depression_detect_underreview.ipynb

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    "colab": {
      "name": "depression_detect1.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Riturao/fyp/blob/master/depression_detect_underreview.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ej59Db-OAoML",
        "colab_type": "code",
        "outputId": "9fc18902-5ccc-43a6-ff86-8efeac6e4410",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        }
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "#import ost as mi\n",
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "#from chart_studio import plotly as py\n",
        "#import plotly.figure_factory as ff\n",
        "from scipy import stats\n",
        "\n",
        "import gensim\n",
        "import json\n",
        "\n",
        "\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import PorterStemmer\n",
        "\n",
        "import itertools\n",
        "\n",
        "import keras\n",
        "\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Dropout, Embedding, LSTM, Input, Activation, GlobalAveragePooling1D, Flatten, Concatenate, Conv1D, MaxPooling1D\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.merge import concatenate\n",
        "from keras.optimizers import SGD, RMSprop, Adagrad, Adam\n",
        "from keras.preprocessing.text import one_hot, text_to_word_sequence, Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.utils.vis_utils import plot_model\n",
        "\n",
        "import fnmatch\n",
        "\n",
        "import warnings\n",
        "\n",
        "import string\n",
        "from pathlib import Path\n",
        "from random import shuffle\n",
        "from ast import literal_eval\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
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              "<IPython.core.display.HTML object>"
            ]
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          "metadata": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "MJU-ByiwAoMS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wordnet_lemmatizer = WordNetLemmatizer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWoE-u_8AoMW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "WINDOWS_SIZE = 10\n",
        "labels=['none','mild','moderate','moderately severe', 'severe']\n",
        "num_classes = len(labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1KPZY71AoMZ",
        "colab_type": "code",
        "outputId": "064f31d8-a9d7-44ed-de21-91b5028ea907",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def plot_acc(history, title=\"Model Accuracy\"):\n",
        "    #plt.plot(history.history['accuracy'])\n",
        "    #plt.plot(history.history['val_accuracy'])\n",
        "\n",
        "    plt.plot(history.history['acc'])\n",
        "    plt.plot(history.history['val_acc'])\n",
        "    plt.title(title)\n",
        "    plt.ylabel('Acc')\n",
        "    \n",
        "    #plt.title(title)\n",
        "    #plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['Train', 'Val'], loc='upper left')\n",
        "    plt.show()\n",
        "    \n",
        "def plot_loss(history, title=\"Model Loss\"):\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.title(title)\n",
        "    plt.ylabel('Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['Train', 'Val'], loc='upper right')\n",
        "    plt.show()\n",
        "    \n",
        "def plot_compare_losses(history1, history2, name1=\"Red 1\", name2=\"Red 2\", title=\"Graph title\"):\n",
        "    plt.plot(history1.history['loss'], color=\"green\")\n",
        "    plt.plot(history1.history['val_loss'], 'r--', color=\"green\")\n",
        "    plt.plot(history2.history['loss'], color=\"blue\")\n",
        "    plt.plot(history2.history['val_loss'], 'r--', color=\"blue\")\n",
        "    plt.title(title)\n",
        "    plt.ylabel('Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['Train ' + name1, 'Val ' + name1, \n",
        "                'Train ' + name2, 'Val ' + name2],\n",
        "               loc='upper right')\n",
        "    plt.show()\n",
        "    \n",
        "def plot_compare_accs(history1, history2, name1=\"Red 1\",\n",
        "                      name2=\"Red 2\", title=\"Graph title\"):\n",
        "    \"\"\"Compara accuracies de dos entrenamientos con nombres name1 y name2\"\"\"\n",
        "    plt.plot(history1.history['acc'], color=\"green\")\n",
        "    plt.plot(history1.history['val_acc'], 'r--', color=\"green\")\n",
        "    plt.plot(history2.history['acc'], color=\"blue\")\n",
        "    plt.plot(history2.history['val_acc'], 'r--', color=\"blue\")\n",
        "    plt.title(title)\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['Train ' + name1, 'Val ' + name1, \n",
        "                'Train ' + name2, 'Val ' + name2], \n",
        "               loc='lower right')\n",
        "    plt.show()\n",
        "\n",
        "def plot_compare_multiple_metrics(history_array, names, colors, title=\"Graph title\", metric='acc'):  \n",
        "    legend = []\n",
        "    for i in range(0, len(history_array)):\n",
        "        plt.plot(history_array[i].history[metric], color=colors[i])\n",
        "        plt.plot(history_array[i].history['val_' + metric], 'r--', color=colors[i])\n",
        "        legend.append('Train ' + names[i])\n",
        "        legend.append('Val ' + names[i])\n",
        "    \n",
        "    plt.title(title)\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')   \n",
        "    plt.axis\n",
        "    plt.legend(legend, \n",
        "               loc='lower right')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ERROR! Session/line number was not unique in database. History logging moved to new session 65\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9P4uiHrAoMb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def transcripts_to_dataframe(directory):\n",
        "    rows_list = []\n",
        "        \n",
        "    filenames = os.listdir(directory)\n",
        "    \n",
        "    if \".DS_Store\" in filenames:\n",
        "        filenames.remove(\".DS_Store\")\n",
        "        \n",
        "    for filename in filenames:\n",
        "        \n",
        "        transcript_path = os.path.join(directory, filename)\n",
        "        transcript = pd.read_csv(transcript_path , sep='\\t')\n",
        "        #print(transcript)\n",
        "        m = re.search(\"(\\d{3})_TRANSCRIPT.csv\", filename)\n",
        "        if m:\n",
        "            print(filename)\n",
        "            person_id = m.group(1)\n",
        "            #print(person_id)\n",
        "            p = {}\n",
        "            question = \"\"\n",
        "            answer = \"\"\n",
        "            lines = len(transcript)\n",
        "            #print(lines)\n",
        "            for i in range(0, lines):\n",
        "                #print(\"entered\")\n",
        "                row = transcript.iloc[i]\n",
        "                #print(row)\n",
        "                if (row[\"speaker\"] == \"Ellie\") or (i == lines - 1):\n",
        "                    p[\"personId\"] = person_id\n",
        "                    #print(p)\n",
        "                    if \"(\" in str(question):\n",
        "                        question = question[question.index(\"(\") + 1:question.index(\")\")]\n",
        "                    p[\"question\"] = question\n",
        "                    p[\"answer\"] = answer\n",
        "                    #print(answer)\n",
        "                    #print(p)\n",
        "                    if question != \"\":\n",
        "                        rows_list.append(p)\n",
        "                        #print(rows_list())\n",
        "                    p = {}\n",
        "                    answer = \"\"\n",
        "                    question = row[\"value\"]\n",
        "                else:\n",
        "                    answer = str(answer) + \" \" + str(row[\"value\"])\n",
        "                    #print(1)\n",
        "                #print(answer)\n",
        "\n",
        "    all_participants = pd.DataFrame(rows_list, columns=['personId', 'question', 'answer'])\n",
        "    #all_participants = pd.DataFrame(*p, columns=['personId', 'question', 'answer'])\n",
        "    all_participants.to_csv(directory + 'all/all.csv', sep=',')\n",
        "    print(\"File was created\")\n",
        "    return all_participants"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tuMyC55Ayef",
        "colab_type": "code",
        "outputId": "56d029a9-27bd-45b6-e4d4-d4a6d6f238c5",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 39
        }
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-75ce70be-4f94-4c1b-9df1-fb851b4aa9c2\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-75ce70be-4f94-4c1b-9df1-fb851b4aa9c2\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smdffqJHA3D4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDRhxAK5AoMe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#data_path = r\"C:/Users/Admin/Desktop/transcripts/\"\n",
        "#transcripts_to_dataframe(data_path) \n",
        "all_participants = pd.read_csv('all.csv', sep=',')\n",
        "#all_participants= pd.read_csv(data_path + '*_TRANSCRIPT.csv', sep=',')\n",
        "#all_participants = pd.concat([pd.read_csv(f) for f in glob.glob('*_TRANSCRIPT.csv')], ignore_index = True)#"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "kvQi8eLNAoMh",
        "colab_type": "code",
        "outputId": "b5b3f81e-7365-4e1b-c5be-e39042961461",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "all_participants.tail() "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>personId</th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14968</th>\n",
              "      <td>14968</td>\n",
              "      <td>492</td>\n",
              "      <td>mm</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14969</th>\n",
              "      <td>14969</td>\n",
              "      <td>492</td>\n",
              "      <td>what are you most proud of in your life</td>\n",
              "      <td>um i am very proud of the fact that uh i don'...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14970</th>\n",
              "      <td>14970</td>\n",
              "      <td>492</td>\n",
              "      <td>okay i think i have asked everything i need to</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14971</th>\n",
              "      <td>14971</td>\n",
              "      <td>492</td>\n",
              "      <td>thanks for sharing your thoughts with me</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14972</th>\n",
              "      <td>14972</td>\n",
              "      <td>492</td>\n",
              "      <td>goodbye</td>\n",
              "      <td>bye and thank you</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Unnamed: 0  ...                                             answer\n",
              "14968       14968  ...                                                NaN\n",
              "14969       14969  ...   um i am very proud of the fact that uh i don'...\n",
              "14970       14970  ...                                                NaN\n",
              "14971       14971  ...                                                NaN\n",
              "14972       14972  ...                                  bye and thank you\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQBtYDKiAoMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_participants.columns =  ['index','personId', 'question', 'answer']\n",
        "all_participants = all_participants.astype({\"index\": int, \"personId\": float, \"question\": str, \"answer\": str })"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1d6KqqFyAoMo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#all_participants = pd.DataFrame(columns=['index','personId', 'question', 'answer'])\n",
        "all_participants.columns =  ['index','personId', 'question', 'answer']\n",
        "all_participants = all_participants.astype({\"index\": int, \"personId\": float, \"question\": str, \"answer\": str })"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RNGJHe_AoMr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def text_to_wordlist(text, remove_stopwords=True, stem_words=False):    \n",
