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 },
 "cells": [
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   "metadata": {
    "id": "YDHCjaH5QrDk",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595407346407,
     "user_tz": -120,
     "elapsed": 22717,
     "user": {
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      "photoUrl": "",
      "userId": "14479436635515447323"
     }
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    "outputId": "543d5b1e-75c5-48ec-c05d-e63b58229c87"
   },
   "source": [
    "%tensorflow_version 2.x\n",
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive/', force_remount=True)"
   ],
   "execution_count": 1,
   "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 /content/drive/\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "_t6gg1ZLRYxS",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595407346408,
     "user_tz": -120,
     "elapsed": 22713,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "import sys\n",
    "projectDir = \"/content/drive/My Drive/code/git/tweepfake_deepfake_text_detection\"\n",
    "sys.path.insert(0, projectDir)\n",
    "resultsDir = projectDir+\"/data/results\"\n",
    "random_state = 523 # Fixed seed for replicability of randomic operations."
   ],
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "V3qW3001RonB",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 680
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595407354242,
     "user_tz": -120,
     "elapsed": 30510,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "c517b1d3-9613-4608-a6c1-d96f9acb8fbc"
   },
   "source": [
    "!pip install pandas transformers torch"
   ],
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (1.0.5)\n",
      "Collecting transformers\n",
      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/27/3c/91ed8f5c4e7ef3227b4119200fc0ed4b4fd965b1f0172021c25701087825/transformers-3.0.2-py3-none-any.whl (769kB)\n",
      "\u001B[K     |████████████████████████████████| 778kB 4.7MB/s \n",
      "\u001B[?25hRequirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.5.1+cu101)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.9)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas) (1.18.5)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
      "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.4)\n",
      "Collecting sacremoses\n",
      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
      "\u001B[K     |████████████████████████████████| 890kB 22.3MB/s \n",
      "\u001B[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
      "Collecting tokenizers==0.8.1.rc1\n",
      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/40/d0/30d5f8d221a0ed981a186c8eb986ce1c94e3a6e87f994eae9f4aa5250217/tokenizers-0.8.1rc1-cp36-cp36m-manylinux1_x86_64.whl (3.0MB)\n",
      "\u001B[K     |████████████████████████████████| 3.0MB 33.5MB/s \n",
      "\u001B[?25hCollecting sentencepiece!=0.1.92\n",
      "\u001B[?25l  Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
      "\u001B[K     |████████████████████████████████| 1.1MB 56.3MB/s \n",
      "\u001B[?25hRequirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.7)\n",
      "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch) (0.16.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas) (1.12.0)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
      "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
      "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.16.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.6.20)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
      "Building wheels for collected packages: sacremoses\n",
      "  Building wheel for sacremoses (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893260 sha256=b11c5cdbfc0fcc10ff9d52c0ce72f88c9c2e20dc0a5440e6d0788ea1aec9d927\n",
      "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
      "Successfully built sacremoses\n",
      "Installing collected packages: sacremoses, tokenizers, sentencepiece, transformers\n",
      "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.8.1rc1 transformers-3.0.2\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "F4xuO0SEMDTN",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595407357799,
     "user_tz": -120,
     "elapsed": 34060,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "e49d694e-fa3e-4614-938f-a58f80b62559"
   },
   "source": [
    "import torch\n",
    "\n",
    "# If there's a GPU available...\n",
    "if torch.cuda.is_available():    \n",
    "\n",
    "    # Tell PyTorch to use the GPU.    \n",
    "    device = torch.device(\"cuda\")\n",
    "\n",
    "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
    "\n",
    "    print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
    "\n",
    "# If not...\n",
    "else:\n",
    "    print('No GPU available, using the CPU instead.')\n",
    "    device = torch.device(\"cpu\")"
   ],
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "There are 1 GPU(s) available.\n",
      "We will use the GPU: Tesla P100-PCIE-16GB\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "lLsNhiofRy5r",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595407359928,
     "user_tz": -120,
     "elapsed": 36187,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "from DataHandler import DataHandler\n",
    "\n",
    "csvTrainDataset = projectDir+\"/data/splits/train.csv\"\n",
    "csvValDataset = projectDir+\"/data/splits/validation.csv\"\n",
    "csvTestDataset = projectDir+\"/data/splits/test.csv\"\n",
