bert_text_classification_pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 34028,
     "status": "ok",
     "timestamp": 1730571478256,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "pzM1_ykHaFur"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import torch\n",
    "import transformers\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch.nn as nn\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
    "from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler\n",
    "from transformers import BertTokenizer, BertModel, BertConfig\n",
    "from sklearn.metrics import accuracy_score, f1_score, matthews_corrcoef, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 34,
     "status": "ok",
     "timestamp": 1730571478258,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "NLxxwd1scQNv"
   },
   "outputs": [],
   "source": [
    "from torch import cuda\n",
    "device = 'cuda' if cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 2149,
     "status": "ok",
     "timestamp": 1730571540826,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "mZ7lTlkyaG7u",
    "outputId": "11a7b31c-776c-4797-ac6c-6c0688ffa5d1"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/train/dataset_en_train_completed.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datos Balanceados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category\n",
      "CRITICAL      2621\n",
      "CONSPIRACY    1379\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df= pd.read_csv('data/train/dataset_en_train_completed.csv')\n",
    "df= df.iloc[:4000]\n",
    "print(df.category.value_counts())\n",
    "\n",
    "df_augmented= pd.read_csv('data/train/dataset_en_train_completed.csv')\n",
    "df_augmented= df_augmented.iloc[4000:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_counts = df['category'].value_counts()\n",
    "df_conspiracy = df[df['category'] == 'CONSPIRACY']\n",
    "df_conspiracy_augmented = df_augmented[df_augmented['category'] == 'CONSPIRACY']\n",
    "critical_count = original_counts.get('CRITICAL', 0)  \n",
    "conspiracy_count = original_counts.get('CONSPIRACY', 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_conspiracy = df_augmented[df_augmented['category'] == 'CONSPIRACY']\n",
    "\n",
    "# Seleccionar 1242 filas aleatorias para balancear el dataset \n",
    "df_conspiracy_sampled = df_conspiracy.sample(n=1242, random_state=42)\n",
    "df_combined = pd.concat([df, df_conspiracy_sampled])\n",
    "\n",
    "df_combined.reset_index(drop=True, inplace=True)\n",
    "df=df_combined.copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this is massive australian senator malcolm roberts exposes nanotech found in the covid vaccines and says they are genocide . he is the first politician to expose this share . me lauraabolichannel. The text reflects the emotion: inspiration and the moral value: no moral'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_context(df):\n",
    "    # Añade información contextual usando corchetes\n",
    "    df['text'] =   df['text'] + '. The text reflects the emotion: ' + df['max_emotion'] + ' and the moral value: ' + df['max_moral'] \n",
    "    return df\n",
    "\n",
    "df=add_context(df)\n",
    "df.loc[0].text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformar etiqueta de categórica a numérica \n",
    "\n",
    "Critical = 1\n",
    "Conspirancy = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>this is massive australian senator malcolm rob...</td>\n",
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       "      <td>anthony fauci once again defended brutal chine...</td>\n",
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       "      <th>4</th>\n",
       "      <td>proof has emerged showing that death from wuha...</td>\n",
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       "      <td>. zickute . com video ubxufaftemjq uk police c...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5238</th>\n",
       "      <td>get a claim in for medical battery. nbcnews. c...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5239</th>\n",
       "      <td>We warn against ... vaccine mandates health pa...</td>\n",
