RoBERTa_LSTM_EmotionalPolarity.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0IYl0s_yfux",
        "outputId": "07e99653-0468-4892-cb30-1baaca1a3d2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.0+cu118)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m76.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.11.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m75.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
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            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.2)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.28.1\n",
            "--2023-05-06 20:09:18--  https://raw.githubusercontent.com/00ber/ml-projects/main/data/wassa2023-shared-task.tar.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4321231 (4.1M) [application/octet-stream]\n",
            "Saving to: ‘wassa2023-shared-task.tar.gz’\n",
            "\n",
            "wassa2023-shared-ta 100%[===================>]   4.12M  --.-KB/s    in 0.02s   \n",
            "\n",
            "2023-05-06 20:09:19 (237 MB/s) - ‘wassa2023-shared-task.tar.gz’ saved [4321231/4321231]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!pip install torch transformers\n",
        "!rm -f wassa2023-shared-task.tar.gz\n",
        "!wget 'https://raw.githubusercontent.com/00ber/ml-projects/main/data/wassa2023-shared-task.tar.gz'\n",
        "!tar -xf wassa2023-shared-task.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqeNheNENmsa",
        "outputId": "84f0c228-4f6a-4d9c-9b05-b8473bdb2959"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
        "\n",
        "class OrdinalLabelEncoder:\n",
        "    def get_feature_names_out(self, prefix=\"X\"):\n",
        "      return list(map(lambda x: f\"{prefix}_{x}\", self.classes_))\n",
        "    \n",
        "    def fit(self, y):\n",
        "      self.classes_ = sorted(list(set(y)))\n",
        "      self.sorter = np.argsort(self.classes_)\n",
        "      \n",
        "    def fit_transform(self, y):\n",
        "        y = y.to_numpy()\n",
        "        self.classes_ = sorted(list(set(y.reshape(-1))))\n",
        "        self.sorter = np.argsort(self.classes_)\n",
        "        idx = self.sorter[np.searchsorted(self.classes_, y, sorter=self.sorter)]\n",
        "        ordinal_labels = np.zeros((y.shape[0], len(self.classes_)))\n",
        "\n",
        "        for row, i in enumerate(idx):\n",
        "          ordinal_labels[row,:i[0]+1] = 1\n",
        "        return ordinal_labels\n",
        "\n",
        "    def transform(self, y):\n",
        "        idx = self.sorter[np.searchsorted(self.classes_, y, sorter=self.sorter)]\n",
        "        ordinal_labels = np.zeros((y.shape[0], len(self.classes_)), dtype=\"int32\")\n",
        "\n",
        "        for row, i in enumerate(idx):\n",
        "          ordinal_labels[row][:i[0]+1] = 1\n",
        "        ordinal_labels = ordinal_labels.astype(\"int32\")\n",
        "        return ordinal_labels\n",
        "\n",
        "    def inverse_transform(self, y_ordinal):\n",
        "        idx = y_ordinal.sum(axis=1, keepdims=True)\n",
        "        idx = idx.numpy()\n",
        "        idx = idx.astype(\"int32\")\n",
        "        classes = np.array(self.classes_)\n",
        "        return classes[idx - 1]\n",
        "\n",
        "\n",
        "class Encoder:\n",
        "  def __init__(self, enc_type=None):\n",
        "    self.enc_type = enc_type\n",
        "    if enc_type is None:\n",
        "      self.enc = None\n",
        "    elif enc_type == \"ohe\":\n",
        "      self.enc = OneHotEncoder()\n",
        "    elif enc_type == \"label\":\n",
        "      self.enc = LabelEncoder()\n",
        "    elif enc_type == \"ordinal\":\n",
        "      self.enc = OrdinalLabelEncoder()\n",
        "\n",
        "  def fit(self, df, target_column):\n",
        "    if self.enc_type is None:\n",
        "      self.target_column = target_column\n",
        "      self.classes = []\n",
        "      self.num_classes = 0 \n",
        "    elif self.enc_type == \"ohe\":\n",
        "      self.target_column = target_column\n",
