validate_kfold_pre_train_ssl_mlm_distilbert_torch.ipynb

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   ],
   "source": [
    "import re\n",
    "import string\n",
    "import gc\n",
    "import os\n",
    "import shutil\n",
    "from datetime import datetime\n",
    "import time\n",
    "from pathlib import Path\n",
    "import random\n",
    "import joblib\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "\n",
    "import torch\n",
    "from torch.utils.data.sampler import RandomSampler, SequentialSampler\n",
    "from torch.optim import lr_scheduler\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import transformers\n",
    "from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification\n",
    "from transformers import DataCollatorWithPadding, TrainingArguments, Trainer\n",
    "from transformers import TextClassificationPipeline, AutoConfig, get_cosine_schedule_with_warmup, get_linear_schedule_with_warmup\n",
    "from transformers import DataCollatorForLanguageModeling, AutoModelForMaskedLM\n",
    "from transformers import TrainerCallback, EarlyStoppingCallback\n",
    "from transformers.modeling_outputs import SequenceClassifierOutput\n",
    "\n",
    "from datasets import Dataset, DatasetDict\n",
    "\n",
    "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold\n",
    "\n",
    "from lightgbm import LGBMRegressor, Booster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w54zGgvrlzBl"
   },
   "source": [
    "# Common Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "def seed_everything(seed=42):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    \n",
    "seed_everything(seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
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    "outputId": "3512d8f0-75bb-4131-8ec1-72bce6d7451b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.11.0\n",
      "4.20.1\n"
     ]
    }
   ],
   "source": [
    "print(torch.__version__)\n",
    "print(transformers.__version__)\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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    },
    "id": "RHHhu6skqOwq"
   },
   "outputs": [],
   "source": [
    "def on_gpu(f):\n",
    "    def wrapper():\n",
    "        if torch.cuda.is_available():\n",
    "            return f()\n",
    "        else:\n",
    "            print('cuda unavailable')\n",
    "    return wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:48.823053Z",
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    },
    "id": "714dce69"
   },
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    from pynvml import *\n",
    "    from numba import cuda\n",
    "\n",
    "@on_gpu\n",
    "def print_gpu_utilization():\n",
    "    try:\n",
    "        nvmlInit()\n",
    "        handle = nvmlDeviceGetHandleByIndex(0)\n",
    "        info = nvmlDeviceGetMemoryInfo(handle)\n",
    "        print(f\"GPU memory occupied: {info.used//1024**2} MB.\")\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "\n",
    "@on_gpu\n",
    "def free_gpu_cache():\n",
    "    print(\"Initial GPU Usage\")\n",
    "    print_gpu_utilization()                          \n",
    "\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    print(\"GPU Usage after emptying the cache\")\n",
    "    print_gpu_utilization()\n",
    "    \n",
    "def print_summary(result):\n",
    "    print(f\"Time: {result.metrics['train_runtime']:.2f}\")\n",
    "    print(f\"Samples/second: {result.metrics['train_samples_per_second']:.2f}\")\n",
    "    print_gpu_utilization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.389293Z",
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    },
    "id": "18d06fcf"
   },
   "outputs": [],
   "source": [
    "def to_train_test_splits(X_train, X_test, y_train):\n",
    "    train_split = pd.concat([X_train, y_train], axis=1)\n",
    "    test_split = pd.concat([X_test, y_test], axis=1)\n",
    "    return train_split, test_split\n",
    "\n",
    "def to_train_test_splits_task(X_train, X_test, y_train, y_test, label):\n",
    "    y_train[f'{label}_labels'] = y_train[label].apply(lambda x: REV_LABELS[x])\n",
    "    y_test[f'{label}_labels'] = y_test[label].apply(lambda x: REV_LABELS[x])\n",
    "\n",
    "    train_split = pd.concat([X_train, y_train], axis=1)\n",
    "    test_split = pd.concat([X_test, y_test], axis=1)\n",
    "    return train_split, test_split\n",
    "\n",
    "def gaussian(x, T):\n",
    "    return x * np.exp(- 5 * np.power(1 - T, 2))\n",
    "\n",
    "def match_with_batchsize(lim, batchsize):\n",
    "    \"\"\"\n",
    "    Function used by modify_datasets below to match return the integer closest to lim\n",
    "    which is multiple of batchsize, i.e., lim%batchsize=0.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    if lim % batchsize == 0:\n",
    "        return lim\n",
    "    else:\n",
    "        return lim - lim % batchsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.403767Z",
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    },
    "id": "QCL0ntrrpP8M"
   },
   "outputs": [],
   "source": [
    "def eval(f):\n",
    "    def wrapper(model, *args, **kwargs):\n",
    "        model.eval()\n",
    "        return f(model, *args, **kwargs)\n",
    "    return wrapper\n",
    "\n",
    "def train(f):\n",
    "    def wrapper(model, *args, **kwargs):\n",
    "        model.train()\n",
    "        return f(model, *args, **kwargs)\n",
    "    return wrapper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qY0leH5yl6wI"
   },
   "source": [
    "# Metrics and evaluation Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.413942Z",
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    },
    "id": "Z-UcwUSiAefg"
   },
   "outputs": [],
   "source": [
    "def MCRMSE(actual, predicted):\n",
    "    squared_diff = (actual - predicted)**2\n",
    "    sqrt_mean = np.sqrt(np.mean(squared_diff, axis=0))\n",
    "    return np.mean(sqrt_mean)\n",
    "\n",
    "def MCRMSE_scores(y_trues, y_preds):\n",
    "    scores = []\n",
    "    idxes = y_trues.shape[1]\n",
    "    for i in range(idxes):\n",
    "        y_true = y_trues[:, i]\n",
    "        y_pred = y_preds[:, i]\n",
    "        score = mean_squared_error(y_true, y_pred, squared = False)\n",
    "        scores.append(score)\n",
    "    mcrmse_score = np.mean(scores)\n",
    "    return mcrmse_score, scores\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    logits, labels = eval_pred.predictions, eval_pred.label_ids\n",
    "    predictions = np.argmax(logits[0], axis=-1)\n",
    "    return {\n",
    "        'accuracy': accuracy_score(labels, predictions), \n",
    "        'rmse': np.sqrt(mean_squared_error(decode(labels), decode(predictions)))\n",
    "        }\n",
    "\n",
    "def decode(predictions):\n",
    "    return np.array(list(map(lambda x: RAW_LABELS[x], predictions)))\n",
    "\n",
    "@eval\n",
    "def perplexity(model, eval_data):\n",
    "    total_loss = 0\n",
    "    batch_count = 0\n",
    "    with torch.no_grad():\n",
    "        for step, val_input in enumerate(tqdm(eval_data)):\n",
    "            labels = val_input['labels'].to(device).clone()\n",
    "            mask = val_input['attention_mask'].to(device)\n",
    "            input_id = val_input['input_ids'].to(device)\n",
    "            \n",
    "            output = model(input_ids=input_id, attention_mask=mask, labels=labels).loss\n",
    "            total_loss += output.cpu().numpy()\n",
    "            batch_count+=1\n",
    "    return np.exp(total_loss.item() / batch_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DoLrHeqhmOvz"
   },
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.430200Z",
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     "shell.execute_reply.started": "2022-11-26T21:02:49.430174Z"
    },
    "id": "4a40b2b7"
   },
   "outputs": [],
   "source": [
    "def tokenization(example):\n",
    "    return tokenizer(example['full_text'], \n",
    "                    max_length=512, \n",
    "                    padding='max_length', \n",
    "                    truncation=True, \n",
    "                    add_special_tokens=True,)\n",
    "#                     return_tensors='pt')\n",
    "\n",
    "def tokenize(example):\n",
    "    return tokenizer(example, \n",
    "                    max_length=512, \n",
    "                    padding='max_length', \n",
    "                    truncation=True, \n",
    "                    add_special_tokens=True,\n",
    "                    return_tensors='pt')\n",
    "    \n",
    "def collate(inputs):\n",
    "    mask_len = int(inputs['attention_mask'].sum(axis=1).max())\n",
    "    for k, v in inputs.items():\n",
    "        inputs[k] = inputs[k][:,:mask_len]\n",
    "    return inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wjyZVyn1mk9s"
   },
   "source": [
    "# Saving and Loading Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.444399Z",
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     "shell.execute_reply.started": "2022-11-26T21:02:49.444369Z"
    },
    "id": "NFm-sAchmkHr"
   },
   "outputs": [],
   "source": [
    "def save_model(fold, model):\n",
    "    print('Saving model...')\n",
    "    try:\n",
    "        Path(f'{BASIC_PATH}/models/{MODEL_NAME}').mkdir(parents=True, exist_ok=True)\n",
    "        torch.save(model.cpu().state_dict(), f'{BASIC_PATH}/models/{MODEL_NAME}/fold_{fold}')\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "\n",
    "def save_model_reg(fold, model):\n",
    "    print('Saving model...')\n",
    "    try:\n",
    "        Path(f'{BASIC_PATH}/models/{MODEL_REG}').mkdir(parents=True, exist_ok=True)\n",
    "        joblib.dump(model, f'{BASIC_PATH}/models/{MODEL_REG}/fold_{fold}.pkl')\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "\n",
    "def save_model_task(task, model):\n",
    "    print('Saving model...')\n",
    "    try:\n",
    "        Path(f'{BASIC_PATH}/models/ssl/{MODEL_NAME}/{task}').mkdir(parents=True, exist_ok=True)\n",
    "        torch.save(model.cpu().state_dict(), f'{BASIC_PATH}/models/ssl/{MODEL_NAME}/{task}/final')\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "\n",
    "def get_tokenizer():\n",
    "    if os.path.exists(f'{BASIC_PATH}/tokenizers/{MODEL_NAME}'):\n",
    "        return f'{BASIC_PATH}/tokenizers/{MODEL_NAME}'\n",
    "    return MODEL_NAME\n",
    "\n",
    "def save_config(model, config_type):\n",
    "    print('Saving config:', config_type)\n",
    "    try:\n",
    "        config_path = f'{BASIC_PATH}/models/{MODEL_NAME}/configs'\n",
    "        model.config.save_pretrained(f'{config_path}/config_type')\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "\n",
    "def get_config_path(config_type):\n",
    "    config_path = f'{BASIC_PATH}/models/{MODEL_NAME}/configs/{config_type}'\n",
    "    if os.path.exists(config_path) and len(os.listdir(config_path)) != 0:\n",
    "        return config_path\n",
    "    return MODEL_NAME\n",
    "\n",
    "def get_checkpoint(task):\n",
    "    chckpt_path = f'{BASIC_PATH}/models/{MODEL_NAME}/{task}'\n",
    "    if os.path.exists(f'{chckpt_path}/final'):\n",
    "        return f'{chckpt_path}/final'\n",
    "    elif os.path.exists(chckpt_path):\n",
    "        checkpoints = list(filter(lambda x: x.startswith('checkpoint-'), os.listdir(chckpt_path)))\n",
    "        checkpoints.sort(reverse=True)\n",
    "        if len(checkpoints) == 0:\n",
    "            return MODEL_NAME\n",
    "        return f'{chckpt_path}/{checkpoints[0]}'\n",
    "    return MODEL_NAME\n",
    "\n",
    "def get_model_ssl_path(task):\n",
    "    model_path = f'{BASIC_PATH}/models/{MODEL_NAME}/ssl/{task}/final'\n",
    "    if os.path.exists(model_path):\n",
    "        return model_path\n",
    "    return MODEL_NAME\n",
    "\n",
    "def copy_weights(modelSrc, modelDst):\n",
    "    print('Copying weights from ------->', modelSrc.__class__)\n",
    "    print('Copying weights to ------->', modelDst.__class__)\n",
    "    paramsSrc = dict(modelSrc.state_dict())\n",
    "    paramsDst = dict(modelDst.state_dict())\n",
    "\n",
    "    dict_paramsDst = paramsDst\n",
    "    for nameSrc, paramSrc in paramsSrc.items():\n",
    "        transformedNameSrc = 'base.' + '.'.join(nameSrc.split('.')[1:])\n",
    "        if transformedNameSrc in dict_paramsDst:\n",
