Roberta_Large.ipynb

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   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "colab_type": "code",
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   "outputs": [],
   "source": [
    "! pip install transformers -q\n",
    "! pip install emoji --upgrade -q\n",
    "! pip install Unidecode -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "colab_type": "code",
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   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from typing import *\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.utils.extmath import softmax\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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     },
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    },
    "id": "-JsXpQNttsRg"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import transformers\n",
    "from transformers import AdamW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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    },
    "id": "IS4kRBVbsh3A"
   },
   "outputs": [],
   "source": [
    "import emoji\n",
    "import regex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
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     "timestamp": 1597663493811,
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   "outputs": [],
   "source": [
    "def seed_all(seed = 42):\n",
    "  \"\"\"\n",
    "  Fix seed for reproducibility\n",
    "  \"\"\"\n",
    "  # python RNG\n",
    "  import random\n",
    "  random.seed(seed)\n",
    "\n",
    "  # pytorch RNGs\n",
    "  import torch\n",
    "  torch.manual_seed(seed)\n",
    "  torch.backends.cudnn.deterministic = True\n",
    "  if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "  # numpy RNG\n",
    "  import numpy as np\n",
    "  np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
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     },
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    },
    "id": "qAbVky6Kt4IV"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('/gdrive/My Drive/COVID_Tweet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
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     "status": "ok",
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    "id": "mMHkNSVGumlX"
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   "outputs": [],
   "source": [
    "class config:\n",
    "  SEED = 42\n",
    "  KFOLD = 5\n",
    "  SAVE_DIR = 'run_roberta_large_oof_fix'\n",
    "  TRAIN_FILE = 'train_fix.csv'\n",
    "  VAL_FILE =  'valid_fix.csv'\n",
    "  TEST_FILE = 'test_fix.csv'\n",
    "  OOF_FILE = os.path.join(SAVE_DIR, 'oof.csv')\n",
    "  MAX_LEN = 96\n",
    "  MODEL = 'roberta-large'\n",
    "  TOKENIZER = transformers.AutoTokenizer.from_pretrained(MODEL)\n",
    "  EPOCHS = 5\n",
    "  TRAIN_BATCH_SIZE = 16\n",
    "  VALID_BATCH_SIZE = 16\n",
    "  DEVICE = torch.device(\"cuda:0\" if (torch.cuda.is_available()) else \"cpu\")"
   ]
  },
  {
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     },
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    },
    "id": "j-_-jJZ9zYYN"
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   "outputs": [],
   "source": [
    "class AverageMeter:\n",
    "    \"\"\"\n",
    "    Computes and stores the average and current value\n",
    "    Source : https://www.kaggle.com/abhishek/bert-base-uncased-using-pytorch/\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.val = 0\n",
    "        self.avg = 0\n",
    "        self.sum = 0\n",
    "        self.count = 0\n",
    "\n",
    "    def update(self, val, n=1):\n",
    "        self.val = val\n",
    "        self.sum += val * n\n",
    "        self.count += n\n",
    "        self.avg = self.sum / self.count"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 32855,
     "status": "ok",
     "timestamp": 1597663496269,
     "user": {
      "displayName": "Priyanshu Kumar",
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     },
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    },
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   "source": [
    "class EarlyStopping:\n",
    "    \"\"\"\n",
    "    Early stopping utility\n",
    "    Source : https://www.kaggle.com/abhishek/bert-base-uncased-using-pytorch/\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, patience=7, mode=\"max\", delta=0.001):\n",
    "        self.patience = patience\n",
    "        self.counter = 0\n",
    "        self.mode = mode\n",
    "        self.best_score = None\n",
    "        self.early_stop = False\n",
    "        self.delta = delta\n",
    "        if self.mode == \"min\":\n",
    "            self.val_score = np.Inf\n",
    "        else:\n",
    "            self.val_score = -np.Inf\n",
    "\n",
    "    def __call__(self, epoch_score, model, model_path):\n",
    "        if self.mode == \"min\":\n",
    "            score = -1.0 * epoch_score\n",
    "        else:\n",
    "            score = np.copy(epoch_score)\n",
    "        if self.best_score is None:\n",
    "            self.best_score = score\n",
    "            self.save_checkpoint(epoch_score, model, model_path)\n",
    "        elif score < self.best_score + self.delta:\n",
    "            self.counter += 1\n",
    "            print('EarlyStopping counter: {} out of {}'.format(self.counter, self.patience))\n",
    "            if self.counter >= self.patience:\n",
    "                self.early_stop = True\n",
    "        else:\n",
    "            self.best_score = score\n",
    "            self.save_checkpoint(epoch_score, model, model_path)\n",
    "            self.counter = 0\n",
    "\n",
    "    def save_checkpoint(self, epoch_score, model, model_path):\n",
    "        if epoch_score not in [-np.inf, np.inf, -np.nan, np.nan]:\n",
    "            print('Validation score improved ({} --> {}). Saving model!'.format(self.val_score, epoch_score))\n",
    "            torch.save(model.state_dict(), model_path)\n",
    "        self.val_score = epoch_score"
   ]
  },
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   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "import html\n",
    "import unicodedata\n",
    "import unidecode\n",
    "\n",
    "control_char_regex = re.compile(r'[\\r\\n\\t]+')\n",
    "transl_table = dict([(ord(x), ord(y)) for x, y in zip(u\"‘’´“”–-\",  u\"'''\\\"\\\"--\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 31837,
     "status": "ok",
     "timestamp": 1597663496272,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
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    "id": "u5JUJgTbjhBv"
   },
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "  text = html.unescape(text)\n",
    "  text = text.translate(transl_table)\n",
    "  text = text.replace('…', '...')\n",
    "  text = re.sub(control_char_regex, ' ', text)\n",
    "  text = ''.join(ch for ch in text if unicodedata.category(ch)[0] != 'C')\n",
    "  text = ' '.join(text.split())\n",
    "  text =  text.strip()\n",
    "  \n",
    "  text = text.replace('HTTPURL', 'URL')\n",
    "  text = emoji.demojize(text)\n",
    "\n",
    "  text = unidecode.unidecode(text)\n",
    "  text = ''.join(ch for ch in text if unicodedata.category(ch)[0] != 'So')\n",
    "\n",
    "\n",
    "  return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 31327,
     "status": "ok",
     "timestamp": 1597663496274,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
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    "id": "IoZ1L2smuMDd"
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   "outputs": [],
   "source": [
    "def process_data(text, tokenizer, max_len, label):\n",
    "  \n",
    "  text = preprocess(text)\n",
    "  \n",
    "  token_ids = tokenizer.encode(text, add_special_tokens=True)\n",
    "  mask = [1] * len(token_ids)\n",
    "\n",
    "  padding = max_len - len(token_ids)\n",
    "  \n",
    "  if padding>=0:\n",
    "    token_ids = token_ids + ([0] * padding)\n",
    "    mask = mask + ([0] * padding)\n",
    "  else:\n",
    "    token_ids = token_ids[0:max_len]\n",
    "    mask = mask[0:max_len]\n",
    "\n",
    "  label = 1 if label=='INFORMATIVE' else 0\n",
    "\n",
    "  assert len(token_ids)==max_len\n",
    "  assert len(mask)==max_len\n",
    "\n",
    "  return {'text':text,\n",
    "          'ids':token_ids,\n",
    "          'mask':mask,\n",
    "          'label':label\n",
    "          }"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30724,
     "status": "ok",
     "timestamp": 1597663496274,
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      "displayName": "Priyanshu Kumar",
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   "outputs": [],
   "source": [
    "class Dataset:\n",
    "    def __init__(self, text, label):\n",
    "        self.text = text\n",
    "        self.label = label\n",
    "        self.tokenizer = config.TOKENIZER\n",
