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Sign-Aware Recommendation Using Graph Neural Networks on ML-1m Dataset in PyTorch"]},{"cell_type":"markdown","metadata":{"id":"dfEu7_3GMP6Z"},"source":["## Setup"]},{"cell_type":"code","metadata":{"id":"3X8DNyvx6-06"},"source":["!pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu111.html\n","!pip install torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+cu111.html\n","!pip install torch-geometric"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"-8cfttRjGXjg","executionInfo":{"status":"ok","timestamp":1637761110365,"user_tz":-330,"elapsed":5264,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"cfef03c8-4508-494c-fd9c-2f80aa77faa3"},"source":["import torch\n","torch.__version__"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'1.10.0+cu111'"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"EaxoGLQO7D5S"},"source":["from torch import nn\n","from torch import Tensor\n","from torch import optim\n","from torch.utils.data import Dataset, DataLoader\n","from torch_geometric.data import Data\n","import torch.nn.functional as F\n","from torch_geometric.nn import MessagePassing\n","from torch_geometric.utils import add_self_loops, degree\n","\n","import pandas as pd\n","import numpy as np\n","import time\n","from tqdm.notebook import tqdm\n","\n","import os\n","import pickle"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jTHP9X3uLtwe"},"source":["import warnings\n","warnings.filterwarnings('ignore')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FkWDKmmUBFJW","executionInfo":{"status":"ok","timestamp":1637761329872,"user_tz":-330,"elapsed":392,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"81d350ac-c184-48a0-84ee-0f55a849e9fe"},"source":["class Args:\n","    dataset = 'ML-1M' # Dataset\n","    version = 1 # Dataset version\n","    batch_size = 1024 # Batch size\n","    dim = 64 # Dimension\n","    lr = 5e-3 # Learning rate\n","    offset = 3.5 # Criterion of likes/dislikes\n","    K = 40 # The number of negative samples\n","    num_layers = 4 # The number of layers of a GNN model for the graph with positive edges\n","    MLP_layers = 2 # The number of layers of MLP for the graph with negative edges\n","    epoch = 5 # The number of epochs\n","    reg = 0.05 # Regularization coefficient\n","\n","args = Args()\n","Args.__dict__"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["mappingproxy({'K': 40,\n","              'MLP_layers': 2,\n","              '__dict__': <attribute '__dict__' of 'Args' objects>,\n","              '__doc__': None,\n","              '__module__': '__main__',\n","              '__weakref__': <attribute '__weakref__' of 'Args' objects>,\n","              'batch_size': 1024,\n","              'dataset': 'ML-1M',\n","              'dim': 64,\n","              'epoch': 5,\n","              'lr': 0.005,\n","              'num_layers': 4,\n","              'offset': 3.5,\n","              'reg': 0.05,\n","              'version': 1})"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"qpVGR7NVMUa-"},"source":["## Dataset"]},{"cell_type":"code","metadata":{"id":"eKhodi5h-aNm"},"source":["!git clone -q --branch v2 https://github.com/RecoHut-Datasets/movielens_1m.git"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xw2tVzO2-HcI"},"source":["class Data_loader():\n","    def __init__(self,dataset,version):\n","        self.dataset=dataset; self.version=version\n","        self.sep='::'\n","        self.names=['userId','movieId','rating','timestemp'];\n","        self.path_for_whole='./movielens_1m/ratings.dat'\n","        self.path_for_train='./movielens_1m/train_1m%s.dat'%(version)\n","        self.path_for_test='./movielens_1m/test_1m%s.dat'%(version)\n","        self.num_u=6040; self.num_v=3952;\n","        \n","    def data_load(self):\n","        self.whole_=pd.read_csv(self.path_for_whole, names = self.names, sep=self.sep, engine='python').drop('timestemp',axis=1).sample(frac=1,replace=False,random_state=self.version)\n","        