    "\n",
    "bertDir = projectDir+\"/data/encoded/bert\"\n",
    "dh = DataHandler()\n",
    "dfTrain = dh.readCSVData(csvTrainDataset)\n",
    "dfVal = dh.readCSVData(csvValDataset)\n",
    "dfTest = dh.readCSVData(csvTestDataset)"
   ],
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "mBCDg773SZKW",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595407359929,
     "user_tz": -120,
     "elapsed": 36181,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "0b84baa4-798c-403c-dadd-bd348e74f1f1"
   },
   "source": [
    "dfTrain"
   ],
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
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       "      <td>22336</td>\n",
       "      <td>x3171109449</td>\n",
       "      <td>x1091463908118941696</td>\n",
       "      <td>2019-02-01 22:30:48</td>\n",
       "      <td>zawvrk</td>\n",
       "      <td>Listen to This Charming Man by The Smiths  htt...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>soundcloud.com/thesmiths/this…</td>\n",
       "      <td>https://t.co/r12OIXkfUO</td>\n",
       "      <td>https://soundcloud.com/thesmiths/this-charming...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA NA</td>\n",
       "      <td>NA NA</td>\n",
       "      <td>NA NA NA NA NA NA NA NA</td>\n",
       "      <td>https://twitter.com/zawvrk/status/109146390811...</td>\n",
       "      <td>zawar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gold experience</td>\n",
       "      <td>https://t.co/J60plTSsKt</td>\n",
       "      <td>False</td>\n",
       "      <td>558</td>\n",
       "      <td>274</td>\n",
       "      <td>8</td>\n",
       "      <td>12216</td>\n",
       "      <td>23633</td>\n",
       "      <td>2015-04-15 19:50:16</td>\n",
       "      <td>False</td>\n",
       "      <td>https://t.co/J60plTSsKt</td>\n",
       "      <td>https://www.instagram.com/zawvr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/profile_banners/31711094...</td>\n",
       "      <td>http://abs.twimg.com/images/themes/theme1/bg.png</td>\n",
       "      <td>http://pbs.twimg.com/profile_images/1229957200...</td>\n",
       "      <td>2019-02-01 22:30:48</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>22:30:48</td>\n",
       "      <td>tweet</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>human</td>\n",
       "      <td>original</td>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24087</td>\n",
       "      <td>x1110686081341632512</td>\n",
       "      <td>x1199055191028293633</td>\n",
       "      <td>2019-11-25 20:00:09</td>\n",
       "      <td>zawarbot</td>\n",
       "      <td>wish i can i would be seeing other hoes on the...</td>\n",
       "      <td>zawar-twitter-bot</td>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA NA</td>\n",
       "      <td>NA NA</td>\n",
       "      <td>NA NA NA NA NA NA NA NA</td>\n",
       "      <td>https://twitter.com/zawarbot/status/1199055191...</td>\n",
       "      <td>zawarbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I’m an AI Bot that tries to tweet like @zawvrk...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4054</td>\n",
       "      <td>9</td>\n",
       "      <td>2019-03-26 23:32:52</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://abs.twimg.com/images/themes/theme1/bg.png</td>\n",
       "      <td>http://pbs.twimg.com/profile_images/1185589257...</td>\n",
       "      <td>2019-11-25 20:00:09</td>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>20:00:09</td>\n",
       "      <td>tweet</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>bot</td>\n",
       "      <td>zawvrk</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7584</td>\n",
       "      <td>x1110307772783124480</td>\n",
       "      <td>x1214698264701722626</td>\n",
       "      <td>2020-01-08 00:00:08</td>\n",
       "      <td>ahadsheriffbot</td>\n",
       "      <td>The decade in the significantly easier schedul...</td>\n",
       "      <td>ahadsheriff-bot</td>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA NA</td>\n",
       "      <td>NA NA</td>\n",
       "      <td>NA NA NA NA NA NA NA NA</td>\n",
       "      <td>https://twitter.com/ahadsheriffbot/status/1214...</td>\n",
       "      <td>Robot Ahad Sheriff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@ahadsheriff in robot form. tweets every hour ...</td>\n",
       "      <td>https://t.co/2G243YsNOt</td>\n",
       "      <td>False</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7946</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-25 22:29:36</td>\n",
       "      <td>False</td>\n",
       "      <td>https://t.co/2G243YsNOt</td>\n",
       "      <td>https://ahadsheriff.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://abs.twimg.com/images/themes/theme1/bg.png</td>\n",
       "      <td>http://pbs.twimg.com/profile_images/1110311752...</td>\n",
       "      <td>2020-01-08 00:00:08</td>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>00:00:08</td>\n",
       "      <td>tweet</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>bot</td>\n",
       "      <td>ahadsheriff</td>\n",
       "      <td>others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19654</td>\n",
       "      <td>x979586167405363200</td>\n",
       "      <td>x1209229478934695937</td>\n",
       "      <td>2019-12-23 21:49:08</td>\n",
       "      <td>kevinhookebot</td>\n",
       "      <td>\"Theim class=\\\"alignnone size-full wp-image-60...</td>\n",
       "      <td>kevinhooke-ml-bot</td>\n",
