[DACON]creditcard_data_anomaly_detection/AutoEncoder_torch_submit.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "신용카드3_AutoEncoder.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## 1. 구글 드라이브 연동"
      ],
      "metadata": {
        "id": "bH1dg8aPLDvK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwFwDe0734L_",
        "outputId": "c6d4c9c9-6e49-4de3-d989-665ff4fc9feb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive/; to attempt to forcibly remount, call drive.mount(\"/content/gdrive/\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# 구글 드라이브 연동\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/')\n",
        "path = '/content/gdrive/MyDrive/Dacon/'\n",
        "file_name_test = 'test.csv'\n",
        "file_name_train = 'train.csv'\n",
        "file_name_val = 'val.csv'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. 데이터 로드"
      ],
      "metadata": {
        "id": "pvT3butdLGy1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "test_df = pd.read_csv(path+file_name_test)\n",
        "train_df = pd.read_csv(path+file_name_train)\n",
        "val_df = pd.read_csv(path+file_name_val)"
      ],
      "metadata": {
        "id": "Dv0ud3IF4BJq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. 데이터 분석"
      ],
      "metadata": {
        "id": "FJbdFfmiLaqI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "AN6TLW8fLZhU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) 데이터 시각화 분석"
      ],
      "metadata": {
        "id": "VvqyDmjTUvDM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "slice_data = val_df[[i for i in val_df.columns[1:-1]]]\n",
        "slice_data['Y'] = pd.Series(val_df.Class, dtype='category')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtCQn_fiL0nd",
        "outputId": "d445fc75-8c13-483b-af29-ff4e8b9b4491"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in slice_data.columns[:-1]:\n",
        "  sns.distplot(slice_data.loc[slice_data['Y'] == 0, i], bins=20, color='skyblue', label='normal')\n",
        "  sns.distplot(slice_data.loc[slice_data['Y'] == 1, i], bins=20, color='red', label='outlier')\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ddLS6f1zOqCx",
        "outputId": "938f0701-25ef-479e-cb65-17e00995a586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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3wt0fd/ffA+puEjkGbO3KYxz5EFdQE1M9Vfuv9H7gZcBuAHdfDYzr45xJwPqy1xuibdVqNbMVZrbMzC6sdICZzY+OWbFly5Z+vLWINMK2rgIjWo58BBNA6R5DGsUUv2oDIhvVAgCIJsvFHd9T3H0W8Fbg38zs5N4HuPtCd5/l7rPGjh0bc3FE5GjtyhYZmTm6yQtpUxNTvVQbEL8ws48DbWZ2DnAH8P0+ztkInFD2enK0rSruvjH6uRb4OfDCas8Vkea0N188quYlCLccBTUx1UO1/1ILgC2EUUV/AywFPtHHOcuBGWY2zcwywKXAkmo+zMxGmllL9HwMoXnrkcOfJSLNrOjOvu7iUY1gAkiakTCNYqqHakcxFc3su8B33b2qxn53z5vZFcA9hBFPi9x9lZndAKxw9yVm9mLgLmAk8AYzu97dTwdOBW4xsyIhxG7sNfpJRAaYzrwf9RDXEt00qD4OGxAWbvd0LXAFUW0jmp/wRXe/oa83d/elhNpG+bZryp4vJzQ99T7v/4DnVVF+ERkg9ka9ykPSCbryR3dxTydMTUx10FeUf4jQvPNidx/l7qOAs4CXmdmHYi+diAwa+/IhII5mDkRJOqFRTPXQ17/U24G3uPu60oao0/htwF/FWTARGVxyhfAXf0sNluhWE1N99BUQaXff2ntj1A+RjqdIIjIYZYtHHxArt3axcmsXuYKzo6vAyq26sWWc+gqI3BHuExE5SDaqQWRqUINImlHQ3Qdi19copueb2e4K2w1ojaE8IjJIZQuh06B0w5+jkUxALn/UbyN9OGxAuLvu1yQiNZErOJmEHdUyGyWhBqFe6rgd/XACEZEqZItek+YlgKRBQfkQOwWEiNRFruC01KB5CdQHUS8KCBGpi2yhxjUI5UPsFBAiUhe5otdkDgRAMmE4YX0niY8CQkTqIht1UtdCKWdUi4iXAkJE6iJXqGENIhoJVdBs6lgpIESkLrI1DYjwUzWIeCkgRCR27l7jYa5RDUJ9ELFSQIhI7EoL69VsmGt05VINIl4KCBGJXc9KrrW55KgGUR8KCBGJXWkl11rOgwDNpo6bAkJEYnegBlHDmdSgGkTcFBAiErtaLvUN6oOoFwWEiMTuQBOTahADigJCRGJXy9uNgvog6kUBISKxy9Y4IBJmGKpBxE0BISKxy9V4FBNoRdd66OuWoyIiwcKFR3xqdvJzSU085UDfQS0kE7onRNxirUGY2Vwze8zMOsxsQYX9Lzez35pZ3sze1GvfZWa2OnpcFmc5RSQ+6/d2s72YJGHGyq1drNzaVZP3VQ0ifrEFhJklgZuB84HTgLeY2Wm9DnsSeAfwzV7njgKuBc4CZgPXmtnIuMoqIvHqTqVI1fhqkzTTaq4xi7MGMRvocPe17p4DFgPzyg9w98fd/fdA77EI5wH3uvt2d98B3AvMjbGsIhKj7lSaVA2blyDUIPLKh1jFGRCTgPVlrzdE22p2rpnNN7MVZrZiy5YtR1xQEYlXdzJNqkZzIErSCSOvGkSsBvQoJndf6O6z3H3W2LFjG10cETmE7lS65k1MqYSRVyd1rOIMiI3ACWWvJ0fb4j5XRJpMdzJV8yamdMLoVg0iVnEGxHJghplNM7MMcCmwpMpz7wHONbORUef0udE2ERmAQg2itgGRMsgXw82IJB6xBYS754ErCBf2PwK3u/sqM7vBzC4AMLMXm9kG4GLgFjNbFZ27HfgUIWSWAzdE20RkgHGiPoja5gPphIX31nIbsYl1opy7LwWW9tp2Tdnz5YTmo0rnLgIWxVk+EYlfIZHEE4na1yCi9+sqFMkkkzV9bwkGdCe1iDS/7mT4OzSOPgjoWedJak8BISKx6k6lAWIYxRR+dikgYqOAEJFYdSdLAaEaxECjgBCRWJVqEDVcyBXoabLq0k0hYqOAEJFYdaeiPoiYahBqYoqPAkJEYnWgianGndSlPgg1McVHASEiscqlM0DPX/y1kjAjadCVVxNTXBQQIhKrbLoF8yLpGK42qYSpBhEjBYSIxCqbbiHTncNq3MQEkDZTH0SMFBAiEqtsOkNLdy6W904l1AcRJwWEiMQqm26hpTsby3unE0anhrnGRgEhIrGKMyAyCaNTt5WLjQJCRGIVAiKeJqZM0tifL2rJ75goIEQkNkWgK91Ca64zlvfPJIyCQ043DoqFAkJEYtOZasETCdpyXbG8fzpav0PNTPFQQIhIbPZkWgFoy8ZXgwDYr8lysVBAiEhs9qVDQLTHVIPoCQjVIOKggBCR2OxNtwHQGlcNIqkaRJxiveWoSE0sXHj4/fPnx3v+YNDXfwOI5b/D7kwbuNOW66Kvga6jv/bVw+7f9leXP2tbqQbRqYCIhWoQIhKbXS3ttGU7SXo8F/CkhYeamOKhgBCR2OzKtDO0a19s729mtKcSamKKiQJCRGKzq6WdITEGBEBbyhQQMVFAiEgsihh7Mm2xB0R7KqF5EDFRQIhILPZkWnFLMLRrf6yfoyam+MQaEGY218weM7MOM1tQYX+LmX072v8bM5sabZ9qZp1mtjJ6fDnOcopI7e1qGQJQlyYm1SDiEdswVzNLAjcD5wAbgOVmtsTdHyk77HJgh7tPN7NLgc8Al0T71rj7C+Iqn4jEa1emHSDWTmoINYhs0ckXnVSNb2t6rItzHsRsoMPd1wKY2WJgHlAeEPOA66LndwL/bnHcdkpEjs6OHbBmDWzeDDt3Qi4H2Whmw5Ah4TFiBJxwAowZA2bsamkHd9pjb2LqmQsxLJOM9bOONXEGxCRgfdnrDcBZhzrG3fNmtgsYHe2bZma/A3YDn3D3X/X+ADObD8wHOPHEE2tbepFj2bZtcO+9cN998JOfwOOPH7w/mYSWFnCHzl6zpNvaYOpURr78fCaeY7HNgTjwcanQUr4/7wzLxPpRx5xmnUm9CTjR3beZ2YuA75rZ6e6+u/wgd18ILASYNWuWGiFFjkZnJ3z/+/D1r8Pdd0M+D8cdB696FbzvffDEEzBhAowcCamyS0ehAPv3h1B58klYvx5Wr+b0Wz7P6bd8ntz4Cew766XsvOAvKIwYWfNit0cBodnUtRdnQGwETih7PTnaVumYDWaWAo4Dtnm4+0cWwN0fNLM1wCnAihjLK3Js2rEDfv5zuOoq2L4dJk6ED30I3vhGeNGLesLgUMt1JJMwbFh4TJ16YPPXR5/K8+/7Hqf88E5Gfu87HHfPD9lx0Zt55gNXkp0xs2bFLzUxaTZ17cUZEMuBGWY2jRAElwJv7XXMEuAy4NfAm4Cfurub2Vhgu7sXzOwkYAawNsayihx7nn4ali6FBx4ITUUXXQTvfW+oMSSPri2/iPHU1Jmc8LqLeGb6NNIbN5Davo2Rt3+DUYtvY+dFF7P5ygVkZ5561F+jVIPYr3tT11xsARH1KVwB3AMkgUXuvsrMbgBWuPsS4KvAbWbWAWwnhAjAy4EbzKybcFOq97j79rjKKnJMKQXDb34D6XQIhFe/Gj7+8Zp9xK6WdoqJBCOyewHonjSZzVddy6aPfZJxX/oCY776ZUbcdQfb//IdbFpwDfnjxx/xZ7VqRdfYxNoH4e5LgaW9tl1T9rwLuLjCed8BvhNn2USