Bidirectional LSTM on MBTI Dataset.ipynb

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      "name": "Bidirectional LSTM on MBTI Dataset.ipynb",
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    "kernelspec": {
      "name": "python3",
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      "cell_type": "code",
      "execution_count": 8,
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        "outputId": "5eacf69d-9464-4ca2-a76a-175cbb4f6422"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.21.6)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.4.1)\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (4.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from gdown) (3.7.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from gdown) (4.64.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.7/dist-packages (from gdown) (2.23.0)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (1.24.3)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1QmH8BuCwltXgQMbzfLkxKJA636aQdBjN\n",
            "To: /content/mbti_full_pull.csv\n",
            "100% 599M/599M [00:07<00:00, 80.2MB/s]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from IPython.display import clear_output\n",
        "import re\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "STOPWORDS = set(stopwords.words('english'))\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "!pip install imbalanced-learn\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "!pip install gdown\n",
        "!gdown https://drive.google.com/uc?id=1QmH8BuCwltXgQMbzfLkxKJA636aQdBjN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#remove mbti-related text, so we dont classify a type based on how much some mbti string appears inside (not generalised)\n",
        "#for loop creates all stuff like XXTJ etc, cause some posts contains thing like this\n",
        "\n",
        "mbti_types = ['ISTJ', 'ISTP', 'ISFJ', 'ISFP', 'INTJ', 'INTP', 'INFJ', 'INFP', 'ESTJ', 'ESTP', 'ESFJ', 'ESFP', 'ENTJ', 'ENTP', 'ENFJ', 'ENFP']\n",
        "mbti_stopwords = ['fe', 'fi', 'ne', 'ni', 'se', 'si', 'te', 'ti']\n",
        "for i in mbti_types:\n",
        "  for mask in range(17):\n",
        "    tmp = list(i)\n",
        "    for j in range(4):\n",
        "      if pow(2, j) & mask != 0:\n",
        "        tmp[j] = 'X'\n",
        "    mbti_stopwords.append(\"\".join(tmp))\n",
        "    mbti_stopwords.append(\"\".join(tmp) + \"s\")\n",
        "\n",
        "\n",
        "class_size = 2000# @param\n",
        "vocab_size = 2000\n",
        "embedding_dim =  64\n",
        "train_size = 0.8\n",
        "max_length = 100\n",
        "trunc_type = 'post'\n",
        "padding_type = 'post'\n",
        "oov_tok = '<OOV>'"
      ],
      "metadata": {
        "id": "97kS-ezuylHZ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"mbti_full_pull.csv\").drop(['subreddit'], axis = 1)\n",
        "df.columns = ['type', 'text']\n",
        "df['type'] = df['type'].str.upper()\n",
        "for i in range(16):\n",
        "  df.loc[df['type'].str.contains(mbti_types[i]), 'type'] = mbti_types[i]\n",
        "df = df[df['text'].str.len() >= 50]\n",
        "df = df.reset_index(drop = True)\n",
        "under = RandomUnderSampler(sampling_strategy = dict(np.minimum(df['type'].value_counts(), class_size)))\n",
        "df, df['type'] = under.fit_resample(df, df['type'])\n",
        "stopwords = [i.lower() for i in nltk.corpus.stopwords.words('english') + mbti_stopwords + [chr(i) for i in range(97, 123)]]\n",
        "df.text = df.text.apply(lambda text: re.sub(\"\\s+\", \" \", ' '.join([i for i in re.sub(\"[^9A-Za-z ]\", \"\" , re.sub(\"\\\\n\", \"\", re.sub(\"\\s+\", \" \", re.sub(r'http\\S+', '', text.lower())))).split(\" \") if i not in stopwords])))\n",
        "df.loc[df['text'].str.len() >= max_length, 'text'] = df.loc[df['text'].str.len() >= max_length, 'text'].str[:max_length]\n",
        "\n",
        "df"
      ],
      "metadata": {
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          "height": 424
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        "outputId": "a95206b4-aac4-47d4-b1d4-3adb33d92d63"
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      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       type                                               text\n",
              "0      ENFJ  yknow point id call impossible really mature w...\n",
              "1      ENFJ  interesting person gaze penetrating speech mea...\n",
              "2      ENFJ  pagsubok lang yan kahit gaano kahirap ang isan...\n",
              "3      ENFJ  doesnt matter im hiding body actually help poi...\n",
