predict.ipynb

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   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "test = pd.read_csv(\"../input/albert-3000-128/dataset/test.csv\", escapechar = \"\\\\\", quoting = csv.QUOTE_NONE)"
   ]
  },
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   ],
   "source": [
    "from transformers import AlbertTokenizerFast\n",
    "tokenizer = AlbertTokenizerFast.from_pretrained('albert-base-v2')"
   ]
  },
  {
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    {
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       "3583"
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "import pickle\n",
    "file = open(\"../input/albert-3000-128/lable_map.pickle\",'rb')\n",
    "label_map = pickle.load(file)\n",
    "file.close()\n",
    "\n",
    "label_to_id = {}\n",
    "for key, value in label_map.items():\n",
    "    label_to_id[value] = key\n",
    "len(label_map)"
   ]
  },
  {
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   "source": [
    "import torch\n",
    "\n",
    "class Dataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, df, tokenizer, is_train=True, label_map={}, max_length=128, load_desc = True, load_bullets = True):\n",
    "        self.df = df\n",
    "        self.load_desc = load_desc\n",
    "        self.load_bullets = load_bullets\n",
    "        self.title = df.TITLE.values\n",
    "        self.desc = df.DESCRIPTION.values\n",
    "        self.bullets = df.BULLET_POINTS.apply(lambda x: x[1:-1] if len(x)>0 and x[0]=='[' else x).values\n",
    "        self.tokenizer = tokenizer\n",
    "        if is_train:\n",
    "            self.labels = df.BROWSE_NODE_ID.apply(lambda x: label_map[x]).values\n",
    "            self.label_map = label_map\n",
    "        self.is_train = is_train\n",
    "        self.max_length = max_length\n",
    " \n",
    "    def __getitem__(self, idx):\n",
    "        req_string = self.title[idx] + ' ~ '\n",
    "        if self.load_desc:\n",
    "            req_string += self.desc[idx]\n",
    "        req_string += ' ~ '\n",
    "        if self.load_bullets:\n",
    "            req_string += self.bullets[idx]\n",
    "        \n",
    "        tokenized_data = tokenizer.tokenize(req_string)\n",
    "        to_append = [\"[CLS]\"] + tokenized_data[:self.max_length - 2] + [\"[SEP]\"]\n",
    "        input_ids = tokenizer.convert_tokens_to_ids(to_append)\n",
    "        input_mask = [1] * len(input_ids)\n",
    "        padding = [0] * (self.max_length - len(input_ids))\n",
    "        input_ids += padding\n",
    "        input_mask += padding\n",
    "        item = {\n",
    "            \"input_ids\": torch.tensor(input_ids, dtype=torch.long),\n",
    "            \"attention_mask\": torch.tensor(input_mask, dtype=torch.long)\n",
    "        }\n",
    "        if self.is_train:\n",
    "            item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)\n",
    "        return item\n",
    " \n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n"
   ]
  },
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   "id": "7d851914",
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   "source": [
    "from transformers import AlbertPreTrainedModel, Trainer, AlbertModel\n",
    "from transformers.modeling_outputs import SequenceClassifierOutput\n",
    "from torch import nn\n",
    "\n",
    "class AlbertForSequenceClassification(AlbertPreTrainedModel):\n",
    "    def __init__(self, config, num_labels=len(label_map)):\n",
    "        super().__init__(config)\n",
    "        self.num_labels = num_labels\n",
    "        self.config = config\n",
    "\n",
    "        self.albert = AlbertModel(config)\n",
    "        self.dropout = nn.Dropout(config.classifier_dropout_prob)\n",
    "        self.classifier = nn.Linear(config.hidden_size, self.num_labels)\n",
    "\n",
    "        self.init_weights()\n",
    "\n",
    "    def forward(\n",
    "        self,\n",
    "        input_ids=None,\n",
    "        attention_mask=None,\n",
    "        token_type_ids=None,\n",
    "        position_ids=None,\n",
    "        head_mask=None,\n",
    "        inputs_embeds=None,\n",
    "        labels=None,\n",
    "        output_attentions=None,\n",
    "        output_hidden_states=None,\n",
    "        return_dict=None,\n",
    "    ):\n",
    "        r\"\"\"\n",
    "        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):\n",
    "            Labels for computing the sequence classification/regression loss. Indices should be in ``[0, ...,\n",
    "            config.num_labels - 1]``. If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),\n",
    "            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).\n",