       "      <td>0</td>\n",
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       "      <th>5240</th>\n",
       "      <td>Vaccination center in leaks Crime against huma...</td>\n",
       "      <td>0</td>\n",
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       "      <th>5241</th>\n",
       "      <td>Premier League footballers had fake vaccine pa...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5242 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  class\n",
       "0     this is massive australian senator malcolm rob...      0\n",
       "1     i m deeply concerned that the push to vaccinat...      1\n",
       "2     they wanted to know your vaccination status an...      1\n",
       "3     anthony fauci once again defended brutal chine...      1\n",
       "4     proof has emerged showing that death from wuha...      1\n",
       "...                                                 ...    ...\n",
       "5237  . zickute . com video ubxufaftemjq uk police c...      0\n",
       "5238  get a claim in for medical battery. nbcnews. c...      0\n",
       "5239  We warn against ... vaccine mandates health pa...      0\n",
       "5240  Vaccination center in leaks Crime against huma...      0\n",
       "5241  Premier League footballers had fake vaccine pa...      0\n",
       "\n",
       "[5242 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'] = df['category'].apply(lambda x: 1 if x == 'CRITICAL' else 0)\n",
    "\n",
    "new_df = df[['text', 'class']].copy()\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SqveDcmDeVrZ"
   },
   "source": [
    "# Parámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304,
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    },
    "executionInfo": {
     "elapsed": 2285,
     "status": "ok",
     "timestamp": 1730571547265,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "ikfbFlNHgi8T",
    "outputId": "07a57560-0ef8-446a-f698-f769ef89f3bf"
   },
   "outputs": [],
   "source": [
    "MAX_LEN = 512 \n",
    "TRAIN_BATCH_SIZE = 32 \n",
    "VALID_BATCH_SIZE = 32\n",
    "EPOCHS = 3 \n",
    "LEARNING_RATE = 2e-5 \n",
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 235,
     "status": "ok",
     "timestamp": 1730571550083,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "oFOylAXqiNYK"
   },
   "outputs": [],
   "source": [
    "class CustomDataset(Dataset):\n",
    "\n",
    "    def __init__(self, dataframe, tokenizer, max_len):\n",
    "        self.tokenizer = tokenizer\n",
    "        self.data = dataframe\n",
    "        self.text = dataframe['text']\n",
    "        self.targets = self.data['class']\n",
    "        self.max_len = max_len\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.text)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        text = str(self.text[index])\n",
    "        text = \" \".join(text.split())\n",
    "\n",
    "        inputs = self.tokenizer.encode_plus(\n",
    "            text,\n",
    "            add_special_tokens=True,\n",
    "            max_length=self.max_len,\n",
    "            padding='max_length',\n",
    "            truncation=True,\n",
    "            return_token_type_ids=True,\n",
    "            return_attention_mask=True,\n",
    "            return_tensors='pt'\n",
    "        )\n",
    "\n",
    "        ids = inputs['input_ids'].squeeze()\n",
    "        mask = inputs['attention_mask'].squeeze()\n",
    "        token_type_ids = inputs[\"token_type_ids\"].squeeze()\n",
    "\n",
    "\n",
    "        return {\n",
    "            'ids': ids,\n",
    "            'mask': mask,\n",
    "            'token_type_ids': token_type_ids,\n",
    "            'targets': torch.tensor(self.targets[index], dtype=torch.float)\n",
    "        }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y8Z3O2tFeVrb"
   },
   "source": [
    "# Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 830,
     "referenced_widgets": [
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      "f3b84121f23344d29477fcf5e546f9bd"
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    },
    "executionInfo": {
     "elapsed": 3486,