        "    # Clean the text, with the option to remove stopwords and to stem words.\n",
        "    \n",
        "    # Convert words to lower case and split them\n",
        "    text = text.lower().split()\n",
        "\n",
        "    # Optionally, remove stop words\n",
        "    if remove_stopwords:\n",
        "        stops = set(stopwords.words(\"english\"))\n",
        "        text = [wordnet_lemmatizer.lemmatize(w) for w in text if not w in stops ]\n",
        "        text = [w for w in text if w != \"nan\" ]\n",
        "    else:\n",
        "        text = [wordnet_lemmatizer.lemmatize(w) for w in text]\n",
        "        text = [w for w in text if w != \"nan\" ]\n",
        "    \n",
        "    text = \" \".join(text)\n",
        "\n",
        "    # Clean the text\n",
        "    text = re.sub(r\"[^A-Za-z0-9^,!.\\/'+-=]\", \" \", text)\n",
        "    text = re.sub(r\"what's\", \"what is \", text)\n",
        "    text = re.sub(r\"\\'s\", \" \", text)\n",
        "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
        "    text = re.sub(r\"can't\", \"cannot \", text)\n",
        "    text = re.sub(r\"n't\", \" not \", text)\n",
        "    text = re.sub(r\"i'm\", \"i am \", text)\n",
        "    text = re.sub(r\"\\'re\", \" are \", text)\n",
        "    text = re.sub(r\"\\'d\", \" would \", text)\n",
        "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
        "    text = re.sub(r\",\", \" \", text)\n",
        "    text = re.sub(r\"\\.\", \" \", text)\n",
        "    text = re.sub(r\"!\", \" ! \", text)\n",
        "    text = re.sub(r\"\\/\", \" \", text)\n",
        "    text = re.sub(r\"\\^\", \" ^ \", text)\n",
        "    text = re.sub(r\"\\+\", \" + \", text)\n",
        "    text = re.sub(r\"\\-\", \" - \", text)\n",
        "    text = re.sub(r\"\\=\", \" = \", text)\n",
        "    \n",
        "    text = re.sub(r\"\\<\", \" \", text)\n",
        "    text = re.sub(r\"\\>\", \" \", text)\n",
        "    \n",
        "    text = re.sub(r\"'\", \" \", text)\n",
        "    text = re.sub(r\"(\\d+)(k)\", r\"\\g<1>000\", text)\n",
        "    text = re.sub(r\":\", \" : \", text)\n",
        "    text = re.sub(r\" e g \", \" eg \", text)\n",
        "    text = re.sub(r\" b g \", \" bg \", text)\n",
        "    text = re.sub(r\" u s \", \" american \", text)\n",
        "    text = re.sub(r\"\\0s\", \"0\", text)\n",
        "    text = re.sub(r\" 9 11 \", \"911\", text)\n",
        "    text = re.sub(r\"e - mail\", \"email\", text)\n",
        "    text = re.sub(r\"j k\", \"jk\", text)\n",
        "    text = re.sub(r\"\\s{2,}\", \" \", text)\n",
        "    \n",
        "    # Optionally, shorten words to their stems\n",
        "    if stem_words:\n",
        "        text = text.split()\n",
        "        stemmer = SnowballStemmer('english')\n",
        "        stemmed_words = [stemmer.stem(word) for word in text]\n",
        "        text = \" \".join(stemmed_words)\n",
        "    \n",
        "    # Return a list of words\n",
        "    return(text)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Qx5TbTXAoMu",
        "colab_type": "code",
        "outputId": "1183d7e6-c6d9-4350-d25a-e276417f1d59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rss97hnJAoMy",
        "colab_type": "code",
        "outputId": "255b99d2-f6b4-4ddf-85e8-97d1a4234319",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "all_participants_mix = all_participants.copy()\n",
        "all_participants_mix['answer'] = all_participants_mix.apply(lambda row: text_to_wordlist(row.answer).split(), axis=1)\n",
        "all_participants_mix.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>personId</th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>hi i'm ellie thanks for coming in today</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>300.0</td>\n",
              "      <td>i was created to talk to people in a safe and ...</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>300.0</td>\n",
              "      <td>think of me as a friend i don't judge i can't ...</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>300.0</td>\n",
              "      <td>i'm here to learn about people and would love ...</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>300.0</td>\n",
              "      <td>i'll ask a few questions to get us started and...</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  personId                                           question answer\n",
              "0      0     300.0            hi i'm ellie thanks for coming in today     []\n",
              "1      1     300.0  i was created to talk to people in a safe and ...     []\n",
              "2      2     300.0  think of me as a friend i don't judge i can't ...     []\n",
              "3      3     300.0  i'm here to learn about people and would love ...     []\n",
              "4      4     300.0  i'll ask a few questions to get us started and...     []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvg2Uum_AoM2",
        "colab_type": "code",
        "outputId": "89ce256e-9936-4755-c0eb-6d529bd50e2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "print(all_participants_mix['answer'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0                                 []\n",
            "1                                 []\n",
            "2                                 []\n",
            "3                                 []\n",
            "4                                 []\n",
            "                    ...             \n",
            "14968                             []\n",
            "14969    [um, proud, fact, uh, give]\n",
            "14970                             []\n",
            "14971                             []\n",
            "14972                   [bye, thank]\n",
            "Name: answer, Length: 14973, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPTs-PWnAoM5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_participants_mix_stopwords = all_participants.copy()\n",
        "all_participants_mix_stopwords['answer'] = all_participants_mix_stopwords.apply(lambda row: text_to_wordlist(row.answer, remove_stopwords=False).split(), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dduv-X0VAoM7",
        "colab_type": "code",
        "outputId": "aeec5706-eda5-4f74-d7d8-50b6a61784a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "words = [w for w in all_participants_mix['answer'].tolist()]\n",
        "words = set(itertools.chain(*words))\n",
        "vocab_size = len(words)\n",
        "print(vocab_size)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7373\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9GyhIGRHAoM-",
        "colab_type": "code",
        "outputId": "ee1d4a0d-01f9-405d-cff7-f0ec256f2de2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "words_stop = [w for w in all_participants_mix_stopwords['answer'].tolist()]\n",
        "words_stop = set(itertools.chain(*words_stop))\n",
        "vocab_size_stop = len(words_stop)\n",
        "print(vocab_size_stop)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7449\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnNApW1PAoNA",
        "colab_type": "code",
        "outputId": "43a82c36-f4a0-4889-f7d7-b76002d90747",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "windows_size = WINDOWS_SIZE\n",
        "tokenizer = Tokenizer(num_words=vocab_size)\n",
        "tokenizer.fit_on_texts(all_participants_mix['answer'])\n",
        "tokenizer.fit_on_sequences(all_participants_mix['answer'])\n",
        "\n",
        "all_participants_mix['t_answer'] = tokenizer.texts_to_sequences(all_participants_mix['answer'])\n",
        "all_participants_mix['answer'].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    []\n",
              "1    []\n",
              "2    []\n",
              "3    []\n",
              "4    []\n",
              "Name: answer, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cru2D4TNAoNF",
        "colab_type": "code",
        "outputId": "36aab970-88eb-4054-ffdf-c691ce2e78a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "windows_size = WINDOWS_SIZE\n",
        "tokenizer = Tokenizer(num_words=vocab_size_stop)\n",
        "tokenizer.fit_on_texts(all_participants_mix_stopwords['answer'])\n",
        "tokenizer.fit_on_sequences(all_participants_mix_stopwords['answer'])\n",
        "\n",
        "all_participants_mix_stopwords['t_answer'] = tokenizer.texts_to_sequences(all_participants_mix_stopwords['answer'])\n",
        "all_participants_mix_stopwords.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>personId</th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "      <th>t_answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>hi i'm ellie thanks for coming in today</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>300.0</td>\n",
              "      <td>i was created to talk to people in a safe and ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>300.0</td>\n",
              "      <td>think of me as a friend i don't judge i can't ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>300.0</td>\n",
              "      <td>i'm here to learn about people and would love ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>300.0</td>\n",
              "      <td>i'll ask a few questions to get us started and...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  personId  ... answer t_answer\n",
              "0      0     300.0  ...     []       []\n",
              "1      1     300.0  ...     []       []\n",
              "2      2     300.0  ...     []       []\n",
              "3      3     300.0  ...     []       []\n",
              "4      4     300.0  ...     []       []\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsrDSTDGAoNK",
        "colab_type": "code",
        "outputId": "c0d01630-d4a3-48a5-8998-f8f217d74b36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "word_index = tokenizer.word_index\n",
        "word_size = len(word_index)\n",
        "print(word_index[\"sad\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "652\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KmbegyeCAoNM",
        "colab_type": "code",
        "outputId": "f5f42c28-5c22-46d6-afa1-781a924c9968",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "all_participants_mix_stopwords.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>personId</th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "      <th>t_answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>hi i'm ellie thanks for coming in today</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>300.0</td>\n",
              "      <td>i was created to talk to people in a safe and ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>300.0</td>\n",
              "      <td>think of me as a friend i don't judge i can't ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>300.0</td>\n",
              "      <td>i'm here to learn about people and would love ...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>300.0</td>\n",
              "      <td>i'll ask a few questions to get us started and...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  personId  ... answer t_answer\n",
              "0      0     300.0  ...     []       []\n",
              "1      1     300.0  ...     []       []\n",
              "2      2     300.0  ...     []       []\n",
              "3      3     300.0  ...     []       []\n",
              "4      4     300.0  ...     []       []\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "yqV984sRAoNP",
        "colab_type": "code",
        "outputId": "93332a66-ca5d-475b-b787-4421adbb781d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "windows_size = WINDOWS_SIZE\n",
        "cont = 0\n",
        "word_index = tokenizer\n",
        "phrases_lp_stop = pd.DataFrame(columns=['personId','answer', 't_answer'])\n",
        "answers = all_participants_mix_stopwords.groupby('personId').agg({'answer':'sum','t_answer':'sum'})\n",
        "\n",
        "#answers = all_participants_mix_stopwords.groupby('personId').agg({' ':\"sum\"},axis=\"columns\")\n",
        "\n",