              "4      ENFJ  tell calm fuck assure meant sign damn papers w...\n",
              "...     ...                                                ...\n",
              "31995  ISTP                    dont worst enemy per conflicts \n",
              "31996  ISTP  yep im good english including reading writinge...\n",
              "31997  ISTP  dont know appealing building part looks fun th...\n",
              "31998  ISTP  struggle often notice kill conversations sayin...\n",
              "31999  ISTP  terribly illogical short sighted reason hate type\n",
              "\n",
              "[32000 rows x 2 columns]"
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              "      <td>ENFJ</td>\n",
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              "      <td>ISTP</td>\n",
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              "      <td>ISTP</td>\n",
              "      <td>yep im good english including reading writinge...</td>\n",
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              "      <th>31997</th>\n",
              "      <td>ISTP</td>\n",
              "      <td>dont know appealing building part looks fun th...</td>\n",
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              "      <td>ISTP</td>\n",
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              "      <th>31999</th>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "x = df.text\n",
        "y = df.type.apply(lambda s: pd.Series(dict(zip([\"I/E\", \"N/S\", \"T/F\", \"P/J\"], [str.index(i) for i, str in zip(list(s), [\"IE\", \"NS\", \"TF\", \"PJ\"])]))))\n",
        "\n",
        "x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.4)\n",
        "x_val, x_test, y_val, y_test = train_test_split(x, y, test_size=0.25)\n",
        "\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(x_train)\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "x_train = pad_sequences(tokenizer.texts_to_sequences(x_train), maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "x_test = pad_sequences(tokenizer.texts_to_sequences(x_test), maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "x_val = pad_sequences(tokenizer.texts_to_sequences(x_val), maxlen=max_length, padding=padding_type, truncating=trunc_type)"
      ],
      "metadata": {
        "id": "XGWOiHI04hzz"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim), # embedding layer\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim, dropout=0.2, recurrent_dropout=0.2)), # LSTM layer\n",
        "    tf.keras.layers.Dropout(rate=0.2), # dropout layer\n",
        "    tf.keras.layers.Dense(embedding_dim, activation='relu'), # fully connected layer\n",
        "    tf.keras.layers.Dense(4, activation='sigmoid') #dropout layer\n",
        "])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myz_GWiG772m",
        "outputId": "9b70e8a1-957b-4d94-9b7e-a11337d0f45c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, None, 64)          128000    \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 128)              66048     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4)                 260       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 202,564\n",
            "Trainable params: 202,564\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy', 'AUC'])\n",
        "num_epochs = 10\n",
        "early_stopping_monitor = EarlyStopping(patience=2)\n",
        "history = model.fit(x_train, y_train, epochs=num_epochs, validation_data=(x_val, y_val), callbacks = [early_stopping_monitor])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5yHE7pW47g6P",
        "outputId": "05a0a71e-57b4-4174-9f8a-8b219ce0c30d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "600/600 [==============================] - 192s 312ms/step - loss: 0.6875 - accuracy: 0.2359 - auc: 0.5457 - val_loss: 0.6758 - val_accuracy: 0.2178 - val_auc: 0.5984\n",
            "Epoch 2/10\n",
            "600/600 [==============================] - 184s 306ms/step - loss: 0.6715 - accuracy: 0.2171 - auc: 0.6085 - val_loss: 0.6626 - val_accuracy: 0.2463 - val_auc: 0.6376\n",
            "Epoch 3/10\n",
            "600/600 [==============================] - 188s 313ms/step - loss: 0.6552 - accuracy: 0.2502 - auc: 0.6493 - val_loss: 0.6513 - val_accuracy: 0.2365 - val_auc: 0.6582\n",
            "Epoch 4/10\n",
            "600/600 [==============================] - 204s 341ms/step - loss: 0.6395 - accuracy: 0.2762 - auc: 0.6781 - val_loss: 0.6466 - val_accuracy: 0.2658 - val_auc: 0.6675\n",
            "Epoch 5/10\n",
            "600/600 [==============================] - 186s 310ms/step - loss: 0.6286 - accuracy: 0.2892 - auc: 0.6960 - val_loss: 0.6396 - val_accuracy: 0.2688 - val_auc: 0.6797\n",