        "      self.enc.fit(df[[target_column]])\n",
        "      self.classes = self.enc.categories_[0]\n",
        "      self.num_classes = len(self.classes)\n",
        "    elif self.enc_type == \"label\":\n",
        "      self.target_column = target_column\n",
        "      self.enc.fit(df[target_column])\n",
        "      self.classes = self.enc.classes_\n",
        "      self.num_classes = len(self.classes)\n",
        "    elif self.enc_type == \"ordinal\":\n",
        "      self.target_column = target_column\n",
        "      self.enc.fit(df[target_column])\n",
        "      self.classes = self.enc.classes_\n",
        "      self.num_classes = len(self.classes)\n",
        "    else:\n",
        "      raise Exception(f\"Unknown encoding type {self.enc_type}\")\n",
        "\n",
        "  def transform(self, df):\n",
        "    if self.enc_type is None:\n",
        "      return df[self.target_column]\n",
        "    elif self.enc_type == \"ohe\":\n",
        "      return self.enc.transform(df[[self.target_column]]).toarray() \n",
        "    elif self.enc_type == \"label\":\n",
        "      return self.enc.transform(df[self.target_column])\n",
        "    elif self.enc_type == \"ordinal\":\n",
        "      return self.enc.transform(df[[self.target_column]])\n",
        "    else:\n",
        "      raise Exception(f\"Unknown encoding type {self.enc_type}\")\n",
        "\n",
        "  def inverse_transform(self, val):\n",
        "    if self.enc_type in [\"ohe\", \"label\", \"ordinal\"]:\n",
        "      return self.enc.inverse_transform(val) \n",
        "    else:\n",
        "      raise Exception(f\"Cannot inverse transform with encoding type {self.enc_type}\")\n",
        "\n",
        "  def get_column_names(self):\n",
        "    if self.enc_type == \"ohe\":\n",
        "      return self.enc.get_feature_names_out()\n",
        "    if self.enc_type == \"ordinal\":\n",
        "      return self.enc.get_feature_names_out(prefix=self.target_column)\n",
        "    else:\n",
        "      return np.array([self.target_column])\n",
        "    \n",
        "class Preprocessor:\n",
        "  def __init__(self, path, tokenizer, text_col, encoder, max_token_len=512):\n",
        "    self.path = path\n",
        "    self.tokenizer = tokenizer\n",
        "    self.text_col = text_col\n",
        "    self.max_token_len = max_token_len \n",
        "    self.encoder = encoder\n",
        "    self._prepare_data()\n",
        "\n",
        "\n",
        "  def _prepare_data(self):\n",
        "    self.df = pd.read_table(self.path[\"convs\"])\n",
        "    if \"conv_labels\" in self.path:\n",
        "      conv_labels_df = pd.read_table(self.path[\"conv_labels\"], names=[\"EmotionalPolarity\", \"Emotion\", \"Empathy\"])\n",
        "      self.df = pd.concat([self.df, conv_labels_df], axis=1)\n",
        "    \n",
        "    def tokenize(target_col):\n",
        "      def _tokenize(row):\n",
        "        text = row[target_col]\n",
        "        tokens = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            add_special_tokens=True,\n",
        "            return_tensors=\"pt\",\n",
        "            truncation=True,\n",
        "            max_length=self.max_token_len,\n",
        "            padding=\"max_length\",\n",
        "            return_attention_mask=True\n",
        "        )\n",
        "        return tokens[\"input_ids\"].numpy(), tokens[\"attention_mask\"].numpy()\n",
        "      return _tokenize\n",
        "\n",
        "    self.tokenized_input = self.df.apply(tokenize(self.text_col), axis=1)\n",
        "    self.labels = self.encoder.transform(self.df)\n",
        "\n",
        "  def get_tokenized_data(self):\n",
        "    tokenized_series = self.tokenized_input.apply(pd.Series)\n",
        "    input_ids = tokenized_series[0].to_numpy().reshape(-1, 1)\n",
        "    attention_mask = tokenized_series[1].to_numpy().reshape(-1, 1)\n",
        "    labels = self.labels\n",
        "    return input_ids, attention_mask, labels"
      ],
      "metadata": {
        "id": "dkAnaZZ5_w-I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_name = \"roberta-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "train_path = {\n",
        "    \"convs\": \"/content/WASSA23_conv_level_with_labels_train.tsv\",\n",
        "    \"essays\": \"/content/WASSA23_essay_level_with_labels_train.tsv\",\n",
        "    \"articles\": \"/content/articles_adobe_AMT.csv\"\n",
        "}\n",
        "val_path = {\n",
        "    \"convs\": \"/content/WASSA23_conv_level_dev.tsv\",\n",
        "    \"conv_labels\": \"/content/goldstandard_CONV_dev.tsv\",\n",
        "    \"essays\": \"/content/WASSA23_essay_level_dev.tsv\",\n",
        "    \"essay_labels\": \"/content/goldstandard_dev.tsv\",\n",
        "    \"articles\": \"/content/articles_adobe_AMT.csv\"\n",
        "}\n",
        "\n",
        "max_token_len = 96\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "label_col = \"EmotionalPolarity\"\n",
        "encoder = Encoder(\"label\")\n",
        "_df = pd.read_table(\"/content/WASSA23_conv_level_with_labels_train.tsv\")\n",
        "encoder.fit(_df, label_col)\n",
        "\n",
        "preprocessor_train = Preprocessor(train_path, tokenizer, \"text\", encoder, max_token_len=max_token_len)\n",
        "preprocessor_val = Preprocessor(val_path, tokenizer, \"text\", encoder, max_token_len=max_token_len)"
      ],
      "metadata": {
        "id": "nO4o5NrtGu_r",
        "colab": {
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          "height": 145,
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        "outputId": "b99a00be-039b-4162-97c4-d048c60a9bb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a3b25bbf8ca4fe393d83ced51937363"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "\n",
        "class ConversationDataset(Dataset):\n",
        "  def __init__(self, input_ids, attention_mask, labels):\n",
        "    self.input_ids = input_ids\n",
        "    self.attention_mask = attention_mask \n",
        "    if len(labels.shape) > 1:\n",
        "      self.labels = torch.FloatTensor(labels)\n",
        "    else:\n",
        "      self.labels = labels if isinstance(labels, np.ndarray) else torch.tensor(labels.to_numpy()).float() \n",
        "  def __len__(self):\n",
        "    return len(self.input_ids)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    input_ids = torch.tensor(self.input_ids[idx][0][0])\n",
        "    attention_mask = torch.tensor(self.attention_mask[idx][0][0])\n",
        "    return {\n",
        "        \"input_ids\": input_ids,\n",
        "        \"attention_mask\": attention_mask,\n",
        "        \"labels\": self.labels[idx]\n",
        "    }"
      ],
      "metadata": {
        "id": "FyQmvx427rWt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "batch_size = 64\n",
        "\n",
        "train_input, train_attn_mask, train_labels = preprocessor_train.get_tokenized_data()\n",
        "val_input, val_attn_mask, val_labels = preprocessor_val.get_tokenized_data()\n",
        "\n",
        "train_ds = ConversationDataset(train_input, train_attn_mask, train_labels)\n",
        "val_ds = ConversationDataset(val_input, val_attn_mask, val_labels)\n",
        "\n",
        "\n",
        "val_dl = DataLoader(\n",
        "        val_ds,\n",
        "        batch_size=batch_size,\n",
        "        shuffle=False\n",
        ")"
      ],
      "metadata": {
        "id": "efgU6YPHWYP0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n",
        "from transformers import AutoModel\n",
        "from transformers.modeling_outputs import SequenceClassifierOutput\n",
        "\n",
        "\n",
        "class CustomClassificationHead(nn.Module):\n",
        "    def __init__(self, hidden_size, hidden_layers_to_use, num_labels, classifier_dropout):\n",
        "        super().__init__()\n",
        "        self.hidden_size = hidden_size \n",
        "        self.num_labels = num_labels\n",
        "        self.classifier_dropout = classifier_dropout\n",
        "        self.hidden_layers_to_use = hidden_layers_to_use\n",
        "\n",
        "        self.dense = nn.Linear(hidden_size * hidden_layers_to_use, hidden_size)\n",
