ACM_AI_FA22_Team_2_Reddit_Dataset.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Neniflight/fa22-reddit-politics-gain/blob/main/ACM_AI_FA22_Team_2_Reddit_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ft2jmE0im9m"
      },
      "source": [
        "## ACM AI Project Team 2 - GAIN\n",
        "\n",
        "### Classification of Political Biases in Reddit Posts Using RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUZZjlcWk-_b"
      },
      "source": [
        "### Cloning dataset "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/Neniflight/fa22-reddit-politics-gain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EQDzJJQwaqd",
        "outputId": "afe8df2c-97c8-43ab-fb21-694181a03a71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'fa22-reddit-politics-gain'...\n",
            "remote: Enumerating objects: 6, done.\u001b[K\n",
            "remote: Counting objects: 100% (6/6), done.\u001b[K\n",
            "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
            "remote: Total 6 (delta 0), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (6/6), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import time\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "from torchtext.data import get_tokenizer\n",
        "from torchtext.vocab import build_vocab_from_iterator\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torchtext\n",
        "import random\n",
        "torch.backends.cudnn.deterministic = True"
      ],
      "metadata": {
        "id": "c1ywP8zehs0L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def full_text(df):\n",
        "  title = df.get('Title')\n",
        "  text = df.get('Text')\n",
        "  all_text = []\n",
        "  for i in range(df.shape[0]):\n",
        "    if type(text[i]) == float:\n",
        "      new_text = title[i].replace(\"\\n\", \"\")\n",
        "    else:\n",
        "      new_text = title[i].replace(\"\\n\", \"\") + \" \" + text[i].replace(\"\\n\", \"\")\n",
        "    all_text.append(new_text)\n",
        "  return all_text"
      ],
      "metadata": {
        "id": "V7sxlCFSm3_M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('fa22-reddit-politics-gain/liberal_vs_conservative.csv')\n",
        "df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "de5b34d6-a0c5-456d-9c53-f0437376aa62"
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        "df['full_text'] = full_text(df)\n",
        "df['full_text'] = df['full_text'].astype('str')"
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      "metadata": {
        "id": "N_J1knyZiaKW"
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      "execution_count": null,
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        "df = df[['Political Lean', 'full_text']]\n",
        "df['Political Lean'] = df['Political Lean'].replace([\"Liberal\", \"Conservative\"], [0, 1])\n",
        "df = df.rename(columns = {\"Political Lean\": \"label\", \"full_text\": \"text\"})\n",
        "df"
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            "<ipython-input-9-f58e51ebf8ff>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Political Lean'] = df['Political Lean'].replace([\"Liberal\", \"Conservative\"], [0, 1])\n"
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              "       label                                               text\n",
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              "4          0                                  Do it for the Dew\n",
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    {
      "cell_type": "code",
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        "nice = [len(i.split()) for i in df['text']]\n",
        "df['count'] = nice\n",
        "df = df[(df['count'] >= 5) & (df['count'] <= 100)]\n",
        "df = df[['label', 'text']]\n",
        "df = df.reset_index(drop = True)"
      ],
      "metadata": {
        "id": "jzHuCW8cqcW0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "#Loading in data\n",
        "df_conservative, df_liberal = df[df.get(\"label\") == 1], df[df.get(\"label\") == 0]\n",
        "df_con, df_test_con = train_test_split(df_conservative, train_size = 0.9)\n",
        "df_lib, df_test_lib = train_test_split(df_liberal, train_size = 0.9)\n",
        "df_test = pd.concat([df_test_lib, df_test_con], ignore_index = True)"
      ],
      "metadata": {
        "id": "2wa9Z6P3ihhS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df_train = pd.concat([df_lib, df_con], ignore_index = True)\n",
