Walkthrough.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Walkthrough.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KZCO3zRqzQpV"
      },
      "source": [
        "**Loading the dataset**\n",
        "\n",
        "We'll start by loading the dataset, which contains reviews of food products on Amazon and their associated ratings."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Qj-f7dCsxlYn",
        "outputId": "1219dc50-6ce9-439a-d501-c1a82ba57b98",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-199e5d7d-084c-44e0-87a4-6ff9e88583ff\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-199e5d7d-084c-44e0-87a4-6ff9e88583ff\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving small_data.csv to small_data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zgJ7ayx30CIl"
      },
      "source": [
        "Here, we load the dataset into a pandas dataframe. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ov5_UocmyTs1",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "for k in uploaded.keys():\n",
        "  df = pd.read_csv(k, sep=',', quotechar='\"')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zzFJzaZW0Hvl",
        "outputId": "cc7d30a2-6d31-4c9f-cb68-750693e405fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.hist(df[\"Score\"])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 1803.,     0.,  1021.,     0.,     0.,  1481.,     0.,  2871.,\n",
              "            0., 12824.]),\n",
              " array([1. , 1.4, 1.8, 2.2, 2.6, 3. , 3.4, 3.8, 4.2, 4.6, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1NfY1NfY1Nc/Vvn6vqobGFp9V/9JZVW0DtvX6OkmGq2r1DLQ0o+xrauxrauxrauZbX3Pl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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5RDiK53ImLDV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def extract_text(df):\n",
        "    return [Soup(text, features=\"html.parser\").get_text() for text in df[\"Text\"]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "em1WHyC4mLDZ",
        "colab_type": "code",
        "outputId": "5f9403b7-8527-49ef-abf4-6b6cd9103c3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "from wordcloud import WordCloud, STOPWORDS\n",
        "from bs4 import BeautifulSoup as Soup\n",
        "\n",
        "stopwords = set(STOPWORDS) \n",
        "wordcloud = WordCloud(width = 800, height = 800, \n",
        "                background_color ='white', \n",
        "                stopwords = stopwords, \n",
        "                min_font_size = 10).generate(\"\\n\".join(extract_text(df))) \n",
        "\n",
        "plt.figure(figsize = (8, 8), facecolor = None) \n",
        "plt.imshow(wordcloud) \n",
        "plt.axis(\"off\") \n",
        "plt.tight_layout(pad = 0) \n",
        "  \n",
        "plt.show() "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OWUXey6xkIpdLuH0zhi0bztClR11zFN3pk3fYsPYEF87d56fVtsOtSyoCYSAy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mvWRdzBZ500A+gjz55jwTlV09So5s3wc291fcVyqRSt1m+ERCZDVkkJwnbsxL8TxlMKCg2OjCsg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5SVFjIVpgNo6/2CVetUlsfecFLm61xzIpEDcqfqsTafYBsuppvXZrEVjlLLjVcB8tSVT5Z9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\n0MimBoFp4XR1b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3HAWKjf+WNS01l7/jq3QiN4FB2v1xwA3o52fNa5NS283LE3r/gqzvIg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Cpld6dmZsm+YoCvlJdqtTk7c37SRXZ97hWH/mAtM6taN2sPuNom1hMpr45YfdjH+iGwAZ\nqdlo1DoO7DQX6Yii4HR+cB5OVdVFRcS4NMh/meSUbNYWCpzGjGjv0Hk9uzbMD5yKIzjIh7dmDGTW\nR+Zgdv2WU8x4rm+x7S/HWOpUNW9iO7+pcOI4OBbsFWXN+uN2A6eSktILU69OlfzAyZ3I5W2JiIwj\nJXk4Wu1pQINC0Rdvn4nI5e0wmXRkZMwkN2cVYEQUQwkJ3YFM5poptS1S4vI+IwlBEZa5UXrdWTKS\n+uDhORQf5XxS7tSw2YfMozN+QcuLHUOTs5zs9Bk2j8kVffBRfopQ6Ek8I3kIeu1RAsPPW5gy67XH\nyUg2bztJJNVQVinwt1PnLEOV/gpBEWWbA3mfsiMr4/38lTqFZz/8lB/bbOftMxVvn6n5K1PmLW/r\nogl3IyAiE/2QiX5WFXV5RPgMJMLHemtUJvrzQGTBQ3+4z4D8/+f11SCoYMejazXH/FZf7dUlP2gC\neKtP9/zAKdLfj63TJuQfC/ByrYKrtDy/ekP+6g/AN48Momc9a71BURCoFxrMivGPWh0ricKBY2GG\nNI1mSNNoHlu6ioPXb2EC3li/jRUTnOu/OHq3fZfmrWsyd8E4q2MjH5pHanI2UpkkP3C6cPo26lwd\nv+8x/57PnbjJ0/8bYHWuI9y3Gy9jzl6w3AaKDFcW09KSvCRpe3TuUBdfH0Wxq1KF2bKjQNunpJil\n6JxdISPLdoJ8YcId/CwEBwKs4rbb/Pxn4uc/s8Rzg4JX2XxdEDxRKj9GWcwNxNfvVXz9LAsGgp3w\nBsvNmnd3IBm+AV8X286gv4wq0/weBNEfmbwjRmMyet0JMOnQaYp/2s/J+pjcrM/v/iRBKm+CIPhj\n0F/EaIhHq95CRvIVlKG788+Rypqh1x7FoDuPVN42/3WdtsAlwGAo0JQCMOjOOPq271MJMZnU5OYU\n+A56+zhnbK7TnUEmc05HLSY9lZEbVyAKAodGWW5bGU0mhx+sKpIGocHFHmsRFV7ssfJky/mCh84h\nTaNtBk2uUsXXh1Gtm5XYpke9Why8fguAy0muCU7aQiaXcOLoNU4fv0GTFmanCJMJVizaR2pyNhFR\nAfxvdsFqbs8BlsFd+27283CL454KnFqtns/E+m15ppH9vdeiTN3zO/vjr3P2EdtP3mVFfEKGxc/v\nzv2Ld+f+5bb+ZTIJPbo2zF/VSktXEaC0rsIxGIxs312gI9SiafGWJEXn3LX/XKfnlZVlP5AL8Hdd\nO+teJydrLrlZXyAInvgG/ojMo3gxSb3uDHrdGXyUH+Ph9Qhg3hY1GpNIizdrTum1J5DKLS8MWvXG\n/KDJw2sEXn5vIIoFF/q0hHYYDbEY9DGAnrzLgVTeHFSg152zCJz02iPIFQ+hVW+kKPr7gdM9jU57\nFKPR/HcvSqogk5d8MyyKVnPA6cCptjKQI6On0W75N1bHev3xE1uHTbRZfVaZqBrgX+yxmkHlsyVl\nD0Oh9IxJ7Vq6te+HousjsRPg1gkucP7I1mjcNvbnCyfzwRt/MOOpX5j0dE9GjO3Imy8s48iBK3Tr\n3YjnXx+Al4MLEM5yTwVOpcWRlQt3c6dIEFIWvDjtwfzAacbMVfz4xXirNu9/sj6/is3bS86ct4cV\n25875qzV6e22Uf6HAidBKLjAqjLfRZ39PYIYyP+xd56BTZVvH76y070Hm7J32RsElI0gCCiCgCAO\nXCju8aqoOFERVPyDCIoiCCh7KnvvVfYs3btNs5PzfkhXaNKMpkt7fYGe8zzPedLmJPe5x+8Ojiw5\nHJuPXHkvCm9rTRmxOAylz6Noc39Bo/oav+AlVudz0p/Iu7YC38DiiaFBEYfRqL5Fnf0xmUm9C0Jv\nCq8HyM16E51mNUqfyQCYjNfQa7cQEGZRbC5qPJmMVzDq3U+yrabiKaotZTYllSiRYAuD4SwAnxzd\nw5or58nQaXiydSde7tiLk8kJfHl8H2dSE2kcFMqsbvfSIsR2JZPOZGTU+t+4lplOk58sHtkrU2Y6\n/HKuKOoG2feaV7YS/KEtm9IswrNVkA+3d5wnW1TWwJPSu42aRrJ41TPs2HSGuR9vYNG8HdStH8rC\nFU9TL6psqz3/M4bT/3qPcXrsCwfWMrf7CI9ct6gaNkCtmkHInag2c5cr15K4cSuVqHqFnoVctZ79\nhwrLT/t4UMoCAAAgAElEQVT2aoZSYT9v6e49F13LWWo7oSn1X0IktuQK5Wb9H9rcxYglkSXmJd2N\n3OsBm8clEktfQ7PZfs9ImaKv3XMKr5Gosz/GZLqNyXgVibQRIEIqa5NnDFlyV4x5YTqptAlSeVuL\n4SQYQSTFaDhvd/1qqgZmc3opF7CE5jdev8TvQx8m1MubMymWIopAhZLhDZvzae9BzDm2j1f3bmHD\nAxNtLqOQSNn4wETqLfqcy4+9VKk9To72Jq8AuYGSqBXgOQHIfJzReiprrl5ORJ8nxJyepiLudlq1\n4XQ3Zf3ccSDpJutvnfeY4XR3TtNzT/SjWyfPxZjz6d65IQeOWJL3X357JX/8/HRBjsCbs9YUKHzX\nqRVcYgK5rT0v+W6Kx/dbVTDoT6O3k0Pk7TsZkcjXqXUkkhpFEsEhKOKYS/uQK+6zfSJf2LME7Si5\ncoDdc2JJTaSyVhgN59Brt+LlaykrV/pMIke3H71mE3KvYWhUeTlYIilKn4mosz9Bq/4Fpc9j6NS/\nW9aqIpo+1RTHbE4t+L9M1hq5wrV0CKnMoujfJCiEgWt+YlD9xszra0nS9pcrOJ+WxP/OHiVbryUx\nV4XRbC4Xoyg3Z77dc67cv7ZwpMNU1jlaJrNr/htnquRcxV9Z/nIZ+fy9+SzffLoRqVTCrC/H0aVH\nYxZ8vY33XllJwyaRvDX7QWrXDXG8kBtUOcNJIirbm21nvGcrB4ODrPONkpI9V05flMH9WxcYTqnp\nKk6duU37aEse05lzsQXjBtkRvCzK3Xv+L6POXYRGvdrmOaX3g0gkzn3wGg3WVUA69cq8fCXnEInd\nr8ixpZpuPcASMhXMhe9NqcyS42I0XkBq7orJeLPIer5IZE0w6o+Bz2MFHidpkR5/1VQtROJCD7FU\n1gI/BwUV9vhxwCjOpibxc8xJvj19iGeiu/LEjr/wkyv4ZdAY4lRZjFr/m+OFPERO9my751y5fysj\nuXrXhHZztJ7LL6oMfPrunwAsW/cC4ZGWVIinZgygddu6vP/qSp6ZuJBnXx1C/yGeb/5e5QwnuURC\njkHHd+f389Olo3hL5XzZbTh9ahb34vx69QTvHN1idez6uDeLjcvQafj2/H5W3zhDlt6S1NxgufUN\n91SLbrwabT/kYY+7G/VeuJTAA0M9/wXTvXMjAvy9yMq2uMy374opMJystJvudWw4ldRc+L+GzkMq\n0mZTIjJFb8TiUHSaNaiyXkUsrYtM7pw8RWkQ7LSOKBxgERcViQtd+WKJRSzOZLiEUX+y2BSZrENB\nNZ9gzgAKja1qqh5FezyazcmlWqt1aASf9x5EsyVf80x0V44nxbFs8FgifXzZG3fT6XWKfm5VU5wc\nFxOtk3LcFDerxIx8uEuB0ZRPjz7NaNSsBlcvJvD5e39VG04An57ayZJLR3mr/X2cj+7LlawUhmy2\n9PRa2HsM99ZqXDB2fKP2jG9kqSLote5b4nJtJz0HKbx4u/19vN3+Pl48uI61N8/ZNLDcoU6tYAbf\n14rNOyxVR1v+PscbLw3xyNpFkUrFfPnRQ0x9bgkAm7ad5bUXBhcobucTGuL4CatOLetqkMPHrtOl\nYwOP7bWqkKv6H2ZTkuOBTiAS++EfYnnS9gn8iPSE5mSnjiY48pyVhlJZoNdutuvdMpvuFFTEyZX9\nbcwt7GsnVxaq/ip9n0Cr/hWDrlDGQOlTvCihmqqBt89kdFpLY2uddmeevIDjh6y7uWflIhLVOcjE\nEtaNsLSvWjpoNG8f2M6dnGyWDxlbkPvzyp4t/H37GmlaNS2WzsVfruDQuKcA+KzXIHqvXEimTsvF\nyTPsXq8kclX/c2teReEjd00vb+P5Sy6N/+tMDM/06lJiQntVYtsR+17R736eBsA/WwurfUd1/xC/\nAC/6D2/HhKf7leraVc5wMgtmfurzMA39LbHLZoHhNA4I40pWCt+c22dlOFUWxjzQqcBwAos0gETi\n+ZBjowbFK1X2Hy5MCvd1szRz0c976diufpnsuTJjL7eptIhEfogl4ZhNyeRkTMM/eDmIyu5WLGrc\n3I1OY3F3iyU1kUht3TvGAsNKVkSaQCK1eHiNhjN5P9crcwOwIija8sSh564C1ywtckUXRCJvhDzv\no1r1EwFBc1xeZ/fYx4sdu6d2FLvGFB6/8thLAHze236u5UNNW/NQU+c6G9ijrO7fssKVRGut0cie\nazddWl8Afjl6ircG9HFpXlWm38BC43/Frtc5fuAqe7ad47XHFzPggfb0vK8lCjcEnqvcN2GHsDoF\nRlM+ExpbvEpn0xMqYksOaRgVRucOhdL23/3onCqtJ9hXpJru3j72m1zeTdH9Xr6WVK57rizodQfL\nbG2/4CWIRF4YdAdRZb3meIIbSKSWZtP2mg6bTXfQ5ClFe/k8jr3SC7PJogYulXcqds5ksDz1SmXF\nlYP/DYiL5P6YjLdLGFmxa5YWkcgb7yIeQ416ZQXuxgMIhjK9f8uCGi5UvS0/fhq13uB4INCzQaFm\n37Kjpzhxp/QCx1URmVxK1z7NeHX2aEZP6smerecY1+9T7txMdTz5Lqqc4dQtvLhwY2X0Mt3N57MK\n5RBWrT3OPUM/Y8jYuRw4co24+Axyc3Wkpqm4diOFX1Yc5NV3V3HP0M+4Z+hn7D3ofLuRXt0Kfxfv\nfryWA3kep/p1Q3hpevFQTEn7/eCtwhL4VWuPM2TsXOYu2MGBI9dQ5eoK9rx7/2V+WXGQgQ9+VbDn\nfwP5T99lgVTWhuAalr+rTr2CtHjb7W9KQ2D4P/gEfAKISYuvTVp8PbJShpKd9igZSZ3JSOqKYM5B\nLIlE6ftEsflyZWFIWaboVkxgE0CX13Pt3xqm8/IeXWDoCIIKfRH19NKsmY+n1vQEfgHvEBi8KO8n\nE4nx9cjOfBWDDbkJszmFnKz3SU3q5bLmU3mgyvmqTO/fsuBuraq+83/keGyc1TGt0chPh4/z8fY9\nTq+7+JFRTOxkyas1CQIPL1nBS39uKrY2QLpaw8fbd9N33o8sy2sdU1X56M1VDOg8q+DnzauPMXPy\nIoZ1eA8BgXfnPsKaA2+TmpzNhAG2u0PYo8qF6gQbElpCFUkifOW5gXz53XZMJjMAubk63njfdsWW\nuwzu37rA0Nq1rzAG7mxD36L07t6EV54byOfztgKW/a5Zf4I166vFDj2F0mci2rxGw0bDGaQuqi87\nXn8CYkk4OelTABNGw2mr8zJFd3wDv7E5t0CvCdveJstzlxGQIHE6H8aE2azCbIrDbM5GEHIKzpjN\naWg1mxGL/ZBIaiES+1kpndtHQBBUmIxxCIIKg+5okXNGtJqNSCQ1LeuJ/BBLIpzcK4AUb9/HUWVb\nPljTUx5AJm+bl/8jQxCyMRhiQDAQGuHsl5n1x27+mnJ5ZwTB4NKaZnMWgpCNYM7BZIrDaLhS5Fwa\net0+RGL/vNdd0ypMaAulV5H8S8GAOncZ6txlSGUtEIsDEAQ9RuMVqwrMyohO57xhUZloGh7KpWSL\nByQuM5txS1cysk0L/JUKVDo9G85fQme0SMu8O6gfc3buQ6VzXF33Rv/e/Hy0sMhjw/lLbDh/iUah\nIQUyBVdT0sjSVo7QsSfIVVknz69aup+BD7Tn7TkPE1Qk17dpy1pkZ7pmZFc5w2lv4g1mtO5tdWx7\n3GU7o12nLJU3hg2KZtigaBYv28fKv46h0ZT8hm/aOJKh/VvTsa399ih306NLIx4a1YkVawq/PLp1\nasi4BzuXMKvkPfft3ZwVa46wdPkBh+Pz91zV0Wo2e2Qdqbx9iY1vfQJm4xNgu2RaKmvpsGmu0udR\nlD7Fm1wWRa4cwMqk1TzdrkvBsUPxsXStWbKXy8t3Ol6+0+2eD6npWpjJkWfCaLxOZvpUm+cia93G\n1seVozUFQU9m+jQ7azoXsvD1exGj8SpatSUfzKA/hUFv/TTuqoZVRM1LZGW+7vaa2Zmvoc79pcRr\nGI3XSU8tXhRg73dZeD4ek/EWKUm9sBjGxeU0iu7Ry9t5ceHywlBFlezXP/Eo8Vk5vLpuC0duWe79\nP89Y/+59FXJOvPIMAGcTklhz2rEArUQs5vLbL7L/+i2+3XeYY7ct3qarqbZ7x4X7+VY65XNXSUmy\nNu5/XPeCzXFePgrWHXnXpbWrnOF0KjWO8xlJtAwqfGr87YrFkm4bUnqXsZ/M8kRWlk0mp0zoybgH\nO3PizG1++f0gGVlqUtNyUCpk+Poq6dqxAS2b1WRAv5ZurT99al+mT3VdOsEePt5ypkzoSdPGkRw+\nfoPLVxK5cTsVo9GEUiGjaeNI6tQK5tGHuxEaXHLV3qaVtt+8JTHx4W5MfLibu9t3C30VfWK1R1Gj\nCWDOkX388cA4O6OruZvAoG8x+j5LVuZrmIy3EIQsROJAxKIAlF5DkMld6wEmEvl5fE1PIpHWIyzy\nCHrtP+h0e9HrDmAWMhEhRiJtgFTaCB/fJ5DJ25LfO7Eaz1AzwI9lj45h9rbdnLgTz4WkFOQSCWG+\nPkzr3pGBzQrTMRqFutYPr0eDevRoUI8fDx3nyK07nI5LIEurQyISUTcokLpBATzWtQMd69aqNG1u\nNq45zoE9l+g7oBX3FZEW+HH+3yXOS7lLM/HkoWu06+oZ8WmRgzCXyzGwXw+f4sMNzicSzx41gJHt\nnDMQOqz5imy9jlClN2+1v4/BdZpzNSuFwXlyBIt6j6VfrUY25+bLETiSGVh36zwzDqxlUpOOTG/Z\ng1ClD7GqDJQSGeFeVVcsrSyJOXSFJe+v4tyBy/gGetPzgU48P3dyRW+rVKQkdbcSfbRFWORxJJKK\n7YD+9bED7L59g1Avb55o2xmpWEy7CMueTiYl0C6iBj+fO0mCKofXulo8tS/s2MjWG1foXqsuiwaP\nRCwSMW7dSkyCmZUjHq7Il1NNNR7BmbyrynD/VuOY+3t/jE5rwMtbztpdrxccL5q/VBL5sgU5WRr8\nAlwSErZrOVY5j9PFh15DbdTzfcwBXj64Hi+pjMV9HqJPjeKW5N0ilncf2zJkGk0CrN3hw+u15L5a\nTRi5bQkrrp3CJAi0DanJ9JY9qg0nO7x47weMe3U4n21+o8yu8clj35OWkMHnWzyjr1USmtzfHBpN\nlYW4nGz+HDWep7autTtmYqt2fHqo0IM2p99gjv0Wx+IhlkbPO25eQywCsUjMgbjbdK9Vt8z3XVG8\ntngTn05xX0ft1PV42jZw3bP92uJNPHxPNO0a1nL72tU4hya3/JTJqyl71u95A4PehExu7dmsXS+E\nxX88Y3feYw/OJy62sAfjnHfW8MCEbjRoEolMXmj6eHnLXd5TlTKcjo96EbCE016N7utQydtdEUtv\nqYytQ2znRVRjmwg3GgG7wqldMdRpWj5Ph1UpsfRyeipmQeB6pkW920dm0SQxms1254hFIrR5CaYA\njYND+HnYGCQi0b9WrXninN+JCPQlLduSBLpwy2EOXbxF79YNmXRvB776cw/x6dnEpWXz1JCu9G5l\nLfgam5rJdxsOkJihwtdLzuxJg/l99ymrNQCmfm0p488/tmDTIa4lpBZctyqSoj1PmNK1tIFkzWnC\nvdxXki/N/Kp0/1bjHHcbTQCBDlqD+fha62KdOHSVQ7svFhu35fQHLu+nShlOZcHa+FXEZJ3BKBjp\nGNyVw2n7SdTGs6BDYeLlU8cfpXVAO+p61+Ng2l6yDVnMb/8TAM+cmMwnbebhJ/UrGP/sicf4tM08\nfKS+/H57KbtSdljNfyzqKRr7NgNg9Z3l7EzeRqA8mA5BnfGT+mMSTAyMHObW61k1dxML3/yd1j2b\nEtWyDjKFlNXfbGHMjCE8/lFhGGbnyoMsm/0XnQdFc+nYNc4fvEKP4R34v+WWHKR1C7bz7cxfeHXR\nk+xbe4z6LWqz47d9/O/Yx3j5KrlxLpaLxwr7+p0/UJig3/3+DgSEWH4fg/wmUathpNV1tqiWIsqL\nn2el5jCz/4fEXk7g4ZfvRyQSsWnxTpbGzMHLV8mZvReIu5ZERnIWPgFebF6yq+A6gyf3cet3VBIG\nw1m0mnUeX7esWPugRZ25SXCo1b9FQ3ZAQZgOLIbT8cmFSd/1/AOtzv3b2HvuBj/PtLz3Zy5az5r9\nZ9EbTfw4Yyzz1x9gzf6zaPRGzIJAjWC/YkYTQJ3QQD6ePISY20m0qGvJr7x7DYAfZ1iSseevP8De\nczdISM/m86nDmLlofTm92rJhV8JbaExp+EgjaB44lvMZv9Knxmx2JbxJy6DxJKqPEa8+ip+sJt0j\nLA+suxPeQWVMZGidhQAcTP6MdN1lanl3oW3INP6Jfw2xSIJEJKdX5LuAiE2x0/CWhtMi0Pk+jkWp\navdvNe4z6ck+JZ4Pjwzg8oXCAhBXE8BLwuOG0/gubRnfpWqJ4YlEIt5oNgupSErP0L68dbZQ4v+q\n6jL9I4bwYG1LIm2f8P68c25mwXmTYOJI+n7uDbeo4KqMObQN7IiP1BLW25Wyo9j8b658xlvNPyxY\nwygYebfFx8jErrsM7ZEcm8YXW98C4ODGk6z6ZjNDH+9HjahwstJymPvcT6yO+x6J1GLJfzplAf+s\nKF41N/e5n1iXaskhm/jOqAKDJ6pVHaJaWaqyvn5mMS27NylmyGSl5dC6RzM+2fCq1XUObTxJt2GW\nxNfvX1lG7OUEpn4wlrEvWYzFCW8+gDTPldqmV3Pa9GrO188sJjgysEyMpaLotdVPq/82aocGFHjS\nbiZlMOnejuw4dQVBgPO3E+ndKooDF27x3P09qR/hfCXR+VuJVmsA5Dvszt9OZGinZlyJt3gEbyZl\nePx1lSe9It9DIpKx5c7TNs9fy9lM78hZBCkKc0x713gfgCz9LQLk9egS9iIikYRVN0bRNuRxMnRX\neKD+CrbHPU+m/iYiJAyu8wMAKZqzbu2z+v797xDdoX6J54eP6Uj7LoUPQdmZag7vuURyQibjn+yL\nVqNHLBYhV/wHlMPLgr5hA5Dmtbzwk/oRqSzMYTiWfpBeYYUhQT+pP1E+jcgxWvRnguWhHEgtvFmP\npB+gR+g9VuvfPT9Wfatgfj6eNJoA7n24R8H/h07ti2AWOJCnv7R71WE0Km2BMQPQd6ztZrM9R3Qs\n+L/IRW/E7lWHGfnMgGLXObylsOx6z5oj1G4cWWA0AQVGU0VQVd388/sPo1ON6vwZW0RFBjNpzu+8\nuWQzdUIDaRNVgw6NazP165V0bFybNlE1MAsCH/6+gyfnreZ2SqZT63ZoXJvH5xau0SaqBo/PXVmw\nblRkMN2b1yu4blVGIrJ8uSgl1oalgCUk3LfGJ1zI/IO43EMF50SIESEmxxCHSdBzOOVLDiZ9gt6c\ngyCYCVY2QSKSoZQEYTCryTHcKZjjLlX1/i3KkCnfsfOQbYmdkzF3GD/jp3LeUdWkbccoho3qUPDz\n4yPm8r8vNvPLd/8A8M/G03z2pns6iv/5UB1AHW9rnaSiRkyiNoGYrLNcFBVqabQL7IREZLm5X276\nFm+dfYmVsb/QI7QPK2OXWYX5gGLzH6n7WMH8siLfGwQWrw3ApeOW0NrJnRbdjxVzNljNmTKruHu8\nQ3/3BRlP7jxPjagwYi9bt8LpcG+hzpPJaKL7sA53T60wqlp/q2qc45eXraUXpg7ozNQBhdpmORod\nPko5JpOZED9vu+vkh+lsrQGFobp8nr2/B/82wpQtEItk7Ep4q0B8+EDSx4hFMtJ0l6jt0x2A3Qlv\nozImMbTOQnSmTDJ11wmQ1ydIbvEC3G0g1fbpzsbYafjJatAs4EGX96XX7fnX3L8iOwVd7VrU5tev\nHyvn3VR+zGbBrhB2fo/VRWtfwD/Qm0HR7wDQe0Arvvt4o1vXq3DDKdugJVFt7X25u9KtIglXRtDE\nrwU1vWw/zQfLQ2nm35Kj6QeRimU23/AlzS8rtLnaYv/39rWUYnr7Wf59aKZ7eVTO4u3nRY/hnWjZ\nreSWOGmJzj3hV1NNWbHw+dGOB/1HKZoY3qeGpSq5d+T7VmPyQ2z5hHtFWyV3KySBxcbkr5X/L1CQ\nD+UOun9RmC42IZ0+476mfu0QlnxuEbhNSs3moecXo5BJ2br02YKxn/2wnYMnb5CcloOPl5zBfVry\n4pR+FbX1cmP96mPs3HqO82diEcz2i1ry5QhSErPwDyx8KDp38hYRtdwT+axww2nirt+KNee99tBb\nFbSb4jxS9zGeOv4o9X0a0DGoK5mGDC7nXOTN5oUaEs80mslrZ55jW+JGHm9gXR45IGIos2Jet5qv\nNWmYUM+2QrKn+Om9VQx41JIQ/M3zSxCJRTz0ssVQevLTRziw/jiJN1OIrF9opGYkZxEUHuCxPTz5\n6SNMjX6VeXvft7qOXmtAnteRusfwDvy9fD91mtZg3CvDATAaTCAIViG7oIgAYi8lYNAZkSnK5m1r\nNqc7HlSN0wiCAZPpBjrNdkzmRIyG85hNyQiCOq/dig4QIRb7AHLEklDE4hDE4mCksuZIpVEolP0Q\nif7bMiCW3+MtdNp/MJluYzal5bU9ycQsqEDQIQgGRCIFIpEMkSgQsTgQsTgIibQBEmktJJLayBXd\nXFY4r0rkqr6v6C14DL3BxK7lM8jV6Dly+iado+sTEerPRzOHM+ubTVZjvb3k/LngCX5bd5R9x66X\nkdEkoNPuxGS8iU63q8h9nIIgqBGJvEDkhUjkjVTaCIm0DnJFT2TSpkikUY6Xd4N5n26iZ9/m/Lhy\nOjKZYxHWqxfimf3qSkRiEVPv/5pHnuxjV03cERVqOGXqNZzPSKzILTjFkw2eZ2fKdv6KW0mALJD6\nPtaaUVKRlC7B3TmcfoC2gR2tzo2q/TBRPg2t5j8W9VSZ7zko3J95M5biH+xL7OUExr44lBpR4QD4\nB/vy6o9P8nTXt7lndBdkcim3L8ZzZt9FNmcv8dge/IN90eRqi13npe8fL5AveObLiVw7c5sl760i\nJ12FXCFnw6K/+WLbW9RvUbtgrT5juvLn/K28Mmg27fq2JCs1m+e/8YzL2mS8gcFwHp12h0fW+y9j\n0J9Arz+OJncpRuNt8lt2lITZnJX3b0rhwSKVUWJJBDJZmwKlapGo5DLkfwMm4030+kMY9CdR5y7H\nmd+jIGgQBA2QjcmU1w5Ht9tqjFjsj9JrJFJZM2SyNsjk7Ty/+XIm//51Q6+50lIr0pIT5+MlJy4p\nq8Sx5y7HYzCYOHUhjkb1PGkYm1GrFqPXH0SvO4TZbL/AQRAMgEWp22S8AYBatRgAsTgEH9+nkCu6\ne/z99sq7I5zWYRo4sgMDR3bAaDAhdcLQKokKNZwOJN2scN2YETWLu+hfafqO1c/tgjrRLshWk9NC\nxtZ5lLF1bPcMK2n+g7XHFVTceZJH3x7F968sIy0hkyc+foRRzw20Ot9taHu6DG7L4c2nEMwCNRqE\nM/ldz4cr5u19nxVfbLC6TmC4f8H5kBpBzN83iz++2sif325FJBbRZ3RXgiOtk2kfe28McoWMPWuO\nsO6HHUS1qn33pZxGEHIxGi6iUf+BwXAeg/64W+ukJJZvbpZCOZCgkMqXGGownEGn3Yleuwu9/rDH\n1zebktCZtqPTbgeRDP+AD1AqB7rYrLcqIKDXHUKn3Uau6gfHw93AbM5Gnbu04Ge5oisyeUd8/WY6\nbABcWRCEXDTq1RgNMf/a+7do6MlRSY7JZGb4Ewvo1KYeTzxc+pw6o+ECGvUqNJo1mE1JpV7PbE4j\nJ/sjACSS2gQG/y+vVU/puXk9meYufhfkG025Ki0mo9kqfOcsHm+54gp9NnxLbG7x/JbKFKqrauTr\nOL297Dl6jSzZ2Pu3Igg6jMZLaNR/YDTEWEJElbybuzNUDsPJhE67F61mLRr1igreC0gkdfHxexpv\nn/GU5jlwUFtLHsSCVc9Sv1G43XF/LNnHj19vo+s9TXlv7ni3r1eUrIwX0ahX44xXqTyQyduh9BqO\nj++TFXJ9QdCh1fyJwXAeoyEGve5ghezD0zh7/w6Z8h0yqYTFn03gxxUHeHnafYjFFvNp//HrzPpm\nk1WO08DJ81Hl6vBSyugcXZ93nh2Ml9K1EntB0JKS2MXa61vGSCQ18PadhrfPZEQipeMJd7H8p32s\n+Hk/6lxdiePyc5zSU3MIDi3UW8xIUzFl2Ff8efAde1MrZ8sVW0ZTNdW4g067C6PR8gSqVa8DTBW9\npX8dJlM86SnDMJkqT3jdZLpNduYbqFWLCY3Y7XhCKcn/4L11LbnUa5nNaeRkfVgpDNCiGPQnMehP\notP+jbfPBBTKwYhErmvdOIvZnI1BfwKjMQZN7kqMxmv81+/f158awINPL6R+7ZACo+nrn3by17bT\nGIwmHnjyB159sj9tm9fm1Sf606NDA7JVWt74fC2rtpzk0Qc6O7hCPmbUuUtRZX9drkYTgMmUQE7W\nLNSqJYRFHnI84S5++t4iKyASgcIJQ9FosO6mYDYLGI32OyyURIUnh1dTjSfISHukorfwr0Wn/Qd1\n7lJ02r8B9z5oyhqj8QpZGc/hH/hJmeZAeftawlmZGaVroaLJ/Y2c7A8K8rsqI3rdPvS6fYjFIYTX\nOIPjoJF7aNQryMnynKpzVWfTYouq/67lM6yOz3isLzMes24zduJcLPd2bwqAUiGjbo1gsnO0OEta\nygMY9MdKuePSYTLdJiNtEv6BHyKR1HE8oQj9h7Rh5v+NKDAuS2Lrn8d5dHph4vz2tSdo0CTS5f1C\nBRpOFZ3b9G9l9AtDGP2C+01Mq6kmn5TEDphMCY4HVhI06tVo1KsJCVuPTF42+SsxpyxJ1zKpe8ml\nBsN5MtOmFiZvVwHM5jTKymiqpnS0b1WHiTOXcicxkyZR4Yy7vyP3dClZ/gUgPXUset2+ctihc+i0\n20lJ3I6P79P4BbyBs6ZJj77NnTKaAO4d1pZ5H67j9vUUatcPZeSE7jz8+D2OJ9qgwgynsxlV5wO5\nmmr+i1Qlo6ko6anjCAr9Fbncszl+RqOJtcstye8Nm7nXcDo9ZRSCkON4YCXi3yxh8G/g5zmTXBpv\nNDA1mJgAACAASURBVF6tVEZTUXJV32MwnCEo5CeHMiRPvzSQT/5vDeOn9iYwyAe5XGqzu0WfARYd\nspp1g3nu7eEe2WeFGU6fnv6noi5dTTXVOIGf/5vkZM92PLCSIQgq0lNGIJO1IiR8m91xo3t/jCpb\nY3XsqdHzHa7f//62zPxglEt7MhlvkpYyuMoZTSAmJLxqNyiuxoJOu5PM9Ml50gGVF71uP0nxTQiv\ncQaxONTuuJ3bzlG/YTj7d10scb18w2nK/V+hUeup1zCcW9eSkculfPT9JGrXt38Ne1SI4aQxGTiR\neqciLl1NNdU4ibfPRFQ53yAIqoreilsYDOdKPP/Hrte5HBPHiUPXWDr/bwDCIgNsaryIAKWXnAlP\n96V73+Yu7UMQ1GSkT6nU+Uz2UCjvQSKpW9HbqMYDZKZPrfRGU1HSUx8iJGytXc/TN4tdE5Hu0rsZ\nj780EIlEjMlk5sevtvLtxxv4+IfJLu+tQgynI8m3MZj/21UT1VRT2RGJ/fH2mUCuaoHH1hSLAxBL\nIpFIauXpBikQBBWCoMKgP4sg5HrsWgBGQwxSWQub50RiEU1b1aZpq9osX7gHvc7AB/MfLVGOwB1y\nsj7CaCj5qdgVRCI5UllzRCI/xOJALB/jJszmLAQhC6MhxmNfkF4+trXpqqla6PVHEATnk8ZLRCRD\nIo6w3MfiUBApEYRsBEGDyZRYIIBZWoyGC2SmP0VQyDKPrDd6cs+CvnUSiZgxk3sxacgct9Yqcx2n\nTL2GS5nJrL55hguZyVzNSkFfDkbT2AZt+bjT0DK/zuxTO9h65yJ3cp1/muwYWoehdZszpE4LQpUV\no4Icm5vJ7FM7OJkaR4rWdY9Ck4Aw+tVszIDaTYkOrlkGOyx/tJoNZKY/4fT4sMjjSCTu5bpUJRLj\nXPn7SpDJ2+Lr/zJyWTtEYn/HU+xgNqeQljICk/Gm22sABIUuR6EoOQl07qy1bF5z3KGOkzu49vsr\nRCKNwtt7HD5+zzoe7ARazTr0uoNWApgl4ev3Ir7+r3jk2uWBq79nd+/f3Wev07NlfSTism3U7ilS\nk+/FaLjg1lyprDnePhNRKgcjlrh+X5jNqWg1G8nOfMOt6wMEBH2Fl/dDbs/P54mR85jyQn+imkRy\n7WICS+Ztp1vf5jz2fH97U8pPx0kAtsRe4EJmMhczk/g7/oqnL1Ep2HbnEvNi9hHjRsuYY6mxHEuN\nZfapv9k0aBoN/ELKYIe2+TZmHxtux3A5q3SaHZezUriclcKCCweY3Wkoo6PaIBFVjQ8ST2ISBBrP\n+5I6/gHsnvx44XGzmTYL5qMxGhjRtDlfDbSudGzwjeVJ5/rzM62OZ2g1zNq9k5jUZJJUKkyCQLDS\ni8YhIXw5YAj+iuLqzusuXWDG1k0EKb04MPUJFBL7t/WqmHO8umMrIV7eHJ32dGleuhVicQAK5SD8\nAt5HXApjyXrNMMIi9qJRr0aV/Ynb+lFq1SKHhlPz6Loc2XcFqdSz72GD/pRb8wKDF6L0GoInq9mU\nXsNReg3H138mOu0udNodaIu0trkbL59qiQ9bzFiwltAAHxa/OJY6YYGOJ1Qw7hpNcnkXgsP+LNW1\nxeJQvH0mIZHUJifrA4zGyy6vkZ35DnJFDyQS97tFAHTr04zZr65ErzPg5S1nzGO9ePjx3m6t5VGP\nU8MVH7m1ibKgLDxOGpOBz8/s5Jcrxzwup7C836N0DvN8LkG6Tk3/TQvI1GscDy4l05p15ZU2/ZDY\nqGwoT54/+CffdBvp8jx3PU62jKBFJ44xe1+hIOPRaU8T4lUo7W9rTp+li7idZfFchvv40Cg4BLFI\nxPnkZDK0lr+fl1TG+enPF9tL/nrta9Rk1RjbLXxis7Pos2QRArB1/GQahzhnsOt1B0hPvbsdj5jA\n4Pl54ojl06pDrfqR7Kz3cEccMbJWvMf34whB0JGc0NyFEImIgKA5eHk/XKb7soVedwCtZj1azV8F\nuVgV8TsrDeXlccpn3aHzrDsUw4mrdxjdM5qRPVrRvI5nvZWlwWSKJTWpT17/QucJrxGTFwIuG1IS\nu2Ayxbo8zz/gQ7x9p5TBjuxSOZXDqxpjdywlJrP0vXtsMWHnMl5p049pzbp6bM10nZre6+ejMZVP\nQuDCi4eIz83iq24PVJj3SW82kaqp+GTmYwlxAAxq1JgtV69wIiGe/g0aWY2523s0tV1Hlp05xcL7\nR1I3IKDguNFsZvGp43yybw8ao+2/pUQsxmQ2cyLB/pfd6pjzCEB0RKTTRhOQ15yzLQb9qbwnyEfx\n8hmPRFK+IVpv36mIxN5kZcx0PLgSoNftdSmvxNf/lQoxmsDyN5YruuMX8D6Z6VMwGv6dkQJPMrxr\nS4Z3bUlCejZPfrOaP/aepmntMJa9+ghSScV737Mz33TZaPL1e6FMjSaAkLB1pKeOwWi86tI8Vc7n\npTKcTh66RruuDd2eX5SK/+tWEc5lJJSZ0QSWkM8np//mk9N/e2zNYIU33SLqe2w9Z9gYe4F3j28t\n12sW5ViK608yZcHxeIsB07d+AwBOJhRqIuV7K1uHW6vWjm8dzdYJk62MJgCpWMwT7UvWJBrUsGTR\nOwFYc/E8AKNbtHL8Au7Cx/dpZLI2hEUew9f/lXI3mvLx8navIbbJVP5VvDrdLpfG+/oV9ySWNyKR\nnKCQZYRG7K3orVQJUrJUbDhygdiUTDo1qUOQrzfjPllGfFrF9sbUatbnKf07j3/AB/j6v1ZGOypE\nLIkgOGy1y/NKW5XaqLnnPrM86nGK8PJzPAjIMWhR23lydmWdkgiQu9400B6d/vqKdJ3jFgvhXr60\nDqpBz8gG+MkUyCVSMnVqEjU5XM9OY8sdx5U1Cy8eoqZ3ABMbd/TE1lnYa6zTIdRIb3/612pCmNKX\nWj4BSEVisg1abuSkczY9nqNOGiXLr53g9eh++Mpsh3C+OLOTA0k3uaXKQCQSEar0oU1wDT7rfL/V\nuCy9lpcPr+NCZhLpOjXBCm9aBdVgQU/rsFGmXsP0/au5mJlElt7yhH/3ay7LxtHdatfl4J3bmAUB\ncV6YMk2jRi6RMLJZC17bsZUlp0/wao9eAGy4bHkfTGgTbbWO2MkQp95kQi6xLpmfN3gY1zPSuZCa\nwi9nTvFoG+vu4y9v28ydbMuH+fjW1td1BqXX/Si97nc8sBwICd9OWvJAXGn/osr+koCgL0scc3cT\n0KKs/nk/Wo2BMZN7Ilc497GpVTufH2LJC6s8z7Eikbyit1Bp+XXnCVbtPYNIJGJs72ge6dOOaYO6\nFJzffuIyYz76mf1feiap3x0y011r0Kz0Goq3r2vl/aVBLA4jIGiOy97j7My38A90LyVozjtreGBC\nNxo0iUQmL7yHvbxdf6971HA6MNy5J6a3j21m+bUTpV6nPFAb9Q6NphClD2Ojonm5Td8Sx13JSmHd\n7fN8F7O/xHEfntxG08BwupRBzlNRRECzwAhean0P7UJqEaTwLnH8/qQbLL18lJ0JVx3meC26dJgZ\nrYon3v185SjfXzhgdSxDp+ZKVoqV4aQ2Guiz4VuyDYWhjgR1NgnqbD4+tYM32t5XcDxdp+ZmTjpK\niQy9xIRRMBPs4LV4kuiISA7euc2trEyiAoMKjjcLDUOaV3mjNRoLjp9PSS6Y5w6CndTDR9u05c1/\ntrPsbHHDaeu18g29jPpgKavfnkS+LXgzKZ2xs5cR5OvF1o+mFYz7Zu0+nh/R06W1ZbKWeHk/gEa9\nxuk5BsOZEs/H3kjliQfn0bF7Yz6YP8Hq3J+/HmThlxYv6j8bTzNr/gRq1XUc6jSbM5zeX1m1iKnG\n83y5Zg/3tG7Al0/YVqH2VsiRStxryeMJBMG1PooSSST+gV+U0W7s4+X9MJrcFej1R5yeo1H/gV/A\nGw5VxW1x4tBVDu0u7rzYcvoDl9fyfFWdIHDpVGEfpmbt6nn6EuXGxtsxPH/Q/lNjQ/8Qtg1+yun1\nGgeEMbN1H2a27sP+pBtM378alUFXbJxJEHjkn1885iX5qNMQ3jq6CYAXWvVmdFQ0Nb1dr3zqERFF\nj4goAJ4/sIaNsfarNead30vzwHAG1m5WcKztmi/IMeg4Nepl/Ox4o/Lxlso4MWpmsey8i5nJDN26\nkFo+AUxsbAlfNfALKTC2Xzq0lkR1Nr/1Kz/9mQltollw/Ah/XojhpW49SNNYPrjyjZemIaFcSkst\nGL8q5jz3NWhIpK+1d+PQnVg+3b+H00nuVY893KoN1zMzWHTiGC9s2cjcQYXFEWqDxcP7Qpfubq3t\nKmvesW4DUT8imHfH9+ebtZ5p9RAQNB+j4QoGw1mnxhsNMRgNl5DKmto8/8MXmxHMAhq19f04fex3\nXL+cyLSXBhLdqQHPjvueqcPnMnPWSPoPb1fq15GPTOa6F7Ay0Wv9PB5q0I5nW7pmBFdFjs+bUeL5\nHi3rs/tzz1WsuoIgaElJ7Ob0eIWyL0Ehv1od27XtfIHatqfY+3cMve69W09NRHDYX2SkPep0WFEQ\nVKSljCA03PWUlnVHPNdI2uOG02czfsVoKHy6fuu7yZ6+RLmQY9Dx4antds+3DIrk5z7ul+v2iIji\n5z6PMGr7T3bH3MhJJ8ov2O1r5DOqfhv2JlxnRL1WDKht+4vDVb7oOoJ4dTYn0+Lsjvk7/oqV4ZST\nZyQ6MprysRW4ahZoqVpJ1XpWKLE01PTzJ8TLmyvpaQCcSrTkM3WqWQuwVLrlG05JuSoytBra2PA2\nPfrnH5gEAYlIRNvImkRHRtImPAI/hQKlVMr4NX843MuE1m1ZdOIYW4p4mPIr9UTA6Bae+UDMryi6\nmZhOzdAA4tOymfvUCEL8vJn4xe9o9Ab2fjHdpTV7vDSf2Y8N4Z7WDZwar/Aa4LThBJbee/YMp9gb\nFnmONh2jCo4dP3iV65ctRuyDE3sAEN0pitNHb7D/nwseNZwEQe+xtSqCeHU2X53bzbmMRMY1bEev\nyAZOh56rGgajiWX/nGD1vrPFPO+bPii/cJcttJq1mM3OS834+b9ThrtxDh/f6S7lYxkNFzDoTyGT\nt3U8uAQ0aj3rfj9Es9Z1iO4U5XhCETxuOCXHZzDnj+c8vWy5M//8XpJLqM5a3PthAuVepbqGI+HI\nr87tdqus/m7kYgnf9niw1OvYWvPejd/brdrbn3Sz2LEQDwl+GgXn81vKgzYRkQWG0/mUZEK9vakb\nYKlOaVejJsvPnSFDq+FyngHV5q7E8LicbEx5H8I7Jz1ObX/3tJDyE8sNpsKS/TUXLEnh3erUpZaf\nZzSWAF4b05elO44xtnc0m49dZPmuk3w4aRDbZk+j18vfubTW2RsJvDdhgNNGE4BC0Q8Vziv/ms32\nmxZnplsM8TpRhX2rNq8+BljnQHTt04zTR29w/ZJ7XkH7e/PseuXN+EYdWH/rPNvjLrE97hK1fAJY\nde9kwr1cD6lUdr7feJCfth1lcKdmHlTZ8gwa9QqXxktlzRwPKmPkCuc9ZPnotNtdNpzmf7Sem1eT\naNA0kumvD+OLt1eTkpjF8v/t5pWPHqRHMY+YfTxqOH00fQlikYhZT/6ERGJ5S1VVj9OiS4ftnhsd\nFe0xxe91A6YyfNuPNs9tvB1DlF8wL7YqWbyvoojw8mP7kKfouX6ezfOJ6uKVJXV9nCt1TVRn88yB\nNZxKi0MultA2pBa+MgXKEsQdK5IJbaKZuu5PbmZm8Mf5czzVoXPBudHNWzJ77y6Wnz1DYq7FGO9d\nr77V/AXHLHH+brXrum005VM/MIibmRmcTExALBLxzZGD+CsU/Di89EZ4URrXCqVXqyjaRNUgOVPF\nzzuOubXOoLcW0qtVA94ad69L82Ry1zw+BsMF7D3qyORSdFoDZlOhB2HfjhgAPvp+YsGx/NymlCRn\nKnxEOCuFp1GvISDIcYPhysqsDoOY1WEQAHG5Way6cZpu6+YCUMcnkIcatmN0VBvClFXfkPr1nxN8\n8/QD9GrlmpeirFHlzEGvO+T0eHs9COd9tgkvbxnNW9dh/LCvWLZuBgFB3mzbcJp2naJYsmAnN68l\n8+3P07h2OZEXpizm8wUTqV03hCsXE2jfuQG/Ld5L5+6NCAz24fuvttHnPueNEmdQ5Xzlsqr9U68O\nQSqTYDZbHrr3/x3DuiPvYjSamDR4jkuGk0fLOB6c1pepb9zP2Kf68eC0vjw4reRk6cpKSX30JCIx\nL9pIenaXlkGRdAqrY/f82pslNyqtaGq4mCsVb8OYssWzeUbT3G4jOTXqZZb3e5SFvcYyr7trXenL\ni/zQ29nkJOJysumQF6bLp21kDS6np3ErM9PmfIXUYhCmqG17OZNznQ9N5udWbbl6uSAp/P4mzUpU\nFK9IBnRoysYj7qkbu4RQPJ8wn5q1LSHxqxcsMhL5H651G4TRIrrwCyZXZSlWkMsd/y7daVHxb6CW\nTwAvtOrNz30eYUS9VsTmZvLFmZ30WDePfYk3St/Hq4KpFxHEnVTb93FFolG7VuKv9Lad3N5/WDRd\nejbBP8ALk8nMjWsWGZ4Bw6IJi/Bn6Mj23LiaRH6UUq830rxVbfz8vWjf2eIx3rLuJI2a1SA03J8n\nX7Db0qQAkajsW48ZjZbvdZPRTHamJQ/VbDajUMrQaY0lTS2GRz9Jm7Wrx+F/YujSz2K5xd1MQZOr\nw8unfJSFPcWOOPuy8IPrNCPSjcTqkhher5XdUv/Y3EyuZqfSyD/U5vmqRpImx+GY26qMgtypYXWt\nnwIcVfOJAHMFfDTnq4LnGyotw6y/NNvXqMmmK5cLkrTvJr/C7mp6erFzh+7E8vrf25zey+gWLZlz\ncB8H7xS+p8a4od1UXrw0qje+SjmZKg2BvqULf5dESWKU0Z2juBwTx/Z1J3ns+ftYMs+SczFygnUy\nfdwtSzg2IMjxB71M1gqdyXntN6PxMlJpE6fHV3byi0n61WzMiusnOZB0k0m7f6OubxCPNensMcmV\n8uaZ+3vw4fIdjOvjuRw3T+BqT0ell23DKapB4WeXQilDnWt54PhuzhZOHr1Bbo4Wo9Fc8HBRbB8m\nM8mJhR7Z8MgALp23nwsLIFd0Qqfd5dL+XeWVKT/SpmMUF87EkhSXga+fksN7LqFQyqhZ17VcYpcN\nJ5M5nXTVUkL9n0OEFEHQIhIVaib9+vUWtv9xhLe/n8z8t1chlUn54KdpJaxYudCZjLx82Hb/Jl+Z\ngi+7jvD4NR9p2J53jm22e/7Ls7v5zsM5ShXBm23vY/apHQzYvIApTbvQPqQ2RsFMfG4Wh5Jv8XY7\ny5NJuJcf3lIZaqOB2NxM6vgEkqDO5perx1h8qeTS1XtqNOLFQ3/x/olt/F/7AWiMBlK1Kur6BpU4\nz1NsumIxuqV3NQB9LLo9Xx60yFDYaklzf5NmfLxvD4mqnIL2KfmIRSJe6NKdRFUOy8+VXFYP4CdX\n8OfY8Qz8dUnBMVvJ6GXBZ3/sYuvxS6g0Onq8NB9fLwVbP5rGe8u2sfHIBYwmMz1nfsvXT42gY+PC\n3lNPDOnKyFlL6NWqAS+N8pxHtyiC2b7RPnXGAGrUDmLe7A0M72IpT5677AmatrLuj7XyJ0tV4IAR\njr80ff1muJT0mprUB5m8I0EhyzzW76+iEICn9v3BP/FXMAsCgXIvpjXryqTGnVh29Tifn9nJnLO7\n+Kv/FI8UwJQ1Ly/cYPVzdFTNYscAvpg2rLy2VCqUXvcjk9l+mFIoZcWO6fVGuvZqwvSZg9i24TRf\nzFprd22JRExYRKGIb0a6Y2+50muky4aTIOhcavc0b3nxSkdNrg5VjpYFq1zT3HLJcNLqz3Ar5RFM\n5gxC/aaDSEpc2vPUDv1fwZiI2iEIgsCd68mYTQISZeURdXOGY6mxaE223XadwupUSCuRs+lVq2eU\nPaY27YLWZODLs7sL5BGKkm84KSVSXmx1Dx+d2kGfDd/iL1MW6Dm90Ko3c8/tsXuNIXWa8+Khv/j5\nylF+vnK04HhZCmA6g4+8MMG4UbBtDaAt4yfx69lTLDh2FLXRgL9cwcjmLRjTohVNQ0JZdeG8U4YT\nYNVS5W4jzhMM72qpzhvYwVKldl+7xtzXzqJe/uqYPsXGvzdhAO9NGFDseFENpz//b7LH91kUe/pX\n+QwZ3YkGTWuw9rdD1G0QVsxoAlAqZSi9ZPQb6lg+QCZvj1gSgdkFr5NBf4y0lMGERZSs9VZZyTZo\nWX3jDMuuHudmjsV7+lHHIYyo3wovieUL+ZU2fZnYuCPd133D1+d2M9cDBTBlTVkaRNN/WcsD7Vsw\noGXJ6v8lIrjWVkuucK21l1wu5fTxm7RpV4/flziWFBk0vC1XLyYQFOLL4m//pmuvkj2p7nhajYYY\nl/Mc78bLR+FWRMylJr8X7zSmcc0jXIprRfPa1xGJFFy4U4/mtW/ZnKzJ1ZGenE2tqDCr444EMCvy\nS+6Rncs4nGz79fyv11jurVmKN3cJOFL3rugv/pIoae+Ved93426T38pIukZDx4WWyrZF94+kX5Tz\n1WpVCVcauyqUAwkKsS//URaYTLdJSXSv/6RYHIyP37P4+DqvFVdRjP17KcdTLW1t/GQKxjRoy1tF\nRGpt0XDFR0jFYi6NecMjeyivJr/frj/Aw/dEE+JfGK69HJfClbhUhnZu7vJ6Ld76imVPjKV9vVqO\nB9tBrVpEdtb/OT0+ouaVcskrch4TiXH1cKUbgJ//G/j4lWkFv2ea/ErEgRiM1rFK2V2Z+RdPWhsd\nVU0A81Jmst1zVcGlXE01ABuvXAIgzNuHe+pXruqf/xL2KpecwWxOJydrFghGvHwmVurwXb7R9H6H\nQYyq3wZvafFwjy3cUfjPMuQQICt9Wy53WbTlMPe1a2xlOMWlZvPVmj1uGU4AkQGlez0Gw3mXxlcu\nowlAglgchNmc5vQMk6niIjEuGU7BflOIS7fEAnN1+9HqYwjxs+6Js3rhTkwmgZuX4qnXOJJ3F1as\nIJgrJGlyyNTb7ybtagVZNdVUFL+dPQ3AuFZtbOZUVVN+iETKEhPTHZGTPRtVzlxCw7cjkdb33MY8\nSJ8ajZjUpBO9I533bN5Xqwl1nJQnKcptdSKtS2loeBovhZQstft/Y43etVDb3RgNMaWaXxkQibwB\nVwynkhPOyxKXDKcQv6cJ8XsaQTAAZnyV/YqNKarbNHOMbX2fysrh5Nslnm+16rNy2kn5kaTJYcnl\no1zITOJ6ThpxuaXrQF1NxdNmwTxUej3PdurKjK7l02KlGvtE1LxMWsooDHr3dK4ABCGXlKTCv6Wv\n/8t4eT+CRFI+Sf+O+LH3Qy7P+aHnGLeulaqrGCmAtQfP88Mmi07Ss9/9iaxIP7qE9GweL9Lo1xVC\n/Xx4Ztk6Vj8zHh+F6w1njcbLLinoV1ZEYj+wrwRUDKPxWtltxgEuGU4CRkRIEYkK3bB64y3k0sJw\n3EfTlwCQmaoivGb5VDJ5CmdK5f8tnEi9wxtHN3I1O9Xx4GoqNeeSk/jy4H5EIhE3MjNQ6S3tO57t\n7F5uTXkgCBoEczZmIQezKRGzOcfiphe0CIIWszkTQdBgFjKLHMvKm5eJkHfMU6xaup89287RuVcT\nJjzlaf05KYHBP5CWPMClUERJqLK/QJX9FYHB36FQDrL6TK7KjNj3gsMxgiDQN7xTOezGmqGdm1Mn\nLJCpX62kZd0I/H0Kq8m7NK3LkE7uhekWTh7FM7+s5dGFK/lj+ngkYtc8xCZjyQ/8tnA1H6xSItiP\nDpU1LhlOF2KjCPAZRbDvY3jlyZ1fTehFizqFf7i3vpvMlt8PUa9JJM3b1/foZsuaO7mVT9TMU6iN\nehZcOMC3MVWzWscW3ba9Tt+IVsyOnkCGXoW3VIFCbP0FkqbLYcrh+SRrLZ60j6Mn0Cei8moauYNC\nIuVEYjy5ej1KmYzXe/ZmQuu2yCuoQ7vJeAt17mL0+pOYjJcxm50TPa1I/vz1IGnJ2dRrEOZ4sBtI\nJDUIr3GW5IRWmM3Ftbrcw0RmemGqhFTaAKX3aHz9Sm5CW1Z8fGpHiR0XwHGxyCtNJ9EzrH2JY/ak\nHHd5b55AKhHTvlEtRnZvxcT7OlA/wjM5ryPn/QJAfCa0fudru+NiPnrR5nGD/rRH9lHVMJszKuza\nLuo4iagVPJdLcS0I9X+BEL/iFUirF+5Ela1h76bTtOnaiIemu9ZGoSLR2ZEhqOrsiLvMuye22myB\nUtW5mpPIJzFrWHvnCGKRmG6hTfmi3aSC8z/f2EWyNot+Ea25rU7lw/N/0CG4IX6yshNaLG8ah4Rw\n6knXdEg8jdmcgk67k5ysWR40DMqPrAyL1kzNeralIjxFSPgWMtIml0lOitF4HVX2Z+h1B/H2GYdC\nOdglnZvSsCPucoHRFKb0JVTpQ2xuJmFKH27kpOMjlTOhcQeH6/jJHCcte0mUDseUJf833rEStis8\n2ce9EF8+phJ6MP6bEUroBFDWuKUcHhWxgTupT5GrLa7ncGhHDJ+veAZBEHj1oW+rlOGkL6HVSlXF\nJJh5ct8fZbK2RCTCSypHZai4N3CsOpVYtSXcaBbM7E+5gMqowVdqMYwOplqqyz6KHo9ZMNNj+5ts\nSjjBQ3V7VNiey5obuYlMOfw5CzrNoKmf/XY+nsBkSiQzbXJejkXVbabhH+BNemoOSi/Xc0xcQSKp\nTUjYepLiG5bZNfS6veh1exGL/QkO/ROpzL0QkiusumHRF9s4cBrNAi3K088dWMO87qP48+ZZ3ji6\nEaXEcTixvo/jEJJ3BRtOnuaF/qXLQ3RFJ6waz+CS4RTq/wwAcmkDGkRaWkDcSh5tNaZ9ryb8PGcz\nl8/E0rFP2d+wznAi8RHaR/7mcJyXgxt7fiXtk2aP5w+sYWOscz3AQpU+9IiIYlqzrjT2D3NJNNGR\nBlVZIhGJWdZ9BvV9LB/WGXoVTx35gWXdLeGKfKMKQJwnXvrH7QP/asOpLBEEHRr1b+TmfFehVS2e\n5vGXBvLZm6tYt/wQI8e73q3dFUQiLyJrxSMIuahy5pKbUzbNfc3mbFKTCx9cff1fw9tnEmKxlkcD\nkQAAIABJREFU65VsjtiTaEnUzTeaAFK1Fi/eyPqt6Rpej57r5/F8y14lruOMzEDLgLIzOt0lLUfN\nfa//wMlvbYfTyhJ3cpyqKR0uGU7hAa8VO1YvfJXVz+Oe7c+W3w/RuV+LMtFwOpE4ARESTIKGjjVW\ncjPzO9K1Bwj16kvdgKnczFpAhuYAAgLtI38hV3+FbN15TidZcgGiI36wu3Z+o1V7DK5TOQxBZ3HG\naJKIRCy95xG6hNdDXAXL1uv6hBYYTQBBcl/iNfZDRT5SBem6/04RgGcxkZLYGbM5paI34nH6DmrN\ntrUnOHX4erldUyTywc//TUzGa2g19lsueQpV9qfk5swjNGIXEklxVfTSYKuHZLpOXfD/Gt7+KCtp\nk2lPkKOuOK97RYas/quU+p18Ob4DTWoWJut9+sIyjAYjx/dcBKzlCUqL1phA+8hlBT/H56zEjIH2\nkcu4nvE18TkrCfPuj8ZwG63R0uDUR94YuSS4RIMpnwZ+ZZvfUJ5M27uyxPMi4GoVUvW2RaRXECna\nbLQmQ0EYwCwIGMwmkrS2ZRVEiDAK/76QbFmSk/UuuapFVOVQnCOOH7xKvyHRRHeKYlBbiwJz8zZ1\nkMrsJ9h//uMUj1w7MPhHAPT6I6iy56DX7fXIurYQBDUpiZ0BEIlkhEUeQywufUJ8r8gG/BN/xerY\n1exUNtyOYVjdFsTmZtptZeUqZ7Ou0DqgbDo42KPdM18xqGNTAnxshwmzcktX4XktOY1D12JJylbZ\nHfPSwJ42jwtCtYRMeeOk4SRgT33cfFfjzOT4DOb8UTYy6ApJGHpTKnJJKAC+8iYkZ24FBLL1Zwnx\nvgcvaV2ah87GYC58M5kFHQJmRJQcfnIkcJmp1xAor/xJxdey04p9iN3N/7Uv3jesqtEuKIrN8Sf4\nNGYN77Z+CJNgZuHV7RgFE9OP/kCUb0SxOSqjlnBlgI3VqrGFyZRIrmphRW+jzHn7mV+KHbtwJrZc\n9yCXdyY4dAUGwznSkgfjkqiNGwiCgZTEnvj6v4CP7/RSrdWnRkObnznvHt+CWCRi4cVDpVq/KGqj\n52QoXOGFEb2IDLYdSoxLy2LLsUturfv55j0s2X+ckruflWQ4Vczv47+MU4bTtcS+iJCiN90pds4s\nWHc+Dg4rO3VtkUjK2eTnEIvkCBhpH/krQcrOnEicQIhXbwIU7Tie8HDeeUuoDiDcZzDH4kejlNam\ndfg3dtev6cBwuq3KIDC48htOv18/WeL5YXVbMLFx+eugeJpx9XqyLeEUWxJOcjLjBhl6FXqzkdda\njOK7K5vZn3IBsUhEmMLaUGrk61nRwGF73iLXqGVnvzkFx/r+MxOACfXvY2qDwQCM2Pt/ZBtyWdbt\nDWp5WYz/H65t4EjaRRI16QQr/OgQ1JQZTW3n0vX9ZyaPNxzC4Bqd+f3WPxxMiyFLn0ukVwj/6+Rc\nbsUvN7fz0/WtPNtkBKNql5xvYjCcISN1glPrehKRyBuxJMzyrzgUkcgbkdgbibjIMbE3IpE3memV\nv4+bq8hkrQiLPIBatQi1egVCGco5CEIOOVkfIpNFI1e4n/c3rG5Lvjizy+oRu21ILU6lxfHcgTUe\n2Ws+alP5GwpNaoUR6Gv/s99X6X714k/7CiM2TWuEcSkhBV+FHJFIRI5Wx+O9OxFdp6Sees73d6vG\nMzhlODWM3AXA5fj2NKlp3Zz3cnxhl/CLJ2/x4LQ+Vv3qPJ3n1KHGcquf6wU8Sb2AJ4uc/73YnCbB\n7zi1dvPACJoHRnAh03aVwqobZ2gTXLmFw5I0OSwuQUtlZP3WfNFleDnuqOxo7FeTff1nk6nPZc2d\nQwTLfekR1pwwhT8P1O5cME5rMtBt2+sAtAmsx6ftJnp0Hw/U6sGvt/62OuYtURCs8GNV7J4Cwynb\nkEun4KbU8grlYnYsz52YR1O/OjzXeCQtAupxKzeJZTd30O+fl/k4eipdQorn1B1MjWFz/BEerteX\noTW7km1Qcyzd9pOuqIiX+Nsra1kVuweFWMY//b5w+JoM+tOkpQx25ddgH5EMuawtXj6PIpO1Ript\nAB4TbPSM4bTl1CyPrOMpJJI6+AW8j1/A+wDodQfJSHukzPJZ0lMtKt4SaX3CIg64PD9AruTkqJlW\nx1bfNxmAmMwkGviFOJXjNPXou/j/P3vnGR1V1bbha3pJJr2RQgKEFrr0DiJIE6QoRUQRUYHXBoqi\nn4qv3RdQLAhWBBWQokgXEOm911BDeu9t+vdjIIWZyZTMJEG51nKtmbP3OXsz5sw8Z+/nuW9x1ZIE\n+dqiGhfAXPla1Q8Q3h7yaiWGf/3YCHo0iQKgw9tf0KFBOAsfHQ6YTIC/nWS9MEkg9MWot+6xagmF\n8s4qdKprOJTj1CB4vdkxL+XwstfN2kViNBgROKh8Wpdo5VfPauC0P+16Dc/GcWLzqk7c7R/WpIZm\nUnP4SD14oqF12Qu5SMJT0QNIKs5iVswIxALXCkM286ps5Ko3GmisCidY7sufqZVtNprf7PvF5d/R\nGfTMbzcVqdB0GzZWhfF2q8fo+9dMPoldw89dX0MkqLy9fC4vjo2930cpKn/CbeVj2cRXcvO6Roys\nTtiNp1jBB21se0caDJnkZk+y2c8WIlEIMsVQVF6v3vShuouzSGVdCQw5RWnJGkqKlqPVnnXLOHpd\nHKUl65ArhtvuXIEbhTlEelp2iojxMd8yt8akqOF1VgDTnbSpX76iJJeIKSytHCAv23+CbtGWFyEE\nOC7P4O3rnkrO6nA+52c8JPWI9DS3crtFbO4qDmeYVvYFCJlQS+YkDgVOElGY2bEQn8pPanOmfMfb\n3z0JmOxXXJkcXhN0CYrk12snLbZdL8gmoTCXCE/Xl/O6ihsFVYsPtvN3bTXNncKkhtZvxurS/LbA\n6XJBEk29Iqgn9zMLnG4FWefy4gDKgqbbSSvN4Wh2rMVVp4pBU1UoRTKMRiMfXVyJv9SLj9s+RUPP\nqpb8waRGPRW9PtWuMSwikODnvxKprDPWciPv4jhCoRdKj0koPSaRn/d/lBb/7hax0dzsaXj5ZKP0\nsD94vnfjQnykCiY0bk8bv1Da+IXiL7ctZnk7d4IAJsDFhHR2nLxidnz6A85pMikk5SuwKrmM7KJy\nOxG5RMyZROtaTQJB7X8eNUWk6j68pVGoDfnsSbFvJ8kd2C/WA6TnfURRqaniIzP/cy4mRqPWlm8T\nvPTQ55zcf5nHerzDuA5vWs13ckQjqKYZHtmSzkHWtxcf3fUzeltZfLVISknV+RBBCk+XjrfiatX5\nVDVFkU5NtqaQLHWBxf/cib/Mix6BLTmYZZJ/+PnGdoaHdWNoaJeywChHU0Cw3JeuATE2r1dfaZJX\n2JV+2qwtSG5/0L46YTf37nyJrSlHWNrlFTuCJkhLboJG7Zwtj1TWg5CwZEJCbyCVdeFu0OQ+vLzf\nJajeWULCkgkKOYnK6zUXXt1Ifu7rqEu32n3G1TGv88cA02rmL1ePM/TPb2m08j0arXyPJr++z+At\n3zD7yEab12nj09SOPrW7av7w+8t4f+VfTH+gGz/8eYTH+3dg1+mr3NvGeX2p9SfLpWN6N23ItYzy\ngLhUq0Mitv6bKRLX7fQRRxDY+M6Qi3wJUXYk0rN2hbUdWnHKK1qDStGfEs1JMvLnEub3Gam5c4gM\nNOUdzV31LB+/8BMz541HKBBY3bKTCWvHQ8teRka14lD6DYttCYW5bIg/x/DIuul3dvvWjrtZdb12\nfZIy1QUsvrKVDUlVO88fGPChW+fRTFWfa4UpdPFvztm8OEIVJmmLJqoIstT5XC1MNluZskZVYbmt\nL5aKrE7YTUPPeiSVZPHW2aV82OZJm38fRqeMM4V4ql7E06vmxf9qg3Mn49GotbTrXDeEGIWiIDxU\n/0EibUtx0Q8u04TKzZ5OQNA2RGLLW8G3E+bhzYste5e9351ylYt56WxLusSNwhx+vXaSDzoOqfa8\npMLaNTS+kZ7L+48PNM1FLKJErWV833b8b/Uuvp/xsFPX3Hz2EiPatwCgT7MGLN1/nF8Pn8ZbaVpN\nahwcYPVcoci9gdP5nJ85lvk59wRM51Le7wgQ0sR7BDG+47j1cHRrC21Mo23sTnmNzNLzeEnr0z34\nLbylUWXXul6wlYPppu/iniHvEO5RuVJQgJAz2Uu4kv8HAkQ3xxnv8Jx3JL1ARulZDEY1bf2n0tx3\nLAKElOizWH1tCFGq/iQW7qGeshMB8haczVlK//Av8Zc1s+v6DgVORnSIhD5cTe1Hg6D1yKWtScqe\nUanPrE/Lk+jSk3IICjPfhGzgVbf1kkY3aMMrhzdYbZ9xcF2dDZzCPKoutc8qLXJqCf12