification(self): # Вернуться назад
self.close()
def api_recommend(rid):
limit = 8
candidates = {}
for r in RoomUsers.select():
candidates[str(r.room.room_key)] = {}
candidates[str(r.room.room_key)]['ucount'] = r.ucount
c = 0
for rc in RoomCommonUsers.select().where(RoomCommonUsers.room1 == int(rid)):
candidates[str(rc.room2.room_key)]['common'] = rc.ucount
c += 1
if c > 0:
rmd = Recommend(str(rid), candidates)
result = rmd.get_result()
else:
r = mongo_db['room'].find_one({'rid': str(rid)})
if r is not None:
js = json.loads(api_cate_room(r['cid'], page=1).data)
js['data'] = js['data'][:limit]
return jsonify(js)
else:
return jsonify({'code': 1, 'msg': 'invalid request'})
meta = []
for doc in mongo_db['room'].find({'isOnline': True, 'rid': {'$in': result}}).limit(limit):
doc.pop('_id')
doc.pop('isOnline')
doc['roomUrl'] = url_for('live', rid=doc['rid'])
doc['cateUrl'] = '/cate/%s' % doc['cid']
meta.append(doc)
return jsonify({'code': 0, 'msg': 'success', 'data': meta})
def main():
global conn, config, recommend
path = os.path.dirname(__file__)
log_filename = os.path.join(path, 'web_log.log')
logging.basicConfig(level=logging.INFO, filename=log_filename, filemode='a', format = "%(asctime)s %(levelname)s %(name)s %(message)s")
logging.info("############configs##############")
conf_path = os.path.join(path, 'config')
logging.info('loading conf ' + conf_path)
conf = configparser.ConfigParser()
conf.read(conf_path)
for section in conf.sections():
for item in conf.items(section):
logging.info(section + "." + item[0] + "=" + item[1])
logging.info("############configs##############")
try:
db = os.path.join(path, 'web_log.db')
conn = sqlite3.connect(db)
createTable()
recommend = Recommend('web_log.db')
recommend.load_exclude()
logging.info("SVM training....")
recommend.train()
run(host=conf.get('server', 'host'), port=conf.get('server', 'port') , debug=conf.get('server', 'debug'))
finally:
if conn: conn.close()
def main():
# コマンドライン引数からターゲットユーザを読み込む
argvs = sys.argv
argc = len(argvs)
if argc == 2:
target_user = argvs[1]
# データセットとinfoファイル名を指定
dataset = os.path.join(DATASET_DIR, "u.data")
info_file = os.path.join(DATASET_DIR, "u.info")
# レコメンドシステム用にデータを生成
rating_data = os.path.join(DATA_DIR, "movie_table.csv")
if not(os.path.isfile(rating_data)):
# データセットの前処理を行う
ppd = PreprocessingDataset(dataset, info_file)
# データセットから「縦：ユーザ、横：映画」の構成で
# 各ユーザが評価した、映画の評価値が表になるように
# data/movie_table.csvを生成
ppd.preprocessing()
# レコメンドシステム用にデータを生成
u_item = os.path.join(DATA_DIR, "u_item.csv")
if not(os.path.isfile(u_item)):
# 映画名が列挙されているデータを読み込む
u_item = os.path.join(DATASET_DIR, "u.item")
ppui = PreprocessingUItem(u_item)
# データセットをクリーニングして
# data/u_item.csvを生成
ppui.preprocessing()
# 生成したデータを使ってレコメンドを行う
rating_data = os.path.join(DATA_DIR, "movie_table.csv")
print("Now target user: %s" % target_user)
rc = Recommend(rating_data, info_file, u_item, target_user)
# ターゲットに似ているユーザを表示する
print("\n========== Similar Users ==========")
rc.get_similar_user()
# ターゲットにレコメンドを行う
print("\n============ Recommend ============")
rc.recommend()
else:
print("Please input target user.\n")
print("For example: \n $ python main.py user1")
def results():
g.data = request.json
g.data = g.data['recommendation_data'][0]
g.books_selected = g.data['books_selected']
g.features_list = g.data['features_list']
g.up_voted = g.data['up_voted']
g.down_voted = g.data['down_voted']
g.books_returned = g.data['books_returned']
g.books_read = g.data['books_read']
g.prev_click = g.data['prev_click']
