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8304f29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | import numpy as np
from collections import defaultdict
from data.data import Data
from data.graph import Graph
import scipy.sparse as sp
import pickle
class Interaction(Data,Graph):
def __init__(self, conf, training, test):
Graph.__init__(self)
Data.__init__(self,conf,training,test)
self.user = {}
self.item = {}
self.id2user = {}
self.id2item = {}
self.training_set_u = defaultdict(dict)
self.training_set_i = defaultdict(dict)
self.test_set = defaultdict(dict)
self.test_set_item = set()
self.__generate_set()
self.user_num = len(self.training_set_u)
self.item_num = len(self.training_set_i)
self.ui_adj = self.__create_sparse_bipartite_adjacency()
self.norm_adj = self.normalize_graph_mat(self.ui_adj)
self.interaction_mat = self.__create_sparse_interaction_matrix()
# popularity_user = {}
# for u in self.user:
# popularity_user[self.user[u]] = len(self.training_set_u[u])
# popularity_item = {}
# for u in self.item:
# popularity_item[self.item[u]] = len(self.training_set_i[u])
def __generate_set(self):
for entry in self.training_data:
user, item, rating = entry
if user not in self.user:
self.user[user] = len(self.user)
self.id2user[self.user[user]] = user
if item not in self.item:
self.item[item] = len(self.item)
self.id2item[self.item[item]] = item
# userList.append
self.training_set_u[user][item] = rating
self.training_set_i[item][user] = rating
for entry in self.test_data:
user, item, rating = entry
if user not in self.user:
continue
self.test_set[user][item] = rating
self.test_set_item.add(item)
def __create_sparse_bipartite_adjacency(self, self_connection=False):
'''
return a sparse adjacency matrix with the shape (user number + item number, user number + item number)
'''
n_nodes = self.user_num + self.item_num
row_idx = [self.user[pair[0]] for pair in self.training_data]
col_idx = [self.item[pair[1]] for pair in self.training_data]
user_np = np.array(row_idx)
item_np = np.array(col_idx)
ratings = np.ones_like(user_np, dtype=np.float32)
tmp_adj = sp.csr_matrix((ratings, (user_np, item_np + self.user_num)), shape=(n_nodes, n_nodes),dtype=np.float32)
adj_mat = tmp_adj + tmp_adj.T
if self_connection:
adj_mat += sp.eye(n_nodes)
return adj_mat
def convert_to_laplacian_mat(self, adj_mat):
adj_shape = adj_mat.get_shape()
n_nodes = adj_shape[0]+adj_shape[1]
(user_np_keep, item_np_keep) = adj_mat.nonzero()
ratings_keep = adj_mat.data
tmp_adj = sp.csr_matrix((ratings_keep, (user_np_keep, item_np_keep + adj_shape[0])),shape=(n_nodes, n_nodes),dtype=np.float32)
tmp_adj = tmp_adj + tmp_adj.T
return self.normalize_graph_mat(tmp_adj)
def __create_sparse_interaction_matrix(self):
"""
return a sparse adjacency matrix with the shape (user number, item number)
"""
row, col, entries = [], [], []
for pair in self.training_data:
row += [self.user[pair[0]]]
col += [self.item[pair[1]]]
entries += [1.0]
interaction_mat = sp.csr_matrix((entries, (row, col)), shape=(self.user_num,self.item_num),dtype=np.float32)
return interaction_mat
def get_user_id(self, u):
if u in self.user:
return self.user[u]
def get_item_id(self, i):
if i in self.item:
return self.item[i]
def training_size(self):
return len(self.user), len(self.item), len(self.training_data)
def test_size(self):
return len(self.test_set), len(self.test_set_item), len(self.test_data)
def contain(self, u, i):
'whether user u rated item i'
if u in self.user and i in self.training_set_u[u]:
return True
else:
return False
def contain_user(self, u):
'whether user is in training set'
if u in self.user:
return True
else:
return False
def contain_item(self, i):
"""whether item is in training set"""
if i in self.item:
return True
else:
return False
def user_rated(self, u):
return list(self.training_set_u[u].keys()), list(self.training_set_u[u].values())
def item_rated(self, i):
return list(self.training_set_i[i].keys()), list(self.training_set_i[i].values())
def row(self, u):
u = self.id2user[u]
k, v = self.user_rated(u)
vec = np.zeros(len(self.item))
# print vec
for pair in zip(k, v):
iid = self.item[pair[0]]
vec[iid] = pair[1]
return vec
def col(self, i):
i = self.id2item[i]
k, v = self.item_rated(i)
vec = np.zeros(len(self.user))
# print vec
for pair in zip(k, v):
uid = self.user[pair[0]]
vec[uid] = pair[1]
return vec
def matrix(self):
m = np.zeros((len(self.user), len(self.item)))
for u in self.user:
k, v = self.user_rated(u)
vec = np.zeros(len(self.item))
# print vec
for pair in zip(k, v):
iid = self.item[pair[0]]
vec[iid] = pair[1]
m[self.user[u]] = vec
return m
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