├── .idea └── vcs.xml ├── DMF.py ├── batch_file_remove.py ├── data ├── ml-1m.test.negative ├── ml-1m.test.rating └── ml-1m.train.rating ├── dataset.py ├── evaluate.py ├── run.sh └── utils.py /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /DMF.py: -------------------------------------------------------------------------------- 1 | # 标准库 2 | import argparse 3 | from time import time 4 | import sys 5 | 6 | # 第三方库 7 | import torch 8 | import torch.nn as nn 9 | import torch.nn.functional as F 10 | import torch.optim as optim 11 | from torch.utils.data import DataLoader 12 | 13 | # 自定义库 14 | from evaluate import evaluate_model 15 | import utils 16 | from dataset import Dataset 17 | 18 | # 是否激活cuda 19 | if torch.cuda.is_available(): 20 | torch.backends.cudnn.deterministic = True 21 | # Device 22 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 23 | 24 | class DMF(nn.Module): 25 | 26 | 27 | def __init__(self, num_users, num_items, layers, dataset): 28 | super(DMF, self).__init__() 29 | self.num_users = num_users 30 | self.num_items = num_items 31 | self.latent_dim = layers[0] 32 | self.layers = layers 33 | 34 | self.user_item_indices = torch.LongTensor([dataset.user_indices, dataset.item_incides]) 35 | self.rating_data = torch.FloatTensor(dataset.rating_data) 36 | self.user_item_matrix = torch.sparse_coo_tensor(self.user_item_indices, self.rating_data, 37 | torch.Size((self.num_users, self.num_items))).to_dense().to(device) 38 | 39 | self.linear_user_1 = nn.Linear(in_features=self.num_items, out_features=self.latent_dim) 40 | self.linear_user_1.weight.detach().normal_(0, 0.01) 41 | self.linear_item_1 = nn.Linear(in_features=self.num_users, out_features=self.latent_dim) 42 | self.linear_item_1.weight.detach().normal_(0, 0.01) 43 | 44 | self.user_fc_layers = nn.ModuleList() 45 | for idx in range(1, len(self.layers)): 46 | self.user_fc_layers.append(nn.Linear(in_features=self.layers[idx - 1], out_features=self.layers[idx])) 47 | 48 | self.item_fc_layers = nn.ModuleList() 49 | for idx in range(1, len(self.layers)): 50 | self.item_fc_layers.append(nn.Linear(in_features=self.layers[idx - 1], out_features=self.layers[idx])) 51 | 52 | def forward(self, user_indices, item_indices): 53 | 54 | user = self.user_item_matrix[user_indices] 55 | item = self.user_item_matrix[:, item_indices].t() 56 | 57 | user = self.linear_user_1(user) 58 | item = self.linear_item_1(item) 59 | 60 | for idx in range(len(self.layers) - 1): 61 | user = F.relu(user) 62 | user = self.user_fc_layers[idx](user) 63 | 64 | for idx in range(len(self.layers) - 1): 65 | item = F.relu(item) 66 | item = self.item_fc_layers[idx](item) 67 | 68 | vector = torch.cosine_similarity(user, item).view(-1, 1) 69 | vector = torch.clamp(vector, min=1e-6, max=1) 70 | 71 | return vector 72 | 73 | #################### Arguments #################### 74 | def parse_args(): 75 | parser = argparse.ArgumentParser(description="Run Conv1.") 76 | parser.add_argument('--path', nargs='?', default='data/', 77 | help='Input data path.') 78 | parser.add_argument('--dataset', nargs='?', default='ml-1m', 79 | help='Choose a dataset.') 80 | parser.add_argument('--epochs', type=int, default=100, 81 | help='Number of epochs.') 82 | parser.add_argument('--batch_size', type=int, default=256, 83 | help='Batch size.') 84 | parser.add_argument('--num_factors', type=int, default=64, 85 | help='Embedding size.') 86 | parser.add_argument('--layers', nargs='?', default='[64,64]', 87 | help="Size of each layer. Note that the first layer is the concatenation of user and item embeddings. So layers[0]/2 is the embedding size.") 88 | parser.add_argument('--reg', type=float, default='0.0', 89 | help="Regularization for each layer") 90 | parser.add_argument('--num_neg', type=int, default=1, 91 | help='Number of negative instances to pair with a positive instance.') 