       "      <td>180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kevinhooke.com/wp-content/upl…</td>\n",
       "      <td>https://t.co/LiAsQsbs99</td>\n",
       "      <td>https://www.kevinhooke.com/wp-content/uploads/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NA NA</td>\n",
       "      <td>NA NA</td>\n",
       "      <td>NA NA NA NA NA NA NA NA</td>\n",
       "      <td>https://twitter.com/kevinhookebot/status/12092...</td>\n",
       "      <td>Kevin Hooke Bot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I'm an AWS Lambda generating content from a Re...</td>\n",
       "      <td>https://t.co/gTSHg4mXqN</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6608</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-30 05:08:36</td>\n",
       "      <td>False</td>\n",
       "      <td>https://t.co/gTSHg4mXqN</td>\n",
       "      <td>https://www.kevinhooke.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://abs.twimg.com/sticky/default_profile_im...</td>\n",
       "      <td>2019-12-23 21:49:08</td>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>21:49:08</td>\n",
       "      <td>tweet</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>bot</td>\n",
       "      <td>kevinhooke</td>\n",
       "      <td>rnn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20707</th>\n",
       "      <td>1538</td>\n",
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       "      <td>x1204245032917700608</td>\n",
       "      <td>2019-12-10 03:42:44</td>\n",
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       "      <td>Met on the Abversion of our science for the co...</td>\n",
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       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
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       "      <td>life.Om</td>\n",
       "      <td>https://t.co/kl5Dfl0y18</td>\n",
       "      <td>http://life.Om</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NA NA</td>\n",
       "      <td>NA NA</td>\n",
       "      <td>NA NA NA NA NA NA NA NA</td>\n",
       "      <td>https://twitter.com/AINarendraModi/status/1204...</td>\n",
       "      <td>AI Narendra Modi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Narendra Modi but with AI\\r\\nBot commands - ht...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1625</td>\n",
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       "      <td>2019-11-22 16:34:45</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://pbs.twimg.com/profile_banners/11979162...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://pbs.twimg.com/profile_images/1197916520...</td>\n",
       "      <td>2019-12-10 03:42:44</td>\n",
       "      <td>2019-12-10</td>\n",
       "      <td>03:42:44</td>\n",
       "      <td>tweet</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>bot</td>\n",
       "      <td>narendramodi</td>\n",
       "      <td>rnn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20708</th>\n",
       "      <td>1885</td>\n",
       "      <td>x1197916267975335939</td>\n",
       "      <td>x1207011474243309570</td>\n",
       "      <td>2019-12-17 18:55:35</td>\n",
       "      <td>AINarendraModi</td>\n",
       "      <td>Land for their during the opportunity to the p...</td>\n",
       "      <td>AI_Narendra_Modi</td>\n",
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       "      <td>https://twitter.com/AINarendraModi/status/1207...</td>\n",
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       "      <td>False</td>\n",
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       "      <td>http://pbs.twimg.com/profile_images/1197916520...</td>\n",
       "      <td>2019-12-17 18:55:35</td>\n",
       "      <td>2019-12-17</td>\n",
       "      <td>18:55:35</td>\n",
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       "      <td>DeepDrumpf</td>\n",
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       "      <td>https://twitter.com/DeepDrumpf/status/71555845...</td>\n",
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       "      <td>http://abs.twimg.com/images/themes/theme1/bg.png</td>\n",
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       "      <td>2016-12-25 03:53:45</td>\n",
       "      <td>2016-12-25</td>\n",
       "      <td>03:53:45</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>human</td>\n",
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       "    <tr>\n",
       "      <th>20711</th>\n",
       "      <td>19366</td>\n",
       "      <td>x979586167405363200</td>\n",
       "      <td>x1194703766303338496</td>\n",
       "      <td>2019-11-13 19:49:09</td>\n",
       "      <td>kevinhookebot</td>\n",
       "      <td>\"Thead has a generate existing the Sparching f...</td>\n",
       "      <td>kevinhooke-ml-bot</td>\n",
       "      <td>205</td>\n",
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       "      <td>NA NA</td>\n",
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       "      <td>NA NA NA NA NA NA NA NA</td>\n",
       "      <td>https://twitter.com/kevinhookebot/status/11947...</td>\n",
       "      <td>Kevin Hooke Bot</td>\n",
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       "      <td>https://t.co/gTSHg4mXqN</td>\n",
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       "      <td>2018-03-30 05:08:36</td>\n",
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       "      <td>https://www.kevinhooke.com</td>\n",