            "Epoch 6/10\n",
            "600/600 [==============================] - 185s 309ms/step - loss: 0.6183 - accuracy: 0.2989 - auc: 0.7112 - val_loss: 0.6364 - val_accuracy: 0.2531 - val_auc: 0.6845\n",
            "Epoch 7/10\n",
            "600/600 [==============================] - 183s 305ms/step - loss: 0.6077 - accuracy: 0.3052 - auc: 0.7246 - val_loss: 0.6319 - val_accuracy: 0.2808 - val_auc: 0.6961\n",
            "Epoch 8/10\n",
            "600/600 [==============================] - 183s 305ms/step - loss: 0.5983 - accuracy: 0.3049 - auc: 0.7356 - val_loss: 0.6291 - val_accuracy: 0.2869 - val_auc: 0.7012\n",
            "Epoch 9/10\n",
            "600/600 [==============================] - 183s 305ms/step - loss: 0.5869 - accuracy: 0.3087 - auc: 0.7492 - val_loss: 0.6269 - val_accuracy: 0.3115 - val_auc: 0.7070\n",
            "Epoch 10/10\n",
            "600/600 [==============================] - 183s 305ms/step - loss: 0.5788 - accuracy: 0.3149 - auc: 0.7570 - val_loss: 0.6234 - val_accuracy: 0.3074 - val_auc: 0.7137\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = pd.DataFrame(model.predict(x_test).round(), columns=[\"I/E\", \"N/S\", \"T/F\", \"P/J\"]).applymap(int)\n",
        "y_pred"
      ],
      "metadata": {
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          "height": 424
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        "id": "E-96OImP8LGe",
        "outputId": "23759e53-f080-4097-94c0-24873baed2ae"
      },
      "execution_count": 30,
      "outputs": [
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              "      I/E  N/S  T/F  P/J\n",
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              "7997    1    0    1    1\n",
              "7998    1    0    0    1\n",
              "7999    1    1    1    0\n",
              "\n",
              "[8000 rows x 4 columns]"
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>7995</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>7996</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>7997</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>7998</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>7999</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "<p>8000 rows × 4 columns</p>\n",
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              "          const element = document.querySelector('#df-b7d0b5d8-4637-461b-9fcb-fe86b5f1bef9');\n",
              "          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",
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              "  "
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    {
      "cell_type": "code",
      "source": [
        "y_test = y_test.reset_index().drop(columns=\"index\")\n",
        "y_test"
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      "metadata": {
        "colab": {
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          "height": 424
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        "id": "n1bEfodXBFbf",
        "outputId": "d060c05d-faf9-4eb0-9d7e-bca5a0b0c38b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      I/E  N/S  T/F  P/J\n",
              "0       0    0    1    1\n",
              "1       1    0    0    1\n",
              "2       0    1    0    0\n",
              "3       0    1    1    0\n",
              "4       1    1    1    0\n",
              "...   ...  ...  ...  ...\n",
              "7995    0    1    1    0\n",
              "7996    1    0    0    0\n",
              "7997    0    0    0    0\n",
              "7998    1    0    0    1\n",
              "7999    0    1    0    0\n",
              "\n",
              "[8000 rows x 4 columns]"
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              "<p>8000 rows × 4 columns</p>\n",
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              "                                                     [key], {});\n",
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              "  "
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          "metadata": {},
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        "(y_pred == y_test).applymap(int).mean(axis=0)"
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      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "Uv86RfChBad4",
        "outputId": "27db6b00-ca76-4c25-abde-9206c5ea38b6"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "I/E    0.620875\n",
              "N/S    0.669250\n",
              "T/F    0.660375\n",