    "        self.max_len = config.MAX_LEN\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.text)\n",
    "\n",
    "    def __getitem__(self, item):\n",
    "        data = process_data(\n",
    "            self.text[item], \n",
    "            self.tokenizer,\n",
    "            self.max_len,\n",
    "            self.label[item],\n",
    "        )\n",
    "\n",
    "        return {\n",
    "            'ids': torch.tensor(data[\"ids\"], dtype=torch.long),\n",
    "            'mask': torch.tensor(data[\"mask\"], dtype=torch.long),\n",
    "            'text': data['text'],\n",
    "            'label': data['label'],\n",
    "        }"
   ]
  },
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   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1597663496275,
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    "id": "9NGJrNcSx33W"
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   "outputs": [],
   "source": [
    "def train_fn(data_loader, model, optimizer, device):\n",
    "  model.train()\n",
    "  losses = AverageMeter()\n",
    "  tk0 = tqdm(data_loader, total=len(data_loader))\n",
    "  \n",
    "  for bi, d in enumerate(tk0):\n",
    "    ids = d['ids']\n",
    "    mask = d['mask']\n",
    "    label = d['label']\n",
    "\n",
    "    ids = ids.to(device, dtype=torch.long)\n",
    "    label = label.to(device, dtype=torch.long)\n",
    "    mask = mask.to(device, dtype=torch.long)\n",
    "\n",
    "    model.zero_grad()\n",
    "    loss, logits = model(input_ids=ids, attention_mask=mask, labels=label)\n",
    "\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    losses.update(loss.item(), ids.size(0))\n",
    "    tk0.set_postfix(loss=losses.avg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 29286,
     "status": "ok",
     "timestamp": 1597663496276,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
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    },
    "id": "pSQNnIl-x3om"
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   "outputs": [],
   "source": [
    "def eval_fn(data_loader, model, device):\n",
    "  model.eval()\n",
    "  losses = AverageMeter()\n",
    "  tk0 = tqdm(data_loader, total=len(data_loader))\n",
    "  yt, yp = [], []\n",
    "\n",
    "  for bi, d in enumerate(tk0):\n",
    "    ids = d['ids']\n",
    "    mask = d['mask']\n",
    "    label = d['label']\n",
    "\n",
    "    ids = ids.to(device, dtype=torch.long)\n",
    "    label = label.to(device, dtype=torch.long)\n",
    "    mask = mask.to(device, dtype=torch.long)\n",
    "\n",
    "    with torch.no_grad():\n",
    "      loss, logits = model(input_ids=ids, attention_mask=mask, labels=label)        \n",
    "       \n",
    "    \n",
    "    logits = logits.detach().cpu().numpy()\n",
    "\n",
    "    preds = softmax(logits)\n",
    "    pred_labels = np.argmax(preds, axis=1).flatten()\n",
    "    ground_labels = label.to('cpu').numpy()\n",
    "\n",
    "    yt = yt + ground_labels.tolist()\n",
    "    yp = yp + pred_labels.tolist()\n",
    "\n",
    "    losses.update(loss.item(), ids.size(0))\n",
    "    tk0.set_postfix(loss=losses.avg)\n",
    "\n",
    "  return f1_score(yt, yp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 28663,
     "status": "ok",
     "timestamp": 1597663496277,
     "user": {
      "displayName": "Priyanshu Kumar",
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      "userId": "03532460980266287093"
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   "source": [
    "def test_fn(data_loader, model, device):\n",
    "  model.eval()\n",
    "  tk0 = tqdm(data_loader, total=len(data_loader))\n",
    "  test_preds = []\n",
    "\n",
    "  for bi, d in enumerate(tk0):\n",
    "    ids = d['ids']\n",
    "    mask = d['mask']\n",
    "    label = d['label']\n",
    "    \n",
    "    ids = ids.to(device, dtype=torch.long)\n",
    "    mask = mask.to(device, dtype=torch.long)\n",
    "    label = label.to(device, dtype=torch.long)\n",
    "\n",
    "    with torch.no_grad():\n",
    "      _, logits = model(input_ids=ids, attention_mask=mask, labels=label)        \n",
    "    \n",
    "    logits = logits.detach().cpu().numpy()\n",
    "    preds = softmax(logits)[:, 1]        \n",
    "    test_preds = test_preds + preds.tolist()\n",
    "\n",
    "  return test_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 27939,
     "status": "ok",
     "timestamp": 1597663496277,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