        "#answers = all_participants_mix_stopwords.groupby('personId',axis=0).aggregate('sum')\n",
        "#answers1 = all_participants_mix_stopwords.groupby('personId',axis=0)\n",
        "\n",
        "#answers= answers1.DataFrame.aggregate('sum', axis=1)\n",
        "#print(answers[\"answer\"])\n",
        "print(phrases_lp_stop.head())\n",
        "#print(answers[1])\n",
        "#print(answers)\n",
        "#answers.head()\n",
        "#answers1.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Empty DataFrame\n",
            "Columns: [personId, answer, t_answer]\n",
            "Index: []\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NL-88Lg3AoNR",
        "colab_type": "code",
        "outputId": "440fd012-da6a-4407-874b-e3bb9770c739",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "print(answers['answer'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "personId\n",
            "300.0    [good, atlanta, georgia, um, my, parent, are, ...\n",
            "301.0    [thank, you, mmm, k, i, am, doing, good, thank...\n",
            "302.0    [i, am, fine, how, about, yourself, i, am, fro...\n",
            "303.0    [okay, how, bout, yourself, here, in, californ...\n",
            "304.0    [i, am, doing, good, um, from, los, angeles, c...\n",
            "                               ...                        \n",
            "488.0    [yes, fine, oh, san, fernando, valley, uh, wel...\n",
            "489.0    [yes, i, am, doing, well, thank, you, san, lui...\n",
            "490.0    [yeah, i, am, doing, already, how, are, you, d...\n",
            "491.0    [yes, huh, overwhelmed, i, have, a, funeral, t...\n",
            "492.0    [yes, doing, pretty, good, thank, you, marylan...\n",
            "Name: answer, Length: 186, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfw7Ka9GAoNV",
        "colab_type": "code",
        "outputId": "a322384c-c382-4529-9248-f8ae2df798a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "cont = 0\n",
        "for p in answers.iterrows():      \n",
        "    words = p[1][\"answer\"]\n",
        "    size = len(words)\n",
        "    word_tokens = p[1][\"t_answer\"]\n",
        "    print(cont)\n",
        "    for i in range(size):\n",
        "        sentence = words[i:min(i+windows_size,size)]  \n",
        "        tokens = word_tokens[i:min(i+windows_size,size)]  \n",
        "        phrases_lp_stop.loc[cont] = [p[0], sentence, tokens]\n",
        "        cont = cont + 1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fieHRqXOwsQj",
        "colab_type": "code",
        "outputId": "b234f000-33e2-4e16-b45b-2a0d797acd03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow\n",
        "print(tensorflow.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLvReiO6AoNY",
        "colab_type": "code",
        "outputId": "d6f01f2d-3cb5-4884-b2db-3ec54a89eded",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "phrases_lp_stop.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>personId</th>\n",
              "      <th>answer</th>\n",
              "      <th>t_answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[good, atlanta, georgia, um, my, parent, are, ...</td>\n",
              "      <td>[42, 1727, 2089, 5, 12, 205, 37, 69, 113, 5]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[atlanta, georgia, um, my, parent, are, from, ...</td>\n",
              "      <td>[1727, 2089, 5, 12, 205, 37, 69, 113, 5, 1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[georgia, um, my, parent, are, from, here, um,...</td>\n",
              "      <td>[2089, 5, 12, 205, 37, 69, 113, 5, 1, 119]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[um, my, parent, are, from, here, um, i, love,...</td>\n",
              "      <td>[5, 12, 205, 37, 69, 113, 5, 1, 119, 6]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[my, parent, are, from, here, um, i, love, it, i]</td>\n",
              "      <td>[12, 205, 37, 69, 113, 5, 1, 119, 6, 1]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   personId  ...                                      t_answer\n",
              "0     300.0  ...  [42, 1727, 2089, 5, 12, 205, 37, 69, 113, 5]\n",
              "1     300.0  ...   [1727, 2089, 5, 12, 205, 37, 69, 113, 5, 1]\n",
              "2     300.0  ...    [2089, 5, 12, 205, 37, 69, 113, 5, 1, 119]\n",
              "3     300.0  ...       [5, 12, 205, 37, 69, 113, 5, 1, 119, 6]\n",
              "4     300.0  ...       [12, 205, 37, 69, 113, 5, 1, 119, 6, 1]\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hU349V48AoNa",
        "colab_type": "code",
        "outputId": "e469cd22-50df-4a82-fd13-66a755028468",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "windows_size = WINDOWS_SIZE\n",
        "cont = 0\n",
        "word_index = tokenizer\n",
        "phrases_lp = pd.DataFrame(columns=['personId','answer', 't_answer'])\n",
        "answers = all_participants_mix.groupby('personId').agg({'answer':'sum','t_answer':'sum'})\n",
        "\n",
        "for p in answers.iterrows():      \n",
        "    \n",
        "    words = p[1][\"answer\"]\n",
        "    size = len(words)\n",
        "    word_tokens = p[1][\"t_answer\"]\n",
        "    print(cont)\n",
        "    for i in range(size):\n",
        "        sentence = words[i:min(i+windows_size,size)]  \n",
        "        tokens = word_tokens[i:min(i+windows_size,size)]  \n",
        "        phrases_lp.loc[cont] = [p[0], sentence, tokens]\n",
        "        cont = cont + 1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s48JCoZ6AoNe",
        "colab_type": "code",
        "outputId": "eb37f645-1b48-425e-a877-53876248a8e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "phrases_lp.to_csv( 'phrases_lp.csv', sep='\\t')\n",
        "print(\"File was created\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File was created\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZaPAS2yAoNg",
        "colab_type": "code",
        "outputId": "1059fe74-7529-4768-edf2-003f23a4aab5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "phrases_lp.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>personId</th>\n",
              "      <th>answer</th>\n",
              "      <th>t_answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[good, atlanta, georgia, um, parent, um, love,...</td>\n",
              "      <td>[16, 1634, 1997, 1, 131, 1, 63, 5, 142, 5]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[atlanta, georgia, um, parent, um, love, like,...</td>\n",
              "      <td>[1634, 1997, 1, 131, 1, 63, 5, 142, 5, 334]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[georgia, um, parent, um, love, like, weather,...</td>\n",
              "      <td>[1997, 1, 131, 1, 63, 5, 142, 5, 334, 1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[um, parent, um, love, like, weather, like, op...</td>\n",
              "      <td>[1, 131, 1, 63, 5, 142, 5, 334, 1, 39]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[parent, um, love, like, weather, like, opport...</td>\n",
              "      <td>[131, 1, 63, 5, 142, 5, 334, 1, 39, 1]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   personId  ...                                     t_answer\n",
              "0     300.0  ...   [16, 1634, 1997, 1, 131, 1, 63, 5, 142, 5]\n",
              "1     300.0  ...  [1634, 1997, 1, 131, 1, 63, 5, 142, 5, 334]\n",
              "2     300.0  ...     [1997, 1, 131, 1, 63, 5, 142, 5, 334, 1]\n",
              "3     300.0  ...       [1, 131, 1, 63, 5, 142, 5, 334, 1, 39]\n",
              "4     300.0  ...       [131, 1, 63, 5, 142, 5, 334, 1, 39, 1]\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGHxhqqxAoNl",
        "colab_type": "code",
        "outputId": "3ca32981-34cb-4674-aecd-f59c59824927",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "phrases_lp[\"t_answer\"] = pad_sequences(phrases_lp[\"t_answer\"], value=0, padding=\"post\", maxlen=windows_size).tolist()\n",
        "phrases_lp.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>personId</th>\n",
              "      <th>answer</th>\n",
              "      <th>t_answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[good, atlanta, georgia, um, parent, um, love,...</td>\n",
              "      <td>[16, 1634, 1997, 1, 131, 1, 63, 5, 142, 5]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[atlanta, georgia, um, parent, um, love, like,...</td>\n",
              "      <td>[1634, 1997, 1, 131, 1, 63, 5, 142, 5, 334]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[georgia, um, parent, um, love, like, weather,...</td>\n",
              "      <td>[1997, 1, 131, 1, 63, 5, 142, 5, 334, 1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[um, parent, um, love, like, weather, like, op...</td>\n",
              "      <td>[1, 131, 1, 63, 5, 142, 5, 334, 1, 39]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>300.0</td>\n",
              "      <td>[parent, um, love, like, weather, like, opport...</td>\n",
              "      <td>[131, 1, 63, 5, 142, 5, 334, 1, 39, 1]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   personId  ...                                     t_answer\n",
              "0     300.0  ...   [16, 1634, 1997, 1, 131, 1, 63, 5, 142, 5]\n",
              "1     300.0  ...  [1634, 1997, 1, 131, 1, 63, 5, 142, 5, 334]\n",
              "2     300.0  ...     [1997, 1, 131, 1, 63, 5, 142, 5, 334, 1]\n",
              "3     300.0  ...       [1, 131, 1, 63, 5, 142, 5, 334, 1, 39]\n",
              "4     300.0  ...       [131, 1, 63, 5, 142, 5, 334, 1, 39, 1]\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LInqZHsqAoNo",
        "colab_type": "code",
        "outputId": "9dabb768-15dc-41b0-cd4b-e6c031d7bff3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "phrases_lp_stop.to_csv( 'phrases_lp_stop.csv', sep='\\t')\n",
        "print(\"File was created\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File was created\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUOTQvtYAoNr",
        "colab_type": "code",
        "outputId": "cfd72076-7c38-4798-851a-d653bac20f00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "phrases_lp = pd.read_csv( 'phrases_lp.csv', sep='\\t', converters={\"t_answer\": literal_eval}) \n",
        "phrases_lp.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>personId</th>\n",
              "      <th>answer</th>\n",
              "      <th>t_answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>['good', 'atlanta', 'georgia', 'um', 'parent',...</td>\n",
              "      <td>[16, 1634, 1997, 1, 131, 1, 63, 5, 142, 5]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>300.0</td>\n",
              "      <td>['atlanta', 'georgia', 'um', 'parent', 'um', '...</td>\n",
              "      <td>[1634, 1997, 1, 131, 1, 63, 5, 142, 5, 334]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>300.0</td>\n",
              "      <td>['georgia', 'um', 'parent', 'um', 'love', 'lik...</td>\n",
              "      <td>[1997, 1, 131, 1, 63, 5, 142, 5, 334, 1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>300.0</td>\n",
              "      <td>['um', 'parent', 'um', 'love', 'like', 'weathe...</td>\n",
              "      <td>[1, 131, 1, 63, 5, 142, 5, 334, 1, 39]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>300.0</td>\n",
              "      <td>['parent', 'um', 'love', 'like', 'weather', 'l...</td>\n",
              "      <td>[131, 1, 63, 5, 142, 5, 334, 1, 39, 1]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ...                                     t_answer\n",
              "0           0  ...   [16, 1634, 1997, 1, 131, 1, 63, 5, 142, 5]\n",
              "1           1  ...  [1634, 1997, 1, 131, 1, 63, 5, 142, 5, 334]\n",
              "2           2  ...     [1997, 1, 131, 1, 63, 5, 142, 5, 334, 1]\n",
              "3           3  ...       [1, 131, 1, 63, 5, 142, 5, 334, 1, 39]\n",
              "4           4  ...       [131, 1, 63, 5, 142, 5, 334, 1, 39, 1]\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIPIGwe2YIy-",
        "colab_type": "code",
