├── losses ├── losses.npy ├── CNN-LSTM_losses.npy ├── GCN-LSTM_losses.npy ├── losses_allUsers.npy ├── C-GCN-LSTM_losses.npy ├── S-GCN-LSTM_losses.npy ├── C-GCN-LSTM_losses_allUsers.npy ├── CNN-LSTM_losses_allUsers.npy ├── GCN-LSTM_losses_allUsers.npy └── S-GCN-LSTM_losses_allUsers.npy ├── 收敛性Training_loss_new ├── Training_loss.jpg ├── Training_loss.pdf ├── Training_loss_AllUsers.pdf ├── Training_loss_oneUser.pdf ├── Training_loss_oneUser.eps ├── Training_loss_AllUsers.eps └── Training_loss.eps ├── pltTrainingLossOfDifferentMethod.py ├── README.md ├── CS-GCN-LSTM_predict.py ├── CS-GCN-LSTM.py ├── CS-GCN-LSTM_AllUsers_predict.py └── CS-GCN-LSTM_AllUsers.py /losses/losses.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/losses.npy -------------------------------------------------------------------------------- /losses/CNN-LSTM_losses.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/CNN-LSTM_losses.npy -------------------------------------------------------------------------------- /losses/GCN-LSTM_losses.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/GCN-LSTM_losses.npy -------------------------------------------------------------------------------- /losses/losses_allUsers.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/losses_allUsers.npy -------------------------------------------------------------------------------- /losses/C-GCN-LSTM_losses.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/C-GCN-LSTM_losses.npy -------------------------------------------------------------------------------- /losses/S-GCN-LSTM_losses.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/S-GCN-LSTM_losses.npy -------------------------------------------------------------------------------- /losses/C-GCN-LSTM_losses_allUsers.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/C-GCN-LSTM_losses_allUsers.npy -------------------------------------------------------------------------------- /losses/CNN-LSTM_losses_allUsers.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/CNN-LSTM_losses_allUsers.npy -------------------------------------------------------------------------------- /losses/GCN-LSTM_losses_allUsers.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/GCN-LSTM_losses_allUsers.npy -------------------------------------------------------------------------------- /losses/S-GCN-LSTM_losses_allUsers.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/losses/S-GCN-LSTM_losses_allUsers.npy -------------------------------------------------------------------------------- /收敛性Training_loss_new/Training_loss.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/收敛性Training_loss_new/Training_loss.jpg -------------------------------------------------------------------------------- /收敛性Training_loss_new/Training_loss.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/收敛性Training_loss_new/Training_loss.pdf -------------------------------------------------------------------------------- /收敛性Training_loss_new/Training_loss_AllUsers.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/收敛性Training_loss_new/Training_loss_AllUsers.pdf -------------------------------------------------------------------------------- /收敛性Training_loss_new/Training_loss_oneUser.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lidongyang1/CS-UPL/HEAD/收敛性Training_loss_new/Training_loss_oneUser.pdf -------------------------------------------------------------------------------- /pltTrainingLossOfDifferentMethod.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2022/4/7 7:54 3 | # @Author : cmdxmm 4 | # @FileName: plotPredictAndGroundTrue_Matrix.py 5 | # @Email ：lidongyang@mail.sdu.edu.cn 6 | 7 | import numpy as np 8 | import matplotlib.pyplot as plt 9 | oneUser_CS_GCN_LSTM = np.load('./losses/losses.npy') 10 | oneUser_S_GCN_LSTM = np.load('./losses/S-GCN-LSTM_losses.npy') 11 | oneUser_C_GCN_LSTM = np.load('./losses/C-GCN-LSTM_losses.npy') 12 | oneUser_GCN_LSTM = np.load('./losses/GCN-LSTM_losses.npy') 13 | AllUser_CS_GCN_LSTM = np.load('./losses/losses_allUsers.npy') 14 | AllUser_S_GCN_LSTM = np.load('./losses/S-GCN-LSTM_losses_allUsers.npy') 15 | AllUser_C_GCN_LSTM = np.load('./losses/C-GCN-LSTM_losses_allUsers.npy') 16 | AllUser_GCN_LSTM = np.load('./losses/GCN-LSTM_losses_allUsers.npy') 17 | labels=['UPL(Single User)','C-UPL(Single User)','S-UPL(Single User)','CS-UPL(Single User)','UPL(Multiple Users)','C-UPL(Multiple Users)','S-UPL(Multiple Users)','CS-UPL(Multiple Users)'] 18 | x = np.arange(100) 19 | 20 | plt.figure() 21 | plt.plot(x,oneUser_GCN_LSTM,label=labels[0],linewidth=2) 22 | plt.plot(x,oneUser_C_GCN_LSTM,label=labels[1],linewidth=2) 23 | plt.plot(x,oneUser_S_GCN_LSTM,label=labels[2],linewidth=2) 24 | plt.plot(x,oneUser_CS_GCN_LSTM,label=labels[3],linewidth=2) 25 | plt.plot(x,AllUser_GCN_LSTM,label=labels[4],linewidth=2) 26 | plt.plot(x,AllUser_C_GCN_LSTM,label=labels[5],linewidth=2) 27 | plt.plot(x,AllUser_S_GCN_LSTM,label=labels[6],linewidth=2) 28 | plt.plot(x,AllUser_CS_GCN_LSTM,label=labels[7],linewidth=2) 29 | plt.legend(ncol=2,fontsize=10.5) 30 | plt.xlabel('Epochs', fontsize=15,fontweight='bold') 31 | plt.ylabel('Training Loss', fontsize=15,fontweight='bold') 32 | plt.grid(ls='--') 33 | plt.show() 34 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # User-Preference-Learning-based-Proactive-Edge-Caching-for-D2D-Assisted-Wireless-Networks 2 | ## Abstract 3 | This work investigates proactive edge caching for device-to-device (D2D) assisted wireless networks, where user equipment (UE) can be selected as caching nodes to assist content delivery for reducing the content transmission latency. Doing so, there are two challenging problems: 1) How to precisely learn the user's preference to cache the proper contents on each UE; 2) How to replace the contents cached on UEs when there are new popular contents continuously emerging. To address these, a user preference learning-based proactive edge caching (UPL-PEC) strategy is proposed in this work. In the strategy, we first propose a novel context and social-aware user preference learning method to precisely predict user's dynamic preferences by jointly exploiting the context correlation among different contents, the influence of social relationships and the time-sequential patterns of user's content requests. Specifically, we utilize the bidirectional long short term memory networks to capture the time-sequential patterns of user's content request. And, the graph convolutional networks are adopted to capture the high-order similarity representation among different contents from the constructed content graph. To learn the social influence representation, an attention mechanism is developed to generate the social influence weights to users with different social relationship. Based on the user preference learning, 4 | a learning-based proactive edge caching architecture is proposed to continuously caching the popular contents on UEs by integrating the offline caching 5 | content placement and the online caching content replacement policy. Real-world trace-based simulation results show that the proposed UPL-PEC strategy 6 | outperforms the compared existing caching strategies at about 2.5\%-45.3\% in terms of the average content transmission latency. 7 | 8 | ## Requirements 9 | stellargraph == 1.2.1 10 | tensorflow-gpu == 2.1.0 11 | pandas = 1.3.4 12 | numpy == 1.19.5 13 | matplotlib == 3.5.0 14 | ## Dataset 15 | We uploaded the processed dataset to：https://pan.baidu.com/s/1i9R6PJDiEhxxhTgAr8fdLQ 16 | Extraction Code：g97g 17 | 18 | 19 | Based on the reviewers' comments, we revised the number of users to 100 in the final version of the paper. 