jMDo7Us4\nmVV76tGfX9rIL3GmFdCXmg8nUOa+ik5bPBjenRdPfEV9ZRC5mnItlrGRffgtaS9Z6nxGhFsuJ76d\nhGJTomfPwFbVmlNFy5W+f83kvp0v83T0UMbW72uxf36uc7peIWHm1bb/ZF6Z8gM6rb7OJZRLZT2Q\nykx/Y0ZjMSVFK8jP+z+nr2c0FpOR1h0v7/+i9HzSrnPO5qRwPDORY5mJbIgv9+OLUvnRO8S5QFNv\nNK8aq+kHxIo0qudPbEIG/do2pk3DUNbsO0NUsC9XkjNtn2yBY2/9B4W0PBjs0qg+O2dNoe/HJhmQ\nVwb35rHu1vO+pNKOlBTZdsaoLtFew2nh+ygaQwGb4idxPPMLJjSuXEiw+tpQxkfvuvmuvMYyo/QM\nWxKmEKxox7hGOwHYmvgUfyfPYmj9ZfjITH8bRzLmM7j+j7Tye7xsnPqevfGUmKcJWUKtz2Vt3AjG\nNtyO4GYu697Ut/jpcjcebVwuidEz5B30Ri2/XhtAh8AXaOn3GMsud2V89N+I7PB3dChw8vEYTXzG\nBDzlvZFLWwMgEVkv7V7yv42VAqlbhCur3m7IVhfjJ6vdZFIPsZQincZq+5bEiwwMty86rUnClFUH\nTkcyE1wy7+9iD9Vq0ASwL8Mksjo+qiejItxrk2ELD7GclJIsLhVUDiJaeEexIekgBboSmqjK88s6\n+jXlSHYsaoMWWYUnaOPN9aYAmTcd/WxvW9jL2Mi+rLixk6+vbLQYOBmNhZQUO+5p6EgezD8BrUaH\nTlv3BVQFAiVKzydAIKWoYEG17HEKC+bbDJzmn/mbo5mJlVbq2weE0z4gginNujj8fW7EyPIbm9mW\ndoBMda5Z+/qenzt0PVdyX9to9p2PA2BA+ya8t3wHcqmYzk3tW1G+nYpB0y2CvT3ZOnMSRWotzepV\nLVAqcvOK0y1kItODqVSooon3CI5lmv8/iPSs+N1Svjp+Jc9UWNbav/zvqJXfE+xIeoHL+b/TMdBU\nkRmlGoBc5FNpnPjC3TdXt2wTV7AdnaGkLGgCaKAawPUC821nkUCCpyQUT8mtz8+IWp+PUmxbENZB\ny5VXkYmboFIOLjsW4FUudrn8i22V+l85Z/lmbV7Bz8gSy6+eYHpM7XqJPd28K/PP7LLaPvPgOlQ9\nZXQPtm8Zu6ZoZuOz/Tv5iksCp/+d/qva16guqSWmL9TREdUzyXQVhboSjmbHoqiQvO0j8eRaUSoa\ngxa5qNxAdnrj4Uw9uoAZx79iSqMhxHhHEl+cxrLr2wF4tsmDiF24pf1UoyGkl+byV9oJLubHm1UC\nqku3YzRaVy22hEgcicr7LZfN8U6guOjOsrdQekxAoXyY4qJvKch716lrGAzmgcvtfHnelBc3IqqV\nKTncP7Ra0i2/J+5kefxmvCWmnMzGnvVJLc2kQFdc6151Y/u0Y+J9HQB4sGtLbqTlkJKdz8uj+7h0\nnAg/+/IZRSL3Gnlb4tYK0e34yS3/tmSrTbnQFbfCbr3OLC3P76q4rXdrnBuF9v/WZKlN11p2uYtd\n/cW3JdYbjPap2ztouSLE26Oyqa+Px9iy1+P+079SW8JVy9oS/nIPxjW6h+VXj1tsn3/mb05mJfFN\nT+f2i13B9Jge9AhpyEPbf7S4VFyq1zHx718YFtmC2W3uc0nStRE4nZ3M1L2rSSsp4M9Bz9DIwW3N\nQLknE6Lb89MVyyW7q66fIkdTwlfdRzvlTbfm+mn+7+gmdIbaF12L8AjgSkFKrS7b386F/HgmNbi/\n0rH00hyzfpEewWzq/T7fXdvM55d/I7kkiwCZF/f4NqkkpOkqBAh4o8UEXmg6imG7/49Ij2CWdJ5V\n1p6f97bD1wwMPuDKKdYYr0z5gVNHrlfaahvV4327zrXkyeYMW47HMvAe160oVoVAIMXDcxoentMo\nKV5JXo7juWjq0m3I5P2tti/r8whHMxP45uIBfos7A5iMwzsERDA1phvNfUIQOfB989ONDYwI78cT\nDR5k/IFXeLj+/XTxN+1yvH1ukcPzdyVKmWmFSKPVUVCqYfoD3ZCIXfOQs+dSHLnFJQgEAjo3jCBQ\nZTutQiSOQirr7lBRh0a9G6msl9PzFAmkFo/LRJaDvfLcTFv3T+V2a+NYP9t0fsVtOXfgVtfFZ98b\nbbVtYHgzq4ETwF/JlynSafAQO/bBuZI2fqE837IX88/8bbXPHzfOsSPpMuMatWN22/ucGie1OJ9P\nzu5mV8pVMkode+q3xJD6MVYDJ4DtSZd4+/hW3m4/0KHrTtu3hq2JF6s7PZfRN7glVwpS2Jpykkcb\n9LZ9gpuxFvBUFQhNbjioTCDT2evfTgOPkLK+0x/4hGsXkvFQyVl94h1UYoXF6xj01sud/2lYslIp\nKvx32FYolGPIz30To9GxStPSkk1VBk7dgqPoFhzF9JjunMtJ5UhGAosvHmBL4kW2JF5EKZZyT0AY\nP/a2L9FXKBCU6a15ipWVtuta17BPnSX2n4/jPwt/w2iEb154iKOXEmjdMJRuzZ0TfN558Rpf/XWQ\ns0mV78PWESGseMb2NpVE0tqxwElzvFqBU466akuv2/GXx5ClvkhW6QVClCbx0iz1xZtt5atQuZrK\nWok56st4S+zf1fGXNecamxyamzO4PHBaNn8Lv323C4PRSINm9fhk7fMW+/UIsf1htF7zPwCUYgmT\nmnQiQO6Bv9wTrUGPRq8jpTifPG0psbnpZJYWkVZSQIHWtJT+Q6+x9KpX/SXd6THdOZIRz55U667p\nRToN38Ye4tsKit0KkYRgpQpfqQIjoDHoSS8pIEddYnEFy5V0CqzPoh6jeWbvaqt9frpyjJ+uHKNj\nYASvtOlHM58gFKLyvfa4gmwu5qWbVv6q8JmSCkW80LI3H9fC1t2gevcQoQzgg3Nr+D3xEG18o/CX\nqixucT0dfed78znDl+tNqwuj27lW80Qsrv2KMq3GOSmMPw69idHKytHSzTMIqmd9iyQ1KYfHh3zi\n1LgVScrO47EFKzmfkMZ7jwxkQLsmnLmRypeb93M+Po2GIf7MHtWXpmGB9H79K954+D5mL9tMg2A/\n5oztT0xEML1f/4pwfx8uJWfQINiPX1+ewNEriTQLDyyzAHntpy0AvD+h/CEpODSW4sJvyc970+75\nlhSvxNNrJiJR1TpwIoGQ1n6hBCtUaA16LuVlsDP5CvnaUvam2i8g3DeoExuSdzGm/v0MC+vL4qur\niPaMIFOdw5rE7YwIr71y9Ec++oWIQB+Of/Ei7aab/hbqB/nyxo9b2PHh0zbONifmddM1ooP8+XXa\nePw9lBgx8teFq6w4dJqY1z9h8WMj6HlTWdwSSs/HKSr8yu4xCws+wcNzCgKB/YVCq68NIcKzN2kl\nx8nTxNHC91G7z+0cNAsPSQjbkp6luc9YjBi5mLuSNv5TaO1XLsqbWXrWbJyKq0fpJSfRGorQGkxW\nb9cKtuAhDsJb2gC5yJdmPg/hKQlhxdV+RKnuQykOIq3kBGnFx80S2auDywOnY7sv0nNoW56c/QDf\nfWiuNF6RquxNKlKs05btodcGX/d8mOf2/8a2JPtNHEv0WuIKsolz37SqpH+YfdsARzISGL19CQAS\noQhvqZwSnbbKxPhbCIC5XYYTZsPjz12M3PNR2euSkmySS6yLAf5bAyd3IZG2re0poNVaFqq1B4GV\nbSMvn6qTmJWeznuSVeTHv47x7iP30zoqlAFzvqFj4wi8lXIG3dOUOWP78+Wm/cxZsY3lM00rNHPX\n7Wbzm5P5assBZv6wgc1vmn5sZo3sQ5ifF19tOYDeYKBDdDhrDpxhVFdTRebfZ6/y6WRziyWl52RK\nilej1ZprhVlDqzmNSGE5cLqcl8H53DTWXD/N+dw0ctTFZW0NVf7cGxpNG3/7c57uC+5CvraQUr2a\nASFd2Zyyl5dPzQdAWMtb81eSM5kyqHOlYz6eCvKKnF+17N44koWPDkciKn/om9C1HWM6tabNm5+x\ndP/xKgMnh/OcjFo06r3I5Pfb7nuTpj6jy2QC2gc8T4zvWNsnVaCl70S8JZHsTX0LBAJ61XvfTCX8\nvrAvOJ/zU6VxKrI1sbLF0r7UOQB0CpxJUx+TbVC4R0/CPXqQWLQPtT4Xf1lz2ga41tPS5YGTXClF\nq9ai8lGSnV61GOP8LsN5cNv3qPX2JWTVFlKhiC+7j6LJr/blQdQVAuWeDm39aQ16MkuLbHcERAIB\n73UcwpCI5uiNBpRiKcV2BFuupDblB6xhNBhZ/9N+vvt4IwIBBNbz4Zs/TblEWWl5fP/xJo7uukhw\nhB8TX7ifDr3Ll6l/XbSTe3o2YeaYL5HJJQwa25lJLw0uO3fakPmUFGt486vHys4rKVbzx9J9/Lpo\nJzqtjrAGgSzcMMN8Yi5GdAevOFlDKhMjV1SdGuDh6RqV5mGdYujVoiEAeoOByymZdGocQf1A02rX\nqK6tmPzFKm4tjI3o3IIALw9mDOvFukPlOT5tokyipjOG9eLwpQS6Notk/ZELZYGTt1JOx2hLP6oC\nPL1mkpP1mN1z1uvjrbYN3PJ1pffBChUTotvTxj/UqQKaJqpIXm1evhLxcZsX+fLKCvylPjXuU3c7\nCpnELEhKzc4nwMv5SvCXBvaqFDTd4taxqpTDnaWkeK1DgVMrv0m08rNcRdvU56GywKUqIjx7My76\nb4ttMb6P2BzH3tylHiGWczYVIv9K1xgU8Z3D1wY3BE4f/jINjVrHkZ0XeGle1fvZTbwDOT/6FTYm\nXOA5FztouxqRQMDVMa8TV5DNG8c2sz8tzu1jSqqpsH5w+POczk5mxLYfXDQjEyFKL/Y9UF5NKRII\nmdq8G/OqyAVzB3/0dqVaMiBw9HaoHPDHnkrghVGfMW76faw7ax5kP9brfb7ZNouX543DaDQybcj8\nSoHTsk+30rV/C7Nzf5y/hVWLd7Ih1rTCNnXwPJZ+upXPfnsehVLGmGfuZcwzpie39ORcfvthNyMm\nOZ+/YA9icc0kNltDozlCSbH1rWhHsVeTSSQS4hvgSU5m9XIRW9Yvl3GRSyQUlmp4bMFKVAoZb44x\n5Urq9AYMN7f16/maHhI85FICvM23VzzkUhKz8gDw9ZCzaMtBJvfvyLfTR2MtJ7uqnCVL6LTWV9yv\njHndLTrxGoOWXI0pH+vxKMf882zj3AP7r689yuPzVrJyl2nFc/6aXRSUaFgxu2oj4KoI8636IdBX\naTtgFwp9MRjMC1GsUVqyHlhsd/+7lOOW5HCpTEzHvuYeW9YYEtGcT1R+XLfhs1YXiFL5sazPI9y3\n6Su3zddHqmBkg9bUt2Ka6Qit/ULpHBRpVQndUfqHNeXDTubqv12qsKm5UxDg2DbM7U71J/ZdAmDg\nw50s9m/QrB716puqJAUCAS06NECj1iGVmW7DqKYhRDQyl5M4vvcSDZqV26W06NCATcsPVjr3FkGh\nPuTnFN9+CZcjENauzpq6xLpArbv5+c+XcIfr0qm4ZBZNHUWQtycHYivfr/EZpuTowlI1GXnmQVth\nqZrwAJOG28iurZi3bjct6gfTM8Z1cilGo/W/K3uCphK9tlIeZVVcKUzg88u/cL0wqaxSqiKu0HG6\n/f61lyAfT5a/+gjTv1xLqL8p4Plp1ji8PZxfjTx4NZ77YqKttvdvYTshXiYfSEnxcgdH1uHmGrEa\np+Fn87j23Eyz40bgx1PH2Xz5MtF+fjzbqQshniqnxqgzn9j2wVMBU6L15N0rOJ6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V7f4q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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jStthL0mLD1",
        "colab_type": "text"
      },
      "source": [
        "# BERT model\n",
        "\n",
        "BERT was our best performing model, so we will give a brief walkthrough to how we used it. We used the ktrain package to simplify training and use, so first we need to install ktrain:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQCoqI-Zq9Wo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "31898767-85d7-4704-c96a-aaf8b68ac918"
      },
      "source": [
        "!pip install ktrain\n",
        "!pip install tensorflow-gpu==1.14"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ktrain in /usr/local/lib/python3.6/dist-packages (0.7.0)\n",
            "Requirement already satisfied: keras-bert in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.80.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from ktrain) (2.21.0)\n",
            "Requirement already satisfied: networkx==2.3 in /usr/local/lib/python3.6/dist-packages (from ktrain) (2.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.14.0)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from ktrain) (3.1.2)\n",
            "Requirement already satisfied: langdetect in /usr/local/lib/python3.6/dist-packages (from ktrain) (1.0.7)\n",
            "Requirement already satisfied: jieba in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.39)\n",
            "Requirement already satisfied: scikit-learn==0.21.3 in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.21.3)\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.25.3)\n",
            "Requirement already satisfied: bokeh in /usr/local/lib/python3.6/dist-packages (from ktrain) (1.0.4)\n",
            "Requirement already satisfied: cchardet in /usr/local/lib/python3.6/dist-packages (from ktrain) (2.1.5)\n",
            "Requirement already satisfied: seqeval in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.0.12)\n",
            "Requirement already satisfied: fastprogress>=0.1.21 in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.1.22)\n",