        "outputId": "27adf286-f94f-4174-94f5-7f9675a82c55",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d8c8be0c-95cb-4d1f-8ab1-ceea93cde4ee\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-d8c8be0c-95cb-4d1f-8ab1-ceea93cde4ee\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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-42-72edf3658aa1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\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[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m     62\u001b[0m   result = _output.eval_js(\n\u001b[1;32m     63\u001b[0m       'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[0;32m---> 64\u001b[0;31m           input_id=input_id, output_id=output_id))\n\u001b[0m\u001b[1;32m     65\u001b[0m   \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_six\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m   \u001b[0;31m# Mapping from original filename to filename as saved locally.\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/python3.6/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result)\u001b[0m\n\u001b[1;32m     37\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\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[1;32m    100\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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[0;32m--> 101\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\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    102\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     if (reply.get('type') == 'colab_reply' and\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSa527eSYKdo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFGdWSFLAoNu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_avec_dataset_file(path, score_column):\n",
        "    ds = pd.read_csv(path, sep=',')\n",
        "    ds['level'] = pd.cut(ds[score_column], bins=[-1,0,5,10,15,25], labels=[0,1,2,3,4])\n",
        "    ds['PHQ8_Score'] = ds[score_column]\n",
        "    ds['cat_level'] = keras.utils.to_categorical(ds['level'], num_classes).tolist()\n",
        "    ds = ds[['Participant_ID', 'level', 'cat_level', 'PHQ8_Score']]\n",
        "    ds = ds.astype({\"Participant_ID\": float, \"level\": int, 'PHQ8_Score': int})\n",
        "    return ds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUMOajPMAoNw",
        "colab_type": "code",
        "outputId": "42838d85-623b-4bc2-ab8c-a02e5ec944d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        }
      },
      "source": [
        "train = load_avec_dataset_file('train_split_Depression_AVEC2017.csv', 'PHQ8_Score')\n",
        "dev = load_avec_dataset_file('dev_split_Depression_AVEC2017.csv', 'PHQ8_Score')\n",
        "test = load_avec_dataset_file('full_test_split.csv', 'PHQ_Score')\n",
        "print(\"Size: train= {}, dev= {}, test= {}\".format(len(train), len(dev), len(test)))\n",
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Size: train= 107, dev= 35, test= 47\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Participant_ID</th>\n",
              "      <th>level</th>\n",
              "      <th>cat_level</th>\n",
              "      <th>PHQ8_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>303.0</td>\n",
              "      <td>0</td>\n",
              "      <td>[1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>304.0</td>\n",
              "      <td>2</td>\n",
              "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>305.0</td>\n",
              "      <td>2</td>\n",
              "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>310.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[0.0, 1.0, 0.0, 0.0, 0.0]</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>312.0</td>\n",
              "      <td>1</td>\n",
              "      <td>[0.0, 1.0, 0.0, 0.0, 0.0]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Participant_ID  level                  cat_level  PHQ8_Score\n",
              "0           303.0      0  [1.0, 0.0, 0.0, 0.0, 0.0]           0\n",
              "1           304.0      2  [0.0, 0.0, 1.0, 0.0, 0.0]           6\n",
              "2           305.0      2  [0.0, 0.0, 1.0, 0.0, 0.0]           7\n",
              "3           310.0      1  [0.0, 1.0, 0.0, 0.0, 0.0]           4\n",
              "4           312.0      1  [0.0, 1.0, 0.0, 0.0, 0.0]           2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuuJM40XAoNy",
        "colab_type": "code",
        "outputId": "6d4094aa-8e01-49a3-b406-852dab7b80d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ds_total = pd.concat([train,dev,test])\n",
        "total_phq8 = len(ds_total)\n",
        "print(\"Total size = {}\".format(total_phq8))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total size = 189\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N60_fF0mAoN1",
        "colab_type": "code",
        "outputId": "89592a78-0d82-4fc0-9c6b-335cf925d842",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ds_total.to_csv( 'ds_total.csv', sep = '\\t')\n",
        "print('done')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGFmOysxAoN3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_by_phq_level(ds):\n",
        "    none_ds = ds[ds['level']==0]\n",
        "    mild_ds = ds[ds['level']==1]\n",
        "    moderate_ds = ds[ds['level']==2]\n",
        "    moderate_severe_ds = ds[ds['level']==3]\n",
        "    severe_ds = ds[ds['level']==4]\n",
        "    return (none_ds, mild_ds, moderate_ds, moderate_severe_ds, severe_ds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-h3sGJfNAoN7",
        "colab_type": "code",
        "outputId": "8f895db6-a628-4084-8ce4-10482566d0e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "none_ds, mild_ds, moderate_ds, moderate_severe_ds, severe_ds = split_by_phq_level(ds_total)\n",
        "print(\"Quantity per none_ds: {}, mild_ds: {}, moderate_ds {}, moderate_severe_ds: {}, severe_ds {}\".format(len(none_ds), len(mild_ds), len(moderate_ds), len(moderate_severe_ds), len(severe_ds)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Quantity per none_ds: 26, mild_ds: 70, moderate_ds 47, moderate_severe_ds: 24, severe_ds 22\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "palLGr2XAoN9",
        "colab_type": "code",
        "outputId": "a1670828-f053-4d97-aa60-50146d3e4cdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "b_none_ds = ds_total[ds_total['level']==0]\n",
        "b_mild_ds = ds_total[ds_total['level']==1].sample(26)\n",
        "b_moderate_ds = ds_total[ds_total['level']==2].sample(26)\n",
        "b_moderate_severe_ds = ds_total[ds_total['level']==3]\n",
        "b_severe_ds = ds_total[ds_total['level']==4]\n",
        "\n",
        "ds_total_b = pd.concat([b_none_ds, b_mild_ds, b_moderate_ds, b_moderate_severe_ds, b_severe_ds])\n",
        "print(\"Quantity per none_ds: {}, mild_ds: {}, moderate_ds {}, moderate_severe_ds: {}, severe_ds {}\".format(len(b_none_ds), len(b_mild_ds), len(b_moderate_ds), len(b_moderate_severe_ds), len(b_severe_ds)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Quantity per none_ds: 26, mild_ds: 26, moderate_ds 26, moderate_severe_ds: 24, severe_ds 22\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhN4Y6nfAoOA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ds_lp = pd.merge(ds_total, phrases_lp,left_on='Participant_ID', right_on='personId')\n",
        "ds_lp_b = pd.merge(ds_total_b, phrases_lp,left_on='Participant_ID', right_on='personId')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UP6S0sUAoOE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def distribute_instances(ds, split_in = [70,14,16]):\n",
        "    ds_shuffled = ds.sample(frac=1)\n",
        "    none_ds, mild_ds, moderate_ds, moderate_severe_ds, severe_ds = split_by_phq_level(ds_shuffled)\n",
        "    eq_ds = dict()\n",
        "    prev_none = prev_mild = prev_moderate = prev_moderate_severe = prev_severe = 0\n",
        "    split = split_in\n",
        "    for p in split:\n",
        "        last_none = min(len(none_ds), prev_none + round(len(none_ds) * p/100))\n",
        "        last_mild = min(len(mild_ds), prev_mild + round(len(mild_ds) * p/100))\n",
        "        last_moderate = min(len(moderate_ds), prev_moderate + round(len(moderate_ds) * p/100))\n",
        "        last_moderate_severe = min(len(moderate_severe_ds), prev_moderate_severe + round(len(moderate_severe_ds) * p/100))\n",
        "        last_severe = min(len(severe_ds), prev_severe + round(len(severe_ds) * p/100))  \n",
        "        eq_ds['d'+str(p)] = pd.concat([none_ds[prev_none: last_none], mild_ds[prev_mild: last_mild], moderate_ds[prev_moderate: last_moderate], moderate_severe_ds[prev_moderate_severe: last_moderate_severe], severe_ds[prev_severe: last_severe]])\n",
        "        prev_none = last_none\n",
        "        prev_mild = last_mild\n",
        "        prev_moderate = last_moderate\n",
        "        prev_moderate_severe = last_moderate_severe\n",
        "        prev_severe = last_severe  \n",
        "    return (eq_ds['d70'], eq_ds['d14'], eq_ds['d16'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_ErUtTwAoOI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_lp, dev_lp, test_lp = distribute_instances(ds_lp)\n",
        "train_lp_b, dev_lp_b, test_lp_b = distribute_instances(ds_lp_b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cri2P9HfAoOM",
        "colab_type": "code",
        "outputId": "3ca3fe3f-f0e9-4172-8cca-9853937bff48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "train_lp.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Participant_ID</th>\n",
              "      <th>level</th>\n",
              "      <th>cat_level</th>\n",
              "      <th>PHQ8_Score</th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>personId</th>\n",
              "      <th>answer</th>\n",
              "      <th>t_answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>109546</th>\n",
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              "      <td>332.0</td>\n",
              "      <td>['christ', 'that', 'mother', 'alive', 'well', ...</td>\n",
              "      <td>[3421, 20, 171, 646, 15, 86, 158, 308, 1119, 7]</td>\n",
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              "    <tr>\n",
              "      <th>81339</th>\n",
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              "      <td>23</td>\n",
              "      <td>30397</td>\n",
              "      <td>346.0</td>\n",
              "      <td>['um', 'uh', 'yeah', 'i', 'am', 'like', 'l', '...</td>\n",
              "      <td>[1, 2, 11, 3, 6, 5, 112, 1542, 295, 1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134886</th>\n",
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              "      <td>17</td>\n",
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              "      <td>453.0</td>\n",
              "      <td>['yes', 'around', 'time', 'p', 't', 's', 'd', ...</td>\n",
              "      <td>[39, 91, 17, 433, 201, 228, 296, 2, 138, 22]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96675</th>\n",
              "      <td>440.0</td>\n",
              "      <td>4</td>\n",
              "      <td>[0.0, 0.0, 0.0, 0.0, 1.0]</td>\n",
              "      <td>19</td>\n",
              "      <td>106049</td>\n",
              "      <td>440.0</td>\n",
              "      <td>['throat', 'husband', 'um', 'asked', 'whether'...</td>\n",
              "      <td>[161, 287, 1, 753, 291, 102, 1342, 185, 893, 2]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106530</th>\n",
              "      <td>311.0</td>\n",
              "      <td>4</td>\n",
              "      <td>[0.0, 0.0, 0.0, 0.0, 1.0]</td>\n",
              "      <td>21</td>\n",
              "      <td>8413</td>\n",
              "      <td>311.0</td>\n",
              "      <td>['well', 'try', 'right', 'thing', 'um', 'alway...</td>\n",
              "      <td>[15, 62, 47, 9, 1, 50, 53, 39, 46, 15]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Participant_ID  ...                                         t_answer\n",
              "109546           332.0  ...  [3421, 20, 171, 646, 15, 86, 158, 308, 1119, 7]\n",
              "81339            346.0  ...           [1, 2, 11, 3, 6, 5, 112, 1542, 295, 1]\n",
              "134886           453.0  ...     [39, 91, 17, 433, 201, 228, 296, 2, 138, 22]\n",
              "96675            440.0  ...  [161, 287, 1, 753, 291, 102, 1342, 185, 893, 2]\n",