self.train_set = pd.read_csv(self.path_for_train,engine='python',names=self.names).drop('timestemp',axis=1)\n","        self.test_set = pd.read_csv(self.path_for_test,engine='python',names=self.names).drop('timestemp',axis=1)            \n","        return self.train_set, self.test_set"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"K26T4NSC-HZl"},"source":["class bipartite_dataset(Dataset): \n","    def __init__(self, train,neg_dist,offset,num_u,num_v,K): \n","        self.edge_1 = torch.tensor(train['userId'].values-1)\n","        self.edge_2 = torch.tensor(train['movieId'].values-1) +num_u\n","        self.edge_3 = torch.tensor(train['rating'].values) - offset\n","        self.neg_dist = neg_dist\n","        self.K = K;\n","        self.num_u = num_u\n","        self.num_v = num_v\n","        self.tot = np.arange(num_v)\n","        self.train = train\n","        \n","    def negs_gen_(self):\n","        print('negative sampling...'); st=time.time()\n","        self.edge_4 = torch.empty((len(self.edge_1),self.K),dtype=torch.long)\n","        prog = tqdm(desc='negative sampling for each epoch...',total=len(set(self.train['userId'].values)),position=0)\n","        for j in set(self.train['userId'].values):\n","            pos=self.train[self.train['userId']==j]['movieId'].values-1\n","            neg = np.setdiff1d(self.tot,pos)\n","            temp = (torch.tensor(np.random.choice(neg,len(pos)*self.K,replace=True,p=self.neg_dist[neg]/self.neg_dist[neg].sum()))+self.num_u).long()\n","            self.edge_4[self.edge_1==j-1]=temp.view(int(len(temp)/self.K),self.K)\n","            prog.update(1)\n","        prog.close()\n","        self.edge_4 = torch.tensor(self.edge_4).long()\n","        print('complete ! %s'%(time.time()-st))\n","        \n","    def negs_gen_EP(self,epoch):\n","        print('negative sampling for next epochs...'); st=time.time()\n","        self.edge_4_tot = torch.empty((len(self.edge_1),self.K,epoch),dtype=torch.long)\n","        prog = tqdm(desc='negative sampling for next epochs...',total=len(set(self.train['userId'].values)),position=0)\n","        for j in set(self.train['userId'].values):\n","            pos=self.train[self.train['userId']==j]['movieId'].values-1\n","            neg = np.setdiff1d(self.tot,pos)\n","            temp = (torch.tensor(np.random.choice(neg,len(pos)*self.K*epoch,replace=True,p=self.neg_dist[neg]/self.neg_dist[neg].sum()))+self.num_u).long()\n","            self.edge_4_tot[self.edge_1==j-1]=temp.view(int(len(temp)/self.K/epoch),self.K,epoch)\n","            prog.update(1)\n","        prog.close()\n","        self.edge_4_tot = torch.tensor(self.edge_4_tot).long()\n","        print('complete ! %s'%(time.time()-st))\n","\n","    def __len__(self):\n","        return len(self.edge_1)\n","\n","    def __getitem__(self,idx):\n","        u = self.edge_1[idx]\n","        v = self.edge_2[idx]\n","        w = self.edge_3[idx]\n","        negs = self.edge_4[idx]\n","        return u,v,w,negs"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"iuji2aOB-HXH"},"source":["def deg_dist(train, num_v):\n","    uni, cou = np.unique(train['movieId'].values-1,return_counts=True)\n","    cou = cou**(0.75)\n","    deg = np.zeros(num_v)\n","    deg[uni] = cou\n","    return torch.tensor(deg)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9bXdW8gMMXF-"},"source":["## Modules"]},{"cell_type":"code","metadata":{"id":"ylnEwRr5AcDM"},"source":["def gen_top_K(data_class,emb,train,directory_):\n","    no_items = np.array(list(set(np.arange(1,data_class.num_v+1))-set(train['movieId'])))\n","    total_users = set(np.arange(1,data_class.num_u+1))\n","    reco = dict()\n","    pbar = tqdm(desc = 'top-k recommendation...',total=len(total_users),position=0)\n","    for j in total_users:\n","        pos = train[train['userId']==j]['movieId'].values-1\n","        embedding_ = emb[j-1].view(1,len(emb[0])).mm(emb[data_class.num_u:].t()).detach();\n","        embedding_[0][no_items-1]=-np.inf;\n","        embedding_[0][pos]=-np.inf;\n","        reco[j]=torch.topk(embedding_[0],300).indices.cpu().numpy()+1\n","        pbar.update(1)\n","    pbar.close()\n","    return reco"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wSZobaqg77bu"},"source":["class LightGConv(MessagePassing):\n","    def __init__(self):\n","        super().