def create_adj_mat(self):
"""Create adjacent matirx from the user-item interaction matrix."""
adj_mat = sp.dok_matrix(
(self.n_users + self.n_items, self.n_users + self.n_items),
dtype=np.float32)
adj_mat = adj_mat.tolil()
R = sp.dok_matrix((self.n_users, self.n_items), dtype=np.float32)
user_np = np.array(self.ratings[DEFAULT_USER_COL])
item_np = np.array(self.ratings[DEFAULT_ITEM_COL])
for u in range(self.n_users):
index = list(np.where(user_np == u)[0])
i = item_np[index]
for item in i:
R[u, item] = 1
R = R.tolil()
adj_mat[:self.n_users, self.n_users:] = R
adj_mat[self.n_users:, :self.n_users] = R.T
adj_mat = adj_mat.todok()
print("already create adjacency matrix", adj_mat.shape)
norm_adj_mat = normalized_adj_single(adj_mat +
sp.eye(adj_mat.shape[0]))
mean_adj_mat = normalized_adj_single(adj_mat)
print("already normalize adjacency matrix")
return adj_mat.tocsr(), norm_adj_mat.tocsr(), mean_adj_mat.tocsr()
def make_fea_sim_mat(self):
"""Make feature similarity matrix.
Note that the first column is the user/item ID.
Returns:
normalized_adj_single
"""
self.load_user_item_fea()
self.init_train_items()
user_sim_mat = sp.csr_matrix(calc_sim(self.user_feat))
item_sim_mat = sp.csr_matrix(calc_sim(self.item_feat))
return (
normalized_adj_single(user_sim_mat + sp.eye(user_sim_mat.shape[0])),
normalized_adj_single(item_sim_mat + sp.eye(item_sim_mat.shape[0])),
)
def create_adj_mat(self):
"""Create adjacent matrix from the user-item interaction."""
t1 = time()
adj_mat = sp.dok_matrix(
(self.n_users + self.n_items, self.n_users + self.n_items), dtype=np.float32
)
adj_mat = adj_mat.tolil()
R = self.R.tolil()
adj_mat[: self.n_users, self.n_users :] = R
adj_mat[self.n_users :, : self.n_users] = R.T
adj_mat = adj_mat.todok()
print("already create adjacency matrix", adj_mat.shape, time() - t1)
t2 = time()
norm_adj_mat = normalized_adj_single(adj_mat + sp.eye(adj_mat.shape[0]))
mean_adj_mat = normalized_adj_single(adj_mat)
print("already normalize adjacency matrix", time() - t2)
return adj_mat.tocsr(), norm_adj_mat.tocsr(), mean_adj_mat.tocsr()