g.more_click = g.data['more_click']
g.collab_start_point = 0
if g.more_click == 'yes':
g.collab_start_point = g.data['collab_returned']
for book in g.books_read:
g.books_returned.append(str(book))
g.Recommend = Recommend(user=g.user, db=db, Read=Read, Book=Book,
book_data=book_data, ipca_model=ipca_model,
dict_vectorizer_fit=dict_vectorizer_fit,
collab_start_point=g.collab_start_point)
g.recommended_books = g.Recommend.recommend_books(books_selected=g.books_selected,
features_list=g.features_list,
books_returned=g.books_returned,
up_votes=g.up_voted,
down_votes=g.down_voted,
n_collab_returned=100)
rec_data = {"recommendations": g.recommended_books, "collab_returned":g.Recommend.collab_returned}
return jsonify(rec_data)
def recommend(self):
print()
print('<recommend>')
uid = int(self.getdata(64))
if not uid:
return
print("uid: ", uid)
recommend = Recommend(uid)
recommend.run()
print(recommend.items)
for i in range(10):
data = str(recommend.items[i]) + '\n'
self.connection.sendall(data.encode())
print('</recommend>')
print()
def test_eng_and_kor(self):
mock = Mock(return_value=[
'유선마우스) 3M마우스 사용후기', '제닉스 TITAN GM AIR WIRELESS (화이트) 마우스 사용 후기',
'마이크로소프트 스컬프트 마우스 한 달이상 사용후기', '로지텍 G603 무선 마우스 사용후기'
])
term = TermProject()
reco = Recommend()
term.search('마우스')
result_1 = reco.titleList
self.assertEqual(result_1[0], mock()[0])
def recommend(self): # Переход в рубрику "Рекомендую"
self.m1 = Recommend(self)
self.m1.show()
def __init__(self, root):
self.root = root
self.create()
self.recommender = Recommend(
"/Users/josephbarbati/Documents/codefiles/Emory/CS378/Final_Project/code/Frequent_Itemsets.txt"
)
def add():
view = ""
registId = ""
form = {}
kind = "入力"
data = [-2, -1, 0, 1, 2]
# GETされた場合
if request.method == 'GET':
# TODO: id指定された場合
# 表示処理
return template('add.html',
form=form,
kind=kind,
registId=registId,
data=data)
# POSTされた場合
if request.method == 'POST':
# POST値の取得
form['evaluation'] = request.forms.decode().get('evaluation')
form['weight'] = request.forms.decode().get('weight')
form['fruity'] = request.forms.decode().get('fruity')
form['acidity'] = request.forms.decode().get('acidity')
form['spicy'] = request.forms.decode().get('spicy')
form['memo'] = request.forms.decode().get('memo')
registId = ""
# idが指定されている場合
if request.forms.decode().get('id') is not None:
registId = request.forms.decode().get('id')
# バリデーション処理
errorMsg = []
errorMsg = Utils.validate(data=form)
# 表示処理
# 確認画面から戻る場合
if request.forms.get('next') == 'back':
return template('add.html',
form=form,
kind=kind,
registId=registId,
data=data)
if not errorMsg:
headers = [
'重み（軽やか - 濃厚）', '果実味（スパイシー - フルーティ）', '酸味（まろやか - シャープ）',
'辛み（甘い - 辛い）', 'メモ'
]
temp_df = Recommend(base_df,
wine=0,
weight=form['weight'],
fruity=form['fruity'],
acidity=form['acidity'],
spicy=form['spicy'])
df = search_info(temp_df, wine_df)
return template('confirm.html',
form=form,
headers=headers,
df=df,
registId=registId)
else:
return template('add.html',
error=errorMsg,
kind=kind,
form=form,
registId=registId,
data=data)
else:
if len(games) < i + 1:
games.append([])
user_id = d[0]
dct[d[1]] = d[4]
if d[4] == "1":
games[i].append(d[1])
# Correctly recommended games / all recommended games
avg_prec = 0.0
# Correctly recommended games / all games that should have been recommended
avg_recall = 0.0
j = 0
for i in range(len(games) - 1):
if len(games[i]) < 2: continue
r = Recommend("Frequent_Itemsets.txt")
r.set_user_games(games[i])
recommended = r.recommend()