              "106530           311.0  ...           [15, 62, 47, 9, 1, 50, 53, 39, 46, 15]\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pH7DWxRaAoOP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def confusion_matrix(model, x, y):\n",
        "    prediction = model.predict(x, batch_size=None, verbose=0, steps=None)\n",
        "    labels=['none','mild','moderate','moderately severe', 'severe']\n",
        "\n",
        "    max_prediction = np.argmax(prediction, axis=1)\n",
        "    max_actual = np.argmax(y, axis=1)\n",
        "\n",
        "    y_pred = pd.Categorical.from_codes(max_prediction, labels)\n",
        "    y_actu = pd.Categorical.from_codes(max_actual, labels)\n",
        "\n",
        "    return pd.crosstab(y_actu, y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCAxsT-nYlWI",
        "colab_type": "code",
        "outputId": "aeb0fe87-89a2-4889-9ce7-be563e12d930",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /gdrive\n",
            "/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fH7aG7ZQ32Zi",
        "colab_type": "code",
        "outputId": "ac4e9573-a5e0-4e9c-d624-7a1c6aa03913",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jqV6vBrAoOT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embeddings_index = dict()\n",
        "f = open('/content/drive/My Drive/glove.6B.100d.txt', encoding=\"utf8\")\n",
        "for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:], dtype='float32')\n",
        "    embeddings_index[word] = coefs\n",
        "f.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6rdeOmlAoOW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fill_embedding_matrix(tokenizer):\n",
        "    vocab_size = len(tokenizer.word_index)\n",
        "    embedding_matrix = np.zeros((vocab_size+1, 100))\n",
        "    for word, i in tokenizer.word_index.items():\n",
        "        embedding_vector = embeddings_index.get(word)\n",
        "        if embedding_vector is not None:        \n",
        "            embedding_matrix[i] = embedding_vector\n",
        "    return embedding_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aITjHr1JAoOZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embedding_matrix_lp = fill_embedding_matrix(tokenizer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOW_kvvjAoOe",
        "colab_type": "code",
        "outputId": "2aa96403-28a7-4c47-b5e8-a76efa3fc201",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "train_lp.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Participant_ID</th>\n",
              "      <th>level</th>\n",
              "      <th>cat_level</th>\n",
              "      <th>PHQ8_Score</th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>personId</th>\n",
              "      <th>answer</th>\n",
              "      <th>t_answer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25816</th>\n",
              "      <td>364.0</td>\n",
              "      <td>0</td>\n",
              "      <td>[1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
              "      <td>0</td>\n",
              "      <td>42314</td>\n",
              "      <td>364.0</td>\n",
              "      <td>['great', 'idea', 'used', 'uh', 'one', 'uh', '...</td>\n",
              "      <td>[84, 307, 134, 2, 24, 2, 636, 499, 143, 134]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32890</th>\n",
              "      <td>370.0</td>\n",
              "      <td>0</td>\n",
              "      <td>[1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
              "      <td>0</td>\n",
              "      <td>51829</td>\n",
              "      <td>370.0</td>\n",
              "      <td>['got', 'engaged', 'labor', 'day', 'uh', 'two'...</td>\n",
              "      <td>[56, 1481, 1540, 42, 2, 75, 418, 523, 557, 8]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76419</th>\n",
              "      <td>487.0</td>\n",
              "      <td>0</td>\n",
              "      <td>[1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
              "      <td>0</td>\n",
              "      <td>140180</td>\n",
              "      <td>487.0</td>\n",
              "      <td>['would', 'know', 'would', 'ss', 'would', 'kno...</td>\n",
              "      <td>[10, 4, 10, 1180, 10, 4, 89, 1768, 18, 29]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127020</th>\n",
              "      <td>411.0</td>\n",
              "      <td>0</td>\n",
              "      <td>[1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
              "      <td>0</td>\n",
              "      <td>83746</td>\n",
              "      <td>411.0</td>\n",
              "      <td>['murphy', 'law', 'that', 'way', 'sure']</td>\n",
              "      <td>[3536, 529, 20, 60, 97]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>563</th>\n",
              "      <td>303.0</td>\n",
              "      <td>0</td>\n",
              "      <td>[1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
              "      <td>0</td>\n",
              "      <td>1852</td>\n",
              "      <td>303.0</td>\n",
              "      <td>['probably', 'would', 'have', 'stayed', 'schoo...</td>\n",
              "      <td>[27, 10, 13, 461, 59, 27, 153, 17, 66, 29]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Participant_ID  ...                                       t_answer\n",
              "25816            364.0  ...   [84, 307, 134, 2, 24, 2, 636, 499, 143, 134]\n",
              "32890            370.0  ...  [56, 1481, 1540, 42, 2, 75, 418, 523, 557, 8]\n",
              "76419            487.0  ...     [10, 4, 10, 1180, 10, 4, 89, 1768, 18, 29]\n",
              "127020           411.0  ...                        [3536, 529, 20, 60, 97]\n",
              "563              303.0  ...     [27, 10, 13, 461, 59, 27, 153, 17, 66, 29]\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPVeczcZAoOh",
        "colab_type": "code",
        "outputId": "d9579f52-a32f-4a17-9bea-1759a6d046c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "train_lp_copy = train_lp\n",
        "print(len(train_lp_copy))\n",
        "for index,row in train_lp_copy.iterrows():\n",
        "    if len(row['t_answer'])<10:\n",
        "        train_lp_copy.drop(index,inplace=True)\n",
        "print(len(train_lp_copy))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "98800\n",
            "98800\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BG2xX01AoOl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_lp = train_lp_copy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sjuvyjv3AoOp",
        "colab_type": "code",
        "outputId": "b948ee16-0d08-44e0-cac5-974078e7030e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "dev_lp_copy = dev_lp\n",
        "print(len(dev_lp_copy))\n",
        "for index,row in dev_lp_copy.iterrows():\n",
        "    if len(row['t_answer'])<10:\n",
        "        dev_lp_copy.drop(index,inplace=True)\n",
        "print(len(dev_lp_copy))  \n",
        "dev_lp = dev_lp_copy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "19993\n",
            "19745\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZaKTtFSrAoOr",
        "colab_type": "code",
        "outputId": "47581c1a-890a-4d33-e360-b5cd4d7b0b6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "test_lp_copy = test_lp\n",
        "print(len(test_lp_copy))\n",
        "for index,row in test_lp_copy.iterrows():\n",
        "    if len(row['t_answer'])<10:\n",
        "        test_lp_copy.drop(index,inplace=True)\n",
        "print(len(test_lp_copy))  \n",
        "test_lp = test_lp_copy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "22849\n",
            "22591\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sE2U1AQzAoOt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_a = np.stack(train_lp['t_answer'], axis=0)\n",
        "dev_a = np.stack(dev_lp['t_answer'], axis=0)\n",
        "train_y = np.stack(train_lp['cat_level'], axis=0)\n",
        "dev_y = np.stack(dev_lp['cat_level'], axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-DP1xTaAoOu",
        "colab_type": "code",
        "outputId": "8a9b4fb2-dbdd-4320-9c48-2fcd67f6fcf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "train_lp_b_copy = train_lp_b\n",
        "print(len(train_lp_b_copy))\n",
        "for index,row in train_lp_b_copy.iterrows():\n",
        "    if len(row['t_answer'])<10:\n",
        "        train_lp_b_copy.drop(index,inplace=True)\n",
        "print(len(train_lp_b_copy))  \n",
        "train_lp_b = train_lp_b_copy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "65054\n",
            "64269\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RamhpafWAoOz",
        "colab_type": "code",
        "outputId": "8357caef-4d68-44f8-c906-6370f471f103",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "dev_lp_b_copy = dev_lp_b\n",
        "print(len(dev_lp_b_copy))\n",
        "for index,row in dev_lp_b_copy.iterrows():\n",
        "    if len(row['t_answer'])<10:\n",
        "        dev_lp_b_copy.drop(index,inplace=True)\n",
        "print(len(dev_lp_b_copy))  \n",
        "dev_lp_b = dev_lp_b_copy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13011\n",
            "12847\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvBJnMClAoO5",
        "colab_type": "code",
        "outputId": "562aac2d-2a64-44b1-bf22-760a9fb7fabf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "test_lp_b_copy = test_lp_b\n",
        "print(len(test_lp_b_copy))\n",
        "for index,row in test_lp_b_copy.iterrows():\n",
        "    if len(row['t_answer'])<10:\n",
        "        test_lp_b_copy.drop(index,inplace=True)\n",
        "print(len(test_lp_b_copy))  \n",
        "test_lp_b = test_lp_b_copy"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "14868\n",
            "14700\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_lKi51WAoO9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_a_b = np.stack(train_lp_b['t_answer'], axis=0)\n",
        "dev_a_b = np.stack(dev_lp_b['t_answer'], axis=0)\n",
        "train_y_b = np.stack(train_lp_b['cat_level'], axis=0)\n",
        "dev_y_b = np.stack(dev_lp_b['cat_level'], axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d32fycvVAoO_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss', patience=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dq7qsj5qAoPG",
        "colab_type": "code",
        "outputId": "76e355db-f97e-4116-fdea-4d945aa7de1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        }
      },
      "source": [
        "answer_inp = Input(shape=(windows_size, ))\n",
        "embedding_size_glove = 100\n",
        "answer_emb1 = Embedding(vocab_size_stop+1, embedding_size_glove, weights=[embedding_matrix_lp], input_length=windows_size, trainable=False)(answer_inp)\n",
        "\n",
        "bt = BatchNormalization()(answer_emb1)\n",
        "lstm = LSTM(embedding_size_glove, return_sequences=True, dropout=0.2, recurrent_dropout=0.2)(bt)\n",
        "\n",
        "dense1 = Dense(units=256, activation=\"relu\")(lstm)\n",
        "dense2 = Dense(units=256, activation=\"relu\")(dense1)\n",
        "\n",
        "flatten = Flatten()(dense2)\n",
        "\n",
        "out = Dense(5,  activation='softmax')(flatten)\n",
        "\n",
        "model = Model(inputs=[answer_inp], outputs=[out])\n",
        "model.compile(optimizer=\"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "embedding_1 (Embedding)      (None, 10, 100)           745000    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 10, 100)           400       \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 10, 100)           80400     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10, 256)           25856     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10, 256)           65792     \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2560)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 5)                 12805     \n",
            "=================================================================\n",
            "Total params: 930,253\n",
            "Trainable params: 185,053\n",
            "Non-trainable params: 745,200\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8YcpRRafIZ1",
        "colab_type": "code",