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       "      <td>http://abs.twimg.com/sticky/default_profile_im...</td>\n",
       "      <td>2019-11-13 19:49:09</td>\n",
       "      <td>2019-11-13</td>\n",
       "      <td>19:49:09</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>bot</td>\n",
       "      <td>kevinhooke</td>\n",
       "      <td>rnn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20712 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      N_prog               user_id  ... finetuning_source class_type\n",
       "0      14692  x1110407881030017024  ...           imranye     others\n",
       "1      22336           x3171109449  ...          original      human\n",
       "2      24087  x1110686081341632512  ...            zawvrk     others\n",
       "3       7584  x1110307772783124480  ...       ahadsheriff     others\n",
       "4      19654   x979586167405363200  ...        kevinhooke        rnn\n",
       "...      ...                   ...  ...               ...        ...\n",
       "20707   1538  x1197916267975335939  ...      narendramodi        rnn\n",
       "20708   1885  x1197916267975335939  ...      narendramodi        rnn\n",
       "20709   3930   x705113652471439361  ...   realDonaldTrump        rnn\n",
       "20710    729            x262794965  ...          original      human\n",
       "20711  19366   x979586167405363200  ...        kevinhooke        rnn\n",
       "\n",
       "[20712 rows x 103 columns]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 6
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "e8b9TFRgN6fz",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595407360755,
     "user_tz": -120,
     "elapsed": 37000,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "fe1ef8c2-96c2-4558-ef1c-b8f729085470"
   },
   "source": [
    "# creating bool series True for NaN values \n",
    "bool_series = dfTrain.isnull() \n",
    "  \n",
    "# filtering data \n",
    "# displayind data only with team = NaN \n",
    "dfTrain[bool_series]"
   ],
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <th>profile_banner_url</th>\n",
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       "      <th>data.tweet</th>\n",
       "      <th>ora.tweet</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20707</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20708</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20709</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20710</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20711</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20712 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      N_prog user_id status_id  ... account.type finetuning_source class_type\n",
       "0        NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "1        NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "2        NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "3        NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "4        NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "...      ...     ...       ...  ...          ...               ...        ...\n",
       "20707    NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "20708    NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "20709    NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "20710    NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "20711    NaN     NaN       NaN  ...          NaN               NaN        NaN\n",
       "\n",
       "[20712 rows x 103 columns]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "c0oedq8-W7u2",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595407360756,
     "user_tz": -120,
     "elapsed": 36999,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "# Select interesting columns for this study.\n",
    "dfTrainDataset = dfTrain[[\"screen_name\", \"text\", \"account.type\"]]\n",
    "dfValDataset = dfVal[[\"screen_name\", \"text\", \"account.type\"]]\n",
    "dfTestDataset = dfTest[[\"screen_name\", \"text\", \"account.type\"]]"
   ],
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "qTeVpVc6I9Lb",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595407360757,
     "user_tz": -120,
     "elapsed": 36993,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "c3eb9f08-30a4-49fa-9aac-4b8d230336a8"
   },
   "source": [
    "dfTrainDataset"
   ],
   "execution_count": 9,
   "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>screen_name</th>\n",
       "      <th>text</th>\n",
       "      <th>account.type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>imranyebot</td>\n",
       "      <td>YEA now that note GOOD</td>\n",
       "      <td>bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>zawvrk</td>\n",
       "      <td>Listen to This Charming Man by The Smiths  htt...</td>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>zawarbot</td>\n",
       "      <td>wish i can i would be seeing other hoes on the...</td>\n",
       "      <td>bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ahadsheriffbot</td>\n",
       "      <td>The decade in the significantly easier schedul...</td>\n",
       "      <td>bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kevinhookebot</td>\n",
       "      <td>\"Theim class=\\\"alignnone size-full wp-image-60...</td>\n",
       "      <td>bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20707</th>\n",
       "      <td>AINarendraModi</td>\n",
       "      <td>Met on the Abversion of our science for the co...</td>\n",
       "      <td>bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20708</th>\n",