        "        self.dropout = nn.Dropout(classifier_dropout)\n",
        "        self.out_proj = nn.Linear(hidden_size, num_labels)\n",
        "\n",
        "    def forward(self, hidden_states, **kwargs):\n",
        "        hidden_state_cls_vectors = []\n",
        "        for i in range(1, self.hidden_layers_to_use + 1):\n",
        "          hidden_state_cls_vectors.append(hidden_states[-1 * i][:, 0, :])\n",
        "        x = torch.cat(hidden_state_cls_vectors, dim=1)\n",
        "        x = self.dropout(x)\n",
        "        x = self.dense(x)\n",
        "        x = torch.tanh(x)\n",
        "        x = self.dropout(x)\n",
        "        x = self.out_proj(x)\n",
        "        return x\n",
        "\n",
        "class CustomClassifier(nn.Module):\n",
        "    def __init__(self, model_name, num_labels, lstm_cells=12, lstm_stack=1, lstm_bidirectional=True, lstm_dropout=0.1):\n",
        "        super().__init__()\n",
        "        self.model_name = model_name \n",
        "        self.num_labels = num_labels\n",
        "        self.lstm_cells = lstm_cells\n",
        "        self.lstm_stack = lstm_stack\n",
        "        self.lstm_bidirectional = lstm_bidirectional\n",
        "        self.lstm_dropout = lstm_dropout\n",
        "        self.pretrained = AutoModel.from_pretrained(model_name)\n",
        "        self.lstm = nn.LSTM(self.pretrained.config.hidden_size, lstm_cells, lstm_stack, batch_first=True, bidirectional=lstm_bidirectional, dropout=lstm_dropout)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.classifier = nn.Linear(lstm_cells * 2, num_labels)\n",
        "\n",
        "    def forward(self,input_ids=None, attention_mask=None,labels=None):\n",
        "        outputs = self.pretrained(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        # out = outputs.last_hidden_state[:, 0, :]\n",
        "        _, (hidden, _) = self.lstm(outputs.last_hidden_state)\n",
        "        out = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)\n",
        "        out = self.relu(out)  \n",
        "        logits = self.classifier(out)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            # move labels to correct device to enable model parallelism\n",
        "            labels = labels.to(logits.device)\n",
        "            loss_fct = CrossEntropyLoss()\n",
        "            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            # if self.config.problem_type is None:\n",
        "            #     if self.num_labels == 1:\n",
        "            #         self.config.problem_type = \"regression\"\n",
        "            #     elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\n",
        "            #         self.config.problem_type = \"single_label_classification\"\n",
        "            #     else:\n",
        "            #         self.config.problem_type = \"multi_label_classification\"\n",
        "\n",
        "            # if self.config.problem_type == \"regression\":\n",
        "            #     loss_fct = MSELoss()\n",
        "            #     if self.num_labels == 1:\n",
        "            #         loss = loss_fct(logits.squeeze(), labels.squeeze())\n",
        "            #     else:\n",
        "            #         loss = loss_fct(logits, labels)\n",
        "            # elif self.config.problem_type == \"single_label_classification\":\n",
        "            #     loss_fct = CrossEntropyLoss()\n",
        "            #     loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            # elif self.config.problem_type == \"multi_label_classification\":\n",
        "            #     loss_fct = BCEWithLogitsLoss()\n",
        "            #     loss = loss_fct(logits, labels)\n",
        "\n",
        "        return SequenceClassifierOutput(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            hidden_states=outputs.hidden_states,\n",
        "            attentions=outputs.attentions,\n",
        "        )\n",
        "\n"
      ],
      "metadata": {
        "id": "_Wg8QSKnbszU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = CustomClassifier(\n",