     "status": "ok",
     "timestamp": 1730571562136,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "DegHNyIEQxB2",
    "outputId": "c0f47e1c-46f0-44db-8c74-8fd8b3688efd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BERTClass(\n",
       "  (l1): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0-11): 12 x BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSdpaSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (l2): Dropout(p=0.3, inplace=False)\n",
       "  (l3): Linear(in_features=768, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class BERTClass(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(BERTClass, self).__init__()\n",
    "        self.l1 = transformers.BertModel.from_pretrained('bert-base-uncased')\n",
    "        self.l2 = torch.nn.Dropout(0.3)\n",
    "        #1 es porque en este caso es una  tarea binaria\n",
    "        self.l3 = torch.nn.Linear(768, 1)\n",
    "\n",
    "    def forward(self, ids, mask, token_type_ids):\n",
    "        _, output_1= self.l1(ids, attention_mask = mask, token_type_ids = token_type_ids, return_dict=False)\n",
    "        output_2 = self.l2(output_1)\n",
    "        output = self.l3(output_2).squeeze(1)\n",
    "        return output\n",
    "\n",
    "model = BERTClass()\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 385,
     "status": "ok",
     "timestamp": 1730571562518,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "7KnNeQx6SI78"
   },
   "outputs": [],
   "source": [
    "def loss_fn(outputs, targets):\n",
    "    return torch.nn.BCEWithLogitsLoss()(outputs, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 6485,
     "status": "ok",
     "timestamp": 1730571570705,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "gUD8j0c7WsA-"
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(params =  model.parameters(), lr=LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fine Tuning (Cross Validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 246,
     "status": "ok",
     "timestamp": 1730571580293,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "BCj3VeoUAMy4"
   },
   "outputs": [],
   "source": [
    "def train(epoch, model, train_loader):\n",
    "    model.train()  # Poner el modelo en modo de entrenamiento\n",
    "    for _, data in enumerate(train_loader):\n",
    "        ids = data['ids'].to(device, dtype=torch.long)\n",
    "        mask = data['mask'].to(device, dtype=torch.long)\n",
    "        token_type_ids = data['token_type_ids'].to(device, dtype=torch.long)\n",
    "        targets = data['targets'].to(device, dtype=torch.float)\n",
    "\n",
    "        optimizer.zero_grad()  # Limpiar los gradientes previos\n",
    "        outputs = model(ids, mask, token_type_ids)  \n",
    "        loss = loss_fn(outputs, targets)  # pérdida\n",
    "\n",
    "        if _ % 5000 == 0:  \n",
    "            print(f'Epoch: {epoch}, Loss: {loss.item()}')\n",
    "\n",
    "        loss.backward()  \n",
    "        optimizer.step()  \n",
    "\n",
    "def validation(model, data_loader, device):\n",
    "    model.eval()  \n",
    "    fin_targets = []  # etiqueta verdadera\n",
    "    fin_outputs = []  # predicciones\n",
    "\n",
    "    with torch.no_grad():  \n",
    "        for data in data_loader:\n",
    "            ids = data['ids'].to(device)\n",
    "            mask = data['mask'].to(device)\n",
    "            token_type_ids = data['token_type_ids'].to(device)\n",
    "            targets = data['targets'].to(device)\n",
    "\n",
    "            # Get model outputs\n",
    "            outputs = model(ids, mask, token_type_ids)  \n",
    "            # funcion sigmoide para la salida como probabilidades\n",
    "            outputs = torch.sigmoid(outputs)\n",
    "\n",
    "            fin_targets.extend(targets.cpu().detach().numpy().tolist())\n",
    "            fin_outputs.extend(outputs.cpu().detach().numpy().tolist())\n",
    "\n",
    "    return fin_outputs, fin_targets\n",
    "\n",
    "\n",
    "def evaluate_metrics(outputs, targets):\n",
    "    # Convertir las salidas a 0 o 1 (clases predichas) basadas en el umbral 0.5\n",