    "        \"\"\"\n",
    "        return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
    "\n",
    "        outputs = self.albert(\n",
    "            input_ids=input_ids,\n",
    "            attention_mask=attention_mask,\n",
    "            token_type_ids=token_type_ids,\n",
    "            position_ids=position_ids,\n",
    "            head_mask=head_mask,\n",
    "            inputs_embeds=inputs_embeds,\n",
    "            output_attentions=output_attentions,\n",
    "            output_hidden_states=output_hidden_states,\n",
    "            return_dict=return_dict,\n",
    "        )\n",
    "\n",
    "        pooled_output = outputs[1]\n",
    "\n",
    "        pooled_output = self.dropout(pooled_output)\n",
    "        logits = self.classifier(pooled_output)\n",
    "\n",
    "        loss = None\n",
    "        if labels is not None:\n",
    "            if self.config.problem_type is None:\n",
    "                if self.num_labels == 1:\n",
    "                    self.config.problem_type = \"regression\"\n",
    "                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\n",
    "                    self.config.problem_type = \"single_label_classification\"\n",
    "                else:\n",
    "                    self.config.problem_type = \"multi_label_classification\"\n",
    "\n",
    "            if self.config.problem_type == \"regression\":\n",
    "                loss_fct = MSELoss()\n",
    "                if self.num_labels == 1:\n",
    "                    loss = loss_fct(logits.squeeze(), labels.squeeze())\n",
    "                else:\n",
    "                    loss = loss_fct(logits, labels)\n",
    "            elif self.config.problem_type == \"single_label_classification\":\n",
    "                loss_fct = CrossEntropyLoss()\n",
    "                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
    "            elif self.config.problem_type == \"multi_label_classification\":\n",
    "                loss_fct = BCEWithLogitsLoss()\n",
    "                loss = loss_fct(logits, labels)\n",
    "\n",
    "        if not return_dict:\n",
    "            output = (logits,) + outputs[2:]\n",
    "            return ((loss,) + output) if loss is not None else output\n",
    "\n",
    "        return SequenceClassifierOutput(\n",
    "            loss=loss,\n",
    "            logits=logits,\n",
    "            hidden_states=outputs.hidden_states,\n",
    "            attentions=outputs.attentions,\n",
    "        )"
   ]
  },
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   "outputs": [],
   "source": [
    "model = AlbertForSequenceClassification.from_pretrained(\"../input/albert-3000-128/results/checkpoint-30000/\", num_labels=len(label_map))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a62c6943",
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   "outputs": [],
   "source": [
    "test_dataset = Dataset(test.fillna(\"\"), tokenizer, is_train=False, load_desc=False, load_bullets = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (541 > 512). Running this sequence through the model will result in indexing errors\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from tqdm.auto import tqdm\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "\n",
    "model.to(device)\n",
    "model.eval()\n",
    "\n",
    "\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
    "logits = []\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(test_loader):\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        attention_mask = batch['attention_mask'].to(device)\n",
    "        outputs = model(input_ids, attention_mask=attention_mask)[\"logits\"]\n",
    "        logits.append(outputs)\n",
    "    labels = torch.cat(logits).argmax(1).cpu()"
   ]
  },
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   "outputs": [],
   "source": [
    "data_dict = {\n",
    "    \"PRODUCT_ID\": range(1, 110776),\n",
    "    \"BROWSE_NODE_ID\": labels\n",
    "}\n",
    "submit = pd.DataFrame.from_dict(data_dict)\n",
    "submit[\"BROWSE_NODE_ID\"] = submit[\"BROWSE_NODE_ID\"].apply(lambda x: label_to_id[x])\n",
    "submit.to_csv(\"Albert_30000_no_desc.csv\", index=False)\n",
    "submit.head()\n",
    "logits = torch.cat(logits).cpu()\n",
    "with open('albert_30000_no_desc.pickle', 'wb') as handle:\n",
    "    pickle.dump(logits, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
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   "outputs": [],
   "source": [
    "test_dataset = Dataset(test.fillna(\"\"), tokenizer, is_train=False, load_desc=False, load_bullets = False)"
   ]
  },
  {