__init__(aggr='add')\n","        \n","    def forward(self,x,edge_index):\n","        row, col = edge_index\n","        deg = degree(col, x.size(0), dtype=x.dtype)\n","        deg_inv_sqrt = deg.pow(-0.5)\n","        deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0\n","        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]\n","        return self.propagate(edge_index, x=x, norm=norm)\n","    \n","    def message(self,x_j,norm):\n","        return norm.view(-1,1) * x_j\n","        \n","    def update(self,inputs: Tensor) -> Tensor:\n","        return inputs"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GQ8cVF6V9Xx8"},"source":["class LRGCCF(MessagePassing):\n","    def __init__(self, in_channels,out_channels):\n","        super(LRGCCF,self).__init__(aggr='mean')\n","        self.lin = torch.nn.Linear(in_channels, out_channels)\n","\n","    def forward(self,x,edge_index):\n","        edge_index, _ = add_self_loops(edge_index, num_nodes = x.size(0));\n","        return self.lin(self.propagate(edge_index,x=x))\n","\n","    def message(self,x_j):\n","        return x_j\n","        \n","    def update(self,inputs: Tensor) -> Tensor:\n","        return inputs"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"P5g2bFwbMeOh"},"source":["## Model"]},{"cell_type":"code","metadata":{"id":"4sKS8Pd2_Pq3"},"source":["class SiReN(nn.Module):\n","    def __init__(self,train,num_u,num_v,offset,num_layers = 2,MLP_layers=2,dim = 64,reg=1e-4\n","                 ,device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")):\n","        super(SiReN,self).__init__()\n","        self.M = num_u; self.N = num_v;\n","        self.num_layers = num_layers\n","        self.MLP_layers = MLP_layers\n","        self.device = device\n","        self.reg = reg\n","        self.embed_dim = dim\n","        edge_user = torch.tensor(train[train['rating']>offset]['userId'].values-1)\n","        edge_item = torch.tensor(train[train['rating']>offset]['movieId'].values-1)+self.M\n","        edge_ = torch.stack((torch.cat((edge_user,edge_item),0),torch.cat((edge_item,edge_user),0)),0)\n","        self.data_p=Data(edge_index=edge_)\n","        # For the graph with positive edges\n","        self.E = nn.Parameter(torch.empty(self.M + self.N, dim))\n","        nn.init.xavier_normal_(self.E.data)\n","        self.convs = nn.ModuleList()\n","        self.mlps = nn.ModuleList()\n","        for _ in range(num_layers):\n","            # self.convs.append(LRGCCF(dim,dim)) \n","            self.convs.append(LightGConv()) \n","        # For the graph with negative edges\n","        self.E2 = nn.Parameter(torch.empty(self.M + self.N, dim))\n","        nn.init.xavier_normal_(self.E2.data)\n","        for _ in range(MLP_layers):\n","            self.mlps.append(nn.Linear(dim,dim,bias=True))\n","            nn.init.xavier_normal_(self.mlps[-1].weight.data)\n","        # Attntion model\n","        self.attn = nn.Linear(dim,dim,bias=True)\n","        self.q = nn.Linear(dim,1,bias=False)\n","        self.attn_softmax = nn.Softmax(dim=1)\n","        \n","    def aggregate(self):\n","        # Generate embeddings z_p\n","        B=[]; B.append(self.E)\n","        x = self.convs[0](self.E,self.data_p.edge_index)\n","        B.append(x)\n","        for i in range(1,self.num_layers):\n","            x = self.convs[i](x,self.data_p.edge_index)\n","            B.append(x)\n","        z_p = sum(B)/len(B) \n","        # Generate embeddings z_n\n","        C = []; C.append(self.E2)\n","        x = F.dropout(F.relu(self.mlps[0](self.E2)),p=0.5,training=self.training)\n","        for i in