        "outputId": "9d63723f-327d-4d91-af37-462aeb176838",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "!pip install tensorflow==1.14.0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow==1.14.0 in /usr/local/lib/python3.6/dist-packages (1.14.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.2.0)\n",
            "Requirement already satisfied: tensorboard<1.15.0,>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.14.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.1.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.3.3)\n",
            "Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.14.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.34.2)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.27.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.18.2)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.8.1)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.0.8)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.12.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.9.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.1.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (3.10.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.12.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (46.1.3)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (1.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.2.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==1.14.0) (2.7.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXlz8hdVedpp",
        "colab_type": "code",
        "outputId": "5ccf1eec-e51c-42a5-fe53-bdbf0904134a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow\n",
        "print(tensorflow.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0cfGyJ2AoPI",
        "colab_type": "code",
        "outputId": "b70d329a-e9d3-428b-fe8f-0cfb4f652cf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_glove_lstm_hist = model.fit(train_a, train_y, validation_data=(dev_a, dev_y), epochs=30, batch_size=64, shuffle=True, callbacks=[early_stopping])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "Train on 98800 samples, validate on 19745 samples\n",
            "Epoch 1/30\n",
            "98800/98800 [==============================] - 62s 627us/step - loss: 1.4481 - acc: 0.3748 - val_loss: 1.3365 - val_acc: 0.4369\n",
            "Epoch 2/30\n",
            "98800/98800 [==============================] - 61s 614us/step - loss: 1.2563 - acc: 0.4792 - val_loss: 1.0794 - val_acc: 0.5702\n",
            "Epoch 3/30\n",
            "98800/98800 [==============================] - 61s 613us/step - loss: 1.0999 - acc: 0.5543 - val_loss: 0.9228 - val_acc: 0.6420\n",
            "Epoch 4/30\n",
            "98800/98800 [==============================] - 61s 614us/step - loss: 0.9941 - acc: 0.6040 - val_loss: 0.8179 - val_acc: 0.6861\n",
            "Epoch 5/30\n",
            "98800/98800 [==============================] - 61s 615us/step - loss: 0.9118 - acc: 0.6394 - val_loss: 0.7374 - val_acc: 0.7218\n",
            "Epoch 6/30\n",
            "98800/98800 [==============================] - 60s 611us/step - loss: 0.8559 - acc: 0.6645 - val_loss: 0.6524 - val_acc: 0.7602\n",
            "Epoch 7/30\n",
            "98800/98800 [==============================] - 60s 610us/step - loss: 0.8062 - acc: 0.6832 - val_loss: 0.6052 - val_acc: 0.7792\n",
            "Epoch 8/30\n",
            "98800/98800 [==============================] - 61s 616us/step - loss: 0.7641 - acc: 0.7039 - val_loss: 0.5445 - val_acc: 0.8041\n",
            "Epoch 9/30\n",
            "98800/98800 [==============================] - 62s 628us/step - loss: 0.7237 - acc: 0.7204 - val_loss: 0.4987 - val_acc: 0.8243\n",
            "Epoch 10/30\n",
            "98800/98800 [==============================] - 61s 619us/step - loss: 0.6904 - acc: 0.7351 - val_loss: 0.4674 - val_acc: 0.8326\n",
            "Epoch 11/30\n",
            "98800/98800 [==============================] - 61s 619us/step - loss: 0.6632 - acc: 0.7469 - val_loss: 0.4277 - val_acc: 0.8528\n",
            "Epoch 12/30\n",
            "98800/98800 [==============================] - 61s 614us/step - loss: 0.6323 - acc: 0.7595 - val_loss: 0.4084 - val_acc: 0.8579\n",
            "Epoch 13/30\n",
            "98800/98800 [==============================] - 61s 616us/step - loss: 0.6090 - acc: 0.7702 - val_loss: 0.3770 - val_acc: 0.8681\n",
            "Epoch 14/30\n",
            "98800/98800 [==============================] - 61s 618us/step - loss: 0.5898 - acc: 0.7765 - val_loss: 0.3522 - val_acc: 0.8781\n",
            "Epoch 15/30\n",
            "98800/98800 [==============================] - 62s 628us/step - loss: 0.5652 - acc: 0.7865 - val_loss: 0.3348 - val_acc: 0.8834\n",
            "Epoch 16/30\n",
            "98800/98800 [==============================] - 62s 625us/step - loss: 0.5443 - acc: 0.7942 - val_loss: 0.3089 - val_acc: 0.8944\n",
            "Epoch 17/30\n",
            "98800/98800 [==============================] - 62s 624us/step - loss: 0.5248 - acc: 0.8035 - val_loss: 0.2991 - val_acc: 0.9008\n",
            "Epoch 18/30\n",
            "98800/98800 [==============================] - 61s 622us/step - loss: 0.5102 - acc: 0.8093 - val_loss: 0.2858 - val_acc: 0.9053\n",
            "Epoch 19/30\n",
            "98800/98800 [==============================] - 63s 639us/step - loss: 0.4992 - acc: 0.8136 - val_loss: 0.2696 - val_acc: 0.9086\n",
            "Epoch 20/30\n",
            "98800/98800 [==============================] - 61s 621us/step - loss: 0.4806 - acc: 0.8215 - val_loss: 0.2517 - val_acc: 0.9165\n",
            "Epoch 21/30\n",
            "98800/98800 [==============================] - 61s 619us/step - loss: 0.4660 - acc: 0.8256 - val_loss: 0.2444 - val_acc: 0.9169\n",
            "Epoch 22/30\n",
            "98800/98800 [==============================] - 61s 615us/step - loss: 0.4569 - acc: 0.8300 - val_loss: 0.2407 - val_acc: 0.9200\n",
            "Epoch 23/30\n",
            "98800/98800 [==============================] - 61s 619us/step - loss: 0.4422 - acc: 0.8351 - val_loss: 0.2168 - val_acc: 0.9284\n",
            "Epoch 24/30\n",
            "98800/98800 [==============================] - 61s 621us/step - loss: 0.4313 - acc: 0.8392 - val_loss: 0.2104 - val_acc: 0.9308\n",
            "Epoch 25/30\n",
            "98800/98800 [==============================] - 61s 622us/step - loss: 0.4202 - acc: 0.8439 - val_loss: 0.2065 - val_acc: 0.9332\n",
            "Epoch 26/30\n",
            "98800/98800 [==============================] - 62s 628us/step - loss: 0.4087 - acc: 0.8499 - val_loss: 0.2032 - val_acc: 0.9293\n",
            "Epoch 27/30\n",
            "98800/98800 [==============================] - 62s 628us/step - loss: 0.4037 - acc: 0.8512 - val_loss: 0.1785 - val_acc: 0.9441\n",
            "Epoch 28/30\n",
            "98800/98800 [==============================] - 62s 630us/step - loss: 0.3911 - acc: 0.8564 - val_loss: 0.1774 - val_acc: 0.9421\n",
            "Epoch 29/30\n",
            "98800/98800 [==============================] - 64s 645us/step - loss: 0.3847 - acc: 0.8578 - val_loss: 0.1730 - val_acc: 0.9430\n",
            "Epoch 30/30\n",
            "98800/98800 [==============================] - 62s 631us/step - loss: 0.3798 - acc: 0.8600 - val_loss: 0.1681 - val_acc: 0.9443\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phpsPO7-AoPK",
        "colab_type": "code",
        "outputId": "90c4193d-873c-4585-fad5-72e3720caefa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        }
      },
      "source": [
        "model_glove_lstm_hist_b = model.fit(train_a_b, train_y_b, validation_data=(dev_a_b, dev_y_b), epochs=30, batch_size=64, shuffle=True, callbacks=[early_stopping])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 64269 samples, validate on 12847 samples\n",
            "Epoch 1/30\n",
            "64269/64269 [==============================] - 40s 629us/step - loss: 0.3851 - acc: 0.8599 - val_loss: 0.0716 - val_acc: 0.9830\n",
            "Epoch 2/30\n",
            "64269/64269 [==============================] - 40s 628us/step - loss: 0.3352 - acc: 0.8789 - val_loss: 0.0596 - val_acc: 0.9846\n",
            "Epoch 3/30\n",
            "64269/64269 [==============================] - 41s 633us/step - loss: 0.3082 - acc: 0.8884 - val_loss: 0.0555 - val_acc: 0.9854\n",
            "Epoch 4/30\n",
            "64269/64269 [==============================] - 40s 626us/step - loss: 0.2888 - acc: 0.8959 - val_loss: 0.0528 - val_acc: 0.9865\n",
            "Epoch 5/30\n",
            "64269/64269 [==============================] - 40s 625us/step - loss: 0.2740 - acc: 0.8998 - val_loss: 0.0512 - val_acc: 0.9867\n",
            "Epoch 6/30\n",
            "64269/64269 [==============================] - 40s 624us/step - loss: 0.2631 - acc: 0.9057 - val_loss: 0.0543 - val_acc: 0.9849\n",
            "Epoch 7/30\n",
            "64269/64269 [==============================] - 40s 627us/step - loss: 0.2575 - acc: 0.9070 - val_loss: 0.0463 - val_acc: 0.9882\n",
            "Epoch 8/30\n",
            "64269/64269 [==============================] - 40s 630us/step - loss: 0.2459 - acc: 0.9129 - val_loss: 0.0430 - val_acc: 0.9880\n",
            "Epoch 9/30\n",
            "64269/64269 [==============================] - 40s 626us/step - loss: 0.2415 - acc: 0.9145 - val_loss: 0.0454 - val_acc: 0.9877\n",
            "Epoch 10/30\n",
            "64269/64269 [==============================] - 40s 622us/step - loss: 0.2360 - acc: 0.9157 - val_loss: 0.0423 - val_acc: 0.9889\n",
            "Epoch 11/30\n",
            "64269/64269 [==============================] - 40s 620us/step - loss: 0.2252 - acc: 0.9188 - val_loss: 0.0429 - val_acc: 0.9884\n",
            "Epoch 12/30\n",
            "64269/64269 [==============================] - 40s 622us/step - loss: 0.2168 - acc: 0.9232 - val_loss: 0.0433 - val_acc: 0.9865\n",
            "Epoch 13/30\n",
            "64269/64269 [==============================] - 41s 643us/step - loss: 0.2124 - acc: 0.9247 - val_loss: 0.0422 - val_acc: 0.9889\n",
            "Epoch 14/30\n",
            "64269/64269 [==============================] - 40s 616us/step - loss: 0.2102 - acc: 0.9249 - val_loss: 0.0408 - val_acc: 0.9879\n",
            "Epoch 15/30\n",
            "64269/64269 [==============================] - 40s 626us/step - loss: 0.2091 - acc: 0.9260 - val_loss: 0.0362 - val_acc: 0.9893\n",
            "Epoch 16/30\n",
            "64269/64269 [==============================] - 41s 641us/step - loss: 0.2033 - acc: 0.9272 - val_loss: 0.0381 - val_acc: 0.9886\n",
            "Epoch 17/30\n",
            "64269/64269 [==============================] - 41s 640us/step - loss: 0.2015 - acc: 0.9276 - val_loss: 0.0422 - val_acc: 0.9862\n",
            "Epoch 18/30\n",
            "64269/64269 [==============================] - 41s 635us/step - loss: 0.1967 - acc: 0.9306 - val_loss: 0.0388 - val_acc: 0.9895\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__zwtdvXAoPN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_a = np.stack(test_lp['t_answer'], axis=0)\n",
        "test_y = np.stack(test_lp['cat_level'], axis=0)\n",
        "test_a_b = np.stack(test_lp_b['t_answer'], axis=0)\n",
        "test_y_b = np.stack(test_lp_b['cat_level'], axis=0)\n",
        "df_confusion = confusion_matrix(model, test_a_b, test_y_b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zwSrqwwAoPR",
        "colab_type": "code",
        "outputId": "24acf426-431a-48a4-bbf6-649f41de1690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "df_confusion"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>col_0</th>\n",
              "      <th>none</th>\n",
              "      <th>mild</th>\n",
              "      <th>moderate</th>\n",
              "      <th>moderately severe</th>\n",
              "      <th>severe</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>row_0</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>none</th>\n",
              "      <td>3495</td>\n",
              "      <td>8</td>\n",
              "      <td>21</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mild</th>\n",
              "      <td>12</td>\n",
              "      <td>2474</td>\n",
