├── assets └── narm.jpeg ├── utils.py ├── README.md ├── .gitignore ├── metric.py ├── requirements.txt ├── narm.py ├── dataset.py ├── main.py ├── datasets └── preprocess.py └── LICENSE /assets/narm.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Wang-Shuo/Neural-Attentive-Session-Based-Recommendation-PyTorch/HEAD/assets/narm.jpeg -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | ''' 4 | Reference: https://medium.com/@sonicboom8/sentiment-analysis-with-variable-length-sequences-in-pytorch-6241635ae130 5 | ''' 6 | 7 | def collate_fn(data): 8 | """This function will be used to pad the sessions to max length 9 | in the batch and transpose the batch from 10 | batch_size x max_seq_len to max_seq_len x batch_size. 11 | It will return padded vectors, labels and lengths of each session (before padding) 12 | It will be used in the Dataloader 13 | """ 14 | data.sort(key=lambda x: len(x[0]), reverse=True) 15 | lens = [len(sess) for sess, label in data] 16 | labels = [] 17 | padded_sesss = torch.zeros(len(data), max(lens)).long() 18 | for i, (sess, label) in enumerate(data): 19 | padded_sesss[i,:lens[i]] = torch.LongTensor(sess) 20 | labels.append(label) 21 | 22 | padded_sesss = padded_sesss.transpose(0,1) 23 | return padded_sesss, torch.tensor(labels).long(), lens -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Neural-Attentive-Session-Based-Recommendation-PyTorch 2 | A PyTorch implementation of the NARM model in [Neural Attentive Session Based Recommendation](https://arxiv.org/abs/1711.04725) (Li, Jing, et al. "Neural attentive session-based recommendation." Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017). 3 | 4 | ![architecture](assets/narm.jpeg) 5 | 6 | # Usage 7 | 1. Install required packages from requirements.txt file. 8 | ```bash 9 | pip install -r requirements.txt 10 | ``` 11 | 12 | 2. Download datasets used in the paper: [YOOCHOOSE](http://2015.recsyschallenge.com/challenge.html) and [DIGINETICA](http://cikm2016.cs.iupui.edu/cikm-cup). Put the two specific files named `train-item-views.csv` and `yoochoose-clicks.dat` into the folder `datasets/` 13 | 14 | 3. Change to `datasets` fold and run `preprocess.py` script to preprocess datasets. Two directories named after dataset should be generated under `datasets/`. 15 | ```bash 16 | python preprocess.py --dataset diginetica 17 | python preprocess.py --dataset yoochoose 18 | ``` 19 | 20 | 4. Run main.py file to train the model. You can configure some training parameters through the command line. 21 | ```bash 22 | python main.py 23 | ``` 24 | 25 | 5. Run main.py file to test the model. 26 | ```bash 27 | python main.py --test 28 | ``` -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /metric.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def get_recall(indices, targets): 5 | """ 6 | Calculates the recall score for the given predictions and targets 7 | 8 | Args: 9 | indices (Bxk): torch.LongTensor. top-k indices predicted by the model. 10 | targets (B): torch.LongTensor. actual target indices. 11 | 12 | Returns: 13 | recall (float): the recall score 14 | """ 15 | 16 | targets = targets.view(-1, 1).expand_as(indices) 17 | hits = (targets == indices).nonzero() 18 | if len(hits) == 0: 19 | return 0 20 | n_hits = (targets == indices).nonzero()[:, :-1].size(0) 21 | recall = float(n_hits) / targets.size(0) 22 | return recall 23 | 24 | 25 | def get_mrr(indices, targets): 26 | """ 27 | Calculates the MRR score for the given predictions and targets 28 | Args: 29 | indices (Bxk): torch.LongTensor. top-k indices predicted by the model. 