    "            dict_paramsDst[transformedNameSrc].data.copy_(paramSrc.data)\n",
    "    modelDst.load_state_dict(dict_paramsDst)\n",
    "    return modelDst\n",
    "\n",
    "def get_model(fold=0, retrain=False, pretrained=None):\n",
    "    model = FeedbackModel()\n",
    "    if pretrained is not None:\n",
    "        if 'mlm' in pretrained:\n",
    "            modelMLM = AutoModelForMaskedLM.from_pretrained(pretrained)\n",
    "            return copy_weights(modelMLM, model)\n",
    "        elif 'ssl' in pretrained:\n",
    "            modelSSL = TemporalEnsembleModel()\n",
    "            modelSSL.load_state_dict(torch.load(pretrained))\n",
    "            return copy_weights(modelSSL.base, model)\n",
    "    if retrain:\n",
    "        return model\n",
    "    if is_trained():\n",
    "        model = FeedbackModel()\n",
    "        model.load_state_dict(torch.load(f'{BASIC_PATH}/models/{MODEL_NAME}/fold_{fold}'))\n",
    "        return model\n",
    "    return model\n",
    "\n",
    "def get_model_reg(fold):\n",
    "    return joblib.load(f'{BASIC_PATH}/models/{MODEL_REG}/fold_{fold}.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NJvqcvgfmpci"
   },
   "source": [
    "# Plotting Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.466921Z",
     "iopub.status.busy": "2022-11-26T21:02:49.466475Z",
     "iopub.status.idle": "2022-11-26T21:02:49.478727Z",
     "shell.execute_reply": "2022-11-26T21:02:49.477774Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.466885Z"
    },
    "id": "szXcd578mov8"
   },
   "outputs": [],
   "source": [
    "def plot_results(fold, results):\n",
    "    epochs_x = list(range(1, len(results['train_losses']) + 1))\n",
    "\n",
    "    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "    ax1.plot(epochs_x, results['train_losses'], color='y')\n",
    "    ax1.plot(epochs_x, results['val_losses'], color='b')\n",
    "    ax1.set_title('Losses')\n",
    "    ax1.set_xlabel('epoch')\n",
    "    ax1.set_ylabel('loss')\n",
    "\n",
    "    ax2.plot(epochs_x, results['val_mcrmse'])\n",
    "    ax2.set_title('MCRMSE')\n",
    "    ax2.set_xlabel('epoch')\n",
    "    ax2.set_ylabel('mcrmse')\n",
    "\n",
    "    plt.title(f'Fold: {fold}')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "def plot_scheduler_stats(stats):\n",
    "    epochs_x = list(range(1, len(stats['lr_history']) + 1))\n",
    "\n",
    "    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
    "    ax1.plot(epochs_x, stats['lr_history'])\n",
    "    ax1.set_title('Learning rates')\n",
    "    ax1.set_xlabel('epoch')\n",
    "    ax1.set_ylabel('learning_rate')\n",
    "\n",
    "    ax2.plot(epochs_x, stats['weights_history'])\n",
    "    ax2.set_title('Weights')\n",
    "    ax2.set_xlabel('epoch')\n",
    "    ax2.set_ylabel('weight')\n",
    "\n",
    "    plt.title('Scheduler stats')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A5nXbKg1mwxu"
   },
   "source": [
    "# Training Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.482308Z",
     "iopub.status.busy": "2022-11-26T21:02:49.481469Z",
     "iopub.status.idle": "2022-11-26T21:02:49.499750Z",
     "shell.execute_reply": "2022-11-26T21:02:49.498766Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.482268Z"
    },
    "id": "dd8cde41"
   },
   "outputs": [],
   "source": [
    "def is_trained(task):\n",
    "    chckpt_path = f'{BASIC_PATH}/models/{MODEL_NAME}/{task}'\n",
    "    if os.path.exists(chckpt_path) and os.path.exists(f'{chckpt_path}/trained'):\n",
    "        return True\n",
    "    return False\n",
    "\n",
    "def clean_checkpoints(task, keep=1):\n",
    "    chckpt_path = f'{BASIC_PATH}/models/{MODEL_NAME}/{task}'\n",
    "    checkpoints = list(filter(lambda x: x.startswith('checkpoint-'), os.listdir(chckpt_path)))\n",
    "    checkpoints.sort(reverse=True)\n",
    "    for chckpt in checkpoints[keep:]:\n",
    "        print('removing...', f'{BASIC_PATH}/models/{MODEL_NAME}/{task}/{chckpt}')\n",
    "        shutil.rmtree(f'{BASIC_PATH}/models/{MODEL_NAME}/{task}/{chckpt}', ignore_errors=True)\n",
    "\n",
    "def get_optimizer_params(model, encoder_lr, decoder_lr, weight_decay=0.0):\n",
    "    param_optimizer = list(model.named_parameters())\n",
    "    no_decay = [\"bias\", \"LayerNorm.bias\", \"LayerNorm.weight\"]\n",
    "    optimizer_parameters = [\n",
    "        {'params': [p for n, p in model.base.named_parameters() if not any(nd in n for nd in no_decay)],\n",
    "            'lr': encoder_lr, 'weight_decay': weight_decay},\n",
    "        {'params': [p for n, p in model.base.named_parameters() if any(nd in n for nd in no_decay)],\n",
    "            'lr': encoder_lr, 'weight_decay': 0.0},\n",
    "        {'params': [p for n, p in model.named_parameters() if \"base\" not in n],\n",
    "            'lr': decoder_lr, 'weight_decay': 0.0}\n",
    "    ]\n",
    "    return optimizer_parameters\n",
    "\n",
    "def retrain(task):\n",
    "    model_path = f'{BASIC_PATH}/models/{MODEL_NAME}/{task}'\n",
    "    print('removing...', model_path)\n",
    "    shutil.rmtree(model_path, ignore_errors=True)\n",
    "\n",
    "def freeze(model, layers, dropout=0.5):\n",
    "    print('Freezing the model...')\n",
    "    for name, param in model.named_parameters():\n",
    "        for layer in layers:\n",
    "            if f'encoder.layer.{layer}.' in name or 'word_embeddings' in name:\n",
    "                param.requires_grad = False\n",
    "                print('Freezing layer:', name)\n",
    "    if dropout is not None:\n",
    "        for layer in layers:\n",
    "            model.base.deberta.encoder.layer[layer].output.dropout = torch.nn.Dropout(dropout)\n",
    "    return model\n",
    "\n",
    "def unfreeze(model, layer, dropout=0.1):\n",
    "    print('Unfreezing the model...')\n",
    "    for name, param in model.named_parameters():\n",
    "        if layer == -1 and 'word_embeddings' in name:\n",
    "            param.requires_grad = True\n",
    "            print('Unfreezing layer:', name)\n",
    "            \n",
    "        if f'encoder.layer.{layer}.' in name:\n",
    "            param.requires_grad = True\n",
    "            print('Unfreezing layer:', name)\n",
    "            if dropout is not None:\n",
    "                model.base.deberta.encoder.layer[layer].output.dropout = torch.nn.Dropout(dropout)\n",
    "    return model\n",
    "    \n",
    "class PreplexityCallback(TrainerCallback):\n",
    "    def on_evaluate(self, args, state, control, model, eval_dataloader, **kwargs):\n",
    "        print(f\"Perplexity: {perplexity(model, eval_dataloader):.3f}\")\n",
    "        print('----------------\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TYVDn9lInjfO"
   },
   "source": [
    "# TRAINING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GUUfVBdEt3Xs"
   },
   "source": [
    "### Domain Adaptation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.501684Z",
     "iopub.status.busy": "2022-11-26T21:02:49.501051Z",
     "iopub.status.idle": "2022-11-26T21:02:49.513924Z",
     "shell.execute_reply": "2022-11-26T21:02:49.513198Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.501640Z"
    },
    "id": "Fk-qJGmEt7PT"
   },
   "outputs": [],
   "source": [
    "def train_masked_lm(model, tokenizer, aug_data, config):\n",
    "    train_split, test_split = train_test_split(aug_data, test_size=0.33, random_state=42)\n",
    "\n",
    "    data_collator = DataCollatorForLanguageModeling(\n",
    "        tokenizer=tokenizer, mlm=True, mlm_probability=0.15, return_tensors='pt'\n",
    "    )\n",
    "\n",
    "    data = DatasetDict({\n",
    "        'train': Dataset.from_pandas(train_split[['full_text']]),\n",
    "        'test': Dataset.from_pandas(test_split[['full_text']]),\n",
    "    })\n",
    "\n",
    "    print(f'Dataset tokenization')\n",
    "    data = data.map(tokenization, batched=True)\n",
    "    data.set_format('pt')\n",
    "\n",
    "    training_args = TrainingArguments(\n",
    "        output_dir=f'{BASIC_PATH}/training',\n",
    "        do_train=True,\n",
    "        do_eval=True,\n",
    "        seed=42,\n",
    "        **config,\n",
    "    )\n",
    "\n",
    "    trainer = Trainer(\n",
    "        model=model,\n",
    "        args=training_args,\n",
    "        data_collator=data_collator,\n",
    "        train_dataset=data['train'],\n",
    "        eval_dataset=data['test'],\n",
    "        tokenizer=tokenizer,\n",
    "        callbacks=[PreplexityCallback]\n",
    "    )\n",
    "\n",
    "    results = trainer.train()\n",
    "    \n",
    "    eval_results = trainer.evaluate()\n",
    "    print('Evaluation results: ', eval_results)\n",
    "    print(f\"Perplexity: {np.exp(eval_results['eval_loss']):.3f}\")\n",
    "    print('----------------\\n')\n",
    "    \n",
    "    model_path = f'{BASIC_PATH}/models/mlm/{MODEL_NAME}'\n",
    "    trainer.save_model(model_path)\n",
    "    return results, model_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mk_Cp7caqZ9p"
   },
   "source": [
    "# SSL Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.516194Z",
     "iopub.status.busy": "2022-11-26T21:02:49.515182Z",
     "iopub.status.idle": "2022-11-26T21:02:49.527950Z",
     "shell.execute_reply": "2022-11-26T21:02:49.527271Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.516156Z"
    },
    "id": "Y4OHSB0tqduz"
   },
   "outputs": [],
   "source": [
    "def train_task(model, data, config, task):\n",
    "    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
    "\n",
    "    training_args = TrainingArguments(\n",
    "        output_dir=f'{BASIC_PATH}/models/{MODEL_NAME}/{task}',\n",
    "        no_cuda=True if device == 'cpu' else False,\n",
    "        label_names=['labels'],\n",
    "        **config['training_args'],\n",
    "    )\n",
    "\n",
    "    # optimizer_parameters = get_optimizer_params(model,\n",
    "    #     encoder_lr=config['optimizer']['encoder_lr'], \n",
    "    #     decoder_lr=config['optimizer']['decoder_lr'],\n",
    "    #     weight_decay=config['optimizer']['weight_decay']\n",
    "    # )\n",
    "\n",
    "    optimizer = torch.optim.AdamW(\n",
    "        model.parameters(),\n",
    "        lr=config['optimizer']['lr'], \n",
    "        eps=config['optimizer']['eps'],\n",
    "        weight_decay=config['optimizer']['weight_decay']\n",
    "    )\n",
    "\n",
    "    num_train_steps = int(len(data['train']) / config['batch_size'] * config['epochs'])\n",
    "    scheduler = get_linear_schedule_with_warmup(\n",
    "        optimizer,\n",
    "        num_warmup_steps=config['scheduler'].pop('num_warmup_steps'),\n",
    "        num_training_steps=num_train_steps,\n",
    "    )\n",
    "\n",
    "    callbacks = []\n",
    "    if 'early_stopping' in config and config['early_stopping']:\n",
    "        callbacks.append(EarlyStoppingCallback(**config['early_stopping_params']))\n",
    "\n",
    "    trainer = TrainerWithUWScheduler(\n",
    "        model=model,\n",
    "        args=training_args,\n",
    "        train_dataset=data['train'],\n",
    "        eval_dataset=data['test'],\n",
    "        data_collator=data_collator,\n",
    "        compute_metrics=compute_metrics,\n",
    "        tokenizer=tokenizer,\n",
    "        callbacks=callbacks,\n",
    "        optimizers=(optimizer, scheduler),\n",
    "        uw_kwargs=config['scheduler'],\n",
    "        unfreeze_after=config['unfreeze_after'],\n",
    "        unfreeze_every=config['unfreeze_every'],\n",
    "        layer_to_unfreeze=config['layer_to_unfreeze'],\n",
    "    )\n",
    "\n",
    "    return trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.530269Z",
     "iopub.status.busy": "2022-11-26T21:02:49.529215Z",
     "iopub.status.idle": "2022-11-26T21:02:49.540992Z",
     "shell.execute_reply": "2022-11-26T21:02:49.540296Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.530211Z"
    },
    "id": "Fa5_yjW9qnTt"
   },
   "outputs": [],
   "source": [
    "@eval\n",
    "def predict_ssl(model, test_data, task):\n",
    "    model = model.to(device)\n",
    "\n",
    "    data = Dataset.from_pandas(test_data[['full_text', f'{task}_labels']].rename({f'{task}_labels': 'label', 'full_text': 'text'}, axis=1))\n",
    "    data = data.remove_columns(['text', '__index_level_0__'])\n",