20 | The update dataset can be seen at:https://pan.baidu.com/s/11KETpeBprwuvOoczMMgXzQ 21 | Extraction Code：s2i8 22 | 23 | ## Training 24 | ``` 25 | python CS-GCN-LSTM.py #To train the model in single user scenario 26 | 27 | python CS-GCN-LSTM_AllUsers.py #To train the model in multiple user scenarios 28 | ``` 29 | ## Predict 30 | 31 | ``` 32 | python CS-GCN-LSTM_predict.py #To predict the results in single user scenario 33 | 34 | python CS-GCN-LSTM_AllUsers_predict.py #To predict the results in multiple user scenarios 35 | ``` 36 | 37 | ## Results 38 | The convergence of the proposed CS-UPL method is investigated, and comparisons are made with UPL, C-UPL and S-UPL in single user and multiple users scenarios. The results are shown in Fig.\ref{convergence}, from which it is seen that the training loss of four methods all gradually converges as the epoch increases. But, the proposed CS-UPL method outperforms UPL, C-UPL and S-UPL methods on the convergence speed and prediction accuracy for all the single user and multiple users scenarios. This proves that the user preference learning method jointly considering the context correlation among different contents and the influence of social relationships can have a better prediction performance. 39 | 40 | 41 | 42 | ## Pleasae cite the work if you would like to use it 43 | 44 | [1] Dongyang Li, Haixia Zhang, Hui Ding, Tiantian Li, Daojun Liang and Dongfeng Yuan, "User Preference Learning-based Proactive Edge Caching for D2D-Assisted Wireless Networks," in IEEE Internet of Things Journal, early access. DOI:10.1109/JIOT.2023.3244621. 45 | 46 | [2] Dongyang Li, Haixia Zhang, Tiantian Li, Hui Ding and Dongfeng Yuan, "Community Detection and Attention-Weighted Federated Learning Based Proactive Edge Caching for D2D-Assisted Wireless Networks," in IEEE Transactions on Wireless Communications,early access. DOI:10.1109/TWC.2023.3249756. 47 | 48 | [3] Dongyang Li, Haixia Zhang, Dongfeng Yuan and Minggao Zhang, "Learning-Based Hierarchical Edge Caching for Cloud-Aided Heterogeneous Networks," in IEEE Transactions on Wireless Communications,early access. DOI:10.1109/TWC.2022.3206236. 49 | 50 | 51 | 52 | -------------------------------------------------------------------------------- /CS-GCN-LSTM_predict.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2022/2/21 16:17 3 | # @Author : cmdxmm 4 | # @FileName: getDifferentUserData_less_30000.py 5 | # @Email ：lidongyang@mail.sdu.edu.cn 6 | import numpy as np 7 | from tensorflow.keras import Model 8 | from stellargraph.layer import GCN_LSTM 9 | import pandas as pd 10 | import tensorflow as tf 11 | from tensorflow import keras 12 | import tensorflow.keras as K 13 | import heapq 14 | import math 15 | from sklearn.metrics import mean_squared_error #MSE 16 | from sklearn.metrics import mean_absolute_error #MAE 17 | from sklearn.metrics import r2_score 18 | 19 | class LossHistory(keras.callbacks.Callback): 20 | def on_train_begin(self, logs={}): 21 | self.losses = [] 22 | def on_epoch_end(self, batch, logs={}): 23 | self.losses.append(logs.get('loss')) 24 | 25 | ####将数据分为训练集和测试集 26 | def train_test_split(data, train_portion): 27 | time_len = data.shape[1] 28 | train_size = int(time_len * train_portion) 29 | train_data = np.array(data.iloc[:, :train_size]) 30 | test_data = np.array(data.iloc[:, train_size:]) 31 | return train_data, test_data 32 | 33 | ###对数据进行归一化 34 | def scale_data(train_data, test_data): 35 | # print(np.argwhere(np.isnan(train_data))) 36 | # print(np.argwhere(np.isnan(test_data))) 37 | train_data = np.abs(train_data) 38 | test_data = np.abs(test_data) 39 | max_speed = train_data.max() 40 | min_speed = train_data.min() 41 | print('max：',max_speed) 42 | print('min：',min_speed) 43 | train_scaled = (train_data - min_speed) / (max_speed - min_speed) 44 | test_scaled = (test_data - min_speed) / (max_speed - min_speed) 45 | return train_scaled, test_scaled 46 | 47 | #将数据转成10个step训练1个step的格式 48 | def sequence_data_preparation(seq_len, pre_len, train_data, test_data): 49 | trainX, trainY, testX, testY = [], [], [], [] 50 | 51 | for i in range(train_data.shape[1] - int(seq_len + pre_len - 1)): 52 | a = train_data[:, i : i + seq_len + pre_len] 53 | trainX.append(a[:, :seq_len]) 54 | trainY.append(a[:, -1]) 55 | 56 | for i in range(test_data.shape[1] - int(seq_len + pre_len - 1)): 57 | b = test_data[:, i : i + seq_len + pre_len] 58 | testX.append(b[:, :seq_len]) 59 | testY.append(b[:, -1]) 60 | 61 | trainX = np.array(trainX) 62 | trainY = np.array(trainY) 63 | testX = np.array(testX) 64 | testY = np.array(testY) 65 | 66 | return trainX, trainY, testX, testY 67 | 68 | #将数据转成10个step训练1个step的格式 69 | def sequence_data_preparationOtherUsers(seq_len, pre_len, train_data, test_data): 70 | trainX, trainY, testX, testY = [], [], [], [] 71 | 72 | for i in range(train_data.shape[1] - int(seq_len + pre_len - 1)): 73 | a = train_data[:, i : i + seq_len + pre_len] 74 | trainX.append(a[:, -2]) 75 | trainY.append(a[:, -1]) 76 | 77 | for i in range(test_data.shape[1] - int(seq_len + pre_len - 1)): 78 | b = test_data[:, i : i + seq_len + pre_len] 79 | testX.append(b[:, -2]) 80 | testY.append(b[:, -1]) 81 | 82 | trainX = np.array(trainX) 83 | trainY = np.array(trainY) 84 | testX = np.array(testX) 85 | testY = np.array(testY) 86 | 87 | return trainX, trainY, testX, testY 88 | 89 | def attention_block(inputs,time_stpes): 90 | input_dim = int(inputs.shape[2]) # shape = (batch_size, time_steps, input_dim) 91 | 92 | a = keras.layers.Permute((2, 1))(inputs) # shape = (batch_size, input_dim, time_steps) 93 | 94 | a = keras.layers.Reshape((input_dim, time_stpes))(a) # this line is not useful. It's just to know which dimension is what. 95 | 96 | a = tf.keras.layers.Dense(time_stpes, activation='softmax')(a)# 为了让输出的维数和时间序列数相同（这样才能生成各个时间点的注意力值） 97 | 98 | a = keras.layers.Lambda(lambda x: K.backend.mean(x, axis=1), name='dim_reduction')(a) 99 | a = keras.layers.RepeatVector(input_dim)(a) 100 | 101 | a_probs = keras.layers.Permute((2, 1), name='attention_vec')(a) # shape = (batch_size, time_steps, input_dim) 102 | 103 | output_attention_mul = keras.layers.Multiply()([inputs, a_probs]) #把注意力值和输入按位相乘，权重乘以输入 104 | 105 | return output_attention_mul 106 | 107 | def getOneDimension(X): 108 | l = [] 109 | for m in range(len(X)): 110 | for i in X[m]: 111 | l.append(i) 112 | return l 113 | def mape(y_true, y_pred): 114 | for i in range(len(y_true)): 115 | if y_true[i]==0: 116 | y_true[i]=0.1 117 | return np.mean(np.abs((y_pred - y_true) / y_true)) 118 | 119 | userId = 0 #当前为用户1的相似用户辅助信息 120 | top_k = 3 121 | seq_len = 24 122 | pre_len = 25 123 | 124 | view_counts = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_1_Contents.npy') 125 | video_dist_adj = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/Content_EdgeMatrix.npy') 126 | view_counts= pd.DataFrame(view_counts) 127 | num_nodes, time_len = view_counts.shape 128 | train_rate = 0.8 129 | train_data, test_data = train_test_split(view_counts, train_rate) 130 | trainX, trainY, testX, testY = sequence_data_preparation( 131 | seq_len, pre_len, train_data, test_data 132 | ) 133 | 134 | 135 | SocialRelationship = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/socialRelationship.npy') 136 | SocialSimilarityUsers = np.delete(heapq.nlargest(top_k+1,range(len(SocialRelationship[userId])),SocialRelationship[userId].take), userId, axis=0) 137 | view_counts_top_1 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[0]+1)+'_Contents.npy') 138 | view_counts_top_2 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[1]+1)+'_Contents.npy') 139 | view_counts_top_3 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[2]+1)+'_Contents.npy') 140 | 141 | view_counts_top_1 = pd.DataFrame(view_counts_top_1) 142 | view_counts_top_2 = pd.DataFrame(view_counts_top_2) 143 | view_counts_top_3 = pd.DataFrame(view_counts_top_3) 144 | 145 | train_data_top_1, test_data_top_1 = train_test_split(view_counts_top_1, train_rate) 146 | train_data_top_2, test_data_top_2 = train_test_split(view_counts_top_2, train_rate) 147 | train_data_top_3, test_data_top_3 = train_test_split(view_counts_top_3, train_rate) 148 | 149 | 150 | trainX_top_1, _, testX_top_1, _ = sequence_data_preparationOtherUsers( 151 | seq_len, pre_len, train_data_top_1, test_data_top_1 152 | ) 153 | trainX_top_2, _, testX_top_2, _ = sequence_data_preparationOtherUsers( 154 | seq_len, pre_len, train_data_top_2, test_data_top_2 155 | ) 156 | trainX_top_3, _, testX_top_3, _ = sequence_data_preparationOtherUsers( 157 | seq_len, pre_len, train_data_top_3, test_data_top_3 158 | ) 159 | 160 | gcn_lstm = GCN_LSTM( 161 | seq_len=seq_len, 162 | adj=video_dist_adj, 163 | gc_layer_sizes=[32, 64], 164 | gc_activations=["relu", "relu"], 165 | lstm_layer_sizes=[32, 64], 166 | lstm_activations=["relu","relu"], 167 | dropout=0.2, 168 | ) 169 | x_input, x_output1 = gcn_lstm.in_out_tensors() 170 | x_output1 = keras.layers.Dense(200,activation='relu')(x_output1) 171 | 172 | input1 = tf.keras.Input(shape=(400,24), name='input1') 173 | x1 = keras.layers.Bidirectional(keras.layers.LSTM(32,activation='tanh',return_sequences=True))(input1) 174 | x2 = keras.layers.Bidirectional(keras.layers.LSTM(64,activation='tanh',return_sequences=False))(x1) 175 | 176 | x3 = tf.keras.layers.Dense(512,activation='relu')(x2) 177 | x_output = keras.layers.Dense(400,activation='relu')(x3) 178 | 179 | 180 | input_top_1 = tf.keras.Input(shape=(400), name='input_top_1') 181 | input_top_2 = tf.keras.Input(shape=(400), name='input_top_2') 182 | input_top_3 = tf.keras.Input(shape=(400), name='input_top_3') 183 | 184 | x1_input_top_1 = keras.layers.Dense(512,activation='relu')(input_top_1) 185 | x2_input_top_1 = keras.layers.Dense(100,activation='relu')(x1_input_top_1) 186 | 187 | x1_input_top_2 = keras.layers.Dense(512,activation='relu')(input_top_2) 188 | x2_input_top_2 = keras.layers.Dense(100,activation='relu')(x1_input_top_2) 189 | 190 | 191 | x1_input_top_3 = keras.layers.Dense(512,activation='relu')(input_top_3) 192 | x2_input_top_3 = keras.layers.Dense(100,activation='relu')(x1_input_top_3) 193 | 194 | top_k_output = keras.layers.Concatenate()([x2_input_top_1,x2_input_top_2,x2_input_top_3]) 195 | 196 | top_k_output = keras.layers.Reshape((3,100))(top_k_output) 197 | 198 | top_k_output = attention_block(top_k_output,3) 199 | # 200 | 201 | top_k_output = keras.layers.Flatten()(top_k_output) 202 | # 203 | # top_k_output = keras.layers.Dense(100)(top_k_output) 204 | 205 | # ,top_k_output 206 | x_output_f1 = keras.layers.Concatenate()([x_output1,x_output]) 207 | x_output_f1 = keras.layers.Dense(100,activation='relu')(x_output_f1) 208 | 209 | x_output_f2 = keras.layers.Concatenate()([x_output,top_k_output]) 210 | x_output_f2 = keras.layers.Dense(100,activation='relu')(x_output_f2) 211 | 212 | 213 | x_output_f = keras.layers.Concatenate()([x_output,x_output_f1,x_output_f2]) 214 | x_output_f = keras.layers.Dense(512,activation='relu')(x_output_f) 215 | x_output_f = keras.layers.Dense(400)(x_output_f) 216 | 217 | model = Model(inputs=[x_input,input1,input_top_1,input_top_2,input_top_3], outputs=x_output_f) 218 | model.load_weights('./model/CS-GCN-LSTM.h5') 219 | predict = model.predict([testX,testX,testX_top_1,testX_top_2,testX_top_3]) 220 | np.save('Results/CS-GCN-LSTM/User=30_FileNum=400_predict.npy', predict) 221 | np.save('Results/CS-GCN-LSTM/User=30_FileNum=400_test.npy', testY) 222 | predict = np.array(getOneDimension(predict)) 223 | test = np.array(getOneDimension(testY)) 224 | 225 | print(mean_absolute_error(predict,test)) 226 | print(np.sqrt(mean_squared_error(predict,test))) 227 | print(r2_score(predict,test)) 228 | -------------------------------------------------------------------------------- /CS-GCN-LSTM.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2022/2/21 16:17 3 | # @Author : cmdxmm 4 | # @FileName: getDifferentUserData_less_30000.py 5 | # @Email ：lidongyang@mail.sdu.edu.cn 6 | import numpy as np 7 | from tensorflow.keras import Model 8 | from stellargraph.layer import GCN_LSTM 9 | import pandas as pd 10 | import tensorflow as tf 11 | from tensorflow.keras.callbacks import ModelCheckpoint 12 | from tensorflow import keras 13 | import tensorflow.keras as K 14 | import heapq 15 | from tensorflow.keras.utils import plot_model 16 | 17 | 18 | class LossHistory(keras.callbacks.Callback): 19 | def on_train_begin(self, logs={}): 20 | self.losses = [] 21 | def on_epoch_end(self, batch, logs={}): 22 | self.losses.append(logs.get('loss')) 23 | 24 | ####将数据分为训练集和测试集 25 | def train_test_split(data, train_portion): 26 | time_len = data.shape[1] 27 | train_size = int(time_len * train_portion) 28 | train_data = np.array(data.iloc[:, :train_size]) 29 | test_data = np.array(data.iloc[:, train_size:]) 30 | return train_data, test_data 31 | 32 | ###对数据进行归一化 33 | def scale_data(train_data, test_data): 34 | # print(np.argwhere(np.isnan(train_data))) 35 | # print(np.argwhere(np.isnan(test_data))) 36 | train_data = np.abs(train_data) 37 | test_data = np.abs(test_data) 38 | max_speed = train_data.max() 39 | min_speed = train_data.min() 40 | print('max：',max_speed) 41 | print('min：',min_speed) 42 | train_scaled = (train_data - min_speed) / (max_speed - min_speed) 43 | test_scaled = (test_data - min_speed) / (max_speed - min_speed) 44 | return train_scaled, test_scaled 45 | 46 | #将数据转成10个step训练1个step的格式 47 | def sequence_data_preparation(seq_len, pre_len, train_data, test_data): 48 | trainX, trainY, testX, testY = [], [], [], [] 49 | 50 | for i in range(train_data.shape[1] - int(seq_len + pre_len - 1)): 51 | a = train_data[:, i : i + seq_len + pre_len] 52 | trainX.append(a[:, :seq_len]) 53 | trainY.append(a[:, -1]) 54 | 55 | for i in range(test_data.shape[1] - int(seq_len + pre_len - 1)): 56 | b = test_data[:, i : i + seq_len + pre_len] 57 | testX.append(b[:, :seq_len]) 58 | testY.append(b[:, -1]) 59 | 60 | trainX = np.array(trainX) 61 | trainY = np.array(trainY) 62 | testX = np.array(testX) 63 | testY = np.array(testY) 64 | 65 | return trainX, trainY, testX, testY 66 | 67 | #将数据转成10个step训练1个step的格式 68 | def sequence_data_preparationOtherUsers(seq_len, pre_len, train_data, test_data): 69 | trainX, trainY, testX, testY = [], [], [], [] 70 | 71 | for i in range(train_data.shape[1] - int(seq_len + pre_len - 1)): 72 | a = train_data[:, i : i + seq_len + pre_len] 73 | trainX.append(a[:, -2]) 74 | trainY.append(a[:, -1]) 75 | 76 | for i in range(test_data.shape[1] - int(seq_len + pre_len - 1)): 77 | b = test_data[:, i : i + seq_len + pre_len] 78 | testX.append(b[:, -2]) 79 | testY.append(b[:, -1]) 80 | 81 | trainX = np.array(trainX) 82 | trainY = np.array(trainY) 83 | testX = np.array(testX) 84 | testY = np.array(testY) 85 | 86 | return trainX, trainY, testX, testY 