30 | targets (B): torch.LongTensor. actual target indices. 31 | 32 | Returns: 33 | mrr (float): the mrr score 34 | """ 35 | 36 | tmp = targets.view(-1, 1) 37 | targets = tmp.expand_as(indices) 38 | hits = (targets == indices).nonzero() 39 | ranks = hits[:, -1] + 1 40 | ranks = ranks.float() 41 | rranks = torch.reciprocal(ranks) 42 | mrr = torch.sum(rranks).data / targets.size(0) 43 | return mrr.item() 44 | 45 | 46 | def evaluate(indices, targets, k=20): 47 | """ 48 | Evaluates the model using Recall@K, MRR@K scores. 49 | 50 | Args: 51 | logits (B,C): torch.LongTensor. The predicted logit for the next items. 52 | targets (B): torch.LongTensor. actual target indices. 53 | 54 | Returns: 55 | recall (float): the recall score 56 | mrr (float): the mrr score 57 | """ 58 | _, indices = torch.topk(indices, k, -1) 59 | recall = get_recall(indices, targets) 60 | mrr = get_mrr(indices, targets) 61 | return recall, mrr 62 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | alabaster==0.7.12 2 | asn1crypto==0.24.0 3 | astroid==2.3.1 4 | attrs==19.2.0 5 | Babel==2.7.0 6 | backcall==0.1.0 7 | bleach==3.1.0 8 | certifi==2019.9.11 9 | cffi==1.12.3 10 | chardet==3.0.4 11 | cloudpickle==1.2.2 12 | cryptography==2.7 13 | decorator==4.4.0 14 | defusedxml==0.6.0 15 | docutils==0.15.2 16 | entrypoints==0.3 17 | idna==2.8 18 | imagesize==1.1.0 19 | ipykernel==5.1.2 20 | ipython==7.8.0 21 | ipython-genutils==0.2.0 22 | ipywidgets==7.5.1 23 | isort==4.3.21 24 | jedi==0.15.1 25 | jeepney==0.4.1 26 | Jinja2==2.10.1 27 | json5==0.8.5 28 | jsonschema==3.0.2 29 | jupyter==1.0.0 30 | jupyter-client==5.3.3 31 | jupyter-console==6.0.0 32 | jupyter-core==4.5.0 33 | jupyterlab==1.1.4 34 | jupyterlab-server==1.0.6 35 | keyring==18.0.0 36 | lazy-object-proxy==1.4.2 37 | MarkupSafe==1.1.1 38 | mccabe==0.6.1 39 | mistune==0.8.4 40 | nbconvert==5.6.0 41 | nbformat==4.4.0 42 | notebook==6.0.1 43 | numpy==1.17.2 44 | numpydoc==0.9.1 45 | packaging==19.2 46 | pandas==0.25.1 47 | pandocfilters==1.4.2 48 | parso==0.5.1 49 | pexpect==4.7.0 50 | pickleshare==0.7.5 51 | Pillow==6.1.0 52 | prometheus-client==0.7.1 53 | prompt-toolkit==2.0.10 54 | psutil==5.6.3 55 | ptyprocess==0.6.0 56 | pycodestyle==2.5.0 57 | pycparser==2.19 58 | pyflakes==2.1.1 59 | Pygments==2.4.2 60 | pylint==2.4.2 61 | pyOpenSSL==19.0.0 62 | pyparsing==2.4.2 63 | pyrsistent==0.15.4 64 | PySocks==1.7.1 65 | python-dateutil==2.8.0 66 | pytz==2019.2 67 | pyzmq==18.1.0 68 | QtAwesome==0.6.0 69 | qtconsole==4.5.5 70 | QtPy==1.9.0 71 | requests==2.22.0 72 | rope==0.14.0 73 | scipy==1.3.1 74 | SecretStorage==3.1.1 75 | Send2Trash==1.5.0 76 | six==1.12.0 77 | snowballstemmer==1.9.1 78 | Sphinx==2.2.0 79 | sphinxcontrib-applehelp==1.0.1 80 | sphinxcontrib-devhelp==1.0.1 81 | sphinxcontrib-htmlhelp==1.0.2 82 | sphinxcontrib-jsmath==1.0.1 83 | sphinxcontrib-qthelp==1.0.2 84 | sphinxcontrib-serializinghtml==1.1.3 85 | spyder==3.3.6 86 | spyder-kernels==0.5.2 87 | terminado==0.8.2 88 | testpath==0.4.2 89 | torch==1.1.0 90 | torchvision==0.3.0 91 | tornado==6.0.3 92 | tqdm==4.36.1 93 | traitlets==4.3.3 94 | urllib3==1.24.2 95 | wcwidth==0.1.7 96 | webencodings==0.5.1 97 | widgetsnbextension==3.5.1 98 | wrapt==1.11.2 99 | wurlitzer==1.0.3 100 | -------------------------------------------------------------------------------- /narm.