    "    data = data.with_format('torch', device=device).map(tokenization, batched=True) # type='torch', columns=['input_ids', 'token_type_ids', 'attention_mask', 'label']\n",
    "    \n",
    "    test_dataloader = torch.utils.data.DataLoader(data, batch_size=2 * TRAIN_CONFIG['batch_size'])\n",
    "\n",
    "    features = []\n",
    "    with torch.no_grad():\n",
    "        for test_input in tqdm(test_dataloader):\n",
    "            mask = test_input['attention_mask'].to(device)\n",
    "            input_id = test_input['input_ids'].to(device)\n",
    "\n",
    "            out_features = model(input_id, mask).hidden_states\n",
    "            features.append(out_features.cpu().numpy())\n",
    "    return np.concatenate(features)\n",
    "\n",
    "def predict_features_ssl(test_split, task, pooling='mean'):\n",
    "\n",
    "    model = TemporalEnsembleModel(pooling=pooling)\n",
    "    model.load_state_dict(torch.load(f'{BASIC_PATH}/models/{MODEL_NAME}/{task}/final'))\n",
    "    print(model.pooler)\n",
    "        \n",
    "    return predict_ssl(model, test_split, task)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aGCMsVP0pP8R"
   },
   "source": [
    "### Supervised Traning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.543187Z",
     "iopub.status.busy": "2022-11-26T21:02:49.542179Z",
     "iopub.status.idle": "2022-11-26T21:02:49.555502Z",
     "shell.execute_reply": "2022-11-26T21:02:49.554692Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.543138Z"
    },
    "id": "ieecZ6xqU19e"
   },
   "outputs": [],
   "source": [
    "@train\n",
    "def train_step(model, criterion, optimizer, scaler, scheduler,  step, train_input, train_labels, config):\n",
    "    step_loss = 0\n",
    "\n",
    "    train_labels = train_labels.to(device).float()\n",
    "    mask = train_input['attention_mask'].to(device)\n",
    "    input_id = train_input['input_ids'].to(device)\n",
    "\n",
    "    with torch.cuda.amp.autocast(enabled=True):\n",
    "        output = model(input_id, mask)\n",
    "        \n",
    "        batch_loss = criterion(output, train_labels)\n",
    "    \n",
    "    if config['gradient_accumulation_steps'] > 1:\n",
    "        batch_loss = batch_loss / config['gradient_accumulation_steps']\n",
    "\n",
    "    step_loss += batch_loss.item()\n",
    "\n",
    "    if config['fp16']:\n",
    "        scaler.scale(batch_loss).backward()\n",
    "    else:\n",
    "        batch_loss.backward()\n",
    "\n",
    "    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config['max_grad_norm'])\n",
    "\n",
    "    if (step + 1) % config['gradient_accumulation_steps'] == 0:\n",
    "        if config['fp16']:\n",
    "            scaler.step(optimizer)\n",
    "            scaler.update()\n",
    "        else:\n",
    "            optimizer.step()\n",
    "\n",
    "        scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "    return step_loss\n",
    "\n",
    "@eval\n",
    "def val_step(model, criterion, val_input, val_label):\n",
    "    step_loss = 0\n",
    "    step_mcrmse = 0\n",
    "\n",
    "    val_label = val_label.to(device)\n",
    "    mask = val_input['attention_mask'].to(device)\n",
    "    input_id = val_input['input_ids'].to(device)\n",
    "\n",
    "    output = model(input_id, mask)\n",
    "\n",
    "    batch_loss = criterion(output, val_label)\n",
    "    step_loss += batch_loss.item()\n",
    "    step_mcrmse += MCRMSE(val_label.to('cpu').numpy(), output.to('cpu').numpy())\n",
    "    return step_loss, step_mcrmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.557608Z",
     "iopub.status.busy": "2022-11-26T21:02:49.556997Z",
     "iopub.status.idle": "2022-11-26T21:02:49.574794Z",
     "shell.execute_reply": "2022-11-26T21:02:49.573927Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.557573Z"
    },
    "id": "fsWPb257_gnh"
   },
   "outputs": [],
   "source": [
    "def training(model, train_data, val_data, config, do_val=False):\n",
    "    model.train()\n",
    "\n",
    "    model = model.to(device)\n",
    "\n",
    "    scaler =  torch.cuda.amp.GradScaler(enabled=config['apex'])\n",
    "\n",
    "    if do_val:\n",
    "        val = FeedbackDataset(val_data)\n",
    "    train = FeedbackDataset(train_data)\n",
    "\n",
    "    criterion = torch.nn.SmoothL1Loss(reduction='mean').to(device)\n",
    "\n",
    "    optimizer_parameters = get_optimizer_params(model,\n",
    "                                                encoder_lr=config['encoder_lr'], \n",
    "                                                decoder_lr=config['decoder_lr'],\n",
    "                                                weight_decay=config['weight_decay'])\n",
    "\n",
    "    optimizer = torch.optim.AdamW(\n",
    "        optimizer_parameters,\n",
    "        lr=config['lr'], \n",
    "        eps=config['eps'],\n",
    "        weight_decay=config['weight_decay']\n",
    "    )\n",
    "\n",
    "    num_train_steps = int(len(train_data) / config['batch_size'] * config['epochs'])\n",
    "    scheduler = get_cosine_schedule_with_warmup(\n",
    "        optimizer, \n",
    "        num_warmup_steps=config['num_warmup_steps'], \n",
    "        num_training_steps=num_train_steps, \n",
    "        num_cycles=config['num_cycles'],\n",
    "    )\n",
    "\n",
    "    train_dataloader = torch.utils.data.DataLoader(train, batch_size=config['batch_size'], shuffle=True)\n",
    "    if do_val:\n",
    "        val_dataloader = torch.utils.data.DataLoader(val, batch_size=config['batch_size'])\n",
    "\n",
    "    results = {\n",
    "        'train_losses': [],\n",
    "        'val_losses': [],\n",
    "        'val_mcrmse': [],\n",
    "    }\n",
    "\n",
    "    layer_to_unfreeze = config['layer_to_unfreeze']\n",
    "    for epoch_num in range(config['epochs']):\n",
    "        \n",
    "        if epoch_num >= config['epochs'] * config['unfreeze_after']:\n",
    "            if layer_to_unfreeze >= -1:\n",
    "                model = unfreeze(model, layer_to_unfreeze, dropout=None)\n",
    "            layer_to_unfreeze-=1\n",
    "\n",
    "        total_loss_train = 0\n",
    "\n",
    "        for step, (train_input, train_labels) in enumerate(tqdm(train_dataloader)):\n",
    "            train_input = collate(train_input)\n",
    "            total_loss_train += train_step(model, criterion, optimizer, scaler, scheduler,  step, train_input, train_labels, config)\n",
    "\n",
    "        if do_val:\n",
    "            total_loss_val = 0\n",
    "            total_mcrmse_val = 0\n",
    "\n",
    "            with torch.no_grad():\n",
    "\n",
    "                for step, (val_input, val_label) in enumerate(tqdm(val_dataloader)):\n",
    "                    step_loss, step_mcrmse = val_step(model, criterion, val_input, val_label)\n",
    "                    total_loss_val += step_loss\n",
    "                    total_mcrmse_val += step_mcrmse\n",
    "        \n",
    "        # torch.save({\n",
    "        #     'epoch': {epoch_num + 1},\n",
    "        #     'model_state_dict': model.to('cpu').state_dict(),\n",
    "        #     'optimizer_state_dict': optimizer.state_dict(),\n",
    "        #     'loss': criterion.to('cpu'),\n",
    "        #     }, f'{BASIC_PATH}/models/{MODEL_NAME}/checkpoints/checkpoint-{str(time.time())}')\n",
    "        \n",
    "        train_steps = len(train_data) / config['batch_size']\n",
    "\n",
    "        if do_val:\n",
    "            val_steps = len(val_data) / config['batch_size']\n",
    "        \n",
    "        results['train_losses'].append(total_loss_train / train_steps)\n",
    "        if do_val:\n",
    "            results['val_losses'].append(total_loss_val / val_steps)\n",
    "            results['val_mcrmse'].append(total_mcrmse_val / val_steps)\n",
    "\n",
    "        print(f'Epoch: {epoch_num + 1} | Train Loss: {total_loss_train / train_steps: .3f}', end=' ')\n",
    "        if do_val:\n",
    "            print(f'| Val Loss: {total_loss_val / val_steps: .3f} | Val MCRMSE: {total_mcrmse_val / val_steps: .3f}')\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.576799Z",
     "iopub.status.busy": "2022-11-26T21:02:49.576416Z",
     "iopub.status.idle": "2022-11-26T21:02:49.589016Z",
     "shell.execute_reply": "2022-11-26T21:02:49.588353Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.576766Z"
    },
    "id": "1-s1Tq95aCBG"
   },
   "outputs": [],
   "source": [
    "@eval\n",
    "def evaluate(model, test_data, config):\n",
    "    model = model.to(device)\n",
    "\n",
    "    test = FeedbackDataset(test_data)\n",
    "\n",
    "    test_dataloader = torch.utils.data.DataLoader(test, batch_size=config['batch_size'])\n",
    "\n",
    "    total_score = 0\n",
    "    with torch.no_grad():\n",
    "        for test_input, test_labels in tqdm(test_dataloader):\n",
    "            mask = test_input['attention_mask'].to(device)\n",
    "            input_id = test_input['input_ids'].squeeze(1).to(device)\n",
    "\n",
    "            output = model(input_id, mask)\n",
    "\n",
    "            total_score += MCRMSE(test_labels.to('cpu').numpy(), output.to('cpu').numpy()) / config['batch_size']\n",
    "    return total_score / len(test_data)\n",
    "\n",
    "@eval\n",
    "def predict(model, test_data, config):\n",
    "    model = model.to(device)\n",
    "\n",
    "    test = PredictDataset(test_data)\n",
    "\n",
    "    test_dataloader = torch.utils.data.DataLoader(test, batch_size=config['batch_size'])\n",
    "\n",
    "    predictions = []\n",
    "    features = []\n",
    "    with torch.no_grad():\n",
    "        for test_input in tqdm(test_dataloader):\n",
    "            mask = test_input['attention_mask'].to(device)\n",
    "            input_id = test_input['input_ids'].to(device)\n",
    "\n",
    "            out_features = model.features(input_id, mask)\n",
    "            output = model.out(out_features)\n",
    "            predictions.append(output.cpu().numpy())\n",
    "            features.append(out_features.cpu().numpy())\n",
    "    return np.concatenate(predictions), np.concatenate(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TTT5ClQknpqQ"
   },
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.590660Z",
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     "shell.execute_reply.started": "2022-11-26T21:02:49.590621Z"
    },
    "id": "tXT-fEgJBjZr"
   },
   "outputs": [],
   "source": [
    "class FeedbackDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, df):\n",
    "        self.labels = df[TARGET_VARIABLES].reset_index(drop=True)\n",
    "        self.texts = df[['full_text']].reset_index(drop=True)\n",
    "\n",
    "    def labels(self):\n",
    "        return self.labels\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def get_batch_labels(self, idx):\n",
    "        return np.array(self.labels.loc[idx].values).astype(float)\n",
    "\n",
    "    def get_batch_texts(self, idx):\n",
    "        if type(idx) == int:\n",
    "            idx = [idx]\n",
    "        return tokenize(list(np.array(self.texts.loc[idx].values)[:, 0]))\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.get_batch_texts(idx), self.get_batch_labels(idx)\n",
    "\n",
    "class PredictDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, df):\n",
    "        self.texts = df[['full_text']].reset_index(drop=True)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.texts)\n",
    "\n",
    "    def get_batch_texts(self, idx):\n",
    "        if type(idx) == int:\n",
    "            idx = [idx]\n",
    "        return tokenize(list(np.array(self.texts.loc[idx].values)[:, 0]))\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.get_batch_texts(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.606220Z",
     "iopub.status.busy": "2022-11-26T21:02:49.605932Z",
     "iopub.status.idle": "2022-11-26T21:02:49.618193Z",
     "shell.execute_reply": "2022-11-26T21:02:49.617306Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.606191Z"
    },
    "id": "szAzU4OjkWBC"
   },
   "outputs": [],
   "source": [
    "class MeanPooling(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MeanPooling, self).__init__()\n",
    "        \n",
    "    def forward(self, last_hidden_state, attention_mask):\n",
    "        input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()\n",