        "df_train"
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test"
      ],
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          "height": 424
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        "id": "TEhqOUDQ5uqf",
        "outputId": "bc867270-530a-4be4-8d6b-01b1ae4064ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      label                                               text\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = list(df_train.to_records(index=False))\n",
        "test_dataset  = list(df_test.to_records(index=False))\n",
        "\n",
        "# test_dataset  = [(0 if label in range(5) else 1, text) \n",
        "#                  for label, text in test_dataset if label != 5]"
      ],
      "metadata": {
        "id": "PuXndYTbihb4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = get_tokenizer('basic_english')\n",
        "\n",
        "def build_vocab(dataset) :\n",
        "  for _, text in dataset :\n",
        "    yield tokenizer(text)\n",
        "\n",
        "vocab = build_vocab_from_iterator(build_vocab(train_dataset), \n",
        "                                  min_freq=1, specials=['<UNK>'])\n",
        "vocab.set_default_index(vocab['<UNK>'])"
      ],
      "metadata": {
        "id": "wZSo_lnkihWB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_words = 100\n",
        "def vectorize_batch(batch) :\n",
        "\n",
        "  Y, X = list(zip(*batch))\n",
        "  X = [vocab(tokenizer(text)) for text in X]\n",
        "  X = [tokens + ([0] * (max_words - len(tokens))) \n",
        "       if len(tokens) < max_words else tokens[:max_words]\n",
        "       for tokens in X]\n",
        "\n",
        "  return torch.tensor(X, dtype=torch.int32), torch.tensor(Y)\n",
        "\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=128, \n",
        "                              collate_fn=vectorize_batch, shuffle=True)\n",
        "test_dataloader  = DataLoader(test_dataset, batch_size=128, \n",
        "                              collate_fn=vectorize_batch)"
      ],
      "metadata": {
        "id": "Kn5HMtOSihTJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i, (text, label) in enumerate(train_dataloader) :\n",
        "  print(text.shape)\n",
        "  print(label.shape)\n",
        "  break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5PV4m8KihQK",
        "outputId": "c9de2575-2343-436b-cda4-79b04149119f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([128, 100])\n",
            "torch.Size([128])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class RNN(nn.Module) :\n",
        "\n",
        "  def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim) :\n",
        "\n",
        "    super().__init__()\n",
        "\n",
        "    self.embedding = nn.Embedding(input_dim, embedding_dim)\n",
        "    self.rnn = nn.RNN(embedding_dim, hidden_dim, nonlinearity='relu', batch_first=True)\n",
        "    # self.rnn = torch.nn.LSTM(embedding_dim, hidden_dim)\n",
        "    self.fc = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "  def forward(self, text) :\n",
        "\n",
        "    embedded = self.embedding(text)\n",
        "\n",
        "    output, hidden = self.rnn(embedded)\n",
        "    hidden.squeeze_(0)\n",
        "    output = self.fc(hidden)\n",
        "\n",
        "    return output"
      ],
      "metadata": {
        "id": "_hEPbJPnihM8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_seed, learning_rate, num_classes = (12, 0.001, 2)\n",
        "embedding_dim, hidden_dim = (128, 256)\n",
        "\n",
        "torch.manual_seed(random_seed)\n",
        "\n",
        "model = RNN(input_dim= vocab.__len__(),\n",
        "            embedding_dim=embedding_dim,\n",
        "            hidden_dim=hidden_dim,\n",
        "            output_dim=num_classes)\n",
        "\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
      ],
      "metadata": {
        "id": "VImhjYgyihHf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_accuracy(model, dataloader) :\n",
        "\n",
        "  with torch.no_grad() :\n",
        "\n",
        "    correct_pred, num_examples = 0, 0\n",
        "\n",
        "    for i, (features, targets) in enumerate(dataloader) :\n",
        "\n",
        "      features = features\n",
        "      targets = targets.float()\n",
        "\n",
        "      logits = model(features)\n",
        "      _, predicted_labels = torch.max(logits, 1)\n",