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 5 | 6 | 7 | class NARM(nn.Module): 8 | """Neural Attentive Session Based Recommendation Model Class 9 | 10 | Args: 11 | n_items(int): the number of items 12 | hidden_size(int): the hidden size of gru 13 | embedding_dim(int): the dimension of item embedding 14 | batch_size(int): 15 | n_layers(int): the number of gru layers 16 | 17 | """ 18 | def __init__(self, n_items, hidden_size, embedding_dim, batch_size, n_layers = 1): 19 | super(NARM, self).__init__() 20 | self.n_items = n_items 21 | self.hidden_size = hidden_size 22 | self.batch_size = batch_size 23 | self.n_layers = n_layers 24 | self.embedding_dim = embedding_dim 25 | self.emb = nn.Embedding(self.n_items, self.embedding_dim, padding_idx = 0) 26 | self.emb_dropout = nn.Dropout(0.25) 27 | self.gru = nn.GRU(self.embedding_dim, self.hidden_size, self.n_layers) 28 | self.a_1 = nn.Linear(self.hidden_size, self.hidden_size, bias=False) 29 | self.a_2 = nn.Linear(self.hidden_size, self.hidden_size, bias=False) 30 | self.v_t = nn.Linear(self.hidden_size, 1, bias=False) 31 | self.ct_dropout = nn.Dropout(0.5) 32 | self.b = nn.Linear(self.embedding_dim, 2 * self.hidden_size, bias=False) 33 | #self.sf = nn.Softmax() 34 | self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 35 | 36 | def forward(self, seq, lengths): 37 | hidden = self.init_hidden(seq.size(1)) 38 | embs = self.emb_dropout(self.emb(seq)) 39 | embs = pack_padded_sequence(embs, lengths) 40 | gru_out, hidden = self.gru(embs, hidden) 41 | gru_out, lengths = pad_packed_sequence(gru_out) 42 | 43 | # fetch the last hidden state of last timestamp 44 | ht = hidden[-1] 45 | gru_out = gru_out.permute(1, 0, 2) 46 | 47 | c_global = ht 48 | q1 = self.a_1(gru_out.contiguous().view(-1, self.hidden_size)).view(gru_out.size()) 49 | q2 = self.a_2(ht) 50 | 51 | mask = torch.where(seq.permute(1, 0) > 0, torch.tensor([1.], device = self.device), torch.tensor([0.], device = self.device)) 52 | q2_expand = q2.unsqueeze(1).expand_as(q1) 53 | q2_masked = mask.unsqueeze(2).expand_as(q1) * q2_expand 54 | 55 | alpha = self.v_t(torch.sigmoid(q1 + q2_masked).view(-1, self.hidden_size)).view(mask.size()) 56 | c_local = torch.sum(alpha.unsqueeze(2).expand_as(gru_out) * gru_out, 1) 57 | 58 | c_t = torch.cat([c_local, c_global], 1) 59 | c_t = self.ct_dropout(c_t) 60 | 61 | item_embs = self.emb(torch.arange(self.n_items).to(self.device)) 62 | scores = torch.matmul(c_t, self.b(item_embs).permute(1, 0)) 63 | # scores = self.sf(scores) 64 | 65 | return scores 66 | 67 | def init_hidden(self, batch_size): 68 | return torch.zeros((self.n_layers, batch_size, self.hidden_size), requires_grad=True).to(self.device) 69 | 70 | -------------------------------------------------------------------------------- /dataset.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | create on 18 Sep, 2019 4 | 5 | @author: wangshuo 6 | 7 | Reference: https://github.com/lijingsdu/sessionRec_NARM/blob/master/data_process.py 8 | """ 9 | 10 | import pickle 11 | import torch 12 | from torch.utils.data import Dataset 13 | import numpy as np 14 | 15 | 16 | def load_data(root, valid_portion=0.1, maxlen=19, sort_by_len=False): 17 | '''Loads the dataset 18 | 19 | :type path: String 20 | :param path: The path to the dataset (here RSC2015) 21 | :type n_items: int 22 | :param n_items: The number of items. 