    "    outputs = [1 if x > 0.5 else 0 for x in outputs]\n",
    "\n",
    "    # Calcular las métricas\n",
    "    accuracy = accuracy_score(targets, outputs)\n",
    "    precision, recall, f1, _ = precision_recall_fscore_support(targets, outputs, average='binary')\n",
    "    mcc = matthews_corrcoef(targets, outputs)  # Calcular MCC\n",
    "\n",
    "    return {\n",
    "        'accuracy': accuracy,\n",
    "        'precision': precision,\n",
    "        'recall': recall,\n",
    "        'f1': f1,\n",
    "        'mcc': mcc\n",
    "    }\n",
    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
    "    print(f\"Precision: {precision:.4f}\")\n",
    "    print(f\"Recall: {recall:.4f}\")\n",
    "    print(f\"F1 Score: {f1:.4f}\")\n",
    "    print(f\"MCC: {mcc:.4f}\")\n",
    "\n",
    "\n",
    "def cross_validate_model(model, dataframe, tokenizer,title, epochs=3, batch_size=16, k_folds=5):\n",
    "    #kfold\n",
    "    #kf = KFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
    "    kf = StratifiedKFold(n_splits=k_folds, shuffle=True, random_state=42)\n",
    "    metrics_list = [] \n",
    "    for fold, (train_idx, val_idx) in enumerate(kf.split(dataframe, dataframe['class'])):\n",
    "        print(f\"\\nFold {fold + 1}/{k_folds}\")\n",
    "        train_df = dataframe.iloc[train_idx].reset_index(drop=True)\n",
    "        val_df = dataframe.iloc[val_idx].reset_index(drop=True)\n",
    "\n",
    "        # Dividir datos en entrenamiento y validación\n",
    "        train_set = CustomDataset(train_df, tokenizer, MAX_LEN)\n",
    "        val_set = CustomDataset(val_df, tokenizer, MAX_LEN)\n",
    "\n",
    "        train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
    "        val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "        # Se entrena para cada fold\n",
    "        for epoch in range(epochs):\n",
    "            train(epoch, model, train_loader)  \n",
    "\n",
    "        # Se valida\n",
    "        outputs, targets = validation(model, val_loader, device)\n",
    "        \n",
    "        fold_metrics=evaluate_metrics(outputs, targets) \n",
    "        metrics_list.append(fold_metrics)\n",
    "        \n",
    "    metrics_df = pd.DataFrame(metrics_list)\n",
    "\n",
    "    # Se guardan las métricas del entreno\n",
    "    metrics_df.to_csv(f'metrics{title}.csv', index=False)\n",
    "\n",
    "    print('Cross-validation complete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YjH9wsfkAPwB",
    "outputId": "713a4a84-fae3-405a-f9c7-37bc9fcacf58"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fold 1/5\n",
      "Epoch: 0, Loss: 0.6655012369155884\n",
      "Epoch: 1, Loss: 0.201736718416214\n",
      "Epoch: 2, Loss: 0.10958785563707352\n",
      "\n",
      "Fold 2/5\n",
      "Epoch: 0, Loss: 0.2270304411649704\n",
      "Epoch: 1, Loss: 0.023486953228712082\n",
      "Epoch: 2, Loss: 0.004546192474663258\n",
      "\n",
      "Fold 3/5\n",
      "Epoch: 0, Loss: 0.004241167567670345\n",
      "Epoch: 1, Loss: 0.16472357511520386\n",
      "Epoch: 2, Loss: 0.001608029706403613\n",
      "\n",
      "Fold 4/5\n",
      "Epoch: 0, Loss: 0.001138518680818379\n",
      "\n",
      "Fold 5/5\n",
      "Epoch: 0, Loss: 0.0008994214003905654\n",
      "Epoch: 1, Loss: 0.005568553693592548\n",
      "Epoch: 2, Loss: 0.0004384811036288738\n",
      "Cross-validation complete\n"
     ]
    }
   ],
   "source": [
    "# Cambiar el nombre del archivo que se guarda\n",
    "cross_validate_model(model, new_df, tokenizer, 'moral_emotions_512_32_2e5_bbu_skfold_dataaugmentation', epochs=3, batch_size=TRAIN_BATCH_SIZE, k_folds=5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WbQB5e2-eVre"
   },
   "source": [
    "# Guardar Modelo (opcional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2052,
     "status": "ok",
     "timestamp": 1730566157795,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "EbRCa-DgtmOe",
    "outputId": "0c6c71bc-c103-4f9c-fa74-1a4c94646bdd"
   },
   "outputs": [],
   "source": [
    "#MODEL_PATH = \"/content/drive/MyDrive/MASTER/NLP/model/bert_finetuned_model_en.pth\"\n",
    "\n",