87 | 88 | def attention_block(inputs,time_stpes): 89 | input_dim = int(inputs.shape[2]) # shape = (batch_size, time_steps, input_dim) 90 | 91 | a = keras.layers.Permute((2, 1))(inputs) # shape = (batch_size, input_dim, time_steps) 92 | 93 | a = keras.layers.Reshape((input_dim, time_stpes))(a) # this line is not useful. It's just to know which dimension is what. 94 | 95 | a = tf.keras.layers.Dense(time_stpes, activation='softmax')(a)# 为了让输出的维数和时间序列数相同（这样才能生成各个时间点的注意力值） 96 | 97 | a = keras.layers.Lambda(lambda x: K.backend.mean(x, axis=1), name='dim_reduction')(a) 98 | a = keras.layers.RepeatVector(input_dim)(a) 99 | 100 | a_probs = keras.layers.Permute((2, 1), name='attention_vec')(a) # shape = (batch_size, time_steps, input_dim) 101 | 102 | output_attention_mul = keras.layers.Multiply()([inputs, a_probs]) #把注意力值和输入按位相乘，权重乘以输入 103 | 104 | return output_attention_mul 105 | 106 | userId = 0 #当前为用户1的相似用户辅助信息 107 | top_k = 3 108 | seq_len = 24 109 | pre_len = 25 110 | 111 | view_counts = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_1_Contents.npy') 112 | video_dist_adj = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/Content_EdgeMatrix.npy') 113 | view_counts= pd.DataFrame(view_counts) 114 | num_nodes, time_len = view_counts.shape 115 | train_rate = 0.8 116 | print(view_counts.shape) 117 | train_data, test_data = train_test_split(view_counts, train_rate) 118 | print("Train data: ", train_data.shape) 119 | print("Test data: ", test_data.shape) 120 | trainX, trainY, testX, testY = sequence_data_preparation( 121 | seq_len, pre_len, train_data, test_data 122 | ) 123 | 124 | 125 | SocialRelationship = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/socialRelationship.npy') 126 | SocialSimilarityUsers = np.delete(heapq.nlargest(top_k+1,range(len(SocialRelationship[userId])),SocialRelationship[userId].take), userId, axis=0) 127 | print(SocialSimilarityUsers[0]) 128 | view_counts_top_1 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[0]+1)+'_Contents.npy') 129 | view_counts_top_2 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[1]+1)+'_Contents.npy') 130 | view_counts_top_3 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[2]+1)+'_Contents.npy') 131 | 132 | view_counts_top_1 = pd.DataFrame(view_counts_top_1) 133 | view_counts_top_2 = pd.DataFrame(view_counts_top_2) 134 | view_counts_top_3 = pd.DataFrame(view_counts_top_3) 135 | 136 | train_data_top_1, test_data_top_1 = train_test_split(view_counts_top_1, train_rate) 137 | train_data_top_2, test_data_top_2 = train_test_split(view_counts_top_2, train_rate) 138 | train_data_top_3, test_data_top_3 = train_test_split(view_counts_top_3, train_rate) 139 | 140 | 141 | print("Train data: ", train_data_top_1.shape) 142 | print("Test data: ", test_data_top_1.shape) 143 | trainX_top_1, _, testX_top_1, _ = sequence_data_preparationOtherUsers( 144 | seq_len, pre_len, train_data_top_1, test_data_top_1 145 | ) 146 | trainX_top_2, _, testX_top_2, _ = sequence_data_preparationOtherUsers( 147 | seq_len, pre_len, train_data_top_2, test_data_top_2 148 | ) 149 | trainX_top_3, _, testX_top_3, _ = sequence_data_preparationOtherUsers( 150 | seq_len, pre_len, train_data_top_3, test_data_top_3 151 | ) 152 | 153 | gcn_lstm = GCN_LSTM( 154 | seq_len=seq_len, 155 | adj=video_dist_adj, 156 | gc_layer_sizes=[32, 64], 157 | gc_activations=["relu", "relu"], 158 | lstm_layer_sizes=[32, 64], 159 | lstm_activations=["relu","relu"], 160 | dropout=0.2, 161 | ) 162 | x_input, x_output1 = gcn_lstm.in_out_tensors() 163 | x_output1 = keras.layers.Dense(200,activation='relu')(x_output1) 164 | 165 | input1 = tf.keras.Input(shape=(400,24), name='input1') 166 | x1 = keras.layers.Bidirectional(keras.layers.LSTM(32,activation='tanh',return_sequences=True))(input1) 167 | x2 = keras.layers.Bidirectional(keras.layers.LSTM(64,activation='tanh',return_sequences=False))(x1) 168 | 169 | x3 = tf.keras.layers.Dense(512,activation='relu')(x2) 170 | x_output = keras.layers.Dense(400,activation='relu')(x3) 171 | 172 | 173 | input_top_1 = tf.keras.Input(shape=(400), name='input_top_1') 174 | input_top_2 = tf.keras.Input(shape=(400), name='input_top_2') 175 | input_top_3 = tf.keras.Input(shape=(400), name='input_top_3') 176 | 177 | x1_input_top_1 = keras.layers.Dense(512,activation='relu')(input_top_1) 178 | x2_input_top_1 = keras.layers.Dense(100,activation='relu')(x1_input_top_1) 179 | 180 | x1_input_top_2 = keras.layers.Dense(512,activation='relu')(input_top_2) 181 | x2_input_top_2 = keras.layers.Dense(100,activation='relu')(x1_input_top_2) 182 | 183 | 184 | x1_input_top_3 = keras.layers.Dense(512,activation='relu')(input_top_3) 185 | x2_input_top_3 = keras.layers.Dense(100,activation='relu')(x1_input_top_3) 186 | 187 | top_k_output = keras.layers.Concatenate()([x2_input_top_1,x2_input_top_2,x2_input_top_3]) 188 | 189 | top_k_output = keras.layers.Reshape((3,100))(top_k_output) 190 | 191 | top_k_output = attention_block(top_k_output,3) 192 | # 193 | 194 | top_k_output = keras.layers.Flatten()(top_k_output) 195 | # 196 | # top_k_output = keras.layers.Dense(100)(top_k_output) 197 | 198 | # ,top_k_output 199 | x_output_f1 = keras.layers.Concatenate()([x_output1,x_output]) 200 | x_output_f1 = keras.layers.Dense(100,activation='relu')(x_output_f1) 201 | 202 | x_output_f2 = keras.layers.Concatenate()([x_output,top_k_output]) 203 | x_output_f2 = keras.layers.Dense(100,activation='relu')(x_output_f2) 204 | 205 | 206 | x_output_f = keras.layers.Concatenate()([x_output,x_output_f1,x_output_f2]) 207 | x_output_f = keras.layers.Dense(512,activation='relu')(x_output_f) 208 | x_output_f = keras.layers.Dense(400)(x_output_f) 209 | 210 | model = Model(inputs=[x_input,input1,input_top_1,input_top_2,input_top_3], outputs=x_output_f) 211 | 212 | adam = tf.keras.optimizers.Adam(learning_rate=0.001) 213 | history = model.compile(optimizer=adam, loss="mae", metrics=["mae"]) 214 | checkpoint = ModelCheckpoint(filepath='model/CS-GCN-LSTM.h5', monitor='val_loss', mode='auto', save_best_only='True', verbose=1) 215 | history = LossHistory() 216 | callback_lists = [checkpoint,history] 217 | model.summary() 218 | plot_model(model,to_file='model.png',show_shapes=True) 219 | 220 | history2 = model.fit( 221 | [trainX,trainX,trainX_top_1,trainX_top_2,trainX_top_3], 222 | trainY, 223 | epochs=100, 224 | batch_size=8, 225 | shuffle=True, 226 | verbose=1, 227 | validation_data=[[testX,testX,testX_top_1,testX_top_2,testX_top_3], testY], 228 | callbacks=callback_lists 229 | ) 230 | 231 | print( 232 | "Train loss: ", 233 | history2.history["loss"][-1], 234 | "\nTest loss:", 235 | history2.history["val_loss"][-1], 236 | ) 237 | losses = history.losses 238 | print(losses) 239 | np.save('./model/losses/' 240 | 'losses.npy',losses) 241 | -------------------------------------------------------------------------------- /CS-GCN-LSTM_AllUsers_predict.