              "P/J    0.640875\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "((y_pred == y_test).applymap(int).sum(axis=1) == 4).mean()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zOvahWVqB9Pf",
        "outputId": "9d7cc35e-a259-402e-fef9-08a37544e2e5"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.217125"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
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      "metadata": {
        "colab": {
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        "id": "5oON8Rh3CaH1",
        "outputId": "ccb3505f-994e-41d3-cd94-98cd997f56e1"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        yknow point id call impossible really mature w...\n",
              "1        interesting person gaze penetrating speech mea...\n",
              "2        pagsubok lang yan kahit gaano kahirap ang isan...\n",
              "3        doesnt matter im hiding body actually help poi...\n",
              "4        tell calm fuck assure meant sign damn papers w...\n",
              "                               ...                        \n",
              "31995                      dont worst enemy per conflicts \n",
              "31996    yep im good english including reading writinge...\n",
              "31997    dont know appealing building part looks fun th...\n",
              "31998    struggle often notice kill conversations sayin...\n",
              "31999    terribly illogical short sighted reason hate type\n",
              "Name: text, Length: 32000, dtype: object"
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      "cell_type": "code",
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      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       I/E  N/S  T/F  P/J\n",
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              "31997    0    1    0    0\n",
              "31998    0    1    0    0\n",
              "31999    0    1    0    0\n",
              "\n",
              "[32000 rows x 4 columns]"
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              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>31995</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>31996</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>31997</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <th>31998</th>\n",
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              "          const element = document.querySelector('#df-6892a532-eb10-48b9-8c86-1a13bcc4909f');\n",
              "          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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.DataFrame(y)\n",
        "data[\"text\"] = x\n",
        "data = data[[\"text\", \"I/E\", \"N/S\", \"T/F\", \"P/J\"]]\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "J_haFD1HCooH",
        "outputId": "55c4c07d-fb20-4ff9-b2c4-5492f48537f3"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text  I/E  N/S  T/F  P/J\n",
              "0      yknow point id call impossible really mature w...    1    0    1    1\n",
              "1      interesting person gaze penetrating speech mea...    1    0    1    1\n",
              "2      pagsubok lang yan kahit gaano kahirap ang isan...    1    0    1    1\n",
              "3      doesnt matter im hiding body actually help poi...    1    0    1    1\n",
              "4      tell calm fuck assure meant sign damn papers w...    1    0    1    1\n",
              "...                                                  ...  ...  ...  ...  ...\n",
              "31995                    dont worst enemy per conflicts     0    1    0    0\n",
              "31996  yep im good english including reading writinge...    0    1    0    0\n",
              "31997  dont know appealing building part looks fun th...    0    1    0    0\n",
              "31998  struggle often notice kill conversations sayin...    0    1    0    0\n",
              "31999  terribly illogical short sighted reason hate type    0    1    0    0\n",
              "\n",
              "[32000 rows x 5 columns]"
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              "      <th>31995</th>\n",
              "      <td>dont worst enemy per conflicts</td>\n",
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              "      <td>0</td>\n",
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              "          const element = document.querySelector('#df-1e7359df-f2e0-440a-8e2f-fd73e3715fce');\n",
              "          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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv(\"mbti.csv\", index=False)"
      ],
      "metadata": {