range(1,self.MLP_layers):\n","            x = self.mlps[i](x);\n","            x = F.relu(x)\n","            x = F.dropout(x,p=0.5,training=self.training)\n","            C.append(x)\n","        z_n = C[-1]\n","        # Attntion for final embeddings Z\n","        w_p = self.q(F.dropout(torch.tanh((self.attn(z_p))),p=0.5,training=self.training))\n","        w_n = self.q(F.dropout(torch.tanh((self.attn(z_n))),p=0.5,training=self.training))\n","        alpha_ = self.attn_softmax(torch.cat([w_p,w_n],dim=1))\n","        Z = alpha_[:,0].view(len(z_p),1) * z_p + alpha_[:,1].view(len(z_p),1) * z_n\n","        return Z\n","    \n","    def forward(self,u,v,w,n,device):\n","        emb = self.aggregate()\n","        u_ = emb[u].to(device);\n","        v_ = emb[v].to(device);\n","        n_ = emb[n].to(device);\n","        w_ = w.to(device)\n","        positivebatch = torch.mul(u_ , v_ ); \n","        negativebatch = torch.mul(u_.view(len(u_),1,self.embed_dim),n_)  \n","        sBPR_loss =  F.logsigmoid((torch.sign(w_).view(len(u_),1) * (positivebatch.sum(dim=1).view(len(u_),1))) - negativebatch.sum(dim=2)).sum(dim=1)\n","        reg_loss = u_.norm(dim=1).pow(2).sum() + v_.norm(dim=1).pow(2).sum() + n_.norm(dim=2).pow(2).sum();\n","        return -torch.sum(sBPR_loss) + self.reg * reg_loss"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8pVwjmftMcku"},"source":["## Evaluator"]},{"cell_type":"code","metadata":{"id":"qZFsrwih-9P7"},"source":["class evaluate():\n","    def __init__(self,reco,train,test,threshold,num_u,num_v,N=[5,10,15,20,25],ratings=[20,50]):\n","        '''\n","        train : training set\n","        test : test set\n","        threshold : To generate ground truth set from test set\n","        '''\n","        self.reco = reco\n","        self.num_u = num_u;\n","        self.num_v = num_v;\n","        self.N=N\n","        self.p=[]\n","        self.r=[]\n","        self.NDCG=[]\n","        self.p_c1=[]; self.p_c2=[]; self.p_c3=[]\n","        self.r_c1=[]; self.r_c2=[]; self.r_c3=[]\n","        self.NDCG_c1=[]; self.NDCG_c2=[]; self.NDCG_c3=[]\n","        self.tr = train; self.te = test;\n","        self.threshold = threshold;\n","        self.gen_ground_truth_set()\n","        self.ratings = ratings\n","        self.partition_into_groups_(self.ratings)\n","        print('\\nevaluating recommendation accuracy....')\n","        self.precision_and_recall_G(self.group1,1)\n","        self.precision_and_recall_G(self.group2,2)\n","        self.precision_and_recall_G(self.group3,3)\n","        self.Normalized_DCG_G(self.group1,1)\n","        self.Normalized_DCG_G(self.group2,2)\n","        self.Normalized_DCG_G(self.group3,3)\n","        self.metric_total()\n","\n","    def gen_ground_truth_set(self):\n","        result = dict()\n","        GT = self.te[self.te['rating']>=self.threshold];\n","        U = set(GT['userId'])\n","        for i in U:\n","            result[i] = list(set([j for j in GT[GT['userId']==i]['movieId']]))#-set(self.TOP))\n","            if len(result[i])==0:\n","                del(result[i])\n","        self.GT = result\n","\n","    def precision_and_recall(self):\n","        user_in_GT=[j for j in self.GT];\n","        for n in self.N:\n","            p=0; r=0;\n","            for i in user_in_GT:\n","                topn=self.reco[i][:n]\n","                num_hit=len(set(topn).intersection(set(self.GT[i])));\n","                p+=num_hit/n; r+=num_hit/len(self.GT[i]);\n","            self.p.append(p/len(user_in_GT)); self.r.append(r/len(user_in_GT));\n","                \n","    def Normalized_DCG(self):\n","        maxn=max(self.N);\n","        user_in_GT=[j for j in self.GT];\n","        ndcg=np.zeros(maxn);\n","        for i in user_in_GT:\n","            idcg_len = min(len(self.GT[i]), maxn)\n","            temp_idcg = np.cumsum(1.0 / np.log2(np.arange(2, maxn + 2)))\n","            temp_idcg[idcg_len:] = temp_idcg[idcg_len-1]\n","            temp_dcg=np.cumsum([1.0/np.log2(idx+2) if