        "    model_name, \n",
        "    num_labels=encoder.num_classes,\n",
        "    lstm_cells=12,\n",
        "    lstm_stack=1,\n",
        "    lstm_bidirectional=True, \n",
        "    lstm_dropout=0.2\n",
        ").to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "a0088de4058441de9d598cd8fb592add",
            "49e4f35546e34e39be0b05cc77595ae6",
            "b7bf7e2e27424bc5808e499ed67551a9",
            "0dbf341facd74888b618394476048aec",
            "61f0b65c09fb4ff7833d2d3129035bd8",
            "f823b529387e4d578e090b3d4a6bb1a3",
            "83ca5a239c0640758c8e6c53b9f6925e",
            "35c14b8e64ee4e63973a1ae879eb4baa",
            "a9255ae817f042d3b7a907e8d1cb0356",
            "95f05cbb42744b179be7f41c7e746276",
            "e5b605ab80b7477da109df72bfdf623e"
          ]
        },
        "id": "0sM7hedHOIYb",
        "outputId": "0e4091a4-ed2d-4ef8-bdea-a464da155a96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/501M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0088de4058441de9d598cd8fb592add"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaModel: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias']\n",
            "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py:71: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n",
            "  warnings.warn(\"dropout option adds dropout after all but last \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "import numpy as np\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='/content/drive/MyDrive/MSML-641-empathy/models/roberta-lstm-emotional-polarity/',\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=64,\n",
        "    warmup_steps=500,\n",
        "    learning_rate=1e-5,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=300,\n",
        "    logging_steps=300,\n",
        "    save_strategy=\"epoch\",\n",
        "    save_total_limit=1\n",
        ")\n"
      ],
      "metadata": {
        "id": "fdPvTy6zQYdw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=val_ds\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "39YLZieuQkvn",
        "outputId": "b3326eb0-ee30-48bc-fd91-44af1e5bfc3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3291' max='3291' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3291/3291 11:14, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>2.092500</td>\n",
              "      <td>2.010440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.913500</td>\n",
              "      <td>1.812799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.816300</td>\n",
              "      <td>1.807244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.814700</td>\n",
              "      <td>1.773693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.769200</td>\n",
              "      <td>1.744691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>1.745200</td>\n",
              "      <td>1.738324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>1.725600</td>\n",
              "      <td>1.728232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>1.687600</td>\n",
              "      <td>1.726015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>1.690300</td>\n",
              "      <td>1.720525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>1.692700</td>\n",
              "      <td>1.722817</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=3291, training_loss=1.7858541019779322, metrics={'train_runtime': 678.1342, 'train_samples_per_second': 38.824, 'train_steps_per_second': 4.853, 'total_flos': 0.0, 'train_loss': 1.7858541019779322, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3000\t1.308600\t1.532028\n",
        "# 2100\t1.428600\t1.482285 (48, 1, 0.1)\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "predictions = np.array([])\n",
        "with torch.no_grad():\n",
        "    model.eval()\n",
        "    val_loop = tqdm(val_dl)\n",
        "    for idx, val_batch in enumerate(val_loop):\n",
        "      input_ids = val_batch[\"input_ids\"].to(device)\n",
        "      attention_mask = val_batch[\"attention_mask\"].to(device)\n",
        "      labels = val_batch[\"labels\"].to(device)\n",
