{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n\n\ndf = pd.read_csv(\"../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv\", dtype={\"article_id\": str})\nprint(df.shape)\ndf.head()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-09-06T02:33:04.462885Z","iopub.execute_input":"2022-09-06T02:33:04.463736Z","iopub.status.idle":"2022-09-06T02:34:06.662948Z","shell.execute_reply.started":"2022-09-06T02:33:04.463602Z","shell.execute_reply":"2022-09-06T02:34:06.662087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df[\"t_dat\"] = pd.to_datetime(df[\"t_dat\"])\ndf[\"t_dat\"].max()","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:34:06.664900Z","iopub.execute_input":"2022-09-06T02:34:06.665417Z","iopub.status.idle":"2022-09-06T02:34:11.361997Z","shell.execute_reply.started":"2022-09-06T02:34:06.665361Z","shell.execute_reply":"2022-09-06T02:34:11.361254Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"active_articles = df.groupby(\"article_id\")[\"t_dat\"].max().reset_index()\nactive_articles = active_articles[active_articles[\"t_dat\"] >= \"2019-09-01\"].reset_index()\nactive_articles.shape","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:34:11.363179Z","iopub.execute_input":"2022-09-06T02:34:11.363442Z","iopub.status.idle":"2022-09-06T02:34:16.206030Z","shell.execute_reply.started":"2022-09-06T02:34:11.363407Z","shell.execute_reply":"2022-09-06T02:34:16.205283Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df[df[\"article_id\"].isin(active_articles[\"article_id\"])].reset_index(drop=True)\ndf.shape","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:34:16.207933Z","iopub.execute_input":"2022-09-06T02:34:16.208271Z","iopub.status.idle":"2022-09-06T02:34:22.983080Z","shell.execute_reply.started":"2022-09-06T02:34:16.208232Z","shell.execute_reply":"2022-09-06T02:34:22.982326Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df[\"week\"] = (df[\"t_dat\"].max() - df[\"t_dat\"]).dt.days // 7\ndf[\"week\"].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:34:22.984357Z","iopub.execute_input":"2022-09-06T02:34:22.984628Z","iopub.status.idle":"2022-09-06T02:34:24.483781Z","shell.execute_reply.started":"2022-09-06T02:34:22.984600Z","shell.execute_reply":"2022-09-06T02:34:24.483045Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\n\n\narticle_ids = np.concatenate([[\"placeholder\"], np.unique(df[\"article_id\"].values)])\n\nle_article = LabelEncoder()\nle_article.fit(article_ids)\ndf[\"article_id\"] = le_article.transform(df[\"article_id\"])","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:34:24.487575Z","iopub.execute_input":"2022-09-06T02:34:24.489801Z","iopub.status.idle":"2022-09-06T02:35:11.428304Z","shell.execute_reply.started":"2022-09-06T02:34:24.489756Z","shell.execute_reply":"2022-09-06T02:35:11.427407Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"WEEK_HIST_MAX = 5\n\ndef create_dataset(df, week):\n    hist_df = df[(df[\"week\"] > week) & (df[\"week\"] <= week + WEEK_HIST_MAX)]\n    hist_df = hist_df.groupby(\"customer_id\").agg({\"article_id\": list, \"week\": list}).reset_index()\n    hist_df.rename(columns={\"week\": 'week_history'}, inplace=True)\n    \n    target_df = df[df[\"week\"] == week]\n    target_df = target_df.groupby(\"customer_id\").agg({\"article_id\": list}).reset_index()\n    target_df.rename(columns={\"article_id\": \"target\"}, inplace=True)\n    target_df[\"week\"] = week\n    \n    return target_df.merge(hist_df, on=\"customer_id\", how=\"left\")\n\nval_weeks = [0]\ntrain_weeks = [1, 2, 3, 4]\n\n\nval_df = pd.concat([create_dataset(df, w) for w in val_weeks]).reset_index(drop=True)\ntrain_df = pd.concat([create_dataset(df, w) for w in train_weeks]).reset_index(drop=True)\ntrain_df.shape, val_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:35:11.429873Z","iopub.execute_input":"2022-09-06T02:35:11.430142Z","iopub.status.idle":"2022-09-06T02:35:42.817980Z","shell.execute_reply.started":"2022-09-06T02:35:11.430090Z","shell.execute_reply":"2022-09-06T02:35:42.817232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import time","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:35:42.819359Z","iopub.execute_input":"2022-09-06T02:35:42.819633Z","iopub.status.idle":"2022-09-06T02:35:42.824319Z","shell.execute_reply.started":"2022-09-06T02:35:42.819598Z","shell.execute_reply":"2022-09-06T02:35:42.823173Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch.utils.data