       "      <td>AINarendraModi</td>\n",
       "      <td>Land for their during the opportunity to the p...</td>\n",
       "      <td>bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20709</th>\n",
       "      <td>DeepDrumpf</td>\n",
       "      <td>@TayandYou doesn't have a clue. You're right. ...</td>\n",
       "      <td>bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20710</th>\n",
       "      <td>jaden</td>\n",
       "      <td>Me And My Bestie https://t.co/vPq2iDkWZm</td>\n",
       "      <td>human</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20711</th>\n",
       "      <td>kevinhookebot</td>\n",
       "      <td>\"Thead has a generate existing the Sparching f...</td>\n",
       "      <td>bot</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20712 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          screen_name  ... account.type\n",
       "0          imranyebot  ...          bot\n",
       "1              zawvrk  ...        human\n",
       "2            zawarbot  ...          bot\n",
       "3      ahadsheriffbot  ...          bot\n",
       "4       kevinhookebot  ...          bot\n",
       "...               ...  ...          ...\n",
       "20707  AINarendraModi  ...          bot\n",
       "20708  AINarendraModi  ...          bot\n",
       "20709      DeepDrumpf  ...          bot\n",
       "20710           jaden  ...        human\n",
       "20711   kevinhookebot  ...          bot\n",
       "\n",
       "[20712 rows x 3 columns]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 9
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "gWNVRnyfJKKH",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595410760761,
     "user_tz": -120,
     "elapsed": 3436990,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "51e449c6-afb9-456e-8fa1-f97235519475"
   },
   "source": [
    "import os\n",
    "from numpy import savetxt\n",
    "from numpy import loadtxt\n",
    "\n",
    "def tokenizeTweets(df):\n",
    "  \n",
    "  import torch\n",
    "  from transformers import BertTokenizer, BertModel, pipeline\n",
    "  import numpy as np\n",
    "\n",
    "  pretrained_weights = \"bert-base-cased\"\n",
    "  tokenizer = BertTokenizer.from_pretrained(pretrained_weights)\n",
    "  model = BertModel.from_pretrained(pretrained_weights)\n",
    "\n",
    "  nlp = pipeline(\"feature-extraction\", tokenizer=tokenizer, model=model)\n",
    "\n",
    "  vectorized_docs = []\n",
    "  count = 0\n",
    "  for index, row in df.iterrows():\n",
    "    # Encode text\n",
    "    vec = np.array(nlp(row[\"text\"][:512]))\n",
    "    meanVec = vec.reshape((vec.shape[1], vec.shape[2])).mean(axis=0)\n",
    "    vectorized_docs.append(meanVec)\n",
    "    count = count + 1\n",
    "    if count % 100 == 0:\n",
    "      print(\"->\"+str(count)) \n",
    "\n",
    "  return np.array(vectorized_docs)\n",
    "\n",
    "\n",
    "fname = bertDir+\"/train.vec\"\n",
    "os.makedirs(bertDir, exist_ok=True)\n",
    "if not os.path.exists(fname):\n",
    "  train_features = tokenizeTweets(dfTrainDataset)\n",
    "  savetxt(fname, train_features, delimiter=';')\n",
    "else:\n",
    "  train_features = loadtxt(fname, delimiter=';')\n",
    "\n",
    "fname = bertDir+\"/val.vec\"\n",
    "if not os.path.exists(fname):\n",
    "  val_features = tokenizeTweets(dfValDataset)\n",
    "  savetxt(fname, val_features, delimiter=';')\n",
    "else:\n",
    "  val_features = loadtxt(fname, delimiter=';')\n",
    "\n",
    "fname = bertDir+\"/test.vec\"\n",
    "if not os.path.exists(fname):\n",
    "  test_features = tokenizeTweets(dfTestDataset)\n",
    "  savetxt(fname, test_features, delimiter=';')\n",
    "else:\n",
    "  test_features = loadtxt(fname, delimiter=';')"
   ],
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6809477e0c124be0bd623c6e9b7288ea",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"
      ]
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": [
      "\n"
     ],
     "name": "stdout"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "271285ba989d4edbb8af96cae83651f1",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
      ]
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": [
      "\n"
     ],
     "name": "stdout"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "95b7d2af01c64788a2a77de3a92d609e",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=435779157.0, style=ProgressStyle(descri…"
      ]
     },
     "metadata": {
      "tags": []
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     ],
     "name": "stdout"
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   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "1tHTftwVR57f",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595410760772,
     "user_tz": -120,
     "elapsed": 3436999,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X_train_all = dfTrainDataset\n",
    "X_val_all = dfValDataset\n",
    "X_test_all = dfTestDataset\n",
    "dictLabels = {\"human\":0, \"bot\":1}\n",
    "dictLabelsReverse = {0:\"human\", 1: \"bot\"}\n",
    "y_train = dfTrainDataset[\"account.type\"].apply(lambda x: dictLabels[x])\n",
    "y_val = dfValDataset[\"account.type\"].apply(lambda x: dictLabels[x])\n",
    "y_test = dfTestDataset[\"account.type\"].apply(lambda x: dictLabels[x])\n"
   ],
   "execution_count": 11,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "xDAhMIgmpxTs",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595410760774,
     "user_tz": -120,