            "Requirement already satisfied: keras-transformer>=0.30.0 in /usr/local/lib/python3.6/dist-packages (from keras-bert->ktrain) (0.31.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from keras-bert->ktrain) (1.17.4)\n",
            "Requirement already satisfied: Keras in /usr/local/lib/python3.6/dist-packages (from keras-bert->ktrain) (2.2.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->ktrain) (2019.11.28)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->ktrain) (1.24.3)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->ktrain) (2.8)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->ktrain) (3.0.4)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx==2.3->ktrain) (4.4.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->ktrain) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->ktrain) (2.6.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->ktrain) (2.4.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->ktrain) (1.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from langdetect->ktrain) (1.12.0)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn==0.21.3->ktrain) (1.3.3)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.2->ktrain) (2018.9)\n",
            "Requirement already satisfied: Jinja2>=2.7 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (2.10.3)\n",
            "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (19.2)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (3.13)\n",
            "Requirement already satisfied: tornado>=4.3 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (4.5.3)\n",
            "Requirement already satisfied: pillow>=4.0 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (4.3.0)\n",
            "Requirement already satisfied: keras-multi-head>=0.22.0 in /usr/local/lib/python3.6/dist-packages (from keras-transformer>=0.30.0->keras-bert->ktrain) (0.22.0)\n",
            "Requirement already satisfied: keras-pos-embd>=0.10.0 in /usr/local/lib/python3.6/dist-packages (from keras-transformer>=0.30.0->keras-bert->ktrain) (0.11.0)\n",
            "Requirement already satisfied: keras-embed-sim>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from keras-transformer>=0.30.0->keras-bert->ktrain) (0.7.0)\n",
            "Requirement already satisfied: keras-position-wise-feed-forward>=0.5.0 in /usr/local/lib/python3.6/dist-packages (from keras-transformer>=0.30.0->keras-bert->ktrain) (0.6.0)\n",
            "Requirement already satisfied: keras-layer-normalization>=0.12.0 in /usr/local/lib/python3.6/dist-packages (from keras-transformer>=0.30.0->keras-bert->ktrain) (0.14.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from Keras->keras-bert->ktrain) (2.8.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from Keras->keras-bert->ktrain) (1.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from Keras->keras-bert->ktrain) (1.0.8)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.0.0->ktrain) (42.0.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.7->bokeh->ktrain) (1.1.1)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=4.0->bokeh->ktrain) (0.46)\n",
            "Requirement already satisfied: keras-self-attention==0.41.0 in /usr/local/lib/python3.6/dist-packages (from keras-multi-head>=0.22.0->keras-transformer>=0.30.0->keras-bert->ktrain) (0.41.0)\n",
            "Requirement already satisfied: tensorflow-gpu==1.14 in /usr/local/lib/python3.6/dist-packages (1.14.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (0.33.6)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (1.17.4)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (0.8.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (1.12.0)\n",
            "Requirement already satisfied: tensorboard<1.15.0,>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (1.14.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (1.1.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (1.11.2)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (0.8.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (3.10.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (1.0.8)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (1.1.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (0.1.8)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (1.15.0)\n",
            "Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (1.14.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14) (0.2.2)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14) (42.0.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14) (3.1.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14) (0.16.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==1.14) (2.8.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgdPajqJ1r7n",
        "colab_type": "text"
      },
      "source": [
        "Let's also upload a balanced version of the dataset for use:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S__a7fGp1rJB",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "3913cfa4-1dec-461d-fc02-bb029f95141c"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2bd444de-ada1-4932-bec2-7402dab879d1\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-2bd444de-ada1-4932-bec2-7402dab879d1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving small_data_balanced.csv to small_data_balanced (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "en5Ab3T72N3S",
        "colab_type": "text"
      },
      "source": [
        "Loading the data into a pandas dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLfJka-M2NOy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from bs4 import BeautifulSoup as Soup\n",
        "\n",
        "def extract_text(df):\n",
        "    return [Soup(text, features=\"html.parser\").get_text() for text in df[\"Text\"]]\n",
        "\n",
        "for k in uploaded.keys():\n",
        "  df = pd.read_csv(k, sep=',', quotechar='\"')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xeQD7gJtO19",
        "colab_type": "text"
      },
      "source": [
        "Now, let's define a BERT wrapper class:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwYFA54HmLD2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import ktrain\n",
        "from ktrain import text\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "class BERT:\n",
        "    def __init__(self, max_len = 320, batch_size = 14, lr = 2e-5, epochs = 3):\n",
        "        self.model = None\n",
        "        self.learner = None\n",
        "        self.predictor = None\n",
        "        self.max_len = max_len\n",
        "        self.batch_size = batch_size\n",
        "        self.lr = lr\n",
        "        self.epochs = epochs\n",
        "\n",
        "    def fit(self, train_strings, y_train, show_plot=False, verbose=True):\n",
        "        tf.random.set_random_seed(0)\n",
        "        (x_train, y_train), (x_test, y_test), preproc = \\\n",
        "            text.texts_from_array(train_strings, y_train, class_names=[\"low\", \"high\"], preprocess_mode=\"bert\", maxlen=self.max_len, lang=\"en\")\n",
        "        self.model = text.text_classifier('bert', (x_train, y_train), preproc=preproc, verbose=verbose)\n",
        "        learner = ktrain.get_learner(self.model, train_data=(x_train, y_train), val_data=(x_test, y_test), batch_size=self.batch_size)\n",
        "        self.learner = learner\n",
        "        learner.fit_onecycle(self.lr,self.epochs, verbose=verbose)\n",
        "        if show_plot:\n",
        "          learner.plot('loss')\n",
        "          plt.show()\n",
        "        self.predictor = ktrain.get_predictor(learner.model, preproc)\n",
        "\n",
        "    def find_lr(self):\n",
        "        self.learner.lr_find(max_epochs=1, show_plot=True)\n",
        "        plt.show()\n",
        "\n",
        "    def predict(self, reviews_test):\n",
        "        return [(0 if i == 'low' else 1) for i in self.predictor.predict(reviews_test)]\n",
        "\n",
        "    def get_params(self, deep = True):\n",
        "        return {'model':self.model, 'learner':self.learner, \n",
        "                'predictor':self.predictor, 'max_len':self.max_len, \n",