    "        sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded, 1)\n",
    "        sum_mask = input_mask_expanded.sum(1)\n",
    "        sum_mask = torch.clamp(sum_mask, min=1e-9)\n",
    "        mean_embeddings = sum_embeddings / sum_mask\n",
    "        return mean_embeddings\n",
    "    \n",
    "class MaxPooling(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MaxPooling, self).__init__()\n",
    "        \n",
    "    def forward(self, last_hidden_state, attention_mask):\n",
    "        input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()\n",
    "        embeddings = last_hidden_state.clone()\n",
    "        embeddings[input_mask_expanded == 0] = -1e4\n",
    "        max_embeddings, _ = torch.max(embeddings, dim = 1)\n",
    "        return max_embeddings\n",
    "    \n",
    "class MinPooling(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MinPooling, self).__init__()\n",
    "        \n",
    "    def forward(self, last_hidden_state, attention_mask):\n",
    "        input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()\n",
    "        embeddings = last_hidden_state.clone()\n",
    "        embeddings[input_mask_expanded == 0] = 1e-4\n",
    "        min_embeddings, _ = torch.min(embeddings, dim = 1)\n",
    "        return min_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.620998Z",
     "iopub.status.busy": "2022-11-26T21:02:49.620392Z",
     "iopub.status.idle": "2022-11-26T21:02:49.633866Z",
     "shell.execute_reply": "2022-11-26T21:02:49.632834Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.620966Z"
    },
    "id": "KKTg-Qjr-np5"
   },
   "outputs": [],
   "source": [
    "class FeedbackModel(torch.nn.Module):\n",
    "\n",
    "    def __init__(self, dropout=0.1, gradient_checkpointing=True):\n",
    "\n",
    "        super(FeedbackModel, self).__init__()\n",
    "\n",
    "        self.config = AutoConfig.from_pretrained(get_config_path('sup'), output_hidden_states=True)\n",
    "        self.config.update({\n",
    "            'hidden_dropout': 0.,\n",
    "            'hidden_dropout_prob': 0.,\n",
    "            'attention_dropout': 0.,\n",
    "            'attention_probs_dropout_prob': 0.,\n",
    "        })\n",
    "\n",
    "        save_config(self, 'sup')\n",
    "\n",
    "        # TODO: FIXME FOR INFERENCE\n",
    "        self.base = AutoModel.from_pretrained(MODEL_NAME, config=self.config)\n",
    "        \n",
    "        if gradient_checkpointing and 'distilbert' not in MODEL_NAME:\n",
    "            self.base.gradient_checkpointing_enable()\n",
    "\n",
    "        self.pooler = MeanPooling()\n",
    "        self.out = torch.nn.Linear(768, 6)\n",
    "        self._init_weights(self.out)\n",
    "    \n",
    "    def _init_weights(self, module):\n",
    "        if isinstance(module, torch.nn.Linear):\n",
    "            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n",
    "            if module.bias is not None:\n",
    "                module.bias.data.zero_()\n",
    "        elif isinstance(module, torch.nn.Embedding):\n",
    "            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n",
    "            if module.padding_idx is not None:\n",
    "                module.weight.data[module.padding_idx].zero_()\n",
    "        elif isinstance(module, torch.nn.LayerNorm):\n",
    "            module.bias.data.zero_()\n",
    "            module.weight.data.fill_(1.0)\n",
    "        \n",
    "    def features(self, input_ids, attention_mask):\n",
    "        input_ids = input_ids.squeeze(1)\n",
    "        attention_mask = attention_mask.squeeze(1)\n",
    "        \n",
    "        out = self.base(input_ids=input_ids,attention_mask=attention_mask,\n",
    "                         output_hidden_states=True)\n",
    "        last_hidden_states = out[0]\n",
    "        features = self.pooler(last_hidden_states, attention_mask)\n",
    "        return features\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        features = self.features(input_ids, attention_mask)\n",
    "        final_layer = self.out(features)\n",
    "        return final_layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cTbRTj3DpxEI"
   },
   "source": [
    "# SSL Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.637667Z",
     "iopub.status.busy": "2022-11-26T21:02:49.637415Z",
     "iopub.status.idle": "2022-11-26T21:02:49.657729Z",
     "shell.execute_reply": "2022-11-26T21:02:49.657062Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.637644Z"
    },
    "id": "94MBQAL3qInz"
   },
   "outputs": [],
   "source": [
    "class TemporalEnsembleModel(torch.nn.Module):\n",
    "\n",
    "    \"\"\"\n",
    "    Implementation of Temporal ensemble model as introduced in <https://arxiv.org/abs/1610.02242>\n",
    "\n",
    "    Args:\n",
    "\n",
    "    alpha (:obj:`float`): memory of the last epochs. For more info please refer\n",
    "    to <https://arxiv.org/abs/1610.02242>.\n",
    "\n",
    "    unsup_weight (:obj:`float`): initial value of weight of the unsupervised loss\n",
    "    component. After setting the initial value, its value is controlled by\n",
    "    unsupervised weight scheduler.\n",
    "\n",
    "    kwargs: remaining dictionary of keyword arguments from the BaseModelClass.\n",
    "\n",
    "    Class attributes:\n",
    "\n",
    "     -**mini_batch_num**: keeps track of the mini_batch_num using forward method.\n",
    "\n",
    "     -**logits_batchwise**: stores the logits of each batch passed through forward method.\n",
    "\n",
    "     -**firstpass**: bool variable to track if its the first pass through the forward method.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, pooling='mean', alpha=0.5, unsup_weight=0, pretrained=None, *args, **kwargs):\n",
    "\n",
    "        super().__init__(*args, **kwargs)\n",
    "\n",
    "        self.config = AutoConfig.from_pretrained(get_config_path('ssl'))\n",
    "\n",
    "        self.config.update({\n",
    "            'id2label': RAW_LABELS,\n",
    "            'num_labels': len(RAW_LABELS),\n",
    "            'label2id': REV_LABELS,\n",
    "            'output_hidden_states': True,\n",
    "            'hidden_dropout_prob': 0.1,\n",
    "            'attention_probs_dropout_prob': 0.1,\n",
    "            'classifier_dropout': 0.3,\n",
    "        })\n",
    "\n",
    "        save_config(self, 'ssl')\n",
    "\n",
    "        self.num_labels = len(RAW_LABELS)\n",
    "\n",
    "        self.base = AutoModelForSequenceClassification.from_pretrained(get_model_ssl_path(TASK), config=self.config)\n",
    "\n",
    "        if pretrained is not None:\n",
    "            modelMLM = AutoModelForMaskedLM.from_pretrained(pretrained)\n",
    "            self.base = copy_weights(modelMLM, self.base)\n",
    "\n",
    "        self.unsup_weight = unsup_weight\n",
    "        self.memory_logits = []\n",
    "        self.alpha = alpha\n",
    "        self.mini_batch_num = 0\n",
    "        self.logits_batchwise = []\n",
    "        self.firstpass = True\n",
    "        \n",
    "        if pooling == 'mean':\n",
    "            self.pooler = MeanPooling()\n",
    "        elif pooling == 'max':\n",
    "            self.pooler = MaxPooling()\n",
    "        elif pooling == 'min':\n",
    "            self.pooler = MinPooling()\n",
    "\n",
    "    def forward(self, input_ids, attention_mask, *args, **kwargs):\n",
    "        \"\"\"\n",
    "        Implementation of forward function calculating the semi supervised loss.\n",
    "        Mixing of the labeled and unlabeled examples in a single batch is not allowed.\n",
    "\n",
    "        Args:\n",
    "\n",
    "        kwargs: Arguments from pretrained_model.forward.\n",
    "\n",
    "        Return:\n",
    "        transformers.modeling_outputs.SequenceClassifierOutput object\n",
    "        with the information of logits and the loss function.\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        labels = kwargs.pop(\"labels\", [])\n",
    "        out = self.base.forward(input_ids.squeeze(1), attention_mask.squeeze(1), *args, **kwargs)\n",
    "        logits = out.logits\n",
    "\n",
    "        sup_loss = torch.nn.CrossEntropyLoss()\n",
    "        unsup_loss = torch.nn.MSELoss()\n",
    "\n",
    "        tot_loss = 0\n",
    "        if len(labels) > 0 and all(labels >= 0):\n",
    "            tot_loss = sup_loss(logits.view(-1, self.num_labels), labels.view(-1))\n",
    "\n",
    "        if self.firstpass is False and self.training:\n",
    "            tot_loss += self.unsup_weight * unsup_loss(self.memory_logits[self.mini_batch_num], logits)\n",
    "\n",
    "        elif self.training:\n",
    "            # required to free the graph\n",
    "            tot_loss += 0 * logits.sum()\n",
    "\n",
    "        if self.training:\n",
    "            self.logits_batchwise.append(logits.detach().clone())\n",
    "            self.mini_batch_num += 1\n",
    "        \n",
    "        hidden_states = None\n",
    "        if not self.training:    \n",
    "            hidden_states = torch.cat([self.pooler(out.hidden_states[0], attention_mask.squeeze(1)), self.pooler(out.hidden_states[-1], attention_mask.squeeze(1))], 1)\n",
    "        return SequenceClassifierOutput(\n",
    "            hidden_states=hidden_states,\n",
    "            loss=tot_loss,\n",
    "            logits=logits\n",
    "        )\n",
    "\n",
    "    def update_memory_logits(self, t):\n",
    "        \"\"\"\n",
    "        Method for updating the memory logits with the exponential average.\n",
    "\n",
    "        Args:\n",
    "        t (:obj:`int`): epoch value for bias normalization.\n",
    "\n",
    "        \"\"\"\n",
    "        return\n",
    "        if self.firstpass is True:\n",
    "\n",
    "            device = self.logits_batchwise[0].device\n",
    "\n",
    "            self.memory_logits = [torch.zeros(self.logits_batchwise[0].shape, device=device)\n",
    "                                  for _ in range(self.mini_batch_num)]\n",
    "            self.firstpass = False\n",
    "\n",
    "        for ind in range(self.mini_batch_num):\n",
    "\n",
    "            self.memory_logits[ind] = (self.alpha * self.memory_logits[ind] +\n",
    "                                       (1 - self.alpha) * self.logits_batchwise[ind])\n",
    "\n",
    "            self.memory_logits[ind] /= (1 - self.alpha**(t) + 1e-8)\n",
    "\n",
    "        self.mini_batch_num = 0\n",
    "        self.logits_batchwise[:] = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "class UWScheduler:\n",
    "\n",
    "    \"\"\"\n",
    "    Unsupervised weights scheduler for changing the unsupervised weight\n",
    "    of the semi supervised learning models. Also contains methods any\n",
    "    other kinds variables updates required by the models. For example,\n",
    "    PiModel, TemporalEnsembling model, and Mean Teacher. In this implementation,\n",
    "    it's  based on the composition with learning scheduler from pytorch and it\n",
    "    works best ~transformers.Trainer without having to rewrite train method.\n",
    "    # TODO: cleaner version with the rewritten Trainer.train method.\n",
    "\n",
    "    Args:\n",
    "\n",
    "    lr_scheduler (:obj:`torch.optim.lr_scheduler`): Learning scheduler object.\n",
    "\n",
    "    trainer (:obj:`~TrainerWithUWScheduler`): Trainer object.\n",
    "\n",
    "    unsup_start_epochs (:obj:`int`): value of epoch at which the unsupervised\n",
    "    weights should start updating.\n",
    "\n",
    "    max_w (:obj:`float`): maximum value of weight that the unsup_weight from model\n",
    "    could reach.\n",
    "\n",
    "    update_teacher_steps (:obj:`int`): useful for MeanTeacher, sets the interval after\n",
    "    which teacher variables should be updated.\n",
    "\n",
    "    w_ramprate (:obj:`float`): linear rate at which the unsupervised weight would be\n",
    "    increased from the initial value.\n",
    "\n",
    "    update_weights_steps (:obj:`int`): interval steps after which unsupervised weight\n",
    "    would be updated by the w_ramprate.\n",
    "\n",
    "    Class attributes:\n",
    "\n",
    "    -**step_in_epochs**: Number of steps (batch passes) in an epoch.\n",
    "\n",