              "      <td>13</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3399</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderately severe</th>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>2769</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>severe</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>2375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0              none  mild  moderate  moderately severe  severe\n",
              "row_0                                                             \n",
              "none               3495     8        21                 20      16\n",
              "mild                 12  2474        13                  7      13\n",
              "moderate              7     3      3399                  2      10\n",
              "moderately severe     7     7        15               2769      11\n",
              "severe                0     2        12                  2    2375"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFYSK7c0AoPU",
        "colab_type": "code",
        "outputId": "4fafe9dc-c6fc-48a4-bd4d-90da9cfdfa8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        }
      },
      "source": [
        "model.save('model_glove_lstm_b.h5')\n",
        "json_dict = model_glove_lstm_hist.history\n",
        "with open('model_glove_lstm_b_hist.json', 'w') as f:\n",
        "    f.write(str(json_dict))\n",
        "print('File got created')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-80-a3ed7f8a9b07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model_glove_lstm_b.h5'\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      2\u001b[0m \u001b[0mjson_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_glove_lstm_hist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model_glove_lstm_b_hist.json'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_dict\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[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'File got created'\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/python3.6/dist-packages/keras/engine/network.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[1;32m   1137\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msave_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m         \u001b[0msave_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_optimizer\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   1140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0msaving\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallow_write_to_gcs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36msave_wrapper\u001b[0;34m(obj, filepath, overwrite, *args, **kwargs)\u001b[0m\n\u001b[1;32m    413\u001b[0m                 \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp_filepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m             \u001b[0msave_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0msave_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36msave_model\u001b[0;34m(model, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[1;32m    504\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mproceed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mH5Dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mh5dict\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    507\u001b[0m             \u001b[0m_serialize_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_optimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'write'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\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[0;32m/usr/local/lib/python3.6/dist-packages/keras/utils/io_utils.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path, mode)\u001b[0m\n\u001b[1;32m    189\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_is_path_instance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\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[0;32m--> 191\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\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    192\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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[0;32m/usr/local/lib/python3.6/dist-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, **kwds)\u001b[0m\n\u001b[1;32m    267\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mphil\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m                 \u001b[0mfapl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_fapl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m                 \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_fid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muserblock_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswmr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswmr\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    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mswmr_support\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/python3.6/dist-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_EXCL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfcpl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_TRUNC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfcpl\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    106\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m         \u001b[0;31m# Open in append mode (read/write).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mh5py/h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.create\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: Unable to create file (unable to open file: name = 'model_glove_lstm_b.h5', errno = 107, error message = 'Transport endpoint is not connected', flags = 13, o_flags = 242)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P821duKEJ-6Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip install -q pyyaml h5py"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qz9o7ihWnyi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "print(tf.version.VERSION)\n",
        "print(keras.__version__)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O29Vut7nEUp3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('model_glove_lstm_b.h5')\n",
        "json_dict = model_glove_lstm_hist.history\n",
        "with open('model_glove_lstm_b_hist.json', 'w') as f:\n",
        "    f.write(str(json_dict))\n",
        "print('File got created')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOBDeIryAoPX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import load_model\n",
        "model = load_model('/media/prahlad/New Volume/DIAC-WOZ/model_glove_lstm_b.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-NLsMkDAoPa",
        "colab_type": "code",
        "outputId": "2cff2a10-04a0-4e3d-f900-178c9df6f506",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "plot_loss(model_glove_lstm_hist)\n",
        "plot_acc(model_glove_lstm_hist)\n",
        "plot_loss(model_glove_lstm_hist_b)\n",
        "plot_acc(model_glove_lstm_hist_b)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4ExdpUG7O4aIK5q89xNvrsigoq9LSglKdRJOBu3GhBuXmNFdamNg3Bi8tLSjV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\nTQZKKaXQZKCUUgpNBkoppdBkoJRSCvj/UfkKU7eW6HQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0tgBjApwlg0BUth4W/gCyT4Fz7/N3NEe0v6mVf36yg8c+KKRyfzPTc1N56MoT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\nsGRgjDEGSwbGGGOA/w96tjFKVvWPYQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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73j3f43UiPBe4AzgrXPQ8cBew9wjbzQJ+RHBNx/3ufneb9V8CPgs0AjuA6919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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NRyOPCzYfiYjIISV0TWlm55vZG2ZWbmY3dTB9nJk9Y2bLzew5MyuJmvY/ZrbSzFab2Y8t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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKQZTWvUAoPb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test_model(text, model):\n",
        "    print(text)\n",
        "    word_list = text_to_wordlist(text)\n",
        "    sequences = tokenizer.texts_to_sequences([word_list])\n",
        "    sequences_input = list(itertools.chain(*sequences))\n",
        "    sequences_input =  pad_sequences([sequences_input], value=0, padding=\"post\", maxlen=windows_size).tolist()\n",
        "    print(sequences_input)\n",
        "    input_a = np.asarray(sequences_input)\n",
        "    pred = model.predict(input_a, batch_size=None, verbose=0, steps=None)\n",
        "    predicted_class = np.argmax(pred)\n",
        "    print(labels[predicted_class])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bQ8oEvfAoPd",
        "colab_type": "code",
        "outputId": "db1df8ed-4456-4392-9211-ea3d1769d08d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        }
      },
      "source": [
        "sen = \"All is going right with the party, I'm happy to know new people\"\n",
        "test_model(sen, model)\n",
        "sen = \"I want an ice cream and have some fries for lunch\"\n",
        "test_model(sen, model)\n",
        "sen = \"I'm afraid of losing my work, I don't have any money\"\n",
        "test_model(sen, model)\n",
        "sen = \"I'm worried about my future, I'm afraid of it\"\n",
        "test_model(sen, model)\n",
        "sen = \"I am a graduate student\"\n",
        "test_model(sen, model)\n",
        "sen = \"I am getting married\"\n",
        "test_model(sen, model)\n",
        "sen = \"This party is great, I know lots of people\"\n",
        "test_model(sen, model)\n",
        "sen = \"I miss my parents, brothers and sisters\"\n",
        "test_model(sen, model)\n",
        "sen = \"I detest my horrible job\"\n",
        "test_model(sen, model)\n",
        "sen = \"suicide\"\n",
        "test_model(sen, model)\n",
        "sen = \"Please kill me\"\n",
        "test_model(sen, model)\n",
        "sen = \"I am depressed\"\n",
        "test_model(sen, model)\n",
        "sen = \"I am scared!!!\"\n",
        "test_model(sen, model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "All is going right with the party, I'm happy to know new people\n",
            "[[90, 99, 559, 1, 18, 128, 14, 136, 39, 0]]\n",
            "mild\n",
            "I want an ice cream and have some fries for lunch\n",
            "[[193, 2448, 2844, 6711, 4205, 0, 0, 0, 0, 0]]\n",
            "moderate\n",
            "I'm afraid of losing my work, I don't have any money\n",
            "[[1, 18, 774, 902, 108, 211, 0, 0, 0, 0]]\n",
            "moderate\n",
            "I'm worried about my future, I'm afraid of it\n",
            "[[1, 18, 1073, 566, 1, 18, 774, 0, 0, 0]]\n",
            "moderate\n",
            "I am a graduate student\n",
            "[[826, 972, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
            "moderate\n",
            "I am getting married\n",
            "[[147, 440, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
            "moderate\n",
            "This party is great, I know lots of people\n",
            "[[559, 150, 14, 51, 39, 0, 0, 0, 0, 0]]\n",
            "moderate\n",
            "I miss my parents, brothers and sisters\n",
            "[[600, 3938, 294, 291, 0, 0, 0, 0, 0, 0]]\n",
            "moderate\n",
            "I detest my horrible job\n",
            "[[851, 123, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
            "moderate\n",
            "suicide\n",
            "[[3364, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
            "moderate\n",
            "Please kill me\n",
            "[[1522, 1850, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
            "moderate\n",
            "I am depressed\n",
            "[[368, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
            "moderate\n",
            "I am scared!!!\n",
            "[[1687, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
            "moderate\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0odlToqAoPe",
        "colab_type": "code",
        "outputId": "a2dd48d1-fbcd-4561-e0de-0a32d2bb251c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "answer_inp = Input(shape=(windows_size, ))\n",
        "embedding_size_glove = 100\n",
        "answer_emb1 = Embedding(vocab_size_stop+1, embedding_size_glove, weights=[embedding_matrix_lp], input_length=windows_size, trainable=False)(answer_inp)\n",
        "\n",
        "\n",
        "lstm1 = LSTM(embedding_size_glove, return_sequences=True, dropout=0.2, recurrent_dropout=0.2)(answer_emb1)\n",