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2022/2/21 16:17 3 | # @Author : cmdxmm 4 | # @FileName: getDifferentUserData_less_30000.py 5 | # @Email ：lidongyang@mail.sdu.edu.cn 6 | import numpy as np 7 | from tensorflow.keras import Model 8 | from stellargraph.layer import GCN_LSTM 9 | import pandas as pd 10 | import tensorflow as tf 11 | from tensorflow.keras.callbacks import ModelCheckpoint 12 | from tensorflow import keras 13 | import tensorflow.keras as K 14 | import heapq 15 | from tensorflow.keras.utils import plot_model 16 | from sklearn.metrics import mean_squared_error #MSE 17 | from sklearn.metrics import mean_absolute_error #MAE 18 | from sklearn.metrics import r2_score 19 | def getOneDimension(X): 20 | l = [] 21 | for m in range(len(X)): 22 | for i in X[m]: 23 | l.append(i) 24 | return l 25 | class LossHistory(keras.callbacks.Callback): 26 | def on_train_begin(self, logs={}): 27 | self.losses = [] 28 | def on_epoch_end(self, batch, logs={}): 29 | self.losses.append(logs.get('loss')) 30 | 31 | ####将数据分为训练集和测试集 32 | def train_test_split(data, train_portion): 33 | time_len = data.shape[1] 34 | train_size = int(time_len * train_portion) 35 | train_data = np.array(data.iloc[:, :train_size]) 36 | test_data = np.array(data.iloc[:, train_size:]) 37 | return train_data, test_data 38 | 39 | ###对数据进行归一化 40 | def scale_data(train_data, test_data): 41 | train_data = np.abs(train_data) 42 | test_data = np.abs(test_data) 43 | max_speed = train_data.max() 44 | min_speed = train_data.min() 45 | print('max：',max_speed) 46 | print('min：',min_speed) 47 | train_scaled = (train_data - min_speed) / (max_speed - min_speed) 48 | test_scaled = (test_data - min_speed) / (max_speed - min_speed) 49 | return train_scaled, test_scaled 50 | 51 | #将数据转成10个step训练1个step的格式 52 | def sequence_data_preparation(seq_len, pre_len, train_data, test_data): 53 | trainX, trainY, testX, testY = [], [], [], [] 54 | 55 | for i in range(train_data.shape[1] - int(seq_len + pre_len - 1)): 56 | a = train_data[:, i : i + seq_len + pre_len] 57 | trainX.append(a[:, :seq_len]) 58 | trainY.append(a[:, -1]) 59 | 60 | for i in range(test_data.shape[1] - int(seq_len + pre_len - 1)): 61 | b = test_data[:, i : i + seq_len + pre_len] 62 | testX.append(b[:, :seq_len]) 63 | testY.append(b[:, -1]) 64 | 65 | trainX = np.array(trainX) 66 | trainY = np.array(trainY) 67 | testX = np.array(testX) 68 | testY = np.array(testY) 69 | 70 | return trainX, trainY, testX, testY 71 | 72 | #将数据转成10个step训练1个step的格式 73 | def sequence_data_preparationOtherUsers(seq_len, pre_len, train_data, test_data): 74 | trainX, trainY, testX, testY = [], [], [], [] 75 | 76 | for i in range(train_data.shape[1] - int(seq_len + pre_len - 1)): 77 | a = train_data[:, i : i + seq_len + pre_len] 78 | trainX.append(a[:, -2]) 79 | trainY.append(a[:, -1]) 80 | 81 | for i in range(test_data.shape[1] - int(seq_len + pre_len - 1)): 82 | b = test_data[:, i : i + seq_len + pre_len] 83 | testX.append(b[:, -2]) 84 | testY.append(b[:, -1]) 85 | 86 | trainX = np.array(trainX) 87 | trainY = np.array(trainY) 88 | testX = np.array(testX) 89 | testY = np.array(testY) 90 | 91 | return trainX, trainY, testX, testY 92 | 93 | def attention_block(inputs,time_stpes): 94 | input_dim = int(inputs.shape[2]) # shape = (batch_size, time_steps, input_dim) 95 | 96 | a = keras.layers.Permute((2, 1))(inputs) # shape = (batch_size, input_dim, time_steps) 97 | 98 | a = keras.layers.Reshape((input_dim, time_stpes))(a) # this line is not useful. It's just to know which dimension is what. 99 | 100 | a = tf.keras.layers.Dense(time_stpes, activation='softmax')(a) # 为了让输出的维数和时间序列数相同（这样才能生成各个时间点的注意力值） 101 | 102 | a = keras.layers.Lambda(lambda x: K.backend.mean(x, axis=1), name='dim_reduction')(a) 103 | a = keras.layers.RepeatVector(input_dim)(a) 104 | 105 | a_probs = keras.layers.Permute((2, 1), name='attention_vec')(a) # shape = (batch_size, time_steps, input_dim) 106 | 107 | output_attention_mul = keras.layers.Multiply()([inputs, a_probs]) # 把注意力值和输入按位相乘，权重乘以输入 108 | 109 | return output_attention_mul 110 | 111 | def getMostNSimilarityUEs(topN,Similarity): 112 | topN_index = [] 113 | tmp = np.sort(Similarity) 114 | MostValues = [] 115 | for i in range(topN): 116 | MostValues.append(tmp[len(Similarity)-i-1]) 117 | for i in range(topN): 118 | for j in range(len(Similarity)): 119 | if MostValues[i] == Similarity[j]: 120 | topN_index.append(j) 121 | return topN_index 122 | 123 | userId = 0 # 当前为用户1的相似用户辅助信息 124 | top_k = 3 125 | seq_len = 24 126 | pre_len = 25 127 | userNum = 30 128 | 129 | trainX_allUser = [] 130 | trainY_allUser = [] 131 | testX_allUser = [] 132 | testY_allUser = [] 133 | trainX_top_1_allUser = [] 134 | trainX_top_2_allUser = [] 135 | trainX_top_3_allUser = [] 136 | testX_top_1_allUser = [] 137 | testX_top_2_allUser = [] 138 | testX_top_3_allUser = [] 139 | 140 | for userId in range(userNum): 141 | view_counts = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(userId+1)+'_Contents.npy') 142 | video_dist_adj = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/Content_EdgeMatrix.npy') 143 | view_counts= pd.DataFrame(view_counts) 144 | num_nodes, time_len = view_counts.shape 145 | train_rate = 0.8 146 | print(view_counts.shape) 147 | train_data, test_data = train_test_split(view_counts, train_rate) 148 | print("Train data: ", train_data.shape) 149 | print("Test data: ", test_data.shape) 150 | trainX, trainY, testX, testY = sequence_data_preparation( 151 | seq_len, pre_len, train_data, test_data 152 | ) 153 | trainX_allUser.extend(trainX) 154 | trainY_allUser.extend(trainY) 155 | testX_allUser.extend(testX) 156 | testY_allUser.extend(testY) 157 | print('userId',userId) 158 | SocialRelationship = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/socialRelationship.npy') 159 | SocialSimilarityUsers = np.delete(getMostNSimilarityUEs(top_k+1,SocialRelationship[userId]), 0, axis=0) 160 | print(SocialSimilarityUsers[0]) 161 | view_counts_top_1 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[0]+1)+'_Contents.npy') 162 | view_counts_top_2 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[1]+1)+'_Contents.npy') 163 | view_counts_top_3 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[2]+1)+'_Contents.npy') 164 | 165 | view_counts_top_1 = pd.DataFrame(view_counts_top_1) 166 | view_counts_top_2 = pd.DataFrame(view_counts_top_2) 167 | view_counts_top_3 = pd.DataFrame(view_counts_top_3) 168 | 169 | train_data_top_1, test_data_top_1 = train_test_split(view_counts_top_1, train_rate) 170 | train_data_top_2, test_data_top_2 = train_test_split(view_counts_top_2, train_rate) 171 | train_data_top_3, test_data_top_3 = train_test_split(view_counts_top_3, train_rate) 172 | 173 | print("Train data: ", train_data_top_1.shape) 174 | print("Test data: ", test_data_top_1.shape) 175 | trainX_top_1, _, testX_top_1, _ = sequence_data_preparationOtherUsers( 176 | seq_len, pre_len, train_data_top_1, test_data_top_1 177 | ) 178 | trainX_top_2, _, testX_top_2, _ = sequence_data_preparationOtherUsers( 179 | seq_len, pre_len, train_data_top_2, test_data_top_2 180 | ) 181 | trainX_top_3, _, testX_top_3, _ = sequence_data_preparationOtherUsers( 182 | seq_len, pre_len, train_data_top_3, test_data_top_3 183 | ) 184 | trainX_top_1_allUser.extend(trainX_top_1) 185 | trainX_top_2_allUser.extend(trainX_top_2) 186 | trainX_top_3_allUser.extend(trainX_top_3) 187 | testX_top_1_allUser.extend(testX_top_1) 188 | testX_top_2_allUser.extend(testX_top_2) 189 | testX_top_3_allUser.extend(testX_top_3) 190 | 191 | trainX_allUser = np.array(trainX_allUser) 192 | trainY_allUser = np.array(trainY_allUser) 193 | testX_allUser = np.array(testX_allUser) 194 | testY_allUser = np.array(testY_allUser) 195 | trainX_top_1_allUser = np.array(trainX_top_1_allUser) 196 | trainX_top_2_allUser = np.array(trainX_top_2_allUser) 197 | trainX_top_3_allUser = np.array(trainX_top_3_allUser) 198 | testX_top_1_allUser = np.array(testX_top_1_allUser) 199 | testX_top_2_allUser = np.array(testX_top_2_allUser) 200 | testX_top_3_allUser = np.array(testX_top_3_allUser) 