23 | :type valid_portion: float 24 | :param valid_portion: The proportion of the full train set used for 25 | the validation set. 26 | :type maxlen: None or positive int 27 | :param maxlen: the max sequence length we use in the train/valid set. 28 | :type sort_by_len: bool 29 | :name sort_by_len: Sort by the sequence lenght for the train, 30 | valid and test set. This allow faster execution as it cause 31 | less padding per minibatch. Another mechanism must be used to 32 | shuffle the train set at each epoch. 33 | 34 | ''' 35 | 36 | # Load the dataset 37 | path_train_data = root + 'train.txt' 38 | path_test_data = root + 'test.txt' 39 | with open(path_train_data, 'rb') as f1: 40 | train_set = pickle.load(f1) 41 | 42 | with open(path_test_data, 'rb') as f2: 43 | test_set = pickle.load(f2) 44 | 45 | if maxlen: 46 | new_train_set_x = [] 47 | new_train_set_y = [] 48 | for x, y in zip(train_set[0], train_set[1]): 49 | if len(x) < maxlen: 50 | new_train_set_x.append(x) 51 | new_train_set_y.append(y) 52 | else: 53 | new_train_set_x.append(x[:maxlen]) 54 | new_train_set_y.append(y) 55 | train_set = (new_train_set_x, new_train_set_y) 56 | del new_train_set_x, new_train_set_y 57 | 58 | new_test_set_x = [] 59 | new_test_set_y = [] 60 | for xx, yy in zip(test_set[0], test_set[1]): 61 | if len(xx) < maxlen: 62 | new_test_set_x.append(xx) 63 | new_test_set_y.append(yy) 64 | else: 65 | new_test_set_x.append(xx[:maxlen]) 66 | new_test_set_y.append(yy) 67 | test_set = (new_test_set_x, new_test_set_y) 68 | del new_test_set_x, new_test_set_y 69 | 70 | # split training set into validation set 71 | train_set_x, train_set_y = train_set 72 | n_samples = len(train_set_x) 73 | sidx = np.arange(n_samples, dtype='int32') 74 | np.random.shuffle(sidx) 75 | n_train = int(np.round(n_samples * (1. - valid_portion))) 76 | valid_set_x = [train_set_x[s] for s in sidx[n_train:]] 77 | valid_set_y = [train_set_y[s] for s in sidx[n_train:]] 78 | train_set_x = [train_set_x[s] for s in sidx[:n_train]] 79 | train_set_y = [train_set_y[s] for s in sidx[:n_train]] 80 | 81 | (test_set_x, test_set_y) = test_set 82 | 83 | def len_argsort(seq): 84 | return sorted(range(len(seq)), key=lambda x: len(seq[x])) 85 | 86 | if sort_by_len: 87 | sorted_index = len_argsort(test_set_x) 88 | test_set_x = [test_set_x[i] for i in sorted_index] 89 | test_set_y = [test_set_y[i] for i in sorted_index] 90 | 91 | sorted_index = len_argsort(valid_set_x) 92 | valid_set_x = [valid_set_x[i] for i in sorted_index] 93 | valid_set_y = [valid_set_y[i] for i in sorted_index] 94 | 95 | train = (train_set_x, train_set_y) 96 | valid = (valid_set_x, valid_set_y) 97 | test = (test_set_x, test_set_y) 98 | 99 | return train, valid, test 100 | 101 | 102 | class RecSysDataset(Dataset): 103 | """define the pytorch Dataset class for yoochoose and diginetica datasets. 104 | """ 105 | def __init__(self, data): 106 | self.data = data 107 | print('-'*50) 108 | print('Dataset info:') 109 | print('Number of sessions: {}'.format(len(data[0]))) 110 | print('-'*50) 111 | 112 | def __getitem__(self, index): 113 | session_items = self.data[0][index] 114 | target_item = self.data[1][index] 115 | return session_items, target_item 116 | 117 | def __len__(self): 118 | return len(self.data[0]) -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python37 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on 19 Sep, 2019 5 | 6 | @author: wangshuo 7 | """ 8 | 9 | import os 10 | import time 11 | import random 12 | import argparse 13 | import pickle 14 | import numpy as np 15 | from tqdm import tqdm 16 | from os.path import join 17 | 18 | import torch 19 | from