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    },
    "id": "BL1F0LV_RNNk"
   },
   "outputs": [],
   "source": [
    "def run(df_train, df_val, fold=None):\n",
    "  train_dataset = Dataset(\n",
    "        text = df_train.Text.values,\n",
    "        label = df_train.Label.values,\n",
    "    )\n",
    "  \n",
    "  valid_dataset = Dataset(\n",
    "      text = df_val.Text.values,\n",
    "      label = df_val.Label.values,\n",
    "    )\n",
    "  \n",
    "  train_data_loader = torch.utils.data.DataLoader(\n",
    "        train_dataset,\n",
    "        batch_size=config.TRAIN_BATCH_SIZE,\n",
    "        num_workers=4\n",
    "    )\n",
    "\n",
    "  valid_data_loader = torch.utils.data.DataLoader(\n",
    "        valid_dataset,\n",
    "        batch_size=config.VALID_BATCH_SIZE,\n",
    "        num_workers=2\n",
    "    )\n",
    "  \n",
    "  \n",
    "  model = transformers.RobertaForSequenceClassification.from_pretrained(config.MODEL, num_labels=2)\n",
    "  device = torch.device(\"cuda:0\" if (torch.cuda.is_available()) else \"cpu\")\n",
    "  model.to(device)\n",
    "\n",
    "  lr = 2e-5\n",
    "  param_optimizer = list(model.named_parameters())\n",
    "  no_decay = ['bias', 'gamma', 'beta']\n",
    "  optimizer_grouped_parameters = [\n",
    "      {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],\n",
    "      'weight_decay_rate': 0.01},\n",
    "      {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],\n",
    "      'weight_decay_rate': 0.0}\n",
    "  ]\n",
    "  optimizer = AdamW(optimizer_grouped_parameters, lr=lr)\n",
    "\n",
    "  es = EarlyStopping(patience=3, mode=\"max\")\n",
    "\n",
    "  print('Starting training....')\n",
    "  for epoch in range(config.EPOCHS):\n",
    "    train_fn(train_data_loader, model, optimizer, device)\n",
    "    valid_loss = eval_fn(valid_data_loader, model, device)\n",
    "    print(f'Epoch :{epoch + 1} | Validation Score :{valid_loss}')\n",
    "    if fold is None:\n",
    "      es(valid_loss, model, model_path=os.path.join(config.SAVE_DIR, f\"model.bin\"))\n",
    "    else:\n",
    "      es(valid_loss, model, model_path=os.path.join(config.SAVE_DIR, f\"model_{fold}.bin\"))\n",
    "    if es.early_stop:\n",
    "      print('Early stopping')\n",
    "      break\n",
    "\n",
    "  print('Predicting for OOF')\n",
    "  if fold is None:\n",
    "    model.load_state_dict(torch.load(os.path.join(config.SAVE_DIR, 'model.bin')))\n",
    "  else:\n",
    "    model.load_state_dict(torch.load(os.path.join(config.SAVE_DIR, f'model_{fold}.bin')))\n",
    "    \n",
    "  model.to(device)\n",
    "  \n",
    "  test_predictions = test_fn(valid_data_loader, model, device)\n",
    "  return test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 27255,
     "status": "ok",
     "timestamp": 1597663496278,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
     },
     "user_tz": -330
    },
    "id": "y7onmf7t7zxP"
   },
   "outputs": [],
   "source": [
    "def run_fold(fold_idx):\n",
    "  \"\"\"\n",
    "    Perform k-fold cross-validation\n",
    "  \"\"\"\n",
    "\n",
    "  seed_all()\n",
    "  scores = pd.DataFrame()\n",
    "\n",
    "  df_train = pd.read_csv(config.TRAIN_FILE)\n",
    "  df_val = pd.read_csv(config.VAL_FILE)\n",
    "  \n",
    "  # concatenating train and validation set\n",
    "  train = pd.concat([df_train, df_val]).reset_index()\n",
    "  \n",
    "  # dividing folds\n",
    "  kf = model_selection.StratifiedKFold(n_splits=config.KFOLD, shuffle=False, random_state=config.SEED)\n",
    "  idx = None\n",
    "\n",
    "  for fold, (train_idx, val_idx) in enumerate(kf.split(X=train, y=train.Label.values)):\n",
    "      train.loc[val_idx, 'kfold'] = fold\n",
    "      if fold==fold_idx:\n",
    "        idx = val_idx\n",
    "\n",
    "  if os.path.isfile(config.OOF_FILE):\n",
    "    scores = pd.read_csv(config.OOF_FILE)\n",
    "    print('Found oof file')\n",
    "  else:\n",
    "    scores = train.copy()\n",
    "    scores['oof'] = 0\n",
    "    scores.to_csv(config.OOF_FILE, index=False)\n",
    "    print('Created oof file')\n",
    "  \n",
    "  df_train = train[train.kfold!=fold_idx]\n",
    "  df_val = train[train.kfold==fold_idx]\n",
    "\n",
    "  y = run(df_train, df_val, fold_idx)\n",
    "  scores.loc[idx, 'oof'] = y\n",
    "  \n",
    "  scores.to_csv(config.OOF_FILE, index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o5iRAN9KrDQ8"
   },
   "outputs": [],
