def train(self, trainset):
# Here again: call base method before doing anything.
AlgoBase.train(self, trainset)
user_num = self.trainset.n_users
item_num = self.trainset.n_items
rating = sparse.lil_matrix((user_num, item_num))
for u, i, r in self.trainset.all_ratings():
if r > 3:
rating[u, i] = r
self.A = st.SparseMatrix(rating)
self.W = st.train(
self.A,
l1_ratio=self.l1_ratio,
eps=self.eps,
n_alphas=self.n_alphas,
alphas=self.alphas,
positive=self.positive,
max_iter=self.max_iter)
self.A = sparse.csc_matrix(self.A)
self.W = sparse.csc_matrix(self.W)
self.estimator = self.A * self.W
def train(self, trainset):
AlgoBase.train(self, trainset)
# Compute baselines and similarities
self.bu, self.bi = self.compute_baselines()
self.sim = self.compute_similarities()
def train(self, trainset):
# Here again: call base method before doing anything.
AlgoBase.train(self, trainset)
# Compute the average rating
self.the_mean = np.mean([r for (_, _, r) in self.trainset.all_ratings()])
def train(self, trainset):
AlgoBase.train(self, trainset)
self.sim = self.compute_similarities()
raw_rating_df = pd.read_csv(
'new_ratings_all.txt',
sep=':',
header=None,
names=['userid', 'movieid', 'rating', 'date', 'comment_level'],
dtype={
'movieid': np.str,
'userid': np.str
})
self.user_comment_indictor = self.get_user_comment_indictor(
raw_rating_df)
self.user_rating_date_indictor = self.get_user_rating_date_indictor(
raw_rating_df)
self.user_professional_indictor = self.get_user_professional_indictor(
self.user_comment_indictor, self.user_rating_date_indictor)
self.inner_uid_2_professional = {
iuid: self.user_professional_indictor['std'][
self.trainset.to_raw_uid(iuid)]
for iuid, _ in self.trainset.ur.iteritems()
}
def train(self, trainset):
AlgoBase.train(self, trainset)
self.trainset = trainset
self.sim = self.compute_similarities()
self.iuid_ifriends = self.read_social(trainset)
self.sbmf(trainset)
def train(self, trainset):
# Here again: call base method before doing anything.
AlgoBase.train(self, trainset)
# Compute the average rating. We might as well use the
# trainset.global_mean attribute ;)
self.the_mean = numpy.mean(
[r for (_, _, r) in self.trainset.all_ratings()])
def train(self, trainset):
# Here again: call base method before doing anything.
AlgoBase.train(self, trainset)
users = np.array([u for (u, _, _) in self.trainset.all_ratings()])
items = np.array([i for (_, i, _) in self.trainset.all_ratings()])
ratings = np.array([r for (_, _, r) in self.trainset.all_ratings()])
user_means, item_means = {}, {}
for user in np.unique(users):
user_means[user] = ratings[users == user].mean()
for item in np.unique(items):
item_means[item] = ratings[items == item].mean()
self.global_mean = ratings.mean()
self.user_means = user_means
self.item_means = item_means
def train(self, trainset):
AlgoBase.train(self, trainset)
self.est = 3
self.bu, self.bi = 1, 1
self.cnt += 1
def train(self, trainset):
AlgoBase.train(self, trainset)
# remember DF is uid, iid, rating
self.mean = np.mean(
[rating for (_, _, rating) in self.trainset.all_ratings()])
def train(self, trainset):
AlgoBase.train(self, trainset)
# Computing the average rating
self.the_mean = np.mean([r for (_, _, r) in self.trainset.all_ratings()])
def train(self, trainset):
AlgoBase.train(self, trainset)
self.est = 3
self.bu, self.bi = 1, 1
self.cnt += 1
def train(self, trainset):
AlgoBase.train(self, trainset)
self.user_bias = bias_user_df
self.business_bias = bias_business_df
def train(self, trainset):
AlgoBase.train(self, trainset)
self.mean = self.trainset.global_mean