torch import nn 20 | from torch.utils.data import DataLoader 21 | import torch.nn.functional as F 22 | import torch.optim as optim 23 | from torch.optim.lr_scheduler import StepLR 24 | from torch.autograd import Variable 25 | from torch.backends import cudnn 26 | 27 | import metric 28 | from utils import collate_fn 29 | from narm import NARM 30 | from dataset import load_data, RecSysDataset 31 | 32 | parser = argparse.ArgumentParser() 33 | parser.add_argument('--dataset_path', default='datasets/diginetica/', help='dataset directory path: datasets/diginetica/yoochoose1_4/yoochoose1_64') 34 | parser.add_argument('--batch_size', type=int, default=512, help='input batch size') 35 | parser.add_argument('--hidden_size', type=int, default=100, help='hidden state size of gru module') 36 | parser.add_argument('--embed_dim', type=int, default=50, help='the dimension of item embedding') 37 | parser.add_argument('--epoch', type=int, default=100, help='the number of epochs to train for') 38 | parser.add_argument('--lr', type=float, default=0.001, help='learning rate') 39 | parser.add_argument('--lr_dc', type=float, default=0.1, help='learning rate decay rate') 40 | parser.add_argument('--lr_dc_step', type=int, default=80, help='the number of steps after which the learning rate decay') 41 | parser.add_argument('--test', action='store_true', help='test') 42 | parser.add_argument('--topk', type=int, default=20, help='number of top score items selected for calculating recall and mrr metrics') 43 | parser.add_argument('--valid_portion', type=float, default=0.1, help='split the portion of training set as validation set') 44 | args = parser.parse_args() 45 | print(args) 46 | 47 | here = os.path.dirname(os.path.abspath(__file__)) 48 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 49 | 50 | def main(): 51 | print('Loading data...') 52 | train, valid, test = load_data(args.dataset_path, valid_portion=args.valid_portion) 53 | 54 | train_data = RecSysDataset(train) 55 | valid_data = RecSysDataset(valid) 56 | test_data = RecSysDataset(test) 57 | train_loader = DataLoader(train_data, batch_size = args.batch_size, shuffle = True, collate_fn = collate_fn) 58 | valid_loader = DataLoader(valid_data, batch_size = args.batch_size, shuffle = False, collate_fn = collate_fn) 59 | test_loader = DataLoader(test_data, batch_size = args.batch_size, shuffle = False, collate_fn = collate_fn) 60 | 61 | if args.dataset_path.split('/')[-2] == 'diginetica': 62 | n_items = 43098 63 | elif args.dataset_path.split('/')[-2] in ['yoochoose1_64', 'yoochoose1_4']: 64 | n_items = 37484 65 | else: 66 | raise Exception('Unknown Dataset!') 67 | 68 | model = NARM(n_items, args.hidden_size, args.embed_dim, args.batch_size).to(device) 69 | 70 | if args.test: 71 | ckpt = torch.load('latest_checkpoint.pth.tar') 72 | model.load_state_dict(ckpt['state_dict']) 73 | recall, mrr = validate(test_loader, model) 74 | print("Test: Recall@{}: {:.4f}, MRR@{}: {:.4f}".format(args.topk, recall, args.topk, mrr)) 75 | return 76 | 77 | optimizer = optim.Adam(model.parameters(), args.lr) 78 | criterion = nn.CrossEntropyLoss() 79 | scheduler = StepLR(optimizer, step_size = args.lr_dc_step, gamma = args.lr_dc) 80 | 81 | for epoch in tqdm(range(args.epoch)): 82 | # train for one epoch 83 | scheduler.step(epoch = epoch) 84 | trainForEpoch(train_loader, model, optimizer, epoch, args.epoch, criterion, log_aggr = 200) 85 | 86 | recall, mrr = validate(valid_loader, model) 87 | print('Epoch {} validation: Recall@{}: {:.4f}, MRR@{}: {:.4f} \n'.format(epoch, args.topk, recall, args.topk, mrr)) 88 | 89 | # store best loss and save a model checkpoint 90 | ckpt_dict = { 91 | 'epoch': epoch + 1, 92 | 'state_dict': model.state_dict(), 93 | 'optimizer': optimizer.state_dict() 94 | } 95 | 96 | torch.save(ckpt_dict, 'latest_checkpoint.pth.tar') 97 | 98 | 99 | def trainForEpoch(train_loader, model, optimizer, epoch, num_epochs, criterion, log_aggr=1): 100 | model.train() 101 | 102 | sum_epoch_loss = 0 103 | 104 | start = time.time() 105 | for i, (seq, target, lens) in tqdm(enumerate(train_loader), total=len(train_loader)): 106 | seq = seq.to(device) 107 | target = target.to(device) 108 | 109 | optimizer.zero_grad() 110 | outputs = model(seq, lens) 111 | loss = criterion(outputs, target) 112 | loss.backward() 113 | optimizer.step() 114 | 115 | loss_val = loss.item() 116 | sum_epoch_loss += loss_val 117 | 118 | iter_num = epoch * len(train_loader) + i + 1 119 | 120 | if i % log_aggr == 0: 121 | print('[TRAIN] epoch %d/%d batch loss: %.4f (avg %.4f) (%.2f im/s)' 122 | % (epoch + 1, num_epochs, loss_val, sum_epoch_loss / (i + 1), 123 | len(seq) / (time.time() - start))) 124 | 125 | start = time.time() 126 | 127 | 128 | def validate(valid_loader, model): 129 | model.eval() 130 | recalls = [] 131 | mrrs = [] 132 | with torch.no_grad(): 133 | for seq, target, lens in tqdm(valid_loader): 134 | seq = seq.to(device) 135 | target = target.to(device) 136 | outputs = model(seq, lens) 137 | logits = F.softmax(outputs, dim = 1) 138 | recall, mrr = metric.evaluate(logits, target, k = args.topk) 139 | recalls.append(recall) 140 | mrrs.append(mrr) 141 | 142 | mean_recall = np.mean(recalls) 143 | mean_mrr = np.mean(mrrs) 144 | return mean_recall, mean_mrr 145 | 146 | 147 | if __name__ == '__main__': 148 | main() 149 | -------------------------------------------------------------------------------- /datasets/preprocess.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python37 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on 17 Sep, 2019 5 | 6 | Reference: https://github.com/CRIPAC-DIG/SR-GNN/blob/master/datasets/preprocess.py 7 | 8 | 预处理基本流程： 9 | 1. 创建两个字典sess_clicks和sess_date来分别保存session的相关信息。两个字典都以sessionId为键，其中session_click以一个Session中用户先后点击的物品id 10 | 构成的List为值；session_date以一个Session中最后一次点击的时间作为值，后续用于训练集和测试集的划分； 11 | 2. 过滤长度为1的Session和出现次数小于5次的物品； 12 | 3. 依据日期划分训练集和测试集。其中Yoochoose数据集以最后一天时长内的Session作为测试集，Diginetica数据集以最后一周时长内的Session作为测试集； 13 | 4. 分解每个Session生成最终的数据格式。每个Session中以不包括最后一个物品的其他物品作为特征，以最后一个物品作为标签。同时把物品的id重新编码成从1开始递增的自然数序列 14 | """ 15 | 16 | import argparse 17 | import time 18 | import csv 19 | import pickle 20 | import operator 21 | import datetime 22 | import os 23 | from tqdm import tqdm 24 | 25 | parser = argparse.ArgumentParser() 26 | parser.add_argument('--dataset', default='diginetica', help='dataset name') 27 | args = parser.parse_args() 28 | 29 | # add a header for yoochoose dataset 30 | with open('yoochoose-clicks.dat', 'r') as f, open('yoochoose-clicks-withHeader.dat', 'w') as fn: 31 | fn.write('sessionId,timestamp,itemId,category'+'\n') 32 | for line in f: 33 | fn.write(line) 34 | 35 | if args.dataset == 'diginetica': 36 | dataset = 'train-item-views.csv' 37 | elif args.dataset =='yoochoose': 38 | dataset = 'yoochoose-clicks-withHeader.dat' 39 | 40 | 41 | print("-- Starting @ %ss" % datetime.datetime.now()) 42 | with open(dataset, "r") as f: 43 | if args.dataset == 