92 | parser.add_argument('--lr', type=float, default=0.0001, 93 | help='Learning rate.') 94 | parser.add_argument('--learner', nargs='?', default='adam', 95 | help='Specify an optimizer: adagrad, adam, rmsprop, sgd') 96 | parser.add_argument('--verbose', type=int, default=1, 97 | help='Show performance per X iterations') 98 | parser.add_argument('--out', type=int, default=1, 99 | help='Whether to save the trained model.') 100 | parser.add_argument('--emlp_pretrain', nargs='?', default='', 101 | help='Specify the pretrain model file for MLP part. If empty, no pretrain will be used') 102 | return parser.parse_args() 103 | 104 | 105 | if __name__ == '__main__': 106 | 107 | 108 | # settings 109 | args = parse_args() 110 | path = args.path 111 | dataset_name = args.dataset 112 | epochs = args.epochs 113 | batch_size = args.batch_size 114 | layers = eval(args.layers) 115 | latent_dim = layers[0] 116 | reg = args.reg 117 | learning_rate = args.lr 118 | num_negative = args.num_neg 119 | verbose = args.verbose 120 | out = args.out 121 | emlp_pretrain = args.emlp_pretrain 122 | topK = 10 123 | evaluation_threads = 1 # mp.cpu_count() 124 | 125 | # Loading data 126 | t1 = time() 127 | dataset = Dataset(path + dataset_name) 128 | train, test_rating, test_negative = dataset.get_train_instances( 129 | num_negative), dataset.test_ratings, dataset.test_negative 130 | num_users, num_items = dataset.num_users, dataset.num_items 131 | t2 = time() 132 | print("header:data, load time:{:.1f}, user:{:d},train:{:d} item:{:d}, test:{:d}" 133 | .format(t2 - t1, num_users, len(train[0]), num_items, len(test_rating))) 134 | 135 | train = utils.UserItemRatingDataset(train[0], train[1], train[2]) 136 | train = DataLoader(train, batch_size=batch_size, shuffle=True) 137 | 138 | # Build model 139 | model = DMF(num_users, num_items, layers, dataset) 140 | model = model.to(device) 141 | criterion = nn.BCELoss() 142 | optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=reg) 143 | print(model) 144 | 145 | 146 | model_name = str(model.__class__)[17:][:-2] 147 | loss_name = str(criterion.__class__)[17 + 13:][:-2] 148 | print( 149 | 'header:class:{},dataset:{}, batch_size:{}, epochs:{}, latent_dim:{}, num_negative:{}, topK:{}, lr:{}, reg:{},loss:{}' 150 | .format(model_name, dataset_name, batch_size, epochs, latent_dim, num_negative, topK, learning_rate, reg, 151 | loss_name)) 152 | 153 | # Init performance 154 | t1 = time() 155 | (hr, ndcg) = evaluate_model(model, test_rating, test_negative, topK, evaluation_threads) 156 | t2 = time() 157 | print('epoch:0,train_time:{:.1f}s, HR:{:.4f}, NDCG:{:.4f}, test_time:{:.1f}s'.format(t1 - t1, hr, ndcg, t2 - t1)) 158 | model_out_file = 'pretrain/'+'/{}-{}-{}-{}-{}-lr_{}-HR_{:.4f}-NDCG_{:.4f}-epoch_{}.model'.format(model_name, 159 | dataset_name, 160 | latent_dim, 161 | layers, 162 | num_negative, 163 | learning_rate, 164 | hr, 165 | ndcg, 166 | 0) 167 | if args.out > 0: 168 | torch.save(model.state_dict(), model_out_file) 169 | 170 | # Train model 171 | best_hr, best_ndcg, best_iter, best_epoch = 0, 0, -1, -1 172 | count = 0 173 | for epoch in range(epochs): 174 | model.train() 175 | epoch = epoch + 1 176 | t1 = time() 177 | # Generate training instances 178 | train = dataset.get_train_instances(num_negative) 179 | train = utils.UserItemRatingDataset(train[0], train[1], train[2]) 180 | train = DataLoader(train, batch_size=batch_size, shuffle=True) 181 | # Training 182 | for batch_idx, (user, item, y) in enumerate(train): 183 | user, item, y = user.cuda(), item.cuda(), y.cuda() 184 | ## forward and backprop 185 | y_hat = model(user, item) 186 | loss = criterion(y_hat, y.view(-1, 1)) 187 | optimizer.zero_grad() 188 | loss.backward() 189 | optimizer.step() 190 | t2 = time() 191 | model.eval() 192 | 193 | # Evaluation 194 | if epoch % verbose == 0: 195 | (hr, ndcg) = evaluate_model(model, test_rating, test_negative, topK, evaluation_threads) 196 | print('epoch:{},train_time:{:.1f}s, HR:{:.4f}, NDCG:{:.4f}, test_time:{:.1f}s, loss:{:.6f}' 197 | .format(epoch, t2 - t1, hr, ndcg, time() - t2, loss)) 198 | if hr > best_hr: 199 | count = 0 200 | best_train_time, best_hr, best_ndcg, best_epoch, best_test_time = t2 - t1, hr, ndcg, epoch, time() - t2 201 | model_out_file = 'pretrain/' + '/{}-{}-{}-{}-{}-lr_{}-HR_{:.4f}-NDCG_{:.4f}-epoch_{}.model'.format( 202 | model_name, 203 | dataset_name, 204 | latent_dim, 205 | layers, 206 | num_negative, 207 | learning_rate, 208 | hr, 209 | ndcg, 210 | epoch) 211 | if args.out > 0: 212 | torch.save(model.state_dict(), model_out_file) 213 | else: 214 | count += 1 215 | if count == 50: 216 | sys.exit(0) 217 | 218 | print('best epoch:{},HR:{:.4f}, NDCG:{:.4f}'.format(best_epoch, best_hr, best_ndcg)) 219 | if args.out > 0: 220 | print("The best model is saved to {}".format(model_out_file)) -------------------------------------------------------------------------------- /batch_file_remove.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | # 指定目录位置 4 | path = 'D:\mylearn\\NCF_Pytorch_Neural-Collaborative-Filtering\pretrain' 5 | # 遍历所有文件 6 | file_names = os.listdir(path) 7 | file_list = [] 8 | # 迭代每个文件名 9 | print(len(file_names)) 10 | file_1 = file_names[0] 11 | file_1_split = file_1.split('_') 12 | for i, item in enumerate(file_names, 1): 13 | file_2 = item 14 | file_2_split = file_2.split('_') 15 | if file_1_split[0:2] != file_2_split[0:2]: 16 | file_list.append(file_1) 17 | file_1 = file_2 18 | file_1_split = file_2_split 19 | file_list.append(file_1) 20 | for e in file_list: 21 | file_names.remove(e) 22 | print(len(file_names)) 23 | for file in file_names: 24 | os.remove(path+'\\'+file) 25 | print('delete') 26 | -------------------------------------------------------------------------------- /data/ml-1m.test.rating: -------------------------------------------------------------------------------- 1 | 0 25 5 978824351 2 | 1 133 3 978300174 3 | 2 207 4 978298504 4 | 3 208 4 978294282 5 | 4 222 2 978246585 6 | 5 396 5 978239019 7 | 6 74 3 978234898 8 | 7 91 3 978247143 9 | 8 514 3 978226678 10 | 9 659 5 980638688 11 | 10 820 1 978909052 12 | 11 829 3 978220273 13 | 12 844 3 978202577 14 | 13 105 4 978201317 15 | 14 519 5 978361393 16 | 15 218 3 978174795 17 | 16 246 5 978161166 18 | 17 228 5 978157434 19 | 18 770 5 994556636 20 | 19 641 4 1009669227 21 | 20 1167 4 978139540 22 | 21 1195 4 991149892 23 | 22 934 1 993707035 24 | 23 603 4 986157459 25 | 24 872 2 978386265 26 | 25 352 1 978385333 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-------------------------------------------------------------------------------- /dataset.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | from scipy.sparse import csr_matrix 4 | 5 | class Dataset(object): 6 | 7 | def __init__(self, path): 8 | self.train_ratings, self.train_num_users, self.train_num_items = self.load_train_rating_file_as_list(path + ".train.rating") 9 | self.test_ratings, self.test_num_users, self.test_num_items = self.load_test_rating_file_as_list(path + ".test.rating") 10 | self.num_users = max(self.train_num_users, self.test_num_users) 11 | self.num_items = max(self.train_num_items, self.test_num_items) 12 | self.test_negative = self.load_negative_file(path + ".test.negative") 13 | self.user_item_rating_indices = self.get_user_item_matrix_indices() 14 | self.user_indices, self.item_incides, self.rating_data = self.user_item_rating_indices 15 | assert len(self.test_ratings) == len(self.test_negative) 16 | self.train_dict = self.get_train_dict() 17 | 18 | def load_test_rating_file_as_list(self, filename): 19 | test_ratings = [] 20 | num_users, num_items = 0, 0 21 | with open(filename, "r") as f: 22 | line = f.readline() 23 | while line != None and line != "": 24 | arr = line.split("\t") 25 | user, item = int(arr[0]), int(arr[1]) 26 | num_users = max(num_users, user) 27 | num_items = max(num_items, item) 28 | test_ratings.append([user, item]) 29 | line = f.readline() 30 | test_num_users = num_users + 1 31 | test_num_items = num_items + 1 