        "id": "bYjeJjoRCrm3"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/terminalai/webdev-ai\n",
        "!cp webdev-ai/data/* .\n",
        "!rm -rf webdev-ai\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcrf23E_DT6_",
        "outputId": "406db9b4-4ef1-468d-94cf-3292653e35ae"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'webdev-ai'...\n",
            "remote: Enumerating objects: 16, done.\u001b[K\n",
            "remote: Counting objects:   6% (1/16)\u001b[K\rremote: Counting objects:  12% (2/16)\u001b[K\rremote: Counting objects:  18% (3/16)\u001b[K\rremote: Counting objects:  25% (4/16)\u001b[K\rremote: Counting objects:  31% (5/16)\u001b[K\rremote: Counting objects:  37% (6/16)\u001b[K\rremote: Counting objects:  43% (7/16)\u001b[K\rremote: Counting objects:  50% (8/16)\u001b[K\rremote: Counting objects:  56% (9/16)\u001b[K\rremote: Counting objects:  62% (10/16)\u001b[K\rremote: Counting objects:  68% (11/16)\u001b[K\rremote: Counting objects:  75% (12/16)\u001b[K\rremote: Counting objects:  81% (13/16)\u001b[K\rremote: Counting objects:  87% (14/16)\u001b[K\rremote: Counting objects:  93% (15/16)\u001b[K\rremote: Counting objects: 100% (16/16)\u001b[K\rremote: Counting objects: 100% (16/16), done.\u001b[K\n",
            "remote: Compressing objects: 100% (11/11), done.\u001b[K\n",
            "remote: Total 16 (delta 3), reused 12 (delta 3), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (16/16), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.text\n",
        "y = data[[\"I/E\", \"N/S\", \"T/F\", \"P/J\"]]\n",
        "\n",
        "x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.4)\n",
        "x_val, x_test, y_val, y_test = train_test_split(x_val, y_val, test_size=0.25)\n",
        "\n",
        "tokenizer = Tokenizer(num_words=2000, oov_token=\"<OOV>\")\n",
        "tokenizer.fit_on_texts(x_train)\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "x_train = pad_sequences(tokenizer.texts_to_sequences(x_train), maxlen=100, padding='post', truncating='post')\n",
        "x_test = pad_sequences(tokenizer.texts_to_sequences(x_test), maxlen=100, padding='post', truncating='post')\n",
        "x_val = pad_sequences(tokenizer.texts_to_sequences(x_val), maxlen=100, padding='post', truncating='post')"
      ],
      "metadata": {
        "id": "WKWXoqW4HR7W"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim), # embedding layer\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim, dropout=0.2, recurrent_dropout=0.2)), # LSTM layer\n",
        "    tf.keras.layers.Dropout(rate=0.2), # dropout layer\n",
        "    tf.keras.layers.Dense(embedding_dim, activation='relu'), # fully connected layer\n",
        "    tf.keras.layers.Dense(4, activation='sigmoid') #dropout layer\n",
        "])\n",
        "clf.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9ENVmxwHYl6",
        "outputId": "adbc0605-e266-47a5-d452-a2205d311adf"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_5 (Embedding)     (None, None, 64)          128000    \n",
            "                                                                 \n",
            " bidirectional_5 (Bidirectio  (None, 128)              66048     \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 4)                 260       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 202,564\n",
            "Trainable params: 202,564\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy', 'AUC'])\n",
        "num_epochs = 10\n",
        "early_stopping_monitor = EarlyStopping(patience=2)\n",
        "history = model.fit(x_train, y_train, epochs=num_epochs, validation_data=(x_val, y_val), callbacks = [early_stopping_monitor])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFROt7PSHfuy",
        "outputId": "06af5611-11f3-4c2b-c1d2-833f78a24c7c"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "600/600 [==============================] - 176s 285ms/step - loss: 0.6891 - accuracy: 0.2531 - auc: 0.5364 - val_loss: 0.6844 - val_accuracy: 0.2429 - val_auc: 0.5583\n",
            "Epoch 2/10\n",
            "600/600 [==============================] - 169s 281ms/step - loss: 0.6804 - accuracy: 0.2867 - auc: 0.5711 - val_loss: 0.6818 - val_accuracy: 0.2488 - val_auc: 0.5647\n",
            "Epoch 3/10\n",
            "600/600 [==============================] - 168s 280ms/step - loss: 0.6709 - accuracy: 0.2772 - auc: 0.6008 - val_loss: 0.6844 - val_accuracy: 0.2300 - val_auc: 0.5649\n",
            "Epoch 4/10\n",
            "600/600 [==============================] - 169s 282ms/step - loss: 0.6611 - accuracy: 0.2820 - auc: 0.6264 - val_loss: 0.6878 - val_accuracy: 0.2668 - val_auc: 0.5695\n"
          ]
        }
      ]
    }
  ]
}