201 | print(trainX_allUser.shape) 202 | 203 | gcn_lstm = GCN_LSTM( 204 | seq_len=seq_len, 205 | adj=video_dist_adj, 206 | gc_layer_sizes=[32, 64], 207 | gc_activations=["relu", "relu"], 208 | lstm_layer_sizes=[32, 64], 209 | lstm_activations=["relu","relu"], 210 | dropout=0.2, 211 | 212 | ) 213 | x_input, x_output1 = gcn_lstm.in_out_tensors() 214 | x_output1 = keras.layers.Dense(200,activation='relu')(x_output1) 215 | 216 | input1 = tf.keras.Input(shape=(400,24), name='input1') 217 | x1 = keras.layers.Bidirectional(keras.layers.LSTM(32,activation='tanh',return_sequences=True))(input1) 218 | x2 = keras.layers.Bidirectional(keras.layers.LSTM(64,activation='tanh',return_sequences=False))(x1) 219 | 220 | x3 = tf.keras.layers.Dense(512,activation='relu')(x2) 221 | x_output = keras.layers.Dense(400,activation='relu')(x3) 222 | 223 | 224 | input_top_1 = tf.keras.Input(shape=(400), name='input_top_1') 225 | input_top_2 = tf.keras.Input(shape=(400), name='input_top_2') 226 | input_top_3 = tf.keras.Input(shape=(400), name='input_top_3') 227 | 228 | x1_input_top_1 = keras.layers.Dense(512,activation='relu')(input_top_1) 229 | x2_input_top_1 = keras.layers.Dense(100,activation='relu')(x1_input_top_1) 230 | 231 | x1_input_top_2 = keras.layers.Dense(512,activation='relu')(input_top_2) 232 | x2_input_top_2 = keras.layers.Dense(100,activation='relu')(x1_input_top_2) 233 | 234 | 235 | x1_input_top_3 = keras.layers.Dense(512,activation='relu')(input_top_3) 236 | x2_input_top_3 = keras.layers.Dense(100,activation='relu')(x1_input_top_3) 237 | 238 | top_k_output = keras.layers.Concatenate()([x2_input_top_1,x2_input_top_2,x2_input_top_3]) 239 | 240 | top_k_output = keras.layers.Reshape((3,100))(top_k_output) 241 | 242 | top_k_output = attention_block(top_k_output,3) 243 | # 244 | 245 | top_k_output = keras.layers.Flatten()(top_k_output) 246 | # 247 | # top_k_output = keras.layers.Dense(100)(top_k_output) 248 | 249 | # ,top_k_output 250 | x_output_f1 = keras.layers.Concatenate()([x_output1,x_output]) 251 | x_output_f1 = keras.layers.Dense(100,activation='relu')(x_output_f1) 252 | 253 | x_output_f2 = keras.layers.Concatenate()([x_output,top_k_output]) 254 | x_output_f2 = keras.layers.Dense(100,activation='relu')(x_output_f2) 255 | 256 | 257 | x_output_f = keras.layers.Concatenate()([x_output,x_output_f1,x_output_f2]) 258 | x_output_f = keras.layers.Dense(512,activation='relu')(x_output_f) 259 | x_output_f = keras.layers.Dense(400)(x_output_f) 260 | 261 | model = Model(inputs=[x_input,input1,input_top_1,input_top_2,input_top_3], outputs=x_output_f) 262 | model.load_weights('./model/CS-GCN-LSTM_allUsers.h5') 263 | predict = model.predict([testX_allUser,testX_allUser,testX_top_1_allUser,testX_top_2_allUser,testX_top_3_allUser]) 264 | np.save('Results/CS-GCN-LSTM_AllUsers/User=30_FileNum=400_predict.npy', predict) 265 | np.save('Results/CS-GCN-LSTM_AllUsers/User=30_FileNum=400_test.npy', testY_allUser) 266 | predict = np.array(getOneDimension(predict)) 267 | test = np.array(getOneDimension(testY_allUser)) 268 | 269 | print(mean_absolute_error(predict,test)) 270 | print(np.sqrt(mean_squared_error(predict,test))) 271 | print(r2_score(predict,test)) 272 | 273 | 274 | 275 | -------------------------------------------------------------------------------- /CS-GCN-LSTM_AllUsers.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Time : 2022/2/21 16:17 3 | # @Author : cmdxmm 4 | # @FileName: getDifferentUserData_less_30000.py 5 | # @Email ：lidongyang@mail.sdu.edu.cn 6 | import numpy as np 7 | from tensorflow.keras import Model 8 | from stellargraph.layer import GCN_LSTM 9 | import pandas as pd 10 | import tensorflow as tf 11 | from tensorflow.keras.callbacks import ModelCheckpoint 12 | from tensorflow import keras 13 | import tensorflow.keras as K 14 | import heapq 15 | from tensorflow.keras.utils import plot_model 16 | 17 | class LossHistory(keras.callbacks.Callback): 18 | def on_train_begin(self, logs={}): 19 | self.losses = [] 20 | def on_epoch_end(self, batch, logs={}): 21 | self.losses.append(logs.get('loss')) 22 | 23 | ####将数据分为训练集和测试集 24 | def train_test_split(data, train_portion): 25 | time_len = data.shape[1] 26 | train_size = int(time_len * train_portion) 27 | train_data = np.array(data.iloc[:, :train_size]) 28 | test_data = np.array(data.iloc[:, train_size:]) 29 | return train_data, test_data 30 | 31 | ###对数据进行归一化 32 | def scale_data(train_data, test_data): 33 | train_data = np.abs(train_data) 34 | test_data = np.abs(test_data) 35 | max_speed = train_data.max() 36 | min_speed = train_data.min() 37 | print('max：',max_speed) 38 | print('min：',min_speed) 39 | train_scaled = (train_data - min_speed) / (max_speed - min_speed) 40 | test_scaled = (test_data - min_speed) / (max_speed - min_speed) 41 | return train_scaled, test_scaled 42 | 43 | #将数据转成10个step训练1个step的格式 44 | def sequence_data_preparation(seq_len, pre_len, train_data, test_data): 45 | trainX, trainY, testX, testY = [], [], [], [] 46 | 47 | for i in range(train_data.shape[1] - int(seq_len + pre_len - 1)): 48 | a = train_data[:, i : i + seq_len + pre_len] 49 | trainX.append(a[:, :seq_len]) 50 | trainY.append(a[:, -1]) 51 | 52 | for i in range(test_data.shape[1] - int(seq_len + pre_len - 1)): 53 | b = test_data[:, i : i + seq_len + pre_len] 54 | testX.append(b[:, :seq_len]) 55 | testY.append(b[:, -1]) 56 | 57 | trainX = np.array(trainX) 58 | trainY = np.array(trainY) 59 | testX = np.array(testX) 60 | testY = np.array(testY) 61 | 62 | return trainX, trainY, testX, testY 63 | 64 | #将数据转成10个step训练1个step的格式 65 | def sequence_data_preparationOtherUsers(seq_len, pre_len, train_data, test_data): 66 | trainX, trainY, testX, testY = [], [], [], [] 67 | 68 | for i in range(train_data.shape[1] - int(seq_len + pre_len - 1)): 69 | a = train_data[:, i : i + seq_len + pre_len] 70 | trainX.append(a[:, -2]) 71 | trainY.append(a[:, -1]) 72 | 73 | for i in range(test_data.shape[1] - int(seq_len + pre_len - 1)): 74 | b = test_data[:, i : i + seq_len + pre_len] 75 | testX.append(b[:, -2]) 76 | testY.append(b[:, -1]) 77 | 78 | trainX = np.array(trainX) 79 | trainY = np.array(trainY) 80 | testX = np.array(testX) 81 | testY = np.array(testY) 82 | 83 | return trainX, trainY, testX, testY 84 | 85 | def attention_block(inputs,time_stpes): 86 | input_dim = int(inputs.shape[2]) # shape = (batch_size, time_steps, input_dim) 87 | 88 | a = keras.layers.Permute((2, 1))(inputs) # shape = (batch_size, input_dim, time_steps) 89 | 90 | a = keras.layers.Reshape((input_dim, time_stpes))(a) # this line is not useful. It's just to know which dimension is what. 91 | 92 | a = tf.keras.layers.Dense(time_stpes, activation='softmax')(a) # 为了让输出的维数和时间序列数相同（这样才能生成各个时间点的注意力值） 93 | 94 | a = keras.layers.Lambda(lambda x: K.backend.mean(x, axis=1), name='dim_reduction')(a) 95 | a = keras.layers.RepeatVector(input_dim)(a) 96 | 97 | a_probs = keras.layers.Permute((2, 1), name='attention_vec')(a) # shape = (batch_size, time_steps, input_dim) 98 | 99 | output_attention_mul = keras.layers.Multiply()([inputs, a_probs]) # 把注意力值和输入按位相乘，权重乘以输入 100 | 101 | return output_attention_mul 102 | 103 | def getMostNSimilarityUEs(topN,Similarity): 104 | topN_index = [] 105 | tmp = np.sort(Similarity) 106 | MostValues = [] 107 | for i in range(topN): 108 | MostValues.append(tmp[len(Similarity)-i-1]) 109 | for i in range(topN): 110 | for j in range(len(Similarity)): 111 | if MostValues[i] == Similarity[j]: 112 | topN_index.append(j) 113 | return