32 | return test_ratings, test_num_users, test_num_items 33 | 34 | def load_negative_file(self, filename): 35 | negativeList = [] 36 | with open(filename, "r") as f: 37 | line = f.readline() 38 | while line != None and line != "": 39 | arr = line.split("\t") 40 | negatives = [] 41 | for x in arr[1: ]: 42 | negatives.append(int(x)) 43 | negativeList.append(negatives) 44 | line = f.readline() 45 | return negativeList 46 | 47 | def load_train_rating_file_as_list(self, filename): 48 | ''' 49 | return: [[user, item, rating]] 50 | ''' 51 | # Get number of users and items 52 | num_users, num_items = 0, 0 53 | with open(filename, "r") as f: 54 | line = f.readline() 55 | max_items = 0 56 | while line != None and line != "": 57 | arr = line.split("\t") 58 | u, i = int(arr[0]), int(arr[1]) 59 | num_users = max(num_users, u) 60 | num_items = max(num_items, i) 61 | line = f.readline() 62 | train_num_users = num_users + 1 63 | train_num_items = num_items + 1 64 | # Construct matrix 65 | train_ratings = [] 66 | with open(filename, "r") as f: 67 | line = f.readline() 68 | while line != None and line != "": 69 | arr = line.split("\t") 70 | user, item, rating = int(arr[0]), int(arr[1]), float(arr[2]) 71 | train_ratings.append([user, item, rating]) 72 | line = f.readline() 73 | return train_ratings, train_num_users, train_num_items 74 | 75 | def get_user_item_matrix_indices(self): 76 | user_indices, item_indices, ratings = [], [], [] 77 | for i in self.train_ratings: 78 | user_indices.append(i[0]) 79 | item_indices.append(i[1]) 80 | ratings.append(1) 81 | return [np.array(user_indices), np.array(item_indices), np.array(ratings)] 82 | 83 | def get_user_item_interact_list(self): 84 | user_item_interact = [] 85 | user, item, rate = [], [], [] 86 | user_idx = int(0) 87 | for i in self.train_ratings: 88 | print(i[0]) 89 | if user_idx != i[0]: 90 | user_item_interact.append([user, item, rate]) 91 | user_idx += 1 92 | user, item, rate = [], [], [] 93 | else: 94 | user.append(i[0]) 95 | item.append(i[1]) 96 | rate.append(i[2]) 97 | return user_item_interact 98 | 99 | def get_item_user_interact_list(self): 100 | item_user_interact = [] 101 | user, item, rate = [], [], [] 102 | item_idx = 0 103 | for i in self.train_ratings: 104 | if item_idx != i[1]: 105 | item_user_interact.append([user, item, rate]) 106 | item_idx += 1 107 | user, item, rate = [], [], [] 108 | else: 109 | user.append(i[0]) 110 | item.append(i[1]) 111 | rate.append(i[2]) 112 | return item_user_interact 113 | 114 | def get_train_instances(self, num_negative): 115 | user, item, rate = [], [], [] 116 | for i in self.train_ratings: 117 | user.append(i[0]) 118 | item.append(i[1]) 119 | rate.append(1) 120 | for t in range(num_negative): 121 | j = np.random.randint(self.num_items) 122 | while (i[0], j) in self.train_dict: 123 | j = np.random.randint(self.num_items) 124 | user.append(i[0]) 125 | item.append(j) 126 | rate.append(0) 127 | return [np.array(user), np.array(item), np.array(rate)] 128 | 129 | 130 | def get_user_and_item_matrix(self): 131 | rom = np.random.rand(1, 100) 132 | user_matrix = self.user_item_matrix 133 | item_matrix = self.user_item_matrix.T 134 | return user_matrix, item_matrix 135 | 136 | def get_train_dict(self): 137 | data_dict = {} 138 | for i in self.train_ratings: 139 | data_dict[(i[0], i[1])] = i[2] 140 | return data_dict 141 | 142 | def get_item_sparse_matrix(self): 143 | num_users, num_items = self.num_users, self.num_items 144 | user_indices, item_incides, rating_data = self.user_item_rating_indices 145 | item_sparse_matrix = csr_matrix((rating_data, (item_incides, user_indices)), shape=(num_items, num_users)) 146 | return item_sparse_matrix 147 | 148 | def get_user_sparse_matrix(self): 149 | user_sparse_matrix = self.get_item_sparse_matrix().T 150 | return user_sparse_matrix 151 | -------------------------------------------------------------------------------- /evaluate.