'yoochoose': 44 | reader = csv.DictReader(f, delimiter=',') 45 | else: 46 | reader = csv.DictReader(f, delimiter=';') 47 | sess_clicks = {} 48 | sess_date = {} 49 | ctr = 0 50 | curid = -1 51 | curdate = None 52 | for data in tqdm(reader): 53 | sessid = data['sessionId'] 54 | if curdate and not curid == sessid: 55 | date = '' 56 | if args.dataset == 'yoochoose': 57 | date = time.mktime(time.strptime(curdate[:19], '%Y-%m-%dT%H:%M:%S')) 58 | else: 59 | date = time.mktime(time.strptime(curdate, '%Y-%m-%d')) 60 | sess_date[curid] = date 61 | curid = sessid 62 | if args.dataset == 'yoochoose': 63 | item = data['itemId'] 64 | else: 65 | item = data['itemId'], int(data['timeframe']) 66 | curdate = '' 67 | if args.dataset == 'yoochoose': 68 | curdate = data['timestamp'] 69 | else: 70 | curdate = data['eventdate'] 71 | 72 | if sessid in sess_clicks: 73 | sess_clicks[sessid] += [item] 74 | else: 75 | sess_clicks[sessid] = [item] 76 | ctr += 1 77 | date = '' 78 | if args.dataset == 'yoochoose': 79 | date = time.mktime(time.strptime(curdate[:19], '%Y-%m-%dT%H:%M:%S')) 80 | else: 81 | date = time.mktime(time.strptime(curdate, '%Y-%m-%d')) 82 | for i in list(sess_clicks): 83 | sorted_clicks = sorted(sess_clicks[i], key=operator.itemgetter(1)) 84 | sess_clicks[i] = [c[0] for c in sorted_clicks] 85 | sess_date[curid] = date 86 | print("-- Reading data @ %ss" % datetime.datetime.now()) 87 | 88 | # Filter out length 1 sessions 89 | for s in list(sess_clicks): 90 | if len(sess_clicks[s]) == 1: 91 | del sess_clicks[s] 92 | del sess_date[s] 93 | 94 | # Count number of times each item appears 95 | iid_counts = {} 96 | for s in sess_clicks: 97 | seq = sess_clicks[s] 98 | for iid in seq: 99 | if iid in iid_counts: 100 | iid_counts[iid] += 1 101 | else: 102 | iid_counts[iid] = 1 103 | 104 | sorted_counts = sorted(iid_counts.items(), key=operator.itemgetter(1)) 105 | 106 | length = len(sess_clicks) 107 | for s in list(sess_clicks): 108 | curseq = sess_clicks[s] 109 | filseq = list(filter(lambda i: iid_counts[i] >= 5, curseq)) 110 | if len(filseq) < 2: 111 | del sess_clicks[s] 112 | del sess_date[s] 113 | else: 114 | sess_clicks[s] = filseq 115 | 116 | # Split out test set based on dates 117 | dates = list(sess_date.items()) 118 | maxdate = dates[0][1] 119 | 120 | for _, date in dates: 121 | if maxdate < date: 122 | maxdate = date 123 | 124 | # 7 days for test 125 | splitdate = 0 126 | if args.dataset == 'yoochoose': 127 | splitdate = maxdate - 86400 * 1 # the number of seconds for a day：86400 128 | else: 129 | splitdate = maxdate - 86400 * 7 130 | 131 | print('Splitting date', splitdate) # Yoochoose: ('Split date', 1411930799.0) 132 | tra_sess = filter(lambda x: x[1] < splitdate, dates) 133 | tes_sess = filter(lambda x: x[1] > splitdate, dates) 134 | 135 | # Sort sessions by date 136 | tra_sess = sorted(tra_sess, key=operator.itemgetter(1)) # [(sessionId, timestamp), (), ] 137 | tes_sess = sorted(tes_sess, key=operator.itemgetter(1)) # [(sessionId, timestamp), (), ] 138 | print(len(tra_sess)) # 186670 # 7966257 139 | print(len(tes_sess)) # 15979 # 15324 140 | print(tra_sess[:3]) 141 | print(tes_sess[:3]) 142 | print("-- Splitting train set and test set @ %ss" % datetime.datetime.now()) 143 | 144 | # Choosing item count >=5 gives approximately the same number of items as reported in paper 145 | item_dict = {} 146 | # Convert training sessions to sequences and renumber items to start from 1 147 | def obtian_tra(): 148 | train_ids = [] 149 | train_seqs = [] 150 | train_dates = [] 151 | item_ctr = 1 152 | for s, date in tra_sess: 153 | seq = sess_clicks[s] 154 | outseq = [] 155 | for i in seq: 156 | if i in item_dict: 157 | outseq += [item_dict[i]] 158 | else: 159 | outseq += [item_ctr] 160 | item_dict[i] = item_ctr 161 | item_ctr += 1 162 | if len(outseq) < 2: # Doesn't occur 163 | continue 164 | train_ids += [s] 165 | train_dates += [date] 166 | train_seqs += [outseq] 167 | print(item_ctr) # 43098, 37484 168 | return train_ids, train_dates, train_seqs 169 | 170 | 171 | # Convert test sessions to sequences, ignoring items that do not appear in training set 172 | def obtian_tes(): 173 | test_ids = [] 174 | test_seqs = [] 175 | test_dates = [] 176 | for s, date in tes_sess: 177 | seq = sess_clicks[s] 178 | outseq = [] 179 | for i in seq: 180 | if i in item_dict: 181 | outseq += [item_dict[i]] 182 | if len(outseq) < 2: 183 | continue 184 | test_ids += [s] 185 | test_dates += [date] 186 | test_seqs += [outseq] 187 | return test_ids, test_dates, test_seqs 188 | 189 | 190 | tra_ids, tra_dates, tra_seqs = obtian_tra() 191 | tes_ids, tes_dates, tes_seqs = obtian_tes() 192 | 193 | 194 | def process_seqs(iseqs, idates): 195 | out_seqs = [] 196 | out_dates = [] 197 | labs = [] 198 | ids = [] 199 | for id, seq, date in zip(range(len(iseqs)), iseqs, idates): 200 | for i in range(1, len(seq)): 201 | tar = seq[-i] 202 | labs += [tar] 203 | out_seqs += [seq[:-i]] 204 | out_dates += [date] 205 | ids += [id] 206 | return out_seqs, out_dates, labs, ids 207 | 208 | 209 | tr_seqs, tr_dates, tr_labs, tr_ids = process_seqs(tra_seqs, tra_dates) 210 | te_seqs, te_dates, te_labs, te_ids = process_seqs(tes_seqs, tes_dates) 211 | tra = (tr_seqs, tr_labs) 212 | tes = (te_seqs, te_labs) 213 | print(len(tr_seqs)) 214 | print(len(te_seqs)) 215 | print(tr_seqs[:3], tr_dates[:3], tr_labs[:3]) 216 | print(te_seqs[:3], te_dates[:3], te_labs[:3]) 217 | all = 0 218 | 219 | for seq in tra_seqs: 220 | all += len(seq) 221 | for seq in tes_seqs: 222 | all += len(seq) 223 | print('avg length: ', all/(len(tra_seqs) + len(tes_seqs) * 1.0)) 224 | if args.dataset == 'diginetica': 225 | if not os.path.exists('diginetica'): 226 | os.makedirs('diginetica') 227 | pickle.dump(tra, open('diginetica/train.txt', 'wb')) 228 | pickle.dump(tes, open('diginetica/test.txt', 'wb')) 229 | pickle.dump(tra_seqs, open('diginetica/all_train_seq.txt', 'wb')) 230 | elif args.dataset == 'yoochoose': 231 | if not os.path.exists('yoochoose1_4'): 232 | os.makedirs('yoochoose1_4') 233 | if not os.path.exists('yoochoose1_64'): 234 | os.makedirs('yoochoose1_64') 235 | pickle.dump(tes, open('yoochoose1_4/test.txt', 'wb')) 236 | pickle.dump(tes, open('yoochoose1_64/test.txt', 'wb')) 237 | 238 | split4, split64 = int(len(tr_seqs) / 4), int(len(tr_seqs) / 64) 239 | print(len(tr_seqs[-split4:])) 240 | print(len(tr_seqs[-split64:])) 241 | 242 | tra4, tra64 = (tr_seqs[-split4:], tr_labs[-split4:]), (tr_seqs[-split64:], tr_labs[-split64:]) 243 | seq4, seq64 = tra_seqs[tr_ids[-split4]:], tra_seqs[tr_ids[-split64]:] 244 | 245 | pickle.dump(tra4, open('yoochoose1_4/train.txt', 'wb')) 246 | pickle.dump(seq4, open('yoochoose1_4/all_train_seq.txt', 'wb')) 247 | 248 | pickle.dump(tra64, open('yoochoose1_64/train.txt', 'wb')) 249 | pickle.dump(seq64, open('yoochoose1_64/all_train_seq.txt', 'wb')) 250 | else: 251 | pass 252 | 253 | print('Done.') 254 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------