import Dataset, DataLoader\nimport torch\nfrom tqdm import tqdm\n\nclass HMDataset(Dataset):\n    def __init__(self, df, seq_len, is_test=False):\n        self.df = df.reset_index(drop=True)\n        self.seq_len = seq_len\n        self.is_test = is_test\n    \n    def __len__(self):\n        return self.df.shape[0]\n    \n    def __getitem__(self, index):\n        row = self.df.iloc[index]\n        \n        if self.is_test:\n            target = torch.zeros(2).float()\n        else:\n#             target = torch.zeros(len(article_ids)).float()\n#             for t in row.target:\n#                 target[t] = 1.0\n            if row.target:\n                target = row.target[0]\n            else:\n                target = torch.tensor([0])\n        \n            \n        article_hist = torch.zeros(self.seq_len).long()\n        week_hist = torch.ones(self.seq_len).float()\n        \n        \n        if isinstance(row.article_id, list):\n            if len(row.article_id) >= self.seq_len:\n                article_hist = torch.LongTensor(row.article_id[-self.seq_len:])\n            else:\n                article_hist[-len(row.article_id):] = torch.LongTensor(row.article_id)\n                \n        return article_hist, week_hist, target\n    \nHMDataset(val_df, 64)[1]","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:35:42.825790Z","iopub.execute_input":"2022-09-06T02:35:42.826040Z","iopub.status.idle":"2022-09-06T02:35:44.520939Z","shell.execute_reply.started":"2022-09-06T02:35:42.826006Z","shell.execute_reply":"2022-09-06T02:35:44.520219Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def adjust_lr(optimizer, epoch):\n    if epoch < 1:\n        lr = 5e-5\n    elif epoch < 6:\n        lr = 1e-3\n    elif epoch < 9:\n        lr = 1e-4\n    else:\n        lr = 1e-5\n\n    for p in optimizer.param_groups:\n        p['lr'] = lr\n    return lr\n    \ndef get_optimizer(net):\n    optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=3e-4, betas=(0.9, 0.999),\n                                 eps=1e-08)\n    return optimizer","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:35:44.523510Z","iopub.execute_input":"2022-09-06T02:35:44.523930Z","iopub.status.idle":"2022-09-06T02:35:44.530025Z","shell.execute_reply.started":"2022-09-06T02:35:44.523889Z","shell.execute_reply":"2022-09-06T02:35:44.529143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch.nn as nn\nimport torch.nn.functional as F\n\nclass HMModel(nn.Module):\n    def __init__(self, article_shape):\n        super(HMModel, self).__init__()\n        \n        self.article_emb = nn.Embedding(article_shape[0]*10, embedding_dim=article_shape[1])\n        \n        self.article_likelihood = nn.Parameter(torch.zeros(article_shape[0]), requires_grad=True)\n        self.top = nn.Sequential(nn.Conv1d(3, 32, kernel_size=1), nn.LeakyReLU(),\n                                 nn.Conv1d(32, 8, kernel_size=1), nn.LeakyReLU(),\n                                 nn.Conv1d(8, 1, kernel_size=1))\n        \n    def forward(self, inputs):\n        article_hist, week_hist = inputs[0], inputs[1]\n        x = self.article_emb(article_hist)\n        x = F.normalize(x, dim=2)\n        x, indices = x.max(axis=1)#customer embedding\n        x = x@F.normalize(self.article_emb.weight).T\n\n        \n        return x\n    \n    \nmodel = HMModel((len(le_article.classes_), 512))\nmodel = model.cuda()","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:35:44.531265Z","iopub.execute_input":"2022-09-06T02:35:44.531591Z","iopub.status.idle":"2022-09-06T02:35:51.584647Z","shell.execute_reply.started":"2022-09-06T02:35:44.531497Z","shell.execute_reply":"2022-09-06T02:35:51.583867Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import sys\n\ndef calc_map(topk_preds, target_array, k=12):\n    metric = []\n    tp, fp = 0, 0\n    \n    for pred in topk_preds:\n        if target_array[pred]:\n            tp += 1\n            metric.append(tp/(tp + fp))\n        else:\n            fp += 1\n            \n    return np.sum(metric) / min(k, target_array.sum())\n\ndef read_data(data):\n    return tuple(d.cuda() for d in data[:-1]), data[-1].cuda()\n\n\ndef validate(model, val_loader, k=12):\n    model.eval()\n    \n    tbar = tqdm(val_loader, file=sys.stdout)\n    \n    maps = []\n    \n    with torch.no_grad():\n        for idx, data in enumerate(tbar):\n            inputs, target = read_data(data)\n\n            logits = model(inputs)\n\n            _, indices = torch.topk(logits, k, dim=1)\n\n            indices = indices.detach().cpu().numpy()\n            target = target.detach().cpu().numpy()\n\n            for i in range(indices.shape[0]):\n                maps.append(calc_map(indices[i], target[i]))\n        \n    \n    return np.mean(maps)\n\nSEQ_LEN = 16\n\nBS = 256\nNW = 8\n\nval_dataset = HMDataset(val_df, SEQ_LEN)\nval_loader = DataLoader(val_dataset, batch_size=BS, shuffle=False, num_workers=NW,\n                          pin_memory=False, drop_last=False)","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:35:51.586053Z","iopub.execute_input":"2022-09-06T02:35:51.586304Z","iopub.status.idle":"2022-09-06T02:35:51.605139Z","shell.execute_reply.started":"2022-09-06T02:35:51.586270Z","shell.execute_reply":"2022-09-06T02:35:51.604313Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Train and validate","metadata":{}},{"cell_type":"code","source":"def train(model, train_loader, epochs):\n    np.random.seed(SEED)\n    \n    optimizer = get_optimizer(model)\n    scaler = torch.cuda.amp.GradScaler()\n\n    criterion = torch.nn.functional.cross_entropy\n    \n    for e in range(epochs):\n        model.train()\n        tbar = tqdm(train_loader, file=sys.stdout)\n        \n        lr = adjust_lr(optimizer, e)\n        \n        loss_list = []\n        read_time_list = []\n        model_time_list = []\n        loss_time_list = []\n        for_loop_time = []\n        \n        initialize = time.time()\n        for idx, data in enumerate(tbar):\n            for_loop_time.append(time.time()-initialize)\n            start = time.time()\n            inputs, target = read_data(data)\n            read_time_list.append(time.time()-start)\n\n            optimizer.zero_grad()\n            \n            with torch.cuda.amp.autocast():\n                start = time.time()\n                logits = model(inputs)\n                model_time_list.append(time.time()-start)\n                start = time.time()\n                try:\n                    target = torch.tensor([0]*target.shape[0]).cuda()\n                except:\n                    import pdb; pdb.set_trace()\n                \n                loss = criterion(logits, target)\n            \n            #loss.backward()\n            scaler.scale(loss).backward()\n            #optimizer.step()\n            scaler.step(optimizer)\n            scaler.update()\n            #print('full softmax loss update',time.time()-start)\n            loss_list.append(loss.detach().cpu().item())\n            loss_time_list.append(time.time()-start)\n            \n            avg_loss = np.round(100*np.mean(loss_list), 4)\n            avg_read_time = np.round(np.sum(read_time_list),4)\n            avg_model_time = np.round(np.sum(model_time_list),4)\n            avg_loss_time = np.round(np.sum(loss_time_list),4)\n            initialize = time.time()\n            initialize_time = np.round(np.sum(for_loop_time),4)\n            \n\n            tbar.set_description(f\"Epoch {e+1} Loss: {avg_loss} lr: {lr} read_time:{avg_read_time} model_time: {avg_model_time} loss_time: {avg_loss_time} Dataloader: {initialize_time}\")\n            \n            \n#         val_map = validate(model, val_loader)\n\n#         log_text = f\"Epoch {e+1}\\nTrain Loss: {avg_loss}\\nValidation MAP: {val_map}\\n\"\n            \n#         print(log_text)\n        \n        #logfile = open(f\"models/{MODEL_NAME}_{SEED}.txt\", 'a')\n        #logfile.write(log_text)\n        #logfile.close()\n    return model\n\n\nMODEL_NAME = \"exp001\"\nSEED = 0\n\ntrain_dataset = HMDataset(train_df, SEQ_LEN)\ntrain_loader = DataLoader(train_dataset, batch_size=BS, shuffle=True, num_workers=NW,\n                          pin_memory=False, drop_last=True)\n\nmodel = train(model, train_loader, epochs=3)","metadata":{"execution":{"iopub.status.busy":"2022-09-06T02:35:51.606971Z","iopub.execute_input":"2022-09-06T02:35:51.607163Z","iopub.status.idle":"2022-09-06T02:43:22.807249Z","shell.execute_reply.started":"2022-09-06T02:35:51.607140Z","shell.execute_reply":"2022-09-06T02:43:22.806222Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}