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Created on Apr 15, 2016 3 | Evaluate the performance of Top-K recommendation: 4 | Protocol: leave-1-out evaluation 5 | Measures: Hit Ratio and NDCG 6 | (more details are in: Xiangnan He, et al. Fast Matrix Factorization for Online Recommendation with Implicit Feedback. SIGIR'16) 7 | @author: hexiangnan 8 | ''' 9 | import math 10 | import heapq # for retrieval topK 11 | import multiprocessing 12 | import numpy as np 13 | import torch 14 | 15 | # 是否激活cuda 16 | if torch.cuda.is_available(): 17 | torch.backends.cudnn.deterministic = True 18 | # Device 19 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 20 | 21 | # Global variables that are shared across processes 22 | _model = None 23 | _testRatings = None 24 | _testNegatives = None 25 | _K = None 26 | 27 | def evaluate_model(model, testRatings, testNegatives, K, num_thread): 28 | """ 29 | Evaluate the performance (Hit_Ratio, NDCG) of top-K recommendation 30 | Return: score of each test rating. 31 | """ 32 | global _model 33 | global _testRatings 34 | global _testNegatives 35 | global _K 36 | global _dataset 37 | _model = model 38 | _testRatings = testRatings 39 | _testNegatives = testNegatives 40 | _K = K 41 | 42 | hits, ndcgs, mrrs = [],[], [] 43 | if(num_thread > 1): # Multi-thread 44 | pool = multiprocessing.Pool(processes=num_thread) 45 | res = pool.map(eval_one_rating, range(len(_testRatings))) 46 | pool.close() 47 | pool.join() 48 | hits = [r[0] for r in res] 49 | ndcgs = [r[1] for r in res] 50 | return (np.mean(hits), np.mean(ndcgs)) 51 | # Single thread 52 | for idx in range(len(_testRatings)): 53 | (hr,ndcg, mrr) = eval_one_rating(idx) 54 | hits.append(hr) 55 | ndcgs.append(ndcg) 56 | mrrs.append(mrr) 57 | return (np.mean(hits), np.mean(ndcgs)) 58 | 59 | def eval_one_rating(idx): 60 | rating = _testRatings[idx] 61 | items = _testNegatives[idx][0:999] 62 | u = rating[0] 63 | gtItem = rating[1] 64 | items.append(gtItem) 65 | # Get prediction scores 66 | map_item_score = {} 67 | users = np.full(len(items), u, dtype='int64') 68 | batch_users, batch_items = torch.LongTensor(users), torch.LongTensor(items) 69 | tensor_users, tensor_items = batch_users.to(device), batch_items.to(device) 70 | y_pred = _model(tensor_users, tensor_items) # model predict 71 | y_pred = y_pred.cpu() 72 | y_pred = y_pred.detach().numpy() 73 | 74 | for i in range(len(items)): 75 | item = items[i] 76 | map_item_score[item] = y_pred[i] 77 | items.pop() 78 | # Evaluate top rank list 79 | ranklist = heapq.nlargest(_K, map_item_score, key=map_item_score.get) 80 | hr = getHitRatio(ranklist, gtItem) 81 | ndcg = getNDCG(ranklist, gtItem) 82 | mrr = getMRR(ranklist, gtItem) 83 | # print(hr, ndcg) 84 | return (hr, ndcg, mrr) 85 | 86 | def getHitRatio(ranklist, gtItem): 87 | for item in ranklist: 88 | if item == gtItem: 89 | return 1 90 | return 0 91 | 92 | def getNDCG(ranklist, gtItem): 93 | for i in range(len(ranklist)): 94 | item = ranklist[i] 95 | if item == gtItem: 96 | return math.log(2) / math.log(i+2) 97 | return 0 98 | 99 | def getMRR(ranklist, gtItem): 100 | for i in range(len(ranklist)): 101 | item = ranklist[i] 102 | if item == gtItem: 103 | return float(1.0) / (i+1) 104 | return 0 -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | 4 | dataset_name='ml-1m' 5 | python GMF.py --dataset $dataset_name --epochs 200 --batch_size 512 --reg 0.0000 --num_factors 128 --num_neg 1 --lr 0.0005 -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.utils.data import DataLoader, Dataset 3 | class UserItemRatingDataset(Dataset): 4 | def __init__(self, user, item, target): 5 | self.user = torch.LongTensor(user) 6 | self.item = torch.LongTensor(item) 7 | self.target = torch.FloatTensor(target) 8 | 9 | def __getitem__(self, index): 10 | return self.user[index], self.item[index], self.target[index] 11 | 12 | def __len__(self): 13 | return self.user.size(0) --------------------------------------------------------------------------------