class Algorithm(object):
def __init__(self):
self._data = Data()
def __repr__(self):
s = '%d rows.' % len(self.get_data())
if len(self.get_data()):
s += '\nE.g: %s' % str(self.get_data()[0])
return s
def __len__(self):
return len(self.get_data())
def get_data(self):
return self._data
def set_data(self, data):
self._data = data
def add_tuple(self, tuple):
self.get_data().add_tuple(tuple)
def load_data(self, filename, sep='\t', format={'value':0, 'row':1, 'col':2}):
self._data.load_file(filename, sep, format)
def compute(self):
if not self._data.get():
raise ValueError('No data set. Matrix is empty!')
print len(r_info),r_info[1:10]
value = RMSE(r_info).compute()
print value
def test_itembased(train,test,data):
itembased = ItemBased()
itembased.set_data(train)
itembased.compute(num = 100)
ITEMID = 1
USERID = 1
#print svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)
print itembased.recommend(USERID, is_row=False) #cols are users and rows are items, thus we set is_row=False
print itembased.recommend(ITEMID)
data = Data()
data.load_file(path='../../data/ratings.dat',sep='::'
, format={'col':0, 'row':1,'value':2, 'ids': int})
print len(data.get())
train,test = data.split_train_test(percent = 100)
#test_svd(train,test,data)
test_itembased(train,test,data)