   "source": [
    "! rm -rf {config.SAVE_DIR} && mkdir {config.SAVE_DIR}"
   ]
  },
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     "status": "ok",
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    "outputId": "3bfb485f-26a4-4569-de5d-901a0d2a5a8f"
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   "source": [
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   ]
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   "source": [
    "run_fold(3)"
   ]
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3979608,
     "status": "ok",
     "timestamp": 1597671511303,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
     },
     "user_tz": -330
    },
    "id": "XpHSD9Oeq6OF",
    "outputId": "63eb6260-f8a6-416c-e005-0274e4fa6713"
   },
   "outputs": [],
   "source": [
    "run_fold(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i39F4lLsmZt6"
   },
   "source": [
    "# Run Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1066431,
     "status": "ok",
     "timestamp": 1597671512409,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
     },
     "user_tz": -330
    },
    "id": "sbl1aWD1a1bp",
    "outputId": "86b172f8-88dd-40b9-9ecf-52da20aec9d8"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(config.OOF_FILE)\n",
    "df['gold'] = df['Label'].map({'INFORMATIVE':1, 'UNINFORMATIVE':0})\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1065444,
     "status": "ok",
     "timestamp": 1597671512414,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
     },
     "user_tz": -330
    },
    "id": "2N33amiJVSXc",
    "outputId": "686cc602-ca88-42aa-a9e5-82daee683499"
   },
   "outputs": [],
   "source": [
    "df['pred'] = (df['oof']>=0.5)*1\n",
    "print(classification_report(df['gold'].values, df['pred'].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1064487,
     "status": "ok",
     "timestamp": 1597671512417,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
     },
     "user_tz": -330
    },
    "id": "NMRs_bcyWCrj",
    "outputId": "b8f0682d-99e8-4691-d348-895619ddd7a0"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(df['gold'].values, df['oof'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1067563,
     "status": "ok",
     "timestamp": 1597671516500,
     "user": {
      "displayName": "Priyanshu Kumar",
      "photoUrl": "",
      "userId": "03532460980266287093"
     },
     "user_tz": -330
    },
    "id": "KnCT1kqbVktq",
    "outputId": "48a96485-6dae-481a-8a23-4d5c464f64b1"
   },
   "outputs": [],
   "source": [
    "thresholds = np.arange(0, 1, 0.001)\n",
    "fscores = [f1_score(df['gold'].values, (df['oof']>=t)*1) for t in thresholds]\n",
    "idx = np.argmax(fscores)\n",
    "print(thresholds[idx], fscores[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gIqYrTO-WdBd"
   },
   "source": [
    "# Test Predictions"
   ]
  },
  {
   "cell_type": "code",
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    "colab": {
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   "source": [
    "df = pd.read_csv(config.TEST_FILE)\n",
    "\n",
    "test_dataset = Dataset(\n",
    "      text = df.Text.values,\n",
    "      label = df.Label.values,\n",
    "    )\n",
    "  \n",
    "test_data_loader = torch.utils.data.DataLoader(\n",
    "      test_dataset,\n",
    "      batch_size=config.VALID_BATCH_SIZE,\n",
    "      num_workers=4\n",
    "  )\n",
    "\n",
    "scores = pd.DataFrame()\n",
    "\n",
    "model = transformers.RobertaForSequenceClassification.from_pretrained(config.MODEL, num_labels=2)\n",
    "device = torch.device(\"cuda:0\" if (torch.cuda.is_available()) else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "for i in range(config.KFOLD):\n",
    "  model.load_state_dict(torch.load(os.path.join(config.SAVE_DIR, f'model_{i}.bin')))\n",
    "  y_preds = test_fn(test_data_loader, model, device)\n",
    "  scores[f'prob_{i}'] = y_preds\n",
    "\n",
    "\n",
    "\n",
    "scores['avg'] = (scores['prob_0'] + scores['prob_1'] + scores['prob_2'] + scores['prob_3'] + scores['prob_4'])/5\n",
    "scores['preds'] = (scores['avg']>=0.5)*1\n",
    "scores['Labels'] = scores['preds'].map({1:'INFORMATIVE', 0:'UNINFORMATIVE'})\n",
    "scores.to_csv(os.path.join(config.SAVE_DIR, 'scores.csv'), index=False)\n",
    "\n",
    "with open(os.path.join(config.SAVE_DIR, 'submission.txt'), 'w') as f:\n",
    "  for i in scores['Labels'].values:\n",
    "    f.write(i+'\\n')"
   ]
  },
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