item in self.GT[i] else 0.0 for idx, item in enumerate(self.reco[i][:maxn])])\n","            ndcg+=temp_dcg/temp_idcg;\n","        ndcg/=len(user_in_GT);\n","        for n in self.N:\n","            self.NDCG.append(ndcg[n-1])\n","            \n","    def metric_total(self):\n","        self.p = self.len1 * np.array(self.p_c1) + self.len2 * np.array(self.p_c2) + self.len3 * np.array(self.p_c3);\n","        self.p/= self.len1 + self.len2 + self.len3\n","        self.p = list(self.p)\n","        self.r = self.len1 * np.array(self.r_c1) + self.len2 * np.array(self.r_c2) + self.len3 * np.array(self.r_c3);\n","        self.r/= self.len1 + self.len2 + self.len3\n","        self.r = list(self.r)\n","        self.NDCG = self.len1 * np.array(self.NDCG_c1) + self.len2 * np.array(self.NDCG_c2) + self.len3 * np.array(self.NDCG_c3);\n","        self.NDCG/= self.len1 + self.len2 + self.len3\n","        self.NDCG = list(self.NDCG)\n","\n","    def partition_into_groups_(self,ratings=[20,50]):\n","        unique_u, counts_u = np.unique(self.tr['userId'].values,return_counts=True)\n","        self.group1 = unique_u[np.argwhere(counts_u<ratings[0])]\n","        temp = unique_u[np.argwhere(counts_u<ratings[1])]\n","        self.group2 = np.setdiff1d(temp,self.group1)\n","        self.group3 = np.setdiff1d(unique_u,temp)\n","        self.cold_groups = ratings\n","        self.group1 = list(self.group1.reshape(-1))\n","        self.group2 = list(self.group2.reshape(-1))\n","        self.group3 = list(self.group3.reshape(-1))\n","    \n","    def precision_and_recall_G(self,group,gn):\n","        user_in_GT=[j for j in self.GT];\n","        leng = 0 ; maxn = max(self.N) ; p = np.zeros(maxn); r = np.zeros(maxn);\n","        for i in user_in_GT:\n","            if i in group:\n","                leng+=1\n","                hit_ = np.cumsum([1.0 if item in self.GT[i] else 0.0 for idx, item in enumerate(self.reco[i][:maxn])])\n","                p+=hit_ / np.arange(1,maxn+1); r+=hit_/len(self.GT[i])\n","        p/= leng; r/=leng;\n","        for n in self.N:\n","            if gn == 1 :\n","                self.p_c1.append(p[n-1])\n","                self.r_c1.append(r[n-1])\n","                self.len1 = leng;\n","            elif gn == 2 :\n","                self.p_c2.append(p[n-1])\n","                self.r_c2.append(r[n-1])\n","                self.len2 = leng;\n","            elif gn == 3 :\n","                self.p_c3.append(p[n-1])\n","                self.r_c3.append(r[n-1])\n","                self.len3 = leng;\n","            \n","    def Normalized_DCG_G(self,group,gn):\n","        maxn=max(self.N);\n","        user_in_GT=[j for j in self.GT];\n","        ndcg=np.zeros(maxn);\n","        leng = 0\n","        for i in user_in_GT:\n","            if i in group:\n","                leng+=1\n","                idcg_len = min(len(self.GT[i]), maxn)\n","                temp_idcg = np.cumsum(1.0 / np.log2(np.arange(2, maxn + 2)))\n","                temp_idcg[idcg_len:] = temp_idcg[idcg_len-1]\n","                temp_dcg=np.cumsum([1.0/np.log2(idx+2) if item in self.GT[i] else 0.0 for idx, item in enumerate(self.reco[i][:maxn])])\n","                ndcg+=temp_dcg/temp_idcg;\n","        ndcg/=leng\n","        for n in self.N:\n","            if gn == 1 :\n","                self.NDCG_c1.append(ndcg[n-1])\n","            elif gn == 2 :\n","                self.NDCG_c2.append(ndcg[n-1])\n","            elif gn == 3 :\n","                self.NDCG_c3.append(ndcg[n-1])"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TEY2dcSCMgDo"},"source":["## Main"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RgXHcZo8Au28","executionInfo":{"status":"ok","timestamp":1637761195212,"user_tz":-330,"elapsed":9968,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"dbad62b2-20cd-4f17-a7c3-2329269c94b6"},"source":["data_class=Data_loader(args.dataset,args.version)\n","threshold = round(args.offset) # To generate ground truth set \n","\n","print('data loading...'); st=time.time()\n","train,test = data_class.data_load();\n","train = train.astype({'userId':'int64', 'movieId':'int64'})\n","print('loading complete! time :: %s'%(time.time()-st))\n","\n","print('generate negative candidates...'); st=time.time()\n","neg_dist = deg_dist(train,data_class.num_v)\n","print('complete ! time : %s'%(time.time()-st))    "],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["data loading...