        "      output = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
        "      pr = torch.sigmoid(output.logits)\n",
        "      pr_idx = pr.argmax(axis=-1)\n",
        "      labels = encoder.classes[pr_idx.cpu()]\n",
        "      predictions = np.concatenate([predictions, labels], axis=0)"
      ],
      "metadata": {
        "id": "OG5z4fCKBK8K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "1083dfe8d4e349a18d2d4ac6e349be01",
            "5f18c2c6f4cd4bfa960b8381d64b8e43",
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            "0686dd3fdbb54eb98991b955227b2906",
            "5cf2cf0dabbf4357bf92552cc2736a15"
          ]
        },
        "outputId": "fa08eda3-38ec-4ef4-e4e2-7846e457010e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/38 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c290269c303c4777bffda6cfbc2e4b1f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ohe = OneHotEncoder()\n",
        "_df = pd.read_table(\"/content/WASSA23_conv_level_with_labels_train.tsv\")\n",
        "ohe.fit(_df[[label_col]])\n",
        "num_classes = len(ohe.categories_[0])\n",
        "\n",
        "predictions_tr = pd.DataFrame(predictions, columns=[label_col])\n",
        "\n",
        "_transformed = ohe.transform(predictions_tr[[label_col]])\n",
        "_ohe_df = pd.DataFrame(_transformed.toarray(), columns=ohe.get_feature_names_out())\n",
        "predictions_tr = pd.concat([predictions_tr, _ohe_df], axis=1).drop([label_col], axis=1)\n",
        "final_pr = predictions_tr.to_numpy()\n",
        "\n",
        "val_data = pd.read_table('/content/goldstandard_CONV_dev.tsv', names=[\"EmotionalPolarity\", \"Emotion\", \"Empathy\"])\n",
        "transformed = ohe.transform(val_data[[label_col]]).toarray()\n",
        "ohe_df = pd.DataFrame(transformed, columns=ohe.get_feature_names_out())\n",
        "val_data = pd.concat([val_data, ohe_df], axis=1).drop([\"Empathy\", \"EmotionalPolarity\", \"Emotion\"], axis=1)\n",
        "labels = val_data.to_numpy()\n",
        "\n",
        "final_pr.shape == labels.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6J68SROgQrCb",
        "outputId": "674ab2a5-8f0a-4be3-93d7-37eeb093deab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot latest\n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "for i, label in enumerate(ohe.get_feature_names_out()):\n",
        "  fpr, tpr, _ = metrics.roc_curve(\n",
        "      labels[:,i].astype(float), final_pr[:,i]\n",
        "  )\n",
        "  auc = metrics.roc_auc_score(\n",
        "      labels[:,i].astype(float), final_pr[:,i]\n",
        "  )\n",
        "  plt.plot(fpr, tpr, label=\"%s %g\" %(label, auc))\n",
        "  plt.xlabel(\"False positive rate\")\n",
        "  plt.ylabel(\"True positive rate\")\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.title(\"RoBERTa + LSTM for Emotional Polarity\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "4tX5Jgg1SIiF",
        "outputId": "e6370a81-e036-4969-e367-c18cfd737e62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot \n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "for i, label in enumerate(ohe.get_feature_names_out()):\n",
        "  fpr, tpr, _ = metrics.roc_curve(\n",
        "      labels[:,i].astype(float), final_pr[:,i]\n",
        "  )\n",
        "  auc = metrics.roc_auc_score(\n",
        "      labels[:,i].astype(float), final_pr[:,i]\n",
        "  )\n",
        "  plt.plot(fpr, tpr, label=\"%s %g\" %(label, auc))\n",
        "  plt.xlabel(\"False positive rate\")\n",
        "  plt.ylabel(\"True positive rate\")\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.title(\"RoBERTa + LSTM for Emotional Polarity\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "c2-_IOibDQ3N",
        "outputId": "371b1dc7-50f5-4a58-b671-c31487436792"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gc \n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pc__LYrQ1CcQ",
        "outputId": "684d6cac-1d22-40c5-8839-35851d0dccc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23204"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H2xFLoLi1wWk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}