    "    -**local_step**: keeps track of the times unsupervised weight has been changed.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, lr_scheduler, trainer, unsup_start_steps=0, max_w=1,\n",
    "                 w_ramprate=1, update_weights_steps=1):\n",
    "\n",
    "        self.trainer = trainer\n",
    "        self.lr_scheduler = lr_scheduler\n",
    "        self.local_step = 0\n",
    "        self.max_w = max_w\n",
    "        self.unsup_start_steps = unsup_start_steps\n",
    "        self.update_weights_steps = update_weights_steps\n",
    "        self.w_ramprate = w_ramprate\n",
    "        self.steps_in_epoch = len(self.trainer.train_dataset) // self.trainer.args.train_batch_size\n",
    "\n",
    "        # debug\n",
    "        self.weight_history = []\n",
    "        self.lr_history = []\n",
    "\n",
    "    def step(self):\n",
    "        \"\"\"\n",
    "        Implementation of composition of the pytorch learning rate scheduler step function\n",
    "        with schedule of unsupervised weights. \n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        self.lr_scheduler.step()\n",
    "\n",
    "        # trainer.state.num_train_epochs is unavailable in the init...\n",
    "        expected_steps = self.steps_in_epoch * self.trainer.state.num_train_epochs\n",
    "        T = 1.0 / (expected_steps - self.unsup_start_steps)\n",
    "\n",
    "        # ramping up T from 0 to 1\n",
    "        if self.trainer.state.global_step >= self.unsup_start_steps and self.is_true(self.update_weights_steps):\n",
    "            rampup_step = self.local_step - self.unsup_start_steps\n",
    "            self.trainer.model.unsup_weight = min(\n",
    "                self.max_w,\n",
    "                gaussian(rampup_step, self.w_ramprate * rampup_step * T)\n",
    "            )\n",
    "\n",
    "        self.debug()\n",
    "\n",
    "        if self.is_true(self.steps_in_epoch):\n",
    "            self.trainer.model.update_memory_logits(int(self.trainer.state.epoch + 1))\n",
    "\n",
    "        self.local_step += 1\n",
    "\n",
    "    def is_true(self, value):\n",
    "        \"\"\"\n",
    "        A simple checker function to if it is time to update things depending on the value of value.\n",
    "        \"\"\"\n",
    "        return (self.trainer.state.global_step and (self.trainer.state.global_step + 1) % value == 0)\n",
    "\n",
    "    def debug(self):\n",
    "        self.weight_history.append(self.trainer.model.unsup_weight)\n",
    "        self.lr_history.append(self.lr_scheduler.get_lr()[0])\n",
    "\n",
    "    def __getattr__(self, name):\n",
    "        \"\"\"\n",
    "        Needed for the calls from ~transformers.Trainer.train() method.\n",
    "        \"\"\"\n",
    "        return getattr(self.lr_scheduler, name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.674899Z",
     "iopub.status.busy": "2022-11-26T21:02:49.674302Z",
     "iopub.status.idle": "2022-11-26T21:02:49.687502Z",
     "shell.execute_reply": "2022-11-26T21:02:49.686598Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.674865Z"
    },
    "id": "yAIf_oMpqUPy"
   },
   "outputs": [],
   "source": [
    "class TrainerWithUWScheduler(Trainer):\n",
    "\n",
    "    \"\"\"\n",
    "    Subclass of ~transformers.Trainer with minimal code change and integration\n",
    "    with unsupervised weight scheduler.\n",
    "\n",
    "    Args:\n",
    "\n",
    "    kwargs_uw: dictionary of arguments to be used by UWScheduler.\n",
    "\n",
    "    kwargs: dictionary arguments for the ~transformers.Trainer, of dataset used\n",
    "    by the trainer and could also include arguments of UWScheduler.\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, uw_kwargs, layer_to_unfreeze, unfreeze_after=0.5, unfreeze_every=1000, *args, **kwargs):\n",
    "\n",
    "        self.uw_kwargs = uw_kwargs\n",
    "\n",
    "        super().__init__(*args, **kwargs)\n",
    "\n",
    "        self.check_for_consistency()\n",
    "\n",
    "        self.unfreeze_after = unfreeze_after\n",
    "        self.unfreeze_every = unfreeze_every\n",
    "        self.layer_to_unfreeze = layer_to_unfreeze\n",
    "\n",
    "    def create_optimizer_and_scheduler(self, num_training_steps):\n",
    "        \"\"\"\n",
    "        Overriden ~transformers.Trainer.create_optimizer_and_scheduler with integration\n",
    "        with the UWScheduler to its Trainer.lr_scheduler object\n",
    "\n",
    "        \"\"\"\n",
    "        super().create_optimizer_and_scheduler(num_training_steps)\n",
    "\n",
    "        self.lr_scheduler = UWScheduler(self.lr_scheduler, self, **self.uw_kwargs)\n",
    "\n",
    "    def get_train_dataloader(self):\n",
    "        \"\"\"\n",
    "        Slightly changed ~transformers.Trainer.get_train_dataloader as models used in\n",
    "        Trainer do not allow for mixing of labeled and unlabeled data. So changing to\n",
    "        SequentialSampler instead of RandomSampler.\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        train_sampler = SequentialSampler(self.train_dataset)\n",
    "\n",
    "        return DataLoader(\n",
    "            self.train_dataset,\n",
    "            batch_size=self.args.train_batch_size,\n",
    "            sampler=train_sampler,\n",
    "            collate_fn=self.data_collator,\n",
    "            drop_last=self.args.dataloader_drop_last,\n",
    "            num_workers=self.args.dataloader_num_workers,\n",
    "        )\n",
    "\n",
    "    def check_for_consistency(self):\n",
    "        \"\"\"\n",
    "        Checks if the labeled and unlabeled are present in the same minibatch, raises error if they are.\n",
    "        \"\"\"\n",
    "\n",
    "        labels = torch.tensor(self.train_dataset['label'])\n",
    "        batch_size = self.args.per_device_train_batch_size\n",
    "\n",
    "        for ind in range(len(labels) // batch_size):\n",
    "\n",
    "            min_batch = labels[ind * batch_size: (ind + 1) * batch_size]\n",
    "            if not (all(min_batch >= 0) or all(min_batch < 0)):\n",
    "                raise RuntimeError('Mixing of labeled and unlabeled examples is not allowed.')\n",
    "    \n",
    "    def training_step(self, *args, **kwargs):\n",
    "        if self.state.epoch >= self.unfreeze_after * self.state.num_train_epochs and self.state.global_step % self.unfreeze_every == 0:\n",
    "            self.model = unfreeze(model, self.layer_to_unfreeze)\n",
    "            if self.layer_to_unfreeze >= 0:\n",
    "                self.layer_to_unfreeze-=1\n",
    "        return super().training_step(*args, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hVn-hIczn21E"
   },
   "source": [
    "# Kfold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.695374Z",
     "iopub.status.busy": "2022-11-26T21:02:49.695106Z",
     "iopub.status.idle": "2022-11-26T21:02:49.704770Z",
     "shell.execute_reply": "2022-11-26T21:02:49.704087Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.695351Z"
    },
    "id": "7WnFG1QpeR18"
   },
   "outputs": [],
   "source": [
    "def kfold_train_features(train_split, test_split, config, plot=False, save_folds=True, pretrained=None):\n",
    "    kf = MultilabelStratifiedKFold(n_splits=config['n_splits'], shuffle=True, random_state=42)\n",
    "    \n",
    "    kfold_results = []\n",
    "    kfold_preds = []\n",
    "    kfold_features = []\n",
    "    for fold, (train_idx, val_idx) in enumerate(kf.split(X=np.zeros(train_split.shape[0]), y=train_split)):\n",
    "        print(f\"\\n{'='*40} Fold: {fold + 1} {'='*40}\")\n",
    "        \n",
    "        train_data = train_split.loc[train_idx]\n",
    "        val_data = train_split.loc[val_idx]\n",
    "        \n",
    "        model = get_model(retrain=True, pretrained=pretrained)\n",
    "        \n",
    "        if config['freeze']:\n",
    "            model = freeze(model, list(range(config['layer_to_unfreeze'] + 1)), dropout=None)\n",
    "\n",
    "        results = training(model, train_data, val_data, config, do_val=True)\n",
    "        kfold_results.append(results)\n",
    "        \n",
    "        preds, feats = predict(model, test_split, config)\n",
    "        kfold_preds.append(preds)\n",
    "        kfold_features.append(feats)\n",
    "\n",
    "        # plot stats from fold training\n",
    "        if plot:\n",
    "            plot_results(fold + 1, kfold_results[-1])\n",
    "        \n",
    "        if save_folds:\n",
    "            save_model(fold + 1, model)\n",
    "\n",
    "        # cleanup after one fold\n",
    "        del model\n",
    "        gc.collect()\n",
    "        free_gpu_cache()\n",
    "        \n",
    "    return kfold_results, np.array(kfold_preds).mean(axis=0), np.array(kfold_features).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.706758Z",
     "iopub.status.busy": "2022-11-26T21:02:49.706133Z",
     "iopub.status.idle": "2022-11-26T21:02:49.718708Z",
     "shell.execute_reply": "2022-11-26T21:02:49.717962Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.706723Z"
    },
    "id": "AxO6WH1QEJBq"
   },
   "outputs": [],
   "source": [
    "def kfold_train_regression(X_train, y_train, X_test, config, save_folds=True):\n",
    "    kf = MultilabelStratifiedKFold(n_splits=config['n_splits'], shuffle=True, random_state=42)\n",
    "    \n",
    "    kfold_predictions = []\n",
    "    kfold_scores = []\n",
    "    for fold, (train_idx, val_idx) in enumerate(kf.split(X=np.zeros(X_train.shape[0]), y=y_train)):\n",
    "        print(f\"\\n{'='*40} Fold: {fold + 1} {'='*40}\")\n",
    "        \n",
    "        train_data = X_train.loc[train_idx]\n",
    "        train_labels = y_train.loc[train_idx]\n",
    "        val_data = X_train.loc[val_idx]\n",
    "        val_labels = y_train.loc[val_idx]\n",
    "\n",
    "        model = MultiOutputRegressor(LGBMRegressor(**config['lgbm']))\n",
    "        model.fit(train_data, train_labels) # , eval_set = (val_data, val_labels)\n",
    "        \n",
    "        kfold_predictions.append(model.predict(X_test))\n",
    "        kfold_scores.append(MCRMSE(val_labels, model.predict(val_data)))\n",
    "\n",
    "        print('Val MCRMSE:', kfold_scores[-1])\n",
    "\n",
    "        if save_folds:\n",
    "            save_model_reg(fold + 1, model)\n",
    "\n",
    "        # cleanup after one fold\n",
    "        del model\n",
    "        gc.collect()\n",
    "        free_gpu_cache()\n",
    "\n",
    "    return np.array(kfold_predictions).mean(axis=0), kfold_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.720723Z",
     "iopub.status.busy": "2022-11-26T21:02:49.720102Z",
     "iopub.status.idle": "2022-11-26T21:02:49.731428Z",
     "shell.execute_reply": "2022-11-26T21:02:49.730723Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.720688Z"
    },
    "id": "OtKB5CnA5N-Y"
   },
   "outputs": [],
   "source": [
    "def kfold_predict_features(test_split, config):\n",
    "    kfold_features = []\n",
    "    kfold_predictions = []\n",
    "    for fold in range(1, config['n_splits'] + 1):\n",
    "        print(f\"\\n{'='*40} Fold: {fold} {'='*40}\")\n",
    "\n",
    "        model = get_model(fold, retrain=False)\n",
    "\n",
    "        preds, feats = predict(model, test_split, config)\n",
    "        kfold_features.append(feats)\n",
    "        kfold_predictions.append(preds)\n",
    "\n",
    "        # cleanup after one fold\n",
    "        del model\n",
    "        gc.collect()\n",
    "        free_gpu_cache()\n",
    "        \n",
    "    return np.array(kfold_predictions).mean(axis=0), np.array(kfold_features).mean(axis=0)\n",
    "\n",
    "def kfold_predict(X_test, config):\n",
    "    kfold_predictions = []\n",
    "    for fold in range(1, config['n_splits'] + 1):\n",
    "        print(f\"\\n{'='*40} Fold: {fold} {'='*40}\")\n",
    "\n",
    "        model = get_model_reg(fold)\n",
    "\n",
    "        kfold_predictions.append(model.predict(X_test))\n",
    "\n",
    "        # cleanup after one fold\n",
    "        del model\n",
    "        gc.collect()\n",
    "        free_gpu_cache()\n",
    "        \n",
    "    return np.array(kfold_predictions).mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cwhZt-kQpP8V"
   },
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.733405Z",
     "iopub.status.busy": "2022-11-26T21:02:49.732782Z",