\n","loading complete! time :: 9.793904066085815\n","generate negative candidates...\n","complete ! time : 0.08616352081298828\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":806,"referenced_widgets":["ead6047367b944f79636469f7f71015b","d7019f566c05455c8b4b62b1344e21cd","107f6502e41b46a1a696ac059ca4f754","9a39640fcf074a78b10217ed37fcd31d","0a809c7dd9ce4c9eb27cda7468391b55","f4f7559bd3714a989420f91381cd3223","972176e5a0914db68c2f273f320ca69f","4f738267f6fe48529bc7642a8cfe21da","11962373c90c4ab4a6894a3ac61c56f9","a3c2d027e0d84f21baa8c68656e7010a","e99793784ef44ddf95284d2bdfeb76eb","014cbdfba6af4da5a74fda8f0f0e0b0c","cb5a582617af4acfbaa5ae5dd786c858","02ee86c7461b40598583b436015fe847","63441a8c0d0d4faa9aa02537952007f0","ce2c4a7eb9b14cbfb1d28d256505a142","7333789cccd54827bfd3e295923ca976","17ec08c591dc41e189925ea3c05b1fd2","0d65d5610232404187802c3028054db9","38af2612ebc3439bb8b72696d58bc363","e3e4a7c24a33428eb5aceaa8ed9c7e78","c1d59aced543427d88ac758f690122c2","0ffd362a9c824471aca634a17ff25235","c20636ca4bc741f8a58d866b70ab4ab8","d96d6f64aa9f4959825dd18e160c02eb","0e92d8d315674c0e9be12a78d457f0cb","c370695bcb1940d29ecee5073d1bb864","8922da611bdf4c8892327edcc8266e7a","c40339c6839c464fbbf87e5e86e03974","947f4b5bd9714813806cb5abe910eafe","a596a3a3b4834269bf6ed0d6bf154a59","0a48f710dd7b424aa22abc343305ba32","1cd92555ca474c15881a04208fb1917a","096e5187287141f2a139151142fe6acd","e5d933ff65294037876a0b3f2ee0e049","ff4945d9a1a043e5a7230cda054d381f","87db259f866c4241abba5bb5058c068e","263404e08b614993ad6f9e959f0cd5cd","f4927693b7bc4cf690b9e86125a49f95","a2de5d902df24b849811bcaf7a9a4ee3","a53299e5b8094edc9dd8169b42c7dd22","632774c513ba487c930e04dbbc330d7e","e29cb7026f58446f8583809fefb161ed","2b8ced7ca75448e7accf3abb3dc73675","29cbaccc00b94ddc99f3b0d92482ad6a","7525cca057714ef691c20080d89e323e","171813ed80f8407586da544dccff22b5","ddcab42cb8db40eea6f847c8da8d8285","50b1849639104386a0fbfa4bdd3cdd19","680b514c97b64c51ab285356e1cf5bec","01ffcc2fc2934165a97bed2ed9d6f2b4","03557b71b8ab489c85e3b04e21210900","a72ca43431d04813935a3c48111a6565","1f19f24bebf34d20bcd403525584bd40","c163af3b4316498e9990e53155a4abc0","ede4338eeb62493fa76f937d603a16a5","75d2e07780df4c10bf06939086b772f0","93e02bae7f4342929a7b05058fe5774b","b009734c5bee44fd8fbf2e0ac49094b5","178d6724c12a4046af8e91e9224b8068","58fc54ccf1214cf0ba48fe75ea115a86","c0a01a0fb8f44031bcb368a2bd1276d6","f50e24eaaab345b98bc209a4a8bf3a73","37ef3244203c4f818de6af92da151a64","3d0ade9c2ef74f5baa35a227e2eaedd4","e04cfbd2a7cc4296804a20d1e33a8a2b","aa1851c2dd8f461489c9059da5470f42","9e87c375e1ad4984a9c61af36ba91081","228f4b3a33924ce69c5f90fcef4fdd2f","b63c999a1d164727ae814d4304854c19","875db3e6a8e144c5b66e421547919c88","79b2d990eff04acba57e6820bdf33e45","37c294403d7b49519dc747054eabc8b3","5795943776ed4abd947e503bb44f7a7a","c3f10f2baf6e4ef6a4a3ac911046eae5","f628f2edeb7349d5b478554bfc94393b","66a8015fdb9146dfbbee0fe8b504fd3f","b481673d99a746d3820e5098f2b62f83","35b1d4537ccf4f30b9285a94cada0cbd","2e26bc06be844e9e8d7102dabba5dbb3","231f6b3a34e4449b92a039ce2d17b7c0","00c14c7d772a4557b946999a2cb7165d","fc4913f1d4e24a6bb02781c8f0a6e05a","636d9b01fbdb485182e5ae62672d8dce","44e2cacb8eb04d3c90f9fae407986a82","31b5bc83ba0b434abf9badec077d32dd","d6e32a2d33694a888c5de660ce577223","fc872aab29b94dde8aa9950cbeb37613","e83e4c84d0a948e2b7e5da1c5b642bd2","0a860f75211c466f8509832ee95f91af","01a0ba7bde004e5282f53473a69d209f","b96c17c668e440288264cd3a929f1551","25a0a94d88a84ed9bf5b94a551ada82e","51a374e955ed4786b75473e485411bd3","fb9fe648cc5044edb3a931ca25d56433","bd083664967