        "\n",
        "      num_examples += targets.size(0)\n",
        "      correct_pred += (predicted_labels == targets).sum()\n",
        "  \n",
        "  return correct_pred.float()/num_examples * 100"
      ],
      "metadata": {
        "id": "dubSRPBW0RRG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = time.time()\n",
        "NUM_EPOCHS = 10\n",
        "train_valid_accuracy = []\n",
        "losses = []\n",
        "\n",
        "for epoch in range(NUM_EPOCHS) :\n",
        "\n",
        "  model.train()\n",
        "\n",
        "  for batch_idx, batch_data in enumerate(train_dataloader) :\n",
        "\n",
        "    text = batch_data[0]\n",
        "    labels = batch_data[1]\n",
        "    \n",
        "    # Forward Propagation\n",
        "    logits = model(text)\n",
        "\n",
        "    # Clear Gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Calculate Cross Entropy Loss\n",
        "    loss = F.cross_entropy(logits, labels)\n",
        "\n",
        "    # Calculating Gradients\n",
        "    loss.backward()\n",
        "\n",
        "    # Update Model Parameters\n",
        "    optimizer.step()\n",
        "\n",
        "    if not batch_idx % 20 :\n",
        "      losses.append(loss.item())\n",
        "      print(f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} | '\n",
        "            f'Batch: {batch_idx:03d}/{len(train_dataloader):03d} | '\n",
        "            f'Loss: {loss:.4f}')\n",
        "      \n",
        "  with torch.set_grad_enabled(False) :\n",
        "    train_accuracy = compute_accuracy(model, train_dataloader)\n",
        "    # test_accuracy = compute_accuracy(model, test_dataloader)\n",
        "    train_valid_accuracy.append([train_accuracy.item()])\n",
        "    print(f'training accuracy: '\n",
        "          f'{train_accuracy:.2f}%')\n",
        "          # f'\\ntest accuracy: '\n",
        "          # f'{test_accuracy:.2f}%')\n",
        "      \n",
        "  print(f'Time elapsed: {(time.time() - start_time)/60:.2f} min')\n",
        "\n",
        "print(f'Total Training Time: {(time.time() - start_time)/60:.2f} min')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "id": "RzQmGMsHig_u",
        "outputId": "6e9f441b-b780-40cd-b986-6e357958331e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 001/010 | Batch: 000/078 | Loss: 0.6415\n",
            "Epoch: 001/010 | Batch: 020/078 | Loss: 0.6318\n",
            "Epoch: 001/010 | Batch: 040/078 | Loss: 0.6708\n",
            "Epoch: 001/010 | Batch: 060/078 | Loss: 0.6241\n",
            "training accuracy: 65.67%\n",
            "valid accuracy: 65.22%\n",
            "Time elapsed: 0.55 min\n",
            "Epoch: 002/010 | Batch: 000/078 | Loss: 0.6104\n",
            "Epoch: 002/010 | Batch: 020/078 | Loss: 0.6430\n",
            "Epoch: 002/010 | Batch: 040/078 | Loss: 0.6742\n",
            "Epoch: 002/010 | Batch: 060/078 | Loss: 0.6404\n",
            "training accuracy: 65.64%\n",
            "valid accuracy: 65.40%\n",
            "Time elapsed: 1.11 min\n",
            "Epoch: 003/010 | Batch: 000/078 | Loss: 0.6203\n",
            "Epoch: 003/010 | Batch: 020/078 | Loss: 0.6419\n",
            "Epoch: 003/010 | Batch: 040/078 | Loss: 0.5785\n",
            "Epoch: 003/010 | Batch: 060/078 | Loss: 0.6791\n",
            "training accuracy: 65.66%\n",
            "valid accuracy: 65.13%\n",
            "Time elapsed: 1.71 min\n",
            "Epoch: 004/010 | Batch: 000/078 | Loss: 0.6171\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-fe94f7be73df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;31m# Calculating Gradients\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;31m# Update Model Parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    485\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m             )\n\u001b[0;32m--> 487\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    488\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    198\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.set_grad_enabled(False) :\n",
        "    print(f'test accuracy: '\n",
        "          f'{compute_accuracy(model, test_dataloader):.2f}%') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E5EuECSYpKDF",
        "outputId": "a64bb9ab-eaa9-4fa5-d726-d26ce624bea7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test accuracy: 65.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TO6ozVFr5FfY"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}