# Caluclate Precision and recall.
all_rec = len(recommended)
should_rec = 0
correct = 0
for key in final_dict[i]:
game = key.split('"')[1]
if final_dict[i][key] == "0":
should_rec += 1
if game in recommended:
correct += 1
if should_rec == 0 or all_rec == 0:
continue
prec = float(float(correct) / float(all_rec))
color = '#303030'
strings.append('<table><tr style="border-bottom: 1px solid #666;"><td width="170px";>'+\
'<img src="'+str(row[1])+'" style="opacity:'+opacity+';""></td>')
strings[-1] += '<td><h2><a href="/document/'+str(uid)+'/'+str(did)+\
'" style="color:'+color+';">' + str(row[2]) + '</a></h2>\n'
strings[
-1] += '<p style="color:#606060; font-size:15"><span style="color:#A0A0A0">'
strings[-1] += parser.parse(str(
row[0])).strftime('%d/%m/%Y') + '</span>'
if str(row[3]) != '':
strings[-1] += ' - ' + str(row[3])
strings[-1] += '</p></td></tr></table>\n'
#strings[-1] += '\n<hr class="dots"/>\n'
return ''.join(strings)
if __name__ == '__main__':
with open('../TextMining/ac.loc', 'rb') as f:
ac = load(f)
root = Home(ac)
root.topic = Topic(ac)
root.document = Document(ac)
root.tag = Tag(ac)
root.recommend = Recommend(ac)
root.search = Search(ac)
root.script = Script()
#cherrypy.server.socket_host = '192.168.1.95'
#cherrypy.engine.start()
cherrypy.quickstart(root)
def main():
conf.init_cnf()
order = Order()
sim = Sim()
conn = MySQLdb.connect(host=mysql_cnf['host'],
port=int(mysql_cnf['port']),
user=mysql_cnf['user'],
passwd=mysql_cnf['passwd'],
db=mysql_cnf['db'],
connect_timeout=3,
local_infile=1)
mysql_read_order(order, conn)
mysql_read_sim(sim, conn)
pool = redis.ConnectionPool(host='192.168.9.36', port=6379, db=0, encoding='utf8')
r = redis.Redis(connection_pool=pool)
recommend = Recommend(order=order, sim=sim)
recommend.set_rec(dict())
recommend.set_rec_by_sim_user(size=20)
users = recommend.get_rec().keys()
for u in users:
redis_write_rec('user:'******':sim_user', recommend.get_user_rec(u), r)
recommend.set_rec(dict())
recommend.set_rec_by_sim_item(size=20)
users = recommend.get_rec().keys()
for u in users:
redis_write_rec('user:'******':sim_item', recommend.get_user_rec(u), r)
import os, sys
import json
import requests
from django.shortcuts import render
from django.views.decorators.csrf import csrf_exempt
from django.http import HttpResponse
cur_dir = os.path.dirname( os.path.abspath(__file__)) or os.getcwd()
sys.path.append(cur_dir + '/../../')
sessionID = "1"
history = ""
from recommend import Recommend
rs = Recommend()
## receive the post input data and process.
@csrf_exempt
def analyze(request):
ctx ={}
if request.method=="POST":
try:
ori = json.loads(request.body)
except:
HttpResponse("json格式错误")
try:
ctx['uid'] = uid = ori['uid']
ctx['input'] = content = ori['user_say']
ctx['flag'] = flag = ori['flag']
except:
HttpResponse("数据格式错误")
# history = request.POST['answer'].strip()
def server_recommend():
global recommend
now = datetime.fromtimestamp(time.time())
ip = request.environ.get('REMOTE_ADDR')
ip_num = Recommend.ip2value(ip)
return recommend.predict([[now.month, now.day, now.weekday(), now.hour, ip_num]])