     "iopub.status.idle": "2022-11-26T21:02:49.750712Z",
     "shell.execute_reply": "2022-11-26T21:02:49.749553Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.733370Z"
    },
    "id": "97fa8861",
    "outputId": "075f8454-585c-4995-84f2-fc69eb30348b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU memory occupied: 2 MB.\n",
      "Initial GPU Usage\n",
      "GPU memory occupied: 2 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 2 MB.\n"
     ]
    }
   ],
   "source": [
    "print_gpu_utilization()\n",
    "free_gpu_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 37
    },
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.752534Z",
     "iopub.status.busy": "2022-11-26T21:02:49.752113Z",
     "iopub.status.idle": "2022-11-26T21:02:49.759619Z",
     "shell.execute_reply": "2022-11-26T21:02:49.758479Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.752499Z"
    },
    "id": "df29c8e2",
    "outputId": "49e8bda0-cf83-415c-9faf-133f1b9bd297"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.762055Z",
     "iopub.status.busy": "2022-11-26T21:02:49.761079Z",
     "iopub.status.idle": "2022-11-26T21:02:49.768224Z",
     "shell.execute_reply": "2022-11-26T21:02:49.767233Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.762019Z"
    },
    "id": "BZTIGCNv2mFw"
   },
   "outputs": [],
   "source": [
    "BASIC_PATH = './output'\n",
    "# BASIC_PATH = 'gdrive/MyDrive/Kaggle/feedback-prize-english-language-learning'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:49.770195Z",
     "iopub.status.busy": "2022-11-26T21:02:49.769730Z",
     "iopub.status.idle": "2022-11-26T21:02:50.229839Z",
     "shell.execute_reply": "2022-11-26T21:02:50.228864Z",
     "shell.execute_reply.started": "2022-11-26T21:02:49.770152Z"
    },
    "id": "c8c3e15b",
    "outputId": "2a23ccae-1b89-4516-953e-1cab2c6fd2a1"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text_id</th>\n",
       "      <th>full_text</th>\n",
       "      <th>cohesion</th>\n",
       "      <th>syntax</th>\n",
       "      <th>vocabulary</th>\n",
       "      <th>phraseology</th>\n",
       "      <th>grammar</th>\n",
       "      <th>conventions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0016926B079C</td>\n",
       "      <td>I think that students would benefit from learn...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0022683E9EA5</td>\n",
       "      <td>When a problem is a change you have to let it ...</td>\n",
       "      <td>2.5</td>\n",
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       "    <tr>\n",
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       "      <td>00299B378633</td>\n",
       "      <td>Dear, Principal\\n\\nIf u change the school poli...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>003885A45F42</td>\n",
       "      <td>The best time in life is when you become yours...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0049B1DF5CCC</td>\n",
       "      <td>Small act of kindness can impact in other peop...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
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       "      <th>3906</th>\n",
       "      <td>FFD29828A873</td>\n",
       "      <td>I believe using cellphones in class for educat...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>2.5</td>\n",
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       "      <th>3907</th>\n",
       "      <td>FFD9A83B0849</td>\n",
       "      <td>Working alone, students do not have to argue w...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3908</th>\n",
       "      <td>FFDC4011AC9C</td>\n",
       "      <td>\"A problem is a chance for you to do your best...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>3909</th>\n",
       "      <td>FFE16D704B16</td>\n",
       "      <td>Many people disagree with Albert Schweitzer's ...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
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       "      <td>FFED00D6E0BD</td>\n",
       "      <td>Do you think that failure is the main thing fo...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
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       "</table>\n",
       "<p>3911 rows × 8 columns</p>\n",
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      "text/plain": [
       "           text_id                                          full_text  \\\n",
       "0     0016926B079C  I think that students would benefit from learn...   \n",
       "1     0022683E9EA5  When a problem is a change you have to let it ...   \n",
       "2     00299B378633  Dear, Principal\\n\\nIf u change the school poli...   \n",
       "3     003885A45F42  The best time in life is when you become yours...   \n",
       "4     0049B1DF5CCC  Small act of kindness can impact in other peop...   \n",
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       "\n",
       "[3911 rows x 8 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_data = pd.read_csv('./train.csv')\n",
    "# test_data = pd.read_csv('./test.csv')\n",
    "# aug_data = pd.read_csv('./aug_data.csv')\n",
    "# train_data\n",
    "\n",
    "train_data = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning/train.csv')\n",
    "test_data = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning/test.csv')\n",
    "aug_data = pd.read_csv(f'/kaggle/input/englishlanguageessaysaugdata/aug_data.csv')\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
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   "outputs": [
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       "['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']"
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   "source": [
    "TARGET_VARIABLES = list(train_data.columns[2:])\n",
    "TARGET_VARIABLES"
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    "RAW_LABELS = {label: key for label, key in zip(range(0, 10), np.arange(1.0, 5.5, 0.5))}\n",
    "REV_LABELS = {v: k for k, v in RAW_LABELS.items()}"
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    "X"
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       "           text_id  cohesion  syntax  vocabulary  phraseology  grammar  \\\n",
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   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:50.312486Z",
     "iopub.status.busy": "2022-11-26T21:02:50.311948Z",
     "iopub.status.idle": "2022-11-26T21:02:50.320312Z",
     "shell.execute_reply": "2022-11-26T21:02:50.319359Z",
     "shell.execute_reply.started": "2022-11-26T21:02:50.312451Z"
    },
    "id": "0cf9cad6"
   },
   "outputs": [],
   "source": [
    "MODEL_NAME = 'microsoft/deberta-v3-base' # distilbert-base-uncased, microsoft/deberta-v3-base\n",
    "MODEL_REG = 'lightboost'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
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    {
     "name": "stderr",
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     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "/opt/conda/lib/python3.7/site-packages/transformers/convert_slow_tokenizer.py:435: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
      "  \"The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option\"\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(get_tokenizer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.147073Z",
     "iopub.status.busy": "2022-11-26T21:02:57.146675Z",
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     "shell.execute_reply.started": "2022-11-26T21:02:57.147034Z"
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   "source": [
    "# try:\n",
    "#     tokenizer.save_pretrained(f'{BASIC_PATH}/tokenizers/{MODEL_NAME}')\n",
    "# except Exception as e:\n",
    "#     print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.153779Z",
     "iopub.status.busy": "2022-11-26T21:02:57.153418Z",
     "iopub.status.idle": "2022-11-26T21:02:57.161458Z",
     "shell.execute_reply": "2022-11-26T21:02:57.160540Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.153742Z"
    },
    "id": "lhfNDJXVw57u"
   },
   "outputs": [],
   "source": [
    "MLM_TRAIN_CONFIG = {\n",
    "    'learning_rate': 2e-5,\n",
    "    'weight_decay': 1e-2,\n",
    "\n",
    "    # 'lr_scheduler_type': 'cosine',\n",
    "    'gradient_accumulation_steps': 1,\n",
    "    'per_device_train_batch_size': 4,\n",
    "    'num_train_epochs': 2,\n",
    "    'warmup_steps': 0,\n",
    "    'max_grad_norm': 1000,\n",
    "    'adam_epsilon': 1e-6,\n",
    "\n",
    "    'log_level': 'debug',\n",
    "    'save_strategy': 'steps',\n",
    "    'disable_tqdm': False,\n",
    "    'logging_steps': 500,\n",
    "    'eval_steps': 500,\n",
    "    'save_steps': 500,\n",
    "    'resume_from_checkpoint': True,\n",
    "    'per_device_eval_batch_size': 8,\n",
    "    'evaluation_strategy': 'steps',\n",
    "    # 'fp16': True,\n",
    "    'metric_for_best_model': 'loss',\n",
    "    'load_best_model_at_end': True,\n",
    "    'greater_is_better': False,\n",
    "    \n",
    "    'save_total_limit': 1,\n",
    "    \n",
    "    'report_to': 'none',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.163415Z",
     "iopub.status.busy": "2022-11-26T21:02:57.162889Z",
     "iopub.status.idle": "2022-11-26T21:02:57.171482Z",
     "shell.execute_reply": "2022-11-26T21:02:57.170546Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.163380Z"
    },
    "id": "IsespJ7-s_sM"
   },
   "outputs": [],
   "source": [
    "RETRAIN = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.173385Z",
     "iopub.status.busy": "2022-11-26T21:02:57.173014Z",
     "iopub.status.idle": "2022-11-26T21:02:57.181472Z",
     "shell.execute_reply": "2022-11-26T21:02:57.180538Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.173349Z"
    },
    "id": "3gfmK_Jfbhe6"
   },
   "outputs": [],
   "source": [
    "mlm_model_path = f'{BASIC_PATH}/models/mlm/{MODEL_NAME}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.183820Z",
     "iopub.status.busy": "2022-11-26T21:02:57.182744Z",
     "iopub.status.idle": "2022-11-26T21:02:57.190999Z",
     "shell.execute_reply": "2022-11-26T21:02:57.190024Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.183783Z"
    },
    "id": "4_wiFeeNtYnI"
   },
   "outputs": [],
   "source": [
    "if RETRAIN:\n",
    "    model = AutoModelForMaskedLM.from_pretrained(MODEL_NAME)\n",
    "    results, mlm_model_path = train_masked_lm(model, tokenizer, aug_data, MLM_TRAIN_CONFIG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.193423Z",
     "iopub.status.busy": "2022-11-26T21:02:57.192444Z",
     "iopub.status.idle": "2022-11-26T21:02:57.202797Z",
     "shell.execute_reply": "2022-11-26T21:02:57.201814Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.193390Z"
    },
    "id": "OPWy4XzvrGp9"
   },
   "outputs": [],
   "source": [
    "SSL_TRAIN_CONFIG = {\n",
    "    'batch_size': 16,\n",
    "    'epochs': 3,\n",
    "    'training_args': {\n",
    "        # 'learning_rate': 2.5e-5,\n",
    "        # 'weight_decay': 1e-2,\n",
    "#         'lr_scheduler_type': 'cosine',\n",
    "        'gradient_accumulation_steps': 1,\n",
    "        'per_device_train_batch_size': 16,\n",
    "        'num_train_epochs': 2,\n",
    "        # 'warmup_steps': 250,\n",
    "        'max_grad_norm': 10,\n",
    "        # 'adam_epsilon': 1e-6,\n",
    "\n",
    "        'log_level': 'debug',\n",
    "        'save_strategy': 'steps',\n",
    "        'disable_tqdm': False,\n",
    "        'logging_steps': 500,\n",
    "        'eval_steps': 500,\n",
    "        'save_steps': 500,\n",
    "        'resume_from_checkpoint': True,\n",
    "        'per_device_eval_batch_size': 32,\n",
    "        'evaluation_strategy': 'steps',\n",
    "        'fp16': True,\n",
    "        'metric_for_best_model': 'rmse',\n",
    "        'load_best_model_at_end': True,\n",
    "        'greater_is_better': False,\n",
    "        \n",
    "        'save_total_limit': 1,\n",
    "        \n",
    "        'report_to': 'none',\n",
    "    },\n",
    "    'optimizer': {\n",
    "        'lr': 2e-5,\n",
    "        'eps': 1e-6,\n",
    "\n",
    "        'encoder_lr': 2e-5,\n",
    "        'decoder_lr': 2e-5,\n",
    "        'weight_decay': 0.01,\n",
    "    },\n",
    "    'scheduler': {\n",
    "        'unsup_start_steps': 0,\n",
    "        'max_w': 10,\n",
    "        'w_ramprate': 1.5,\n",