    "#torch.save(model.state_dict(), MODEL_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gXrZVs7VuPyU"
   },
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 277,
     "status": "ok",
     "timestamp": 1730569342132,
     "user": {
      "displayName": "Anny Álvarez",
      "userId": "01047458995033959693"
     },
     "user_tz": -60
    },
    "id": "GUnpQCwytmRR"
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
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       "                                                  text  class\n",
       "0    elon now confirming what we ve been suspecting...      0\n",
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       "3    cdc report admits . million people in the usa ...      1\n",
       "4    how to use health to acquire totalitarian cont...      0\n",
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       "996  fact check biden white house falsely accuses d...      1\n",
       "997  w onset acral hand lesions following mrna vacc...      1\n",
       "998  we will fire unvaccinated workers cohen hadad ...      1\n",
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       "\n",
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     },
     "execution_count": 16,
     "metadata": {},
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    }
   ],
   "source": [
    "test_df = pd.read_csv(\"data/test/dataset_en_test_cleaned.csv\")\n",
    "test_df['class'] = test_df['category'].apply(lambda x: 1 if x == 'CRITICAL' else 0)\n",
    "test_df = test_df[['text', 'class']].copy()\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test results saved to test_evaluation_results_512_2e5_emotions_morals_dataaugmentation2.json\n",
      "Test MCC = 0.7912231864845695\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "\n",
    "#usar modelo\n",
    "def predict(text, model, tokenizer):\n",
    "    inputs = tokenizer.encode_plus(\n",
    "        text,\n",
    "        None,\n",
    "        add_special_tokens=True,\n",
    "        max_length=MAX_LEN,\n",
    "        padding='max_length',\n",
    "        truncation=True,\n",
    "        return_tensors='pt'\n",
    "    )\n",
    "\n",
    "    ids = inputs['input_ids'].to(device)\n",
    "    mask = inputs['attention_mask'].to(device)\n",
    "    token_type_ids = inputs['token_type_ids'].to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        outputs = model(ids, mask, token_type_ids) \n",
    "        probabilities = torch.sigmoid(outputs).cpu().numpy()\n",
    "        prediction = 1 if probabilities[0] >= 0.5 else 0\n",
    "\n",
    "    return prediction, probabilities[0]\n",
    "\n",
    "\n",
    "    \n",
    "def test_and_evaluate(model, tokenizer, test_df, filename=\"test_results.json\"):\n",
    "    predictions = []\n",
    "    probabilities = []\n",
    "    for index, row in test_df.iterrows():\n",
    "        text = row['text']\n",
    "        prediction, probability = predict(text, model, tokenizer) \n",
    "        predictions.append(prediction)\n",
    "        probabilities.append(probability)\n",
    "\n",
    "    test_df['predictions'] = predictions\n",
    "    test_df['probabilities'] = probabilities\n",
    "\n",
    "    mcc = matthews_corrcoef(test_df['class'], test_df['predictions'])\n",
    "    \n",
    "    results = classification_report(\n",
    "        test_df['class'], \n",
    "        test_df['predictions'], \n",
    "        target_names=['CONSPIRANCY', 'CRITICAL'], \n",
    "        digits=5, \n",
    "        output_dict=True\n",
    "    )\n",
    "\n",
    "\n",
    "\n",
    "    # Guardar\n",
    "    output_data = {\n",
    "        \"mcc\": mcc,\n",
    "        \"classification_report\": results\n",
    "    }\n",
    "\n",
    "    with open(filename, \"w\") as f:\n",
    "        json.dump(output_data, f, indent=4)\n",
    "\n",
    "    print(f\"Test results saved to {filename}\")\n",
    "    print(f\"Test MCC = {mcc}\")\n",
    "\n",
    "test_and_evaluate(model, tokenizer, test_df, filename=\"test_evaluation_results_512_2e5_emotions_morals_dataaugmentation2.json\")"
   ]
  }
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