     "elapsed": 3436999,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "train_labels = y_train.tolist()\n",
    "val_labels = y_val.tolist()\n",
    "test_labels = y_test.tolist()"
   ],
   "execution_count": 12,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "WyBJOn97WM9a",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413182099,
     "user_tz": -120,
     "elapsed": 5858322,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "ecdf5cf8-8664-4e14-f9e1-2fad1cd74caa"
   },
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Test SVC.\n",
    "tuned_parameters = [#{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
    "                     # 'C': [1, 10, 100, 1000]},\n",
    "                      #{'kernel': ['linear'], 'C': [0.1, 1, 10, 100]},\n",
    "                    {'kernel': ['linear'], 'C': [1.0]}]\n",
    "model = SVC()\n",
    "y_pred_svc, best_params_svc = dh.testSKLearnModel(model, tuned_parameters, train_features, val_features, test_features, train_labels, val_labels, test_labels)"
   ],
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Model tested: <class 'sklearn.svm._classes.SVC'>\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'C': 1.0, 'kernel': 'linear'}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.842 (+/-0.000) for {'C': 1.0, 'kernel': 'linear'}\n",
      "\n",
      "Detailed classification report:\n",
      "\n",
      "The model is trained on the full development set.\n",
      "The scores are computed on the full evaluation set.\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.860     0.818     0.838      1278\n",
      "           1      0.827     0.867     0.846      1280\n",
      "\n",
      "    accuracy                          0.842      2558\n",
      "   macro avg      0.843     0.842     0.842      2558\n",
      "weighted avg      0.843     0.842     0.842      2558\n",
      "\n",
      "\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "al7KYUkJVcFY",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413182493,
     "user_tz": -120,
     "elapsed": 5858714,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "import pandas as pd\n",
    "\n",
    "predictionLabels = [dictLabelsReverse[t] for t in y_pred_svc]\n",
    "dfResults = pd.DataFrame(predictionLabels, columns=[\"prediction\"])\n",
    "dfResults[\"gold\"] = dfTestDataset[[\"account.type\"]]\n",
    "file_name = resultsDir+\"/svc_bert.csv\"\n",
    "dfResults.to_csv(file_name, sep='\\t', encoding='utf-8')"
   ],
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "mgsdzMkdXMLf",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413202640,
     "user_tz": -120,
     "elapsed": 5878859,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "958a2ebd-a67c-456c-bdb2-68329ab2b5c5"
   },
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Test logistic regression.\n",
    "model = LogisticRegression(solver='liblinear')\n",
    "tuned_parameters = {'C': [1]}\n",
    "y_pred_log_reg, best_params_log_reg = dh.testSKLearnModel(model, tuned_parameters, train_features, val_features, test_features, train_labels, val_labels, test_labels)"
   ],
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Model tested: <class 'sklearn.linear_model._logistic.LogisticRegression'>\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'C': 1}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.835 (+/-0.000) for {'C': 1}\n",
      "\n",
      "Detailed classification report:\n",
      "\n",
      "The model is trained on the full development set.\n",
      "The scores are computed on the full evaluation set.\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.846     0.820     0.833      1278\n",
      "           1      0.826     0.851     0.838      1280\n",
      "\n",
      "    accuracy                          0.835      2558\n",
      "   macro avg      0.836     0.835     0.835      2558\n",
      "weighted avg      0.836     0.835     0.835      2558\n",
      "\n",
      "\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "__ccAXZaVwzh",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413202641,
     "user_tz": -120,
     "elapsed": 5878857,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "import pandas as pd\n",
    "\n",
    "predictionLabels = [dictLabelsReverse[t] for t in y_pred_log_reg]\n",
    "dfResults = pd.DataFrame(predictionLabels, columns=[\"prediction\"])\n",
    "dfResults[\"gold\"] = dfTestDataset[[\"account.type\"]]\n",
    "file_name = resultsDir+\"/logistic_regression_bert.csv\"\n",
    "dfResults.to_csv(file_name, sep='\\t', encoding='utf-8')"
   ],
   "execution_count": 16,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "xYKqumdxwCnC",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413202642,
     "user_tz": -120,
     "elapsed": 5878857,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "#X_test_all[\"label_log_reg\"] = [dictLabelsReverse[t] for t in y_pred_log_reg]"
   ],
   "execution_count": 17,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "GCRSpAQ5PELE",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413516003,
     "user_tz": -120,
     "elapsed": 6192216,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "d042a0f1-24c6-4ecc-8e8c-17f7261edbf2"
   },
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "n_estimators = [100]\n",
    "max_depth = [30]\n",
    "min_samples_split = [15]\n",
    "min_samples_leaf = [2] \n",
    "\n",
    "tuned_parameters = dict(n_estimators = n_estimators, max_depth = max_depth,  \n",
    "              min_samples_split = min_samples_split, \n",