    "        'update_weights_steps': 1,\n",
    "        # sup\n",
    "        'num_warmup_steps': 0,\n",
    "    },\n",
    "    'model': {\n",
    "        'alpha': 0.8,\n",
    "        'unsup_weight': 0,\n",
    "    },\n",
    "    'early_stopping': False,\n",
    "    'early_stopping_params': {\n",
    "        'early_stopping_patience': 3,\n",
    "        'early_stopping_threshold': 0.02,\n",
    "    },\n",
    "    'unfreeze_after': 0.55,\n",
    "    'unfreeze_every': 100,\n",
    "    'layer_to_unfreeze': 5,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.206363Z",
     "iopub.status.busy": "2022-11-26T21:02:57.206064Z",
     "iopub.status.idle": "2022-11-26T21:02:57.216184Z",
     "shell.execute_reply": "2022-11-26T21:02:57.215304Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.206338Z"
    },
    "id": "Bow9PdorrIeI"
   },
   "outputs": [],
   "source": [
    "RETRAIN = False\n",
    "FREEZE = True\n",
    "TASK = 'cohesion'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.218597Z",
     "iopub.status.busy": "2022-11-26T21:02:57.217830Z",
     "iopub.status.idle": "2022-11-26T21:02:57.231313Z",
     "shell.execute_reply": "2022-11-26T21:02:57.230298Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.218472Z"
    },
    "id": "MhcyWH-9Z-3M"
   },
   "outputs": [],
   "source": [
    "train_split, test_split = to_train_test_splits_task(X_train, X_test, y_train, y_test, TASK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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    },
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.233475Z",
     "iopub.status.busy": "2022-11-26T21:02:57.232870Z",
     "iopub.status.idle": "2022-11-26T21:02:57.244787Z",
     "shell.execute_reply": "2022-11-26T21:02:57.243842Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.233442Z"
    },
    "id": "_xCbtWBUrQr0",
    "outputId": "ede4de39-3482-4be0-cc56-8c3196c2e9c5"
   },
   "outputs": [],
   "source": [
    "if RETRAIN:\n",
    "    print(f'Training: {TASK}...')\n",
    "\n",
    "    lab_lim = match_with_batchsize(train_split.shape[0], SSL_TRAIN_CONFIG['training_args']['per_device_train_batch_size'])\n",
    "    train_split_aug = pd.concat([train_split[:lab_lim].sample(frac=1, random_state=42).reset_index(drop=True), aug_data], axis=0).reset_index(drop=True)\n",
    "    train_split_aug[f'{TASK}_labels'] = train_split_aug[f'{TASK}_labels'].fillna(-1).astype(int)\n",
    "\n",
    "    data = DatasetDict({\n",
    "        'train': Dataset.from_pandas(train_split_aug[['full_text', f'{TASK}_labels']].rename({f'{TASK}_labels': 'label'}, axis=1)),\n",
    "        'test': Dataset.from_pandas(test_split[['full_text', f'{TASK}_labels']].rename({f'{TASK}_labels': 'label'}, axis=1)),\n",
    "    })\n",
    "\n",
    "    print(f'Dataset tokenization')\n",
    "    data = data.map(tokenization, batched=True).remove_columns(['full_text'])\n",
    "    data.set_format('pt')\n",
    "    \n",
    "    print('Loading model')\n",
    "    model = TemporalEnsembleModel(pretrained=mlm_model_path, **SSL_TRAIN_CONFIG['model']).to(device)\n",
    "\n",
    "    if RETRAIN:\n",
    "        retrain(TASK)\n",
    "    if not is_trained(TASK):\n",
    "        print('Training model')\n",
    "        if FREEZE:\n",
    "            model = freeze(model, list(range(SSL_TRAIN_CONFIG['layer_to_unfreeze'] + 1)))\n",
    "\n",
    "        trainer = train_task(model, data, SSL_TRAIN_CONFIG, TASK)\n",
    "\n",
    "        result = trainer.train()\n",
    "        print_summary(result)\n",
    "\n",
    "        save_model_task(TASK, model)\n",
    "        ssl_model_path = f'{BASIC_PATH}/models/ssl/{MODEL_NAME}/{TASK}/final'\n",
    "\n",
    "        with open(f'{BASIC_PATH}/models/{MODEL_NAME}/{TASK}/trained', 'w') as f:\n",
    "            f.write(str(datetime.now()))\n",
    "    else:\n",
    "        print(TASK, 'trained, skipping...')\n",
    "\n",
    "    print('Removing cache')\n",
    "    free_gpu_cache()\n",
    "    gc.collect()\n",
    "    clean_checkpoints(TASK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.248559Z",
     "iopub.status.busy": "2022-11-26T21:02:57.248165Z",
     "iopub.status.idle": "2022-11-26T21:02:57.495261Z",
     "shell.execute_reply": "2022-11-26T21:02:57.494182Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.248533Z"
    },
    "id": "ySGxfbZwriTa",
    "outputId": "a9b13cba-1a67-4dd2-ebb4-32d0c7d3cb65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial GPU Usage\n",
      "GPU memory occupied: 2 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 2 MB.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "free_gpu_cache()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.497275Z",
     "iopub.status.busy": "2022-11-26T21:02:57.496810Z",
     "iopub.status.idle": "2022-11-26T21:02:57.503024Z",
     "shell.execute_reply": "2022-11-26T21:02:57.501896Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.497219Z"
    },
    "id": "qpZLuonNripa",
    "outputId": "a615945a-815b-4aa7-c9fd-3e2acf2250b5"
   },
   "outputs": [],
   "source": [
    "if RETRAIN:\n",
    "    plot_scheduler_stats({'lr_history': trainer.lr_scheduler.lr_history, 'weights_history': trainer.lr_scheduler.weight_history})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.505289Z",
     "iopub.status.busy": "2022-11-26T21:02:57.504893Z",
     "iopub.status.idle": "2022-11-26T21:02:57.512482Z",
     "shell.execute_reply": "2022-11-26T21:02:57.511492Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.505204Z"
    },
    "id": "MpWWNrfKTirK"
   },
   "outputs": [],
   "source": [
    "TRAIN_CONFIG = {\n",
    "    'epochs': 5,\n",
    "    'fp16': True,\n",
    "    'gradient_accumulation_steps': 1,\n",
    "    'max_grad_norm': 1000, \n",
    "    'batch_size': 16,\n",
    "    'apex': True,\n",
    "    # optimizer\n",
    "    'lr': 2e-5,\n",
    "    'min_lr': 1e-6,\n",
    "    'weight_decay': 0.01,\n",
    "    'encoder_lr': 2e-5,\n",
    "    'decoder_lr': 2e-5,\n",
    "    'eps': 1e-6,\n",
    "    # scheduler\n",
    "    'T_0': 500,\n",
    "    'T_max': 500,\n",
    "    'num_cycles': 0.5,\n",
    "    'num_warmup_steps': 0,\n",
    "    # kfold:\n",
    "    'n_splits': 4,\n",
    "    # freezing\n",
    "    'freeze': True,\n",
    "    'unfreeze_after': 0.4,\n",
    "    'layer_to_unfreeze': 1,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T21:02:57.515437Z",
     "iopub.status.busy": "2022-11-26T21:02:57.514522Z",
     "iopub.status.idle": "2022-11-26T21:02:57.525553Z",
     "shell.execute_reply": "2022-11-26T21:02:57.524522Z",
     "shell.execute_reply.started": "2022-11-26T21:02:57.515402Z"
    }
   },
   "outputs": [],
   "source": [
    "# ssl_model_path = f'{BASIC_PATH}/models/mlm/{MODEL_NAME}'\n",
    "ssl_model_path = f'{BASIC_PATH}/models/ssl/{MODEL_NAME}/{TASK}/final'\n",
    "# ssl_model_path = None\n",
    "RETRAIN = True"
   ]
  },
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    },
    "execution": {
     "iopub.execute_input": "2022-11-26T21:04:35.765979Z",
     "iopub.status.busy": "2022-11-26T21:04:35.765609Z"
    },
    "id": "7llg-yL1lAC8",
    "outputId": "2992eb3d-a1c2-47dd-a30f-fe785553509e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model\n",
      "\n",
      "======================================== Fold: 1 ========================================\n",
      "Saving config: sup\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2044e29c73a4cacabb91e751a52baa1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/354M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.classifier.bias', 'mask_predictions.LayerNorm.bias', 'mask_predictions.classifier.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.dense.weight', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.bias', 'lm_predictions.lm_head.dense.bias']\n",
      "- This IS expected if you are initializing DebertaV2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving config: ssl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2ForSequenceClassification: ['lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.classifier.bias', 'mask_predictions.LayerNorm.bias', 'mask_predictions.classifier.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.dense.weight', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.bias', 'lm_predictions.lm_head.dense.bias']\n",
      "- This IS expected if you are initializing DebertaV2ForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DebertaV2ForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DebertaV2ForSequenceClassification were not initialized from the model checkpoint at microsoft/deberta-v3-base and are newly initialized: ['pooler.dense.bias', 'classifier.bias', 'classifier.weight', 'pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying weights from -------> <class 'transformers.models.deberta_v2.modeling_deberta_v2.DebertaV2ForSequenceClassification'>\n",
      "Copying weights to -------> <class '__main__.FeedbackModel'>\n",
      "Freezing the model...\n",
      "Freezing layer: base.embeddings.word_embeddings.weight\n",
      "Freezing layer: base.embeddings.word_embeddings.weight\n",
      "Freezing layer: base.encoder.layer.0.attention.self.query_proj.weight\n",
      "Freezing layer: base.encoder.layer.0.attention.self.query_proj.bias\n",
      "Freezing layer: base.encoder.layer.0.attention.self.key_proj.weight\n",
      "Freezing layer: base.encoder.layer.0.attention.self.key_proj.bias\n",
      "Freezing layer: base.encoder.layer.0.attention.self.value_proj.weight\n",
      "Freezing layer: base.encoder.layer.0.attention.self.value_proj.bias\n",
      "Freezing layer: base.encoder.layer.0.attention.output.dense.weight\n",
      "Freezing layer: base.encoder.layer.0.attention.output.dense.bias\n",
      "Freezing layer: base.encoder.layer.0.attention.output.LayerNorm.weight\n",
      "Freezing layer: base.encoder.layer.0.attention.output.LayerNorm.bias\n",
      "Freezing layer: base.encoder.layer.0.intermediate.dense.weight\n",
      "Freezing layer: base.encoder.layer.0.intermediate.dense.bias\n",
      "Freezing layer: base.encoder.layer.0.output.dense.weight\n",
      "Freezing layer: base.encoder.layer.0.output.dense.bias\n",
      "Freezing layer: base.encoder.layer.0.output.LayerNorm.weight\n",
      "Freezing layer: base.encoder.layer.0.output.LayerNorm.bias\n",
      "Freezing layer: base.encoder.layer.1.attention.self.query_proj.weight\n",
      "Freezing layer: base.encoder.layer.1.attention.self.query_proj.bias\n",
      "Freezing layer: base.encoder.layer.1.attention.self.key_proj.weight\n",
      "Freezing layer: base.encoder.layer.1.attention.self.key_proj.bias\n",
      "Freezing layer: base.encoder.layer.1.attention.self.value_proj.weight\n",
      "Freezing layer: base.encoder.layer.1.attention.self.value_proj.bias\n",
      "Freezing layer: base.encoder.layer.1.attention.output.dense.weight\n",
      "Freezing layer: base.encoder.layer.1.attention.output.dense.bias\n",
      "Freezing layer: base.encoder.layer.1.attention.output.LayerNorm.weight\n",
      "Freezing layer: base.encoder.layer.1.attention.output.LayerNorm.bias\n",
      "Freezing layer: base.encoder.layer.1.intermediate.dense.weight\n",
      "Freezing layer: base.encoder.layer.1.intermediate.dense.bias\n",
      "Freezing layer: base.encoder.layer.1.output.dense.weight\n",
      "Freezing layer: base.encoder.layer.1.output.dense.bias\n",
      "Freezing layer: base.encoder.layer.1.output.LayerNorm.weight\n",
      "Freezing layer: base.encoder.layer.1.output.LayerNorm.bias\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1100b73a60fe424a89de41ac0752be56",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/123 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if RETRAIN:\n",
    "    print('Training model')\n",
    "\n",
    "    Path(f'{BASIC_PATH}/models/{MODEL_NAME}').mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    kfold_results, kfold_preds, kfold_features = kfold_train_features(train_split.reset_index(drop=True), test_split, config=TRAIN_CONFIG, plot=True, save_folds=False, pretrained=ssl_model_path)\n",
    "    \n",
    "    with open(f'{BASIC_PATH}/models/{MODEL_NAME}/trained', 'w') as f:\n",
    "        f.write(str(datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-11-26T20:42:01.284406Z",
     "iopub.status.busy": "2022-11-26T20:42:01.284037Z",