e4c94b130763e63484916","75c6a649a14f4834a984f95af808cbea","79a8c652316048df93621c205b0d2c9e","8ab9f40f74ef4ebeb94937ded992516e","e145f46ecd36420284bcbde614f6f768","8f0a81812e8e443a9db4e3180e8ca2b3","766b730d130b4b4590dcbd57cf01a085","d1f4619fb6ec4f1cbc2e24a84928fbc5","e9060be8b2504d458a78e171359eceb8","24c41df87d8541a9b94251d0c1b6f0bb","ded72cecb54d448d987b5e0e9659578d","cdd7970076f447a097af679ecd5a6344","2b8cd80de36f409a8542c53fdd28adb0","145a81cf42064d6ca67483796af406da","df131514b9d6434eb2969da766ba3065"]},"id":"93VSXEfkH9CG","executionInfo":{"status":"ok","timestamp":1637762281358,"user_tz":-330,"elapsed":924484,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"2ac89b0a-7791-4e03-e658-5fcde15e2451"},"source":["device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","model= SiReN(train, data_class.num_u,data_class.num_v,offset=args.offset,num_layers = args.num_layers,MLP_layers=args.MLP_layers,dim=args.dim,device=device,reg=args.reg)#.to(device);\n","model.data_p.to(device)\n","model.to(device)\n","optimizer = optim.Adam(model.parameters(), lr = args.lr)\n","\n","print(\"\\nTraining on {}...\\n\".format(device))\n","model.train()\n","training_dataset=bipartite_dataset(train,neg_dist,args.offset,data_class.num_u,data_class.num_v,args.K);\n","\n","for EPOCH in range(1,args.epoch+1):\n","    if EPOCH%2-1==0:training_dataset.negs_gen_EP(2)\n","    LOSS=0\n","    training_dataset.edge_4 = training_dataset.edge_4_tot[:,:,EPOCH%2-1]\n","    ds = DataLoader(training_dataset,batch_size=args.batch_size,shuffle=True)\n","    q=0\n","    pbar = tqdm(desc = 'Version : {} Epoch {}/{}'.format(args.version,EPOCH,args.epoch),total=len(ds),position=0)\n","    for u,v,w,negs in ds:\n","        q+=len(u)\n","        st=time.time()\n","        optimizer.zero_grad()\n","        loss = model(u,v,w,negs,device) # original\n","        loss.backward()                \n","        optimizer.step()\n","        LOSS+=loss.item() * len(ds)\n","        pbar.update(1);\n","        pbar.set_postfix({'loss':loss.item()})\n","    pbar.close()\n","\n","    if EPOCH%2==0 :\n","        directory = os.getcwd() + '/results/%s/SiReN/epoch%s_batch%s_dim%s_lr%s_offset%s_K%s_num_layers%s_MLP_layers%s_threshold%s_reg%s/'%(args.dataset,EPOCH,args.batch_size,args.dim,args.lr,args.offset,args.K,args.num_layers,args.MLP_layers,threshold,args.reg)\n","        if not os.path.exists(directory):\n","            os.makedirs(directory)\n","        model.eval()\n","        emb = model.aggregate();\n","        top_k_list = gen_top_K(data_class,emb,train,directory+'r%s_reco.pickle'%(args.version)) \n","        eval_ = evaluate(top_k_list,train,test,threshold,data_class.num_u,data_class.num_v,N=[10,15,20],ratings=[20,50])\n","        print(\"\\n***************************************************************************************\")\n","        print(\" /* Recommendation Accuracy */\")\n","        print('Precision at [10, 15, 20] :: ',eval_.p)\n","        print('Recall at [10, 15, 20] :: ',eval_.r)\n","        print('NDCG at [10, 15, 20] :: ',eval_.NDCG)\n","        print(\"***************************************************************************************\")\n","        directory_ = directory+'r%s_reco.pickle'%(args.version)\n","        with open(directory_,'wb') as fw:\n","            pickle.dump(eval_,fw)\n","        model.train()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Training on cuda:0...\n","\n","negative sampling for next epochs...\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ead6047367b944f79636469f7f71015b","version_minor":0,"version_major":2},"text/plain":["negative sampling for next epochs...:   0%|          | 0/6040 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["comlete ! 