        "lstm2 = LSTM(embedding_size_glove, dropout=0.2, recurrent_dropout=0.2)(lstm1)\n",
        "\n",
        "X = Dropout(0.2)(lstm2)\n",
        "bt = BatchNormalization()(X)\n",
        "dense1 = Dense(units=256, activation=\"relu\")(bt)\n",
        "\n",
        "out = Dense(5,  activation='softmax')(dense1)\n",
        "\n",
        "model_2lstm = Model(inputs=[answer_inp], outputs=[out])\n",
        "model_2lstm.compile(optimizer=\"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model_2lstm.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "embedding_2 (Embedding)      (None, 10, 100)           745000    \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 10, 100)           80400     \n",
            "_________________________________________________________________\n",
            "lstm_3 (LSTM)                (None, 100)               80400     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 100)               400       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 256)               25856     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 5)                 1285      \n",
            "=================================================================\n",
            "Total params: 933,341\n",
            "Trainable params: 188,141\n",
            "Non-trainable params: 745,200\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3dHg3vNAoPh",
        "colab_type": "code",
        "outputId": "2df64938-6a96-4e82-f551-6f87729a8944",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_glove_2lstm_b_hist = model_2lstm.fit(train_a_b, train_y_b, validation_data=(dev_a_b, dev_y_b), epochs=30, batch_size=64, shuffle=True, callbacks=[early_stopping])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 64269 samples, validate on 12847 samples\n",
            "Epoch 1/30\n",
            "64269/64269 [==============================] - 48s 746us/step - loss: 1.6233 - acc: 0.2424 - val_loss: 1.5855 - val_acc: 0.2594\n",
            "Epoch 2/30\n",
            "64269/64269 [==============================] - 45s 698us/step - loss: 1.5782 - acc: 0.2766 - val_loss: 1.5537 - val_acc: 0.3002\n",
            "Epoch 3/30\n",
            "64269/64269 [==============================] - 45s 696us/step - loss: 1.5446 - acc: 0.3071 - val_loss: 1.4968 - val_acc: 0.3429\n",
            "Epoch 4/30\n",
            "64269/64269 [==============================] - 45s 696us/step - loss: 1.4889 - acc: 0.3491 - val_loss: 1.4079 - val_acc: 0.4023\n",
            "Epoch 5/30\n",
            "64269/64269 [==============================] - 45s 702us/step - loss: 1.4114 - acc: 0.4037 - val_loss: 1.3271 - val_acc: 0.4435\n",
            "Epoch 6/30\n",
            "64269/64269 [==============================] - 45s 707us/step - loss: 1.3305 - acc: 0.4465 - val_loss: 1.1914 - val_acc: 0.5206\n",
            "Epoch 7/30\n",
            "64269/64269 [==============================] - 45s 705us/step - loss: 1.2539 - acc: 0.4851 - val_loss: 1.1086 - val_acc: 0.5644\n",
            "Epoch 8/30\n",
            "64269/64269 [==============================] - 46s 710us/step - loss: 1.1844 - acc: 0.5189 - val_loss: 1.0289 - val_acc: 0.5988\n",
            "Epoch 9/30\n",
            "64269/64269 [==============================] - 45s 708us/step - loss: 1.1271 - acc: 0.5489 - val_loss: 0.9754 - val_acc: 0.6251\n",
            "Epoch 10/30\n",
            "64269/64269 [==============================] - 47s 730us/step - loss: 1.0769 - acc: 0.5737 - val_loss: 0.9166 - val_acc: 0.6613\n",
            "Epoch 11/30\n",
            "64269/64269 [==============================] - 45s 708us/step - loss: 1.0336 - acc: 0.5931 - val_loss: 0.8514 - val_acc: 0.6834\n",
            "Epoch 12/30\n",
            "64269/64269 [==============================] - 46s 711us/step - loss: 0.9985 - acc: 0.6065 - val_loss: 0.8400 - val_acc: 0.6885\n",
            "Epoch 13/30\n",
            "64269/64269 [==============================] - 46s 708us/step - loss: 0.9670 - acc: 0.6218 - val_loss: 0.7810 - val_acc: 0.7125\n",
            "Epoch 14/30\n",
            "64269/64269 [==============================] - 45s 707us/step - loss: 0.9422 - acc: 0.6325 - val_loss: 0.7587 - val_acc: 0.7146\n",
            "Epoch 15/30\n",
            "64269/64269 [==============================] - 46s 710us/step - loss: 0.9118 - acc: 0.6470 - val_loss: 0.6993 - val_acc: 0.7501\n",
            "Epoch 16/30\n",
            "64269/64269 [==============================] - 45s 705us/step - loss: 0.9009 - acc: 0.6491 - val_loss: 0.6687 - val_acc: 0.7612\n",
            "Epoch 17/30\n",
            "64269/64269 [==============================] - 45s 700us/step - loss: 0.8784 - acc: 0.6605 - val_loss: 0.6426 - val_acc: 0.7691\n",
            "Epoch 18/30\n",
            "64269/64269 [==============================] - 45s 700us/step - loss: 0.8567 - acc: 0.6690 - val_loss: 0.6194 - val_acc: 0.7769\n",
            "Epoch 19/30\n",
            "64269/64269 [==============================] - 46s 708us/step - loss: 0.8343 - acc: 0.6797 - val_loss: 0.6137 - val_acc: 0.7844\n",
            "Epoch 20/30\n",
            "64269/64269 [==============================] - 45s 700us/step - loss: 0.8286 - acc: 0.6828 - val_loss: 0.5849 - val_acc: 0.7937\n",
            "Epoch 21/30\n",
            "64269/64269 [==============================] - 45s 701us/step - loss: 0.8077 - acc: 0.6915 - val_loss: 0.5695 - val_acc: 0.8038\n",
            "Epoch 22/30\n",
            "64269/64269 [==============================] - 45s 704us/step - loss: 0.7991 - acc: 0.6948 - val_loss: 0.5380 - val_acc: 0.8124\n",
            "Epoch 23/30\n",
            "64269/64269 [==============================] - 46s 723us/step - loss: 0.7828 - acc: 0.7031 - val_loss: 0.5385 - val_acc: 0.8169\n",
            "Epoch 24/30\n",
            "64269/64269 [==============================] - 45s 702us/step - loss: 0.7746 - acc: 0.7054 - val_loss: 0.5209 - val_acc: 0.8215\n",
            "Epoch 25/30\n",
            "64269/64269 [==============================] - 45s 701us/step - loss: 0.7635 - acc: 0.7087 - val_loss: 0.5088 - val_acc: 0.8249\n",
            "Epoch 26/30\n",
            "64269/64269 [==============================] - 45s 701us/step - loss: 0.7523 - acc: 0.7144 - val_loss: 0.4796 - val_acc: 0.8339\n",
            "Epoch 27/30\n",
            "64269/64269 [==============================] - 45s 707us/step - loss: 0.7464 - acc: 0.7171 - val_loss: 0.4679 - val_acc: 0.8372\n",
            "Epoch 28/30\n",
            "64269/64269 [==============================] - 45s 704us/step - loss: 0.7325 - acc: 0.7229 - val_loss: 0.4703 - val_acc: 0.8445\n",
            "Epoch 29/30\n",
            "64269/64269 [==============================] - 45s 703us/step - loss: 0.7277 - acc: 0.7256 - val_loss: 0.4638 - val_acc: 0.8413\n",
            "Epoch 30/30\n",
            "64269/64269 [==============================] - 45s 701us/step - loss: 0.7218 - acc: 0.7292 - val_loss: 0.4423 - val_acc: 0.8516\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4HfaDbVAoPj",
        "colab_type": "code",
        "outputId": "ba6ec41d-5dff-403e-f89e-2a4bd40b70e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "plot_loss(model_glove_2lstm_b_hist)\n",
        "plot_acc(model_glove_2lstm_b_hist)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tm44CY40x66p4XyLweQUDyG8APxpjZthebwUGGmMqXYZLB5CdaM2HMP8BiGwJ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\nTQZKKaXQZKCUUgpNBkoppdBkoJRSCvh/h6/MTJ2uh48AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mL4CILMSpSnpQVT+t+0IicgtwC0C3bt08EqwB5j8G3z4GJ0yEi/4Fwc2/ALuq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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5HUNLiwAoPk",
        "colab_type": "code",
        "outputId": "ea420e8c-6522-4fe1-cedb-390372bcb136",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "score = model_2lstm.evaluate(test_a_b, test_y_b, verbose=0)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.4467240418138958\n",
            "Test accuracy: 0.8504761904924094\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrC15pQOAoPo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_confusion = confusion_matrix(model_2lstm, test_a_b, test_y_b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pme6rz_VAoPq",
        "colab_type": "code",
        "outputId": "966227ce-7941-4c1d-fa0b-9b3e8b468f8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "df_confusion\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>col_0</th>\n",
              "      <th>none</th>\n",
              "      <th>mild</th>\n",
              "      <th>moderate</th>\n",
              "      <th>moderately severe</th>\n",
              "      <th>severe</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>row_0</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>none</th>\n",
              "      <td>3209</td>\n",
              "      <td>109</td>\n",
              "      <td>116</td>\n",
              "      <td>53</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mild</th>\n",
              "      <td>172</td>\n",
              "      <td>2118</td>\n",
              "      <td>108</td>\n",
              "      <td>49</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>174</td>\n",
              "      <td>148</td>\n",
              "      <td>2924</td>\n",
              "      <td>82</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderately severe</th>\n",
              "      <td>204</td>\n",
              "      <td>127</td>\n",
              "      <td>124</td>\n",
              "      <td>2233</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>severe</th>\n",
              "      <td>114</td>\n",
              "      <td>82</td>\n",
              "      <td>94</td>\n",
              "      <td>83</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0              none  mild  moderate  moderately severe  severe\n",
              "row_0                                                             \n",
              "none               3209   109       116                 53      73\n",
              "mild                172  2118       108                 49      72\n",
              "moderate            174   148      2924                 82      93\n",
              "moderately severe   204   127       124               2233     121\n",
              "severe              114    82        94                 83    2018"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsXGUBV8fB2h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip uninstall h5py"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXrlEgqhfOO9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip install h5py==2.7.1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csNOJNoJAoPr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('model_glove_2lstm_b.h5')\n",
        "json_dict = model_glove_2lstm_b_hist.history\n",
        "with open('model_glove_2lstm_b_hist.json', 'w') as f:\n",
        "    f.write(str(json_dict))\n",
        "print('File got created')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUu1DmzCAoPt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sen = \"All is going right with the party, I'm happy to know new people\"\n",
        "test_model(sen, model_2lstm)\n",
        "sen = \"I want an ice cream and have some fries for lunch\"\n",
        "test_model(sen, model_2lstm)\n",
        "sen = \"I'm afraid of losing my work, I don't have any money\"\n",
        "test_model(sen, model_2lstm)\n",
        "sen = \"I'm worried about my future, I'm afraid of it\"\n",
        "test_model(sen, model_2lstm)\n",
        "sen = \"My father loves me\"\n",
        "test_model(sen, model_2lstm)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APqHsaybAoPv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sen = \"Please kill me\"\n",
        "test_model(sen, model_2lstm)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmifWa83AoPx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sen = \"I am depressed\"\n",
        "test_model(sen, model_2lstm)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dA37D9F-AoP0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sen = \"life is hell\"\n",
        "test_model(sen, model_2lstm)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}