     "iopub.status.idle": "2022-11-26T20:42:01.536029Z",
     "shell.execute_reply": "2022-11-26T20:42:01.535002Z",
     "shell.execute_reply.started": "2022-11-26T20:42:01.284375Z"
    },
    "id": "9kfyj0AadpvV",
    "outputId": "b4d3f99d-1aaa-47fb-deea-a213ddde51b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial GPU Usage\n",
      "GPU memory occupied: 3411 MB.\n",
      "GPU Usage after emptying the cache\n",
      "GPU memory occupied: 3411 MB.\n"
     ]
    }
   ],
   "source": [
    "gc.collect()\n",
    "free_gpu_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Freezing all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T20:43:03.697893Z",
     "iopub.status.busy": "2022-11-26T20:43:03.697530Z",
     "iopub.status.idle": "2022-11-26T20:43:03.709213Z",
     "shell.execute_reply": "2022-11-26T20:43:03.708155Z",
     "shell.execute_reply.started": "2022-11-26T20:43:03.697861Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4547738337660865"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = MCRMSE(y_test[TARGET_VARIABLES], kfold_preds)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T20:43:27.463426Z",
     "iopub.status.busy": "2022-11-26T20:43:27.463050Z",
     "iopub.status.idle": "2022-11-26T20:43:27.482246Z",
     "shell.execute_reply": "2022-11-26T20:43:27.481390Z",
     "shell.execute_reply.started": "2022-11-26T20:43:27.463393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohesion</th>\n",
       "      <th>syntax</th>\n",
       "      <th>vocabulary</th>\n",
       "      <th>phraseology</th>\n",
       "      <th>grammar</th>\n",
       "      <th>conventions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.487132</td>\n",
       "      <td>0.445335</td>\n",
       "      <td>0.425267</td>\n",
       "      <td>0.449531</td>\n",
       "      <td>0.472373</td>\n",
       "      <td>0.449006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cohesion    syntax  vocabulary  phraseology   grammar  conventions\n",
       "0  0.487132  0.445335    0.425267     0.449531  0.472373     0.449006"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_score, scores = MCRMSE_scores(y_test[TARGET_VARIABLES].values, kfold_preds)\n",
    "pd.DataFrame([scores], columns=TARGET_VARIABLES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Freezing basic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T18:40:07.930067Z",
     "iopub.status.busy": "2022-11-26T18:40:07.929398Z",
     "iopub.status.idle": "2022-11-26T18:40:07.942563Z",
     "shell.execute_reply": "2022-11-26T18:40:07.941459Z",
     "shell.execute_reply.started": "2022-11-26T18:40:07.930028Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4581978944565348"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = MCRMSE(y_test[TARGET_VARIABLES], kfold_preds)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T18:40:21.995527Z",
     "iopub.status.busy": "2022-11-26T18:40:21.995146Z",
     "iopub.status.idle": "2022-11-26T18:40:22.014073Z",
     "shell.execute_reply": "2022-11-26T18:40:22.012952Z",
     "shell.execute_reply.started": "2022-11-26T18:40:21.995477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohesion</th>\n",
       "      <th>syntax</th>\n",
       "      <th>vocabulary</th>\n",
       "      <th>phraseology</th>\n",
       "      <th>grammar</th>\n",
       "      <th>conventions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.495208</td>\n",
       "      <td>0.449318</td>\n",
       "      <td>0.429247</td>\n",
       "      <td>0.448714</td>\n",
       "      <td>0.478544</td>\n",
       "      <td>0.448157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cohesion    syntax  vocabulary  phraseology   grammar  conventions\n",
       "0  0.495208  0.449318    0.429247     0.448714  0.478544     0.448157"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_score, scores = MCRMSE_scores(y_test[TARGET_VARIABLES].values, kfold_preds)\n",
    "pd.DataFrame([scores], columns=TARGET_VARIABLES)\n",
    "# 0.489043\t0.445325\t0.426208\t0.447804\t0.470864\t0.448128 <-- layerwise\n",
    "# 0.493565\t0.449979\t0.424176\t0.449213\t0.477018\t0.451502 <-- all together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-26T03:52:31.471831Z",
     "iopub.status.busy": "2022-11-26T03:52:31.470840Z",
     "iopub.status.idle": "2022-11-26T03:52:31.482627Z",
     "shell.execute_reply": "2022-11-26T03:52:31.481515Z",
     "shell.execute_reply.started": "2022-11-26T03:52:31.471786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.461390576958493"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = MCRMSE(y_test[TARGET_VARIABLES], kfold_preds)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-11-26T03:52:32.816428Z"
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   "outputs": [
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       "   cohesion    syntax  vocabulary  phraseology   grammar  conventions\n",
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     "execution_count": 75,
     "metadata": {},
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   ],
   "source": [
    "avg_score, scores = MCRMSE_scores(y_test[TARGET_VARIABLES].values, kfold_preds)\n",
    "pd.DataFrame([scores], columns=TARGET_VARIABLES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nothing but layerwise LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
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     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "score = MCRMSE(y_test[TARGET_VARIABLES], kfold_preds)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
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     "execution_count": 67,
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   "source": [
    "avg_score, scores = MCRMSE_scores(y_test[TARGET_VARIABLES].values, kfold_preds)\n",
    "pd.DataFrame([scores], columns=TARGET_VARIABLES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-11-25T22:27:06.453296Z",
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    "id": "z79Z7haipP8Y",
    "outputId": "9c4e125d-0cf6-4b4c-bad6-64874ab80fef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45757551145760883"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = MCRMSE(y_test[TARGET_VARIABLES], kfold_preds)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
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    "execution": {
     "iopub.execute_input": "2022-11-25T22:27:15.358332Z",
     "iopub.status.busy": "2022-11-25T22:27:15.357931Z",
     "iopub.status.idle": "2022-11-25T22:27:15.377349Z",
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     "shell.execute_reply.started": "2022-11-25T22:27:15.358300Z"
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    "id": "7FbxNG-OpP8Y",
    "outputId": "5b33f8d3-9ccc-4ab9-e0bc-9ec8d07728b3"
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   "outputs": [
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   "source": [
    "avg_score, scores = MCRMSE_scores(y_test[TARGET_VARIABLES].values, kfold_preds)\n",
    "pd.DataFrame([scores], columns=TARGET_VARIABLES)"
   ]
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     "text": [
      "\n",
      "\n",
      "======================================== Fold: 1 ========================================\n"
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    {
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     "text": [
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight']\n",
      "\n",
      "- This IS expected if you are initializing DebertaV2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "\n",
      "- This IS NOT expected if you are initializing DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "\n",
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight']\n",
      "\n",
      "- This IS expected if you are initializing DebertaV2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "\n",
      "- This IS NOT expected if you are initializing DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial GPU Usage\n",
      "\n",
      "GPU memory occupied: 3240 MB.\n",
      "\n",
      "GPU Usage after emptying the cache\n",
      "\n",
      "GPU memory occupied: 814 MB.\n",
      "\n",
      "\n",
      "\n",
      "======================================== Fold: 2 ========================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight']\n",
      "\n",
      "- This IS expected if you are initializing DebertaV2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "\n",
      "- This IS NOT expected if you are initializing DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "\n",
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight']\n",
      "\n",
      "- This IS expected if you are initializing DebertaV2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "\n",
      "- This IS NOT expected if you are initializing DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial GPU Usage\n",
      "\n",
      "GPU memory occupied: 3240 MB.\n",
      "\n",
      "GPU Usage after emptying the cache\n",
      "\n",
      "GPU memory occupied: 814 MB.\n",
      "\n",
      "\n",
      "\n",
      "======================================== Fold: 3 ========================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight']\n",
      "\n",
      "- This IS expected if you are initializing DebertaV2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "\n",
      "- This IS NOT expected if you are initializing DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "\n",
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight']\n",
      "\n",
      "- This IS expected if you are initializing DebertaV2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "\n",
      "- This IS NOT expected if you are initializing DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
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     "text": [
      "Initial GPU Usage\n",
      "\n",
      "GPU memory occupied: 3048 MB.\n",
      "\n",
      "GPU Usage after emptying the cache\n",
      "\n",
      "GPU memory occupied: 814 MB.\n",
      "\n",
      "\n",
      "\n",
      "======================================== Fold: 4 ========================================\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight']\n",
      "\n",
      "- This IS expected if you are initializing DebertaV2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "\n",
      "- This IS NOT expected if you are initializing DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "\n",
      "Some weights of the model checkpoint at microsoft/deberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.LayerNorm.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.weight']\n",
      "\n",
      "- This IS expected if you are initializing DebertaV2Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "\n",
      "- This IS NOT expected if you are initializing DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
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      "Initial GPU Usage\n",
      "\n",
      "GPU memory occupied: 3432 MB.\n",
      "\n",
      "GPU Usage after emptying the cache\n",
      "\n",
      "GPU memory occupied: 814 MB.\n"
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   ],
   "source": [
    "kfold_train_preds, kfold_train_features = kfold_predict_features(train_split, config)"
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  },
  {
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    "id": "xYpdzfQ78SG4",
    "outputId": "55beee15-604a-4138-ba01-440426e46555"
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     "data": {
      "text/plain": [
       "array([[3.01340138, 2.96729451, 3.01048335, 3.06610858, 2.85042437,\n",
       "        2.81598375],\n",
       "       [2.85349467, 2.39097571, 2.71609242, 2.42150926, 2.26266024,\n",
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       "       [3.71905806, 3.37728188, 3.58308175, 3.72029037, 3.58006818,\n",
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     "execution_count": 84,
     "metadata": {},
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    "predictions"
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       "   cohesion    syntax  vocabulary  phraseology   grammar  conventions  \\\n",
       "0  3.013401  2.967295    3.010483     3.066109  2.850424     2.815984   \n",
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