47.54057741165161\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"014cbdfba6af4da5a74fda8f0f0e0b0c","version_minor":0,"version_major":2},"text/plain":["Version : 1 Epoch 1/5:   0%|          | 0/782 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0ffd362a9c824471aca634a17ff25235","version_minor":0,"version_major":2},"text/plain":["Version : 1 Epoch 2/5:   0%|          | 0/782 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"096e5187287141f2a139151142fe6acd","version_minor":0,"version_major":2},"text/plain":["top-k recommendation...:   0%|          | 0/6040 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","evaluating recommendation accuracy....\n","\n","***************************************************************************************\n"," /* Recommendation Accuracy */\n","Precision at [10, 15, 20] ::  [0.17743606886177699, 0.15966349100228705, 0.14585492227979271]\n","Recall at [10, 15, 20] ::  [0.10826288445828194, 0.1428493921120366, 0.1714220782607128]\n","NDCG at [10, 15, 20] ::  [0.19677987212069598, 0.1955777256736281, 0.19695887816559263]\n","***************************************************************************************\n","negative sampling for next epochs...\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"29cbaccc00b94ddc99f3b0d92482ad6a","version_minor":0,"version_major":2},"text/plain":["negative sampling for next epochs...:   0%|          | 0/6040 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["comlete ! 97.63998460769653\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ede4338eeb62493fa76f937d603a16a5","version_minor":0,"version_major":2},"text/plain":["Version : 1 Epoch 3/5:   0%|          | 0/782 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"aa1851c2dd8f461489c9059da5470f42","version_minor":0,"version_major":2},"text/plain":["Version : 1 Epoch 4/5:   0%|          | 0/782 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b481673d99a746d3820e5098f2b62f83","version_minor":0,"version_major":2},"text/plain":["top-k recommendation...:   0%|          | 0/6040 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","evaluating recommendation accuracy....\n","\n","***************************************************************************************\n"," /* Recommendation Accuracy */\n","Precision at [10, 15, 20] ::  [0.21833528330269186, 0.19131985068806362, 0.1724887180344305]\n","Recall at [10, 15, 20] ::  [0.1381091856308494, 0.17661215295682997, 0.2087517778197392]\n","NDCG at [10, 15, 20] ::  [0.26384756370294105, 0.2564248659896275, 0.2555622447550156]\n","***************************************************************************************\n","negative sampling for next epochs...\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e83e4c84d0a948e2b7e5da1c5b642bd2","version_minor":0,"version_major":2},"text/plain":["negative sampling for next epochs...:   0%|          | 0/6040 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["comlete ! 45.37535858154297\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e145f46ecd36420284bcbde614f6f768","version_minor":0,"version_major":2},"text/plain":["Version : 1 Epoch 5/5:   0%|          | 0/782 [00:00<?, ?it/s]"]},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"N1SNkv69OkWM"},"source":["---"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QqAlsR9BOkWN","executionInfo":{"status":"ok","timestamp":1637762417561,"user_tz":-330,"elapsed":3668,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"68ec4782-32d3-4fc0-bc2b-32eb6f7deea9"},"source":["!pip install -q watermark\n","%reload_ext watermark\n","%watermark -a \"Sparsh A.\" -m -iv -u -t -d -p torch_geometric"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Author: Sparsh A.\n","\n","Last updated: 2021-11-24 14:00:16\n","\n","torch_geometric: 2.0.2\n","\n","Compiler    : GCC 7.5.0\n","OS          : Linux\n","Release     : 5.4.104+\n","Machine     : x86_64\n","Processor   : x86_64\n","CPU cores   : 2\n","Architecture: 64bit\n","\n","torch  : 1.10.0+cu111\n","pandas : 1.1.5\n","IPython: 5.5.0\n","numpy  : 1.19.5\n","\n"]}]},{"cell_type":"markdown","metadata":{"id":"_UTXo_AeOkWO"},"source":["---"]},{"cell_type":"markdown","metadata":{"id":"ARVn9tFAOkWO"},"source":["**END**"]}]}