    "            min_samples_leaf = min_samples_leaf)\n",
    "model = RandomForestClassifier(random_state = random_state)\n",
    "\n",
    "\n",
    "y_pred_rand_forest, best_params_rand_forest = dh.testSKLearnModel(model, tuned_parameters, train_features, val_features, test_features, train_labels, val_labels, test_labels)"
   ],
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Model tested: <class 'sklearn.ensemble._forest.RandomForestClassifier'>\n",
      "Best parameters set found on development set:\n",
      "\n",
      "{'max_depth': 30, 'min_samples_leaf': 2, 'min_samples_split': 15, 'n_estimators': 100}\n",
      "\n",
      "Grid scores on development set:\n",
      "\n",
      "0.820 (+/-0.000) for {'max_depth': 30, 'min_samples_leaf': 2, 'min_samples_split': 15, 'n_estimators': 100}\n",
      "\n",
      "Detailed classification report:\n",
      "\n",
      "The model is trained on the full development set.\n",
      "The scores are computed on the full evaluation set.\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.864     0.776     0.818      1278\n",
      "           1      0.797     0.878     0.836      1280\n",
      "\n",
      "    accuracy                          0.827      2558\n",
      "   macro avg      0.831     0.827     0.827      2558\n",
      "weighted avg      0.831     0.827     0.827      2558\n",
      "\n",
      "\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Bw1Idk3WV_J2",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413516004,
     "user_tz": -120,
     "elapsed": 6192215,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "import pandas as pd\n",
    "\n",
    "predictionLabels = [dictLabelsReverse[t] for t in y_pred_rand_forest]\n",
    "dfResults = pd.DataFrame(predictionLabels, columns=[\"prediction\"])\n",
    "dfResults[\"gold\"] = dfTestDataset[[\"account.type\"]]\n",
    "file_name = resultsDir+\"/random_forest_bert.csv\"\n",
    "dfResults.to_csv(file_name, sep='\\t', encoding='utf-8')"
   ],
   "execution_count": 19,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "w2vCK1FRrAOk",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413516005,
     "user_tz": -120,
     "elapsed": 6192214,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "f3c3002e-9adf-4ffe-d123-de3a97306073"
   },
   "source": [
    "X_test_all[\"label_rand_forest\"] = [dictLabelsReverse[t] for t in y_pred_rand_forest]"
   ],
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ],
     "name": "stderr"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "5z4JICTNSTbX",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413516005,
     "user_tz": -120,
     "elapsed": 6192213,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "def plotErrorRatio(X_test_all, prediction_column, min_sample_num=30):\n",
    "  X_verify = X_test_all[[\"screen_name\", \"text\", \"account.type\", prediction_column]]\n",
    "  X_count_accounts = X_test_all[[\"screen_name\", \"account.type\"]].groupby(\"screen_name\").count()\n",
    "  X_verify = X_verify[X_verify[\"account.type\"] != X_verify[prediction_column]]\n",
    "  X_verify = X_verify.groupby(\"screen_name\").count()[\"text\"].to_frame()\n",
    "  X_verify[\"total\"] = X_count_accounts[\"account.type\"]\n",
    "  X_verify.columns = [\"errors\", \"num_samples\"]\n",
    "  X_verify[\"error_ratio\"] = X_verify[\"errors\"] / X_verify[\"num_samples\"]\n",
    "  X_verify[X_verify[\"num_samples\"] >= min_sample_num][\"error_ratio\"].plot.bar(figsize=(15,5))\n",
    "  return X_verify"
   ],
   "execution_count": 21,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "HkD_xLJ9zCRR",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413516006,
     "user_tz": -120,
     "elapsed": 6192212,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    "#plotErrorRatio(X_test_all, \"label_log_reg\")"
   ],
   "execution_count": 22,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "gsu9Qs8eCtbJ",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413516006,
     "user_tz": -120,
     "elapsed": 6192210,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    },
    "outputId": "2f160b37-aee0-4509-eaf9-45a66041ff30"
   },
   "source": [
    "plotErrorRatio(X_test_all, \"label_rand_forest\")"
   ],
   "execution_count": 23,
   "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>errors</th>\n",
       "      <th>num_samples</th>\n",
       "      <th>error_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>screen_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AINarendraModi</th>\n",
       "      <td>7</td>\n",
       "      <td>125</td>\n",
       "      <td>0.056000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeepDrumpf</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0.206897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenePark_GPT2</th>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gpt2Wint</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Musk_from_Mars</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thorin</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtilityLimb</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VBoterin</th>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VitalikButerin</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ahadsheriff</th>\n",
       "      <td>29</td>\n",
       "      <td>95</td>\n",
       "      <td>0.305263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ahadsheriffbot</th>\n",
       "      <td>2</td>\n",
       "      <td>95</td>\n",