        "                'lr':self.lr, \"batch_size\":self.batch_size, \"epochs\":self.epochs}\n",
        "    \n",
        "    def set_params(self, **parameters):\n",
        "        for parameter, value in parameters.items():\n",
        "            setattr(self, parameter, value)\n",
        "        return self\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KpO_ELTzv6ZZ",
        "colab_type": "text"
      },
      "source": [
        "Set up data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOh1IcgrtYUj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def label_func(item):\n",
        "    return 1 if item > 3 else 0\n",
        "\n",
        "label_col = \"Score\"\n",
        "#df[label_col] = df[label_col].apply(label_func)\n",
        "text_data = extract_text(df)\n",
        "labels = df[label_col]\n",
        "p_train = 0.5\n",
        "\n",
        "n_train = int(p_train * len(text_data))\n",
        "\n",
        "reviews_train = text_data[:n_train]\n",
        "reviews_test = text_data[n_train:]\n",
        "\n",
        "labels_train = labels[:n_train]\n",
        "labels_test = labels[n_train:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsdM8c5MzXVA",
        "colab_type": "text"
      },
      "source": [
        "Define a function to help evaluate a model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2x2517K6wD-a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score, fbeta_score\n",
        "import numpy as np\n",
        "\n",
        "metrics = {\"Accurracy\": accuracy_score, \"F1\": lambda a, b: fbeta_score(a, b, 1)}\n",
        "\n",
        "def evaluate(models, names, show_plot = False, verbose = True):\n",
        "  results = {}\n",
        "  for metric in metrics.keys():\n",
        "    results[metric] = []\n",
        "\n",
        "  for model, name in zip(models, names):\n",
        "    model.fit(reviews_train, np.array(labels_train), show_plot = show_plot, verbose = verbose)\n",
        "    predictions = np.round(model.predict(reviews_test))\n",
        "    print(\"Model: %s\" % name)\n",
        "    for k, metric in metrics.items():\n",
        "      score = metric(predictions, np.array(labels_test))\n",
        "      print(k + \": \" + str(score))\n",
        "      results[k].append(score)\n",
        "\n",
        "  return results"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4HxhuQAkziyD",
        "colab_type": "text"
      },
      "source": [
        "Let's get a baseline for BERT. We'll use a max (starting) learning rate of 2e-5 (per Google's recommendations), 3 epochs, a maximum sequence length of 320, and a minibatch size of 14. \n",
        "\n",
        "64\t64\n",
        "...\t128\t32\n",
        "...\t256\t16\n",
        "...\t320\t14\n",
        "...\t384\t12\n",
        "...\t512\t6"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CK3aa0K-zexx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cf55ff11-d263-45bf-b8b5-36120e081e53"
      },
      "source": [
        "model = BERT()\n",
        "evaluate([model], [\"Baseline Bert\"], show_plot=True)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 ... 1 1 0]\n",
            "preprocessing train...\n",
            "language: en\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "done."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "preprocessing test...\n",
            "language: en\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "done."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[[1. 0.]\n",
            " [0. 1.]\n",
            " [0. 1.]\n",
            " ...\n",
            " [0. 1.]\n",
            " [1. 0.]\n",
            " [1. 0.]]\n",
            "Is Multi-Label? False\n",
            "maxlen is 320\n",
            "done.\n",
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 2e-05...\n",
            "Train on 9000 samples, validate on 1000 samples\n",
            "Epoch 1/3\n",
            "9000/9000 [==============================] - 710s 79ms/sample - loss: 0.3596 - acc: 0.8412 - val_loss: 0.2500 - val_acc: 0.9030\n",
            "Epoch 2/3\n",
            "9000/9000 [==============================] - 715s 79ms/sample - loss: 0.2047 - acc: 0.9236 - val_loss: 0.2235 - val_acc: 0.9030\n",
            "Epoch 3/3\n",
            "9000/9000 [==============================] - 716s 80ms/sample - loss: 0.0729 - acc: 0.9756 - val_loss: 0.2782 - val_acc: 0.9010\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KuTcNiApsUKvaLJnUk7gGYTy1cBsPfRTPmbQsV5ellPIQGhAVXK3QAD66vyN/G9yc1fvP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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-fa5a30645d76>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBERT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Baseline Bert\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_plot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-5-8c31a613ecc8>\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(models, names, show_plot, verbose)\u001b[0m\n\u001b[1;32m     11\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_plot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshow_plot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindividual_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Model: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'individual_model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9W39vcqd7Dju",
        "colab_type": "text"
      },
      "source": [
        "We can graph the effectiveness of learning rates by simulating training:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBOz7CLW7MQM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.find_lr()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzfpHM3Z7Mzw",
        "colab_type": "text"
      },
      "source": [
        "This lines up with Google's recommendation of 2e-5\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GUzOf3L7S1a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rf6XUvnWB7qI",
        "colab_type": "text"
      },
      "source": [
        "Through an ad hoc search, we determined that the best hyperparameters were:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q2CjzJqVB2K6",
        "colab_type": "text"
      },
      "source": [
        "# Logistic Regression\n",
        "\n",
        "The runner up to BERT was Logistic Regression, so let's do some hyperparameter search:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNENS2zODQx5",
        "colab_type": "text"
      },
      "source": [
        "First, we'll define a helpful Model and Vectorizer wrapper classes. This is useful because we need to vectorize the text before we can use it in a logistic regression, and the Model class helps bundle the functionality of the vectorizer for easier use."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cn0Z1NI6DzDV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from typing import List\n",
        "\n",
        "import numpy as np\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "\n",
        "class Vectorizer:\n",
        "\n",
        "    def __init__(self, pca=True, base_model=TfidfVectorizer(lowercase=True), make_array=False):\n",
        "        self.base_model = base_model\n",
        "        self.dimensionality_reducer = None\n",
        "        self.pca = pca\n",
        "        self.make_array = make_array\n",
        "\n",
        "    def fit_transform(self, reviews: List[str]):\n",
        "        X_train = self.base_model.fit_transform(reviews)\n",
        "        if self.pca:\n",
        "            self.dimensionality_reducer = TruncatedSVD(n_components=1000)\n",
        "            X_train = self.dimensionality_reducer.fit_transform(X_train)\n",
        "        elif self.make_array:\n",
        "            X_train = X_train.toarray()\n",
        "        return X_train\n",
        "\n",
        "    def transform(self, comments: List[str]):\n",
        "        if self.pca:\n",
        "            comments = self.dimensionality_reducer.transform(self.base_model.transform(comments))\n",
        "        else:\n",
        "            comments = self.base_model.transform(comments)\n",