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   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from tqdm.auto import tqdm\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "\n",
    "model.to(device)\n",
    "model.eval()\n",
    "\n",
    "\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
    "logits = []\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(test_loader):\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        attention_mask = batch['attention_mask'].to(device)\n",
    "        outputs = model(input_ids, attention_mask=attention_mask)[\"logits\"]\n",
    "        logits.append(outputs)\n",
    "    labels = torch.cat(logits).argmax(1).cpu()"
   ]
  },
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   "source": [
    "data_dict = {\n",
    "    \"PRODUCT_ID\": range(1, 110776),\n",
    "    \"BROWSE_NODE_ID\": labels\n",
    "}\n",
    "submit = pd.DataFrame.from_dict(data_dict)\n",
    "submit[\"BROWSE_NODE_ID\"] = submit[\"BROWSE_NODE_ID\"].apply(lambda x: label_to_id[x])\n",
    "submit.to_csv(\"Albert_30000_no_desc_no_bullet.csv\", index=False)\n",
    "submit.head()\n",
    "logits = torch.cat(logits).cpu()\n",
    "with open('albert_30000_no_desc_no_bullet.pickle', 'wb') as handle:\n",
    "    pickle.dump(logits, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
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   "id": "fc97ff12",
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   "execution_count": 13,
   "id": "aa8e02a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-08-01T12:45:21.059194Z",
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   "outputs": [],
   "source": [
    "test_dataset = Dataset(test.fillna(\"\"), tokenizer, is_train=False, load_desc=True, load_bullets = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95ea89e2",
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    {
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from tqdm.auto import tqdm\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "\n",
    "model.to(device)\n",
    "model.eval()\n",
    "\n",
    "\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
    "logits = []\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(test_loader):\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        attention_mask = batch['attention_mask'].to(device)\n",
    "        outputs = model(input_ids, attention_mask=attention_mask)[\"logits\"]\n",
    "        logits.append(outputs)\n",
    "    labels = torch.cat(logits).argmax(1).cpu()"
   ]
  },
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    "data_dict = {\n",
    "    \"PRODUCT_ID\": range(1, 110776),\n",
    "    \"BROWSE_NODE_ID\": labels\n",
    "}\n",
    "submit = pd.DataFrame.from_dict(data_dict)\n",
    "submit[\"BROWSE_NODE_ID\"] = submit[\"BROWSE_NODE_ID\"].apply(lambda x: label_to_id[x])\n",
    "submit.to_csv(\"Albert_30000_no_bullet.csv\", index=False)\n",
    "submit.head()\n",
    "logits = torch.cat(logits).cpu()\n",
    "with open('albert_30000_no_bullet.pickle', 'wb') as handle:\n",
    "    pickle.dump(logits, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
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   "source": [
    "test_dataset = Dataset(test.fillna(\"\"), tokenizer, is_train=False, load_desc=True, load_bullets = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
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    {
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     "metadata": {},
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   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from tqdm.auto import tqdm\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "\n",
    "model.to(device)\n",
    "model.eval()\n",
    "\n",
    "\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
    "logits = []\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(test_loader):\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        attention_mask = batch['attention_mask'].to(device)\n",
    "        outputs = model(input_ids, attention_mask=attention_mask)[\"logits\"]\n",
    "        logits.append(outputs)\n",
    "    labels = torch.cat(logits).argmax(1).cpu()"
   ]
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    "data_dict = {\n",
    "    \"PRODUCT_ID\": range(1, 110776),\n",
    "    \"BROWSE_NODE_ID\": labels\n",
    "}\n",
    "submit = pd.DataFrame.from_dict(data_dict)\n",
    "submit[\"BROWSE_NODE_ID\"] = submit[\"BROWSE_NODE_ID\"].apply(lambda x: label_to_id[x])\n",
    "submit.to_csv(\"Albert_30000.csv\", index=False)\n",
    "submit.head()\n",
    "logits = torch.cat(logits).cpu()\n",
    "with open('albert_30000.pickle', 'wb') as handle:\n",
    "    pickle.dump(logits, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
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