       "      <td>0.021053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>awhalefact</th>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>0.085714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>botustrump</th>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "      <td>0.485714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calebgamman</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calebgamman2</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deep_potus</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deep_thorin</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dril</th>\n",
       "      <td>100</td>\n",
       "      <td>180</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dril_gpt2</th>\n",
       "      <td>54</td>\n",
       "      <td>179</td>\n",
       "      <td>0.301676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elonmusk</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gpt2_trump</th>\n",
       "      <td>29</td>\n",
       "      <td>55</td>\n",
       "      <td>0.527273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imranye</th>\n",
       "      <td>30</td>\n",
       "      <td>129</td>\n",
       "      <td>0.232558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imranyebot</th>\n",
       "      <td>3</td>\n",
       "      <td>129</td>\n",
       "      <td>0.023256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jaden</th>\n",
       "      <td>20</td>\n",
       "      <td>51</td>\n",
       "      <td>0.392157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kevinhooke</th>\n",
       "      <td>7</td>\n",
       "      <td>241</td>\n",
       "      <td>0.029046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>narendramodi</th>\n",
       "      <td>1</td>\n",
       "      <td>124</td>\n",
       "      <td>0.008065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ninjasexparty</th>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nsp_gpt2</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>realDonaldTrump</th>\n",
       "      <td>22</td>\n",
       "      <td>129</td>\n",
       "      <td>0.170543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theJadenTrudeau</th>\n",
       "      <td>3</td>\n",
       "      <td>103</td>\n",
       "      <td>0.029126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whalefakes</th>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>0.228571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zawarbot</th>\n",
       "      <td>10</td>\n",
       "      <td>152</td>\n",
       "      <td>0.065789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zawvrk</th>\n",
       "      <td>49</td>\n",
       "      <td>152</td>\n",
       "      <td>0.322368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 errors  num_samples  error_ratio\n",
       "screen_name                                      \n",
       "AINarendraModi        7          125     0.056000\n",
       "DeepDrumpf            6           29     0.206897\n",
       "GenePark_GPT2         4           22     0.181818\n",
       "Gpt2Wint              2            2     1.000000\n",
       "Musk_from_Mars        1            2     0.500000\n",
       "Thorin                2           10     0.200000\n",
       "UtilityLimb           1            4     0.250000\n",
       "VBoterin              4           13     0.307692\n",
       "VitalikButerin        1           13     0.076923\n",
       "ahadsheriff          29           95     0.305263\n",
       "ahadsheriffbot        2           95     0.021053\n",
       "awhalefact            3           35     0.085714\n",
       "botustrump           17           35     0.485714\n",
       "calebgamman           7           13     0.538462\n",
       "calebgamman2          3           13     0.230769\n",
       "deep_potus            1            3     0.333333\n",
       "deep_thorin           1           10     0.100000\n",
       "dril                100          180     0.555556\n",
       "dril_gpt2            54          179     0.301676\n",
       "elonmusk              2            6     0.333333\n",
       "gpt2_trump           29           55     0.527273\n",
       "imranye              30          129     0.232558\n",
       "imranyebot            3          129     0.023256\n",
       "jaden                20           51     0.392157\n",
       "kevinhooke            7          241     0.029046\n",
       "narendramodi          1          124     0.008065\n",
       "ninjasexparty        12           23     0.521739\n",
       "nsp_gpt2              1           23     0.043478\n",
       "realDonaldTrump      22          129     0.170543\n",
       "theJadenTrudeau       3          103     0.029126\n",
       "whalefakes            8           35     0.228571\n",
       "zawarbot             10          152     0.065789\n",
       "zawvrk               49          152     0.322368"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 23
    },
    {
     "output_type": "display_data",
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": [],
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "MFxq59MLEtkh",
    "colab_type": "code",
    "colab": {},
    "executionInfo": {
     "status": "ok",
     "timestamp": 1595413516007,
     "user_tz": -120,
     "elapsed": 6192209,
     "user": {
      "displayName": "tizfa data",
      "photoUrl": "",
      "userId": "14479436635515447323"
     }
    }
   },
   "source": [
    ""
   ],
   "execution_count": 23,
   "outputs": []
  }
 ]
}