        "            if self.make_array:\n",
        "                comments = comments.toarray()\n",
        "        return comments\n",
        "\n",
        "\n",
        "class Model:\n",
        "\n",
        "    def __init__(self, vectorizer=Vectorizer(pca=True), model=SVC()):\n",
        "        self.vectorizer = vectorizer\n",
        "        self.model = model\n",
        "\n",
        "    def fit(self, reviews_train: List[str], y_train, show_plot = True, verbose = False):\n",
        "        X_train = self.vectorizer.fit_transform(reviews_train) if self.vectorizer is not None else reviews_train\n",
        "        self.model.fit(X_train, y_train)\n",
        "\n",
        "    def predict(self, reviews_test: List[str]):\n",
        "        X_test = self.vectorizer.transform(reviews_test) if self.vectorizer is not None else reviews_test\n",
        "        return np.round(np.array(self.model.predict(X_test)))\n",
        "\n",
        "    def get_params(self, deep = True):\n",
        "        return {'model' : self.model, 'vectorizer' : self.vectorizer}\n",
        "\n",
        "    def set_params(self, **parameters):\n",
        "        for parameter, value in parameters.items():\n",
        "            setattr(self, parameter, value)\n",
        "        return self\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOBFhW93CL3Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c7d26c12-1c6a-4b54-993d-24899f1bebae"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "models = []\n",
        "names = []\n",
        "\n",
        "for c in [0.25, 0.5, 1, 1.5, 2, 4, 8, 16, 32]:\n",
        "  for rat in [0, 0.25, 0.5, 0.75, 1]:\n",
        "    models.append(Model(model=LogisticRegression(C=c, penalty = 'elasticnet', solver='saga', l1_ratio = rat), vectorizer=Vectorizer(pca=False)))\n",
        "    names.append(\"Logistic: C: %s, l1_ratio: %s\" % (c, rat))\n",
        "\n",
        "results = evaluate(models, names)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 0.25, l1_ratio: 0\n",
            "Accurracy: 0.8254\n",
            "F1: 0.8224527150701648\n",
            "Model: Logistic: C: 0.25, l1_ratio: 0.25\n",
            "Accurracy: 0.8093\n",
            "F1: 0.806533428020696\n",
            "Model: Logistic: C: 0.25, l1_ratio: 0.5\n",
            "Accurracy: 0.7993\n",
            "F1: 0.7963470319634703\n",
            "Model: Logistic: C: 0.25, l1_ratio: 0.75\n",
            "Accurracy: 0.7925\n",
            "F1: 0.7891045837991665\n",
            "Model: Logistic: C: 0.25, l1_ratio: 1\n",
            "Accurracy: 0.7846\n",
            "F1: 0.7791222313371616\n",
            "Model: Logistic: C: 0.5, l1_ratio: 0\n",
            "Accurracy: 0.8327\n",
            "F1: 0.8294771175211496\n",
            "Model: Logistic: C: 0.5, l1_ratio: 0.25\n",
            "Accurracy: 0.8235\n",
            "F1: 0.8206118507978454\n",
            "Model: Logistic: C: 0.5, l1_ratio: 0.5\n",
            "Accurracy: 0.8173\n",
            "F1: 0.8147622427253371\n",
            "Model: Logistic: C: 0.5, l1_ratio: 0.75\n",
            "Accurracy: 0.8141\n",
            "F1: 0.8113264995432864\n",
            "Model: Logistic: C: 0.5, l1_ratio: 1\n",
            "Accurracy: 0.8128\n",
            "F1: 0.8092520888526594\n",
            "Model: Logistic: C: 1, l1_ratio: 0\n",
            "Accurracy: 0.8373\n",
            "F1: 0.834368319250738\n",
            "Model: Logistic: C: 1, l1_ratio: 0.25\n",
            "Accurracy: 0.8341\n",
            "F1: 0.8314880650076181\n",
            "Model: Logistic: C: 1, l1_ratio: 0.5\n",
            "Accurracy: 0.832\n",
            "F1: 0.8295108585346052\n",
            "Model: Logistic: C: 1, l1_ratio: 0.75\n",
            "Accurracy: 0.8278\n",
            "F1: 0.8253195374315277\n",
            "Model: Logistic: C: 1, l1_ratio: 1\n",
            "Accurracy: 0.8258\n",
            "F1: 0.8236127987039287\n",
            "Model: Logistic: C: 1.5, l1_ratio: 0\n",
            "Accurracy: 0.8402\n",
            "F1: 0.8374364191251271\n",
            "Model: Logistic: C: 1.5, l1_ratio: 0.25\n",
            "Accurracy: 0.8373\n",
            "F1: 0.8348055640166515\n",
            "Model: Logistic: C: 1.5, l1_ratio: 0.5\n",
            "Accurracy: 0.8349\n",
            "F1: 0.8325727613832268\n",
            "Model: Logistic: C: 1.5, l1_ratio: 0.75\n",
            "Accurracy: 0.8328\n",
            "F1: 0.8307692307692308\n",
            "Model: Logistic: C: 1.5, l1_ratio: 1\n",
            "Accurracy: 0.8327\n",
            "F1: 0.8306165839829908\n",
            "Model: Logistic: C: 2, l1_ratio: 0\n",
            "Accurracy: 0.8405\n",
            "F1: 0.8380217325073627\n",
            "Model: Logistic: C: 2, l1_ratio: 0.25\n",
            "Accurracy: 0.8388\n",
            "F1: 0.8364448051948051\n",
            "Model: Logistic: C: 2, l1_ratio: 0.5\n",
            "Accurracy: 0.837\n",
            "F1: 0.8348865478119935\n",
            "Model: Logistic: C: 2, l1_ratio: 0.75\n",
            "Accurracy: 0.8356\n",
            "F1: 0.83373786407767\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 2, l1_ratio: 1\n",
            "Accurracy: 0.832\n",
            "F1: 0.8304400484457005\n",
            "Model: Logistic: C: 4, l1_ratio: 0\n",
            "Accurracy: 0.8404\n",
            "F1: 0.8385269121813032\n",
            "Model: Logistic: C: 4, l1_ratio: 0.25\n",
            "Accurracy: 0.8385\n",
            "F1: 0.8365550045541948\n",
            "Model: Logistic: C: 4, l1_ratio: 0.5\n",
            "Accurracy: 0.8389\n",
            "F1: 0.8368607594936709\n",
            "Model: Logistic: C: 4, l1_ratio: 0.75\n",
            "Accurracy: 0.8347\n",
            "F1: 0.8325736858097842\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 4, l1_ratio: 1\n",
            "Accurracy: 0.8295\n",
            "F1: 0.8279342012312041\n",
            "Model: Logistic: C: 8, l1_ratio: 0\n",
            "Accurracy: 0.836\n",
            "F1: 0.8344771901493742\n",
            "Model: Logistic: C: 8, l1_ratio: 0.25\n",
            "Accurracy: 0.8331\n",
            "F1: 0.8313971108192746\n",
            "Model: Logistic: C: 8, l1_ratio: 0.5\n",
            "Accurracy: 0.8311\n",
            "F1: 0.8295488949439904\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 8, l1_ratio: 0.75\n",
            "Accurracy: 0.826\n",
            "F1: 0.824313408723748\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 8, l1_ratio: 1\n",
            "Accurracy: 0.818\n",
            "F1: 0.8166431593794076\n",
            "Model: Logistic: C: 16, l1_ratio: 0\n",
            "Accurracy: 0.8278\n",
            "F1: 0.826271186440678\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 16, l1_ratio: 0.25\n",
            "Accurracy: 0.8253\n",
            "F1: 0.8238023197175997\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 16, l1_ratio: 0.5\n",
            "Accurracy: 0.8238\n",
            "F1: 0.8224863993552287\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 16, l1_ratio: 0.75\n",
            "Accurracy: 0.8199\n",
            "F1: 0.8183193785937658\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 16, l1_ratio: 1\n",
            "Accurracy: 0.8136\n",
            "F1: 0.8123993558776167\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 32, l1_ratio: 0\n",
            "Accurracy: 0.8196\n",
            "F1: 0.8185840707964602\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 32, l1_ratio: 0.25\n",
            "Accurracy: 0.8196\n",
            "F1: 0.8183282980866063\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 32, l1_ratio: 0.5\n",
            "Accurracy: 0.8167\n",
            "F1: 0.8153520701118163\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 32, l1_ratio: 0.75\n",
            "Accurracy: 0.8138\n",
            "F1: 0.8125251711639145\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: Logistic: C: 32, l1_ratio: 1\n",
            "Accurracy: 0.81\n",
            "F1: 0.8086606243705942\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}