topN_index 114 | 115 | userId = 0 # 当前为用户1的相似用户辅助信息 116 | top_k = 3 117 | seq_len = 24 118 | pre_len = 25 119 | userNum = 30 120 | 121 | trainX_allUser = [] 122 | trainY_allUser = [] 123 | testX_allUser = [] 124 | testY_allUser = [] 125 | trainX_top_1_allUser = [] 126 | trainX_top_2_allUser = [] 127 | trainX_top_3_allUser = [] 128 | testX_top_1_allUser = [] 129 | testX_top_2_allUser = [] 130 | testX_top_3_allUser = [] 131 | 132 | for userId in range(userNum): 133 | view_counts = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(userId+1)+'_Contents.npy') 134 | video_dist_adj = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/Content_EdgeMatrix.npy') 135 | view_counts= pd.DataFrame(view_counts) 136 | num_nodes, time_len = view_counts.shape 137 | train_rate = 0.8 138 | print(view_counts.shape) 139 | train_data, test_data = train_test_split(view_counts, train_rate) 140 | print("Train data: ", train_data.shape) 141 | print("Test data: ", test_data.shape) 142 | trainX, trainY, testX, testY = sequence_data_preparation( 143 | seq_len, pre_len, train_data, test_data 144 | ) 145 | trainX_allUser.extend(trainX) 146 | trainY_allUser.extend(trainY) 147 | testX_allUser.extend(testX) 148 | testY_allUser.extend(testY) 149 | print('userId',userId) 150 | SocialRelationship = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/socialRelationship.npy') 151 | SocialSimilarityUsers = np.delete(getMostNSimilarityUEs(top_k+1,SocialRelationship[userId]), 0, axis=0) 152 | print(SocialSimilarityUsers[0]) 153 | view_counts_top_1 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[0]+1)+'_Contents.npy') 154 | view_counts_top_2 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[1]+1)+'_Contents.npy') 155 | view_counts_top_3 = np.load('./YouTube_NewDATA/data/userData/users=30_FileNum=400/User_'+str(SocialSimilarityUsers[2]+1)+'_Contents.npy') 156 | 157 | view_counts_top_1 = pd.DataFrame(view_counts_top_1) 158 | view_counts_top_2 = pd.DataFrame(view_counts_top_2) 159 | view_counts_top_3 = pd.DataFrame(view_counts_top_3) 160 | 161 | train_data_top_1, test_data_top_1 = train_test_split(view_counts_top_1, train_rate) 162 | train_data_top_2, test_data_top_2 = train_test_split(view_counts_top_2, train_rate) 163 | train_data_top_3, test_data_top_3 = train_test_split(view_counts_top_3, train_rate) 164 | 165 | print("Train data: ", train_data_top_1.shape) 166 | print("Test data: ", test_data_top_1.shape) 167 | trainX_top_1, _, testX_top_1, _ = sequence_data_preparationOtherUsers( 168 | seq_len, pre_len, train_data_top_1, test_data_top_1 169 | ) 170 | trainX_top_2, _, testX_top_2, _ = sequence_data_preparationOtherUsers( 171 | seq_len, pre_len, train_data_top_2, test_data_top_2 172 | ) 173 | trainX_top_3, _, testX_top_3, _ = sequence_data_preparationOtherUsers( 174 | seq_len, pre_len, train_data_top_3, test_data_top_3 175 | ) 176 | trainX_top_1_allUser.extend(trainX_top_1) 177 | trainX_top_2_allUser.extend(trainX_top_2) 178 | trainX_top_3_allUser.extend(trainX_top_3) 179 | testX_top_1_allUser.extend(testX_top_1) 180 | testX_top_2_allUser.extend(testX_top_2) 181 | testX_top_3_allUser.extend(testX_top_3) 182 | 183 | trainX_allUser = np.array(trainX_allUser) 184 | trainY_allUser = np.array(trainY_allUser) 185 | testX_allUser = np.array(testX_allUser) 186 | testY_allUser = np.array(testY_allUser) 187 | trainX_top_1_allUser = np.array(trainX_top_1_allUser) 188 | trainX_top_2_allUser = np.array(trainX_top_2_allUser) 189 | trainX_top_3_allUser = np.array(trainX_top_3_allUser) 190 | testX_top_1_allUser = np.array(testX_top_1_allUser) 191 | testX_top_2_allUser = np.array(testX_top_2_allUser) 192 | testX_top_3_allUser = np.array(testX_top_3_allUser) 193 | print(trainX_allUser.shape) 194 | 195 | gcn_lstm = GCN_LSTM( 196 | seq_len=seq_len, 197 | adj=video_dist_adj, 198 | gc_layer_sizes=[32, 64], 199 | gc_activations=["relu", "relu"], 200 | lstm_layer_sizes=[32, 64], 201 | lstm_activations=["relu","relu"], 202 | dropout=0.2, 203 | 204 | ) 205 | x_input, x_output1 = gcn_lstm.in_out_tensors() 206 | x_output1 = keras.layers.Dense(200,activation='relu')(x_output1) 207 | 208 | input1 = tf.keras.Input(shape=(400,24), name='input1') 209 | x1 = keras.layers.Bidirectional(keras.layers.LSTM(32,activation='tanh',return_sequences=True))(input1) 210 | x2 = keras.layers.Bidirectional(keras.layers.LSTM(64,activation='tanh',return_sequences=False))(x1) 211 | 212 | x3 = tf.keras.layers.Dense(512,activation='relu')(x2) 213 | x_output = keras.layers.Dense(400,activation='relu')(x3) 214 | 215 | 216 | input_top_1 = tf.keras.Input(shape=(400), name='input_top_1') 217 | input_top_2 = tf.keras.Input(shape=(400), name='input_top_2') 218 | input_top_3 = tf.keras.Input(shape=(400), name='input_top_3') 219 | 220 | x1_input_top_1 = keras.layers.Dense(512,activation='relu')(input_top_1) 221 | x2_input_top_1 = keras.layers.Dense(100,activation='relu')(x1_input_top_1) 222 | 223 | x1_input_top_2 = keras.layers.Dense(512,activation='relu')(input_top_2) 224 | x2_input_top_2 = keras.layers.Dense(100,activation='relu')(x1_input_top_2) 225 | 226 | 227 | x1_input_top_3 = keras.layers.Dense(512,activation='relu')(input_top_3) 228 | x2_input_top_3 = keras.layers.Dense(100,activation='relu')(x1_input_top_3) 229 | 230 | top_k_output = keras.layers.Concatenate()([x2_input_top_1,x2_input_top_2,x2_input_top_3]) 231 | 232 | top_k_output = keras.layers.Reshape((3,100))(top_k_output) 233 | 234 | top_k_output = attention_block(top_k_output,3) 235 | # 236 | 237 | top_k_output = keras.layers.Flatten()(top_k_output) 238 | # 239 | # top_k_output = keras.layers.Dense(100)(top_k_output) 240 | 241 | # ,top_k_output 242 | x_output_f1 = keras.layers.Concatenate()([x_output1,x_output]) 243 | x_output_f1 = keras.layers.Dense(100,activation='relu')(x_output_f1) 244 | 245 | x_output_f2 = keras.layers.Concatenate()([x_output,top_k_output]) 246 | x_output_f2 = keras.layers.Dense(100,activation='relu')(x_output_f2) 247 | 248 | 249 | x_output_f = keras.layers.Concatenate()([x_output,x_output_f1,x_output_f2]) 250 | x_output_f = keras.layers.Dense(512,activation='relu')(x_output_f) 251 | x_output_f = keras.layers.Dense(400)(x_output_f) 252 | 253 | model = Model(inputs=[x_input,input1,input_top_1,input_top_2,input_top_3], outputs=x_output_f) 254 | 255 | adam = tf.keras.optimizers.Adam(learning_rate=0.0005) 256 | history = model.compile(optimizer=adam, loss="mae", metrics=["mae"]) 257 | checkpoint = ModelCheckpoint(filepath='model/CS-GCN-LSTM_allUsers.h5', monitor='val_loss', mode='auto', save_best_only='True', verbose=1) 258 | history = LossHistory() 259 | callback_lists = [checkpoint,history] 260 | model.summary() 261 | plot_model(model,to_file='model.png',show_shapes=True) 262 | 263 | history2 = model.fit( 264 | [trainX_allUser,trainX_allUser,trainX_top_1_allUser,trainX_top_2_allUser,trainX_top_3_allUser], 265 | trainY_allUser, 266 | epochs=100, 267 | batch_size=512, 268 | shuffle=True, 269 | verbose=1, 270 | validation_data=[[testX_allUser,testX_allUser,testX_top_1_allUser,testX_top_2_allUser,testX_top_3_allUser], testY_allUser], 271 | callbacks=callback_lists 272 | ) 273 | 274 | print( 275 | "Train loss: ", 276 | history2.history["loss"][-1], 277 | "\nTest loss:", 278 | history2.history["val_loss"][-1], 279 | ) 280 | losses = history.losses 281 | print(losses) 282 | np.save('./model/losses/losses_allUsers.npy',losses) 283 | -------------------------------------------------------------------------------- /收敛性Training_loss_new/Training_loss_oneUser.eps: -------------------------------------------------------------------------------- 1 | %!PS-Adobe-3.0 EPSF-3.0 2 | %%Title: Training_loss_oneUser.eps 3 | %%Creator: Matplotlib v3.5.0, https://matplotlib.org/ 4 | %%CreationDate: Fri May 20 17:28:52 2022 5 | %%Orientation: portrait 6 | %%BoundingBox: 75 223 537 569 7 | %%HiResBoundingBox: 75.600000 223.200000 536.400000 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1097 c 501 | 584 1130 659 1147 748 1147 c 502 | 761 1147 775 1146 790 1144 c 503 | 805 1143 822 1140 841 1137 c 504 | 842 948 l 505 | 506 | ce} _d 507 | /s{1067 0 111 -29 967 1147 sc 508 | 907 1087 m 509 | 907 913 l 510 | 855 940 801 960 745 973 c 511 | 689 986 631 993 571 993 c 512 | 480 993 411 979 365 951 c 513 | 320 923 297 881 297 825 c 514 | 297 782 313 749 346 724 c 515 | 379 700 444 677 543 655 c 516 | 606 641 l 517 | 737 613 829 573 884 522 c 518 | 939 471 967 400 967 309 c 519 | 967 205 926 123 843 62 c 520 | 761 1 648 -29 504 -29 c 521 | 444 -29 381 -23 316 -11 c 522 | 251 0 183 18 111 41 c 523 | 111 231 l 524 | 179 196 246 169 312 151 c 525 | 378 134 443 125 508 125 c 526 | 595 125 661 140 708 169 c 527 | 755 199 778 241 778 295 c 528 | 778 345 761 383 727 410 c 529 | 694 437 620 462 506 487 c 530 | 442 502 l 531 | 328 526 246 563 195 612 c 532 | 144 662 119 730 119 817 c 533 | 119 922 156 1004 231 1061 c 534 | 306 1118 412 1147 549 1147 c 535 | 617 1147 681 1142 741 1132 c 536 | 801 1122 856 1107 907 1087 c 537 | 538 | ce} _d 539 | /t{803 0 55 0 754 1438 sc 540 | 375 1438 m 541 | 375 1120 l 542 | 754 1120 l 543 | 754 977 l 544 | 375 977 l 545 | 375 369 l 546 | 375 278 387 219 412 193 c 547 | 437 167 488 154 565 154 c 548 | 754 154 l 549 | 754 0 l 550 | 565 0 l 551 | 423 0 325 26 271 79 c 552 | 217 132 190 229 190 369 c 553 | 190 977 l 554 | 55 977 l 555 | 55 1120 l 556 | 190 1120 l 557 | 190 1438 l 558 | 375 1438 l 559 | 560 | ce} _d 561 | /u{1298 0 174 -29 1112 1147 sc 562 | 174 442 m 563 | 174 1120 l 564 | 358 1120 l 565 | 358 449 l 566 | 358 343 379 263 420 210 c 567 | 461 157 523 131 606 131 c 568 | 705 131 784 163 841 226 c 569 | 899 289 928 376 928 485 c 570 | 928 1120 l 571 | 1112 1120 l 572 | 1112 0 l 573 | 928 0 l 574 | 928 172 l 575 | 883 104 831 53 772 20 c 576 | 713 -13 645 -29 567 -29 c 577 | 438 -29 341 11 274 91 c 578 | 207 171 174 288 174 442 c 579 | 580 | 637 1147 m 581 | 637 1147 l 582 | 583 | ce} _d 584 | end readonly def 585 | 586 | /BuildGlyph { 587 | exch begin 588 | CharStrings exch 589 | 2 copy known not {pop /.notdef} if 590 | true 3 1 roll get exec 591 | end 592 | } _d 593 | 594 | /BuildChar { 595 | 1 index /Encoding get exch get 596 | 1 index /BuildGlyph get exec 597 | } _d 598 | 599 | FontName currentdict end definefont pop 600 | %!PS-Adobe-3.0 Resource-Font 601 | %%Creator: Converted from TrueType to Type 3 by Matplotlib. 602 | 10 dict begin 603 | /FontName /DejaVuSans-Bold def 604 | /PaintType 0 def 605 | /FontMatrix [0.00048828125 0 0 0.00048828125 0 0] def 606 | /FontBBox [-2190 -850 4045 2405] def 607 | /FontType 3 def 608 | /Encoding [/space /a /c /E /g /h /i /L /n /o /p /r /s /T] def 609 | /CharStrings 15 dict dup begin 610 | /.notdef 0 def 611 | /space{713 0 0 0 0 0 sc 612 | ce} _d 613 | /a{1382 0 88 -29 1221 1147 sc 614 | 674 504 m 615 | 599 504 543 491 505 466 c 616 | 468 441 449 403 449 354 c 617 | 449 309 464 273 494 247 c 618 | 525 222 567 209 621 209 c 619 | 688 209 745 233 791 281 c 620 | 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88 -29 1319 1147 sc 791 | 705 891 m 792 | 626 891 565 862 523 805 c 793 | 482 748 461 666 461 559 c 794 | 461 452 482 369 523 312 c 795 | 565 255 626 227 705 227 c 796 | 783 227 843 255 884 312 c 797 | 925 369 946 452 946 559 c 798 | 946 666 925 748 884 805 c 799 | 843 862 783 891 705 891 c 800 | 801 | 705 1147 m 802 | 898 1147 1048 1095 1156 991 c 803 | 1265 887 1319 743 1319 559 c 804 | 1319 375 1265 231 1156 127 c 805 | 1048 23 898 -29 705 -29 c 806 | 512 -29 360 23 251 127 c 807 | 142 231 88 375 88 559 c 808 | 88 743 142 887 251 991 c 809 | 360 1095 512 1147 705 1147 c 810 | 811 | ce} _d 812 | /p{1466 0 172 -426 1374 1147 sc 813 | 530 162 m 814 | 530 -426 l 815 | 172 -426 l 816 | 172 1120 l 817 | 530 1120 l 818 | 530 956 l 819 | 579 1021 634 1069 694 1100 c 820 | 754 1131 823 1147 901 1147 c 821 | 1039 1147 1152 1092 1241 982 c 822 | 1330 873 1374 732 1374 559 c 823 | 1374 386 1330 245 1241 135 c 824 | 1152 26 1039 -29 901 -29 c 825 | 823 -29 754 -14 694 17 c 826 | 634 48 579 97 530 162 c 827 | 828 | 768 887 m 829 | 691 887 632 859 591 802 c 830 | 550 746 530 665 530 559 c 831 | 530 453 550 372 591 315 c 832 | 632 259 691 231 768 231 c 833 | 845 231 903 259 943 315 c 834 | 984 371 1004 452 1004 559 c 835 | 1004 666 984 747 943 803 c 836 | 903 859 845 887 768 887 c 837 | 838 | ce} _d 839 | /r{1010 0 172 0 1004 1147 sc 840 | 1004 815 m 841 | 973 830 941 840 910 847 c 842 | 879 854 848 858 817 858 c 843 | 725 858 654 828 604 769 c 844 | 555 710 530 626 530 516 c 845 | 530 0 l 846 | 172 0 l 847 | 172 1120 l 848 | 530 1120 l 849 | 530 936 l 850 | 576 1009 629 1063 688 1096 c 851 | 748 1130 820 1147 903 1147 c 852 | 915 1147 928 1146 942 1145 c 853 | 956 1144 976 1142 1003 1139 c 854 | 1004 815 l 855 | 856 | ce} _d 857 | /s{1219 0 106 -29 1122 1147 sc 858 | 1047 1085 m 859 | 1047 813 l 860 | 970 845 896 869 825 885 c 861 | 754 901 686 909 623 909 c 862 | 555 909 504 900 471 883 c 863 | 438 866 422 840 422 805 c 864 | 422 776 434 754 459 739 c 865 | 484 724 529 712 594 705 c 866 | 657 696 l 867 | 840 673 964 634 1027 581 c 868 | 1090 528 1122 444 1122 330 c 869 | 1122 211 1078 121 990 61 c 870 | 902 1 771 -29 596 -29 c 871 | 522 -29 445 -23 366 -11 c 872 | 287 0 206 18 123 41 c 873 | 123 313 l 874 | 194 278 267 252 342 235 c 875 | 417 218 494 209 571 209 c 876 | 641 209 694 219 729 238 c 877 | 764 257 782 286 782 324 c 878 | 782 356 770 380 745 395 c 879 | 721 411 673 423 600 432 c 880 | 537 440 l 881 | 378 460 266 497 202 551 c 882 | 138 605 106 687 106 797 c 883 | 106 916 147 1004 228 1061 c 884 | 309 1118 434 1147 602 1147 c 885 | 668 1147 737 1142 810 1132 c 886 | 883 1122 962 1106 1047 1085 c 887 | 888 | ce} _d 889 | /T{1397 0 10 0 1386 1493 sc 890 | 10 1493 m 891 | 1386 1493 l 892 | 1386 1202 l 893 | 891 1202 l 894 | 891 0 l 895 | 506 0 l 896 | 506 1202 l 897 | 10 1202 l 898 | 10 1493 l 899 | 900 | ce} _d 901 | end readonly def 902 | 903 | /BuildGlyph { 904 | exch begin 905 | CharStrings exch 906 | 2 copy known not {pop /.notdef} if 907 | 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/C glyphshow 2623 | 7.331543 0 m /S glyphshow 2624 | 13.996582 0 m /hyphen glyphshow 2625 | 17.785400 0 m /U glyphshow 2626 | 25.470703 0 m /P glyphshow 2627 | 31.802490 0 m /L glyphshow 2628 | 37.652344 0 m /parenleft glyphshow 2629 | 41.748779 0 m /M glyphshow 2630 | 50.808105 0 m /u glyphshow 2631 | 57.462891 0 m /l glyphshow 2632 | 60.380127 0 m /t glyphshow 2633 | 64.497070 0 m /i glyphshow 2634 | 67.414307 0 m /p glyphshow 2635 | 74.079346 0 m /l glyphshow 2636 | 76.996582 0 m /e glyphshow 2637 | 83.456543 0 m /space glyphshow 2638 | 86.794189 0 m /U glyphshow 2639 | 94.479492 0 m /s glyphshow 2640 | 99.949951 0 m /e glyphshow 2641 | 106.409912 0 m /r glyphshow 2642 | 110.726807 0 m /s glyphshow 2643 | 116.197266 0 m /parenright glyphshow 2644 | grestore 2645 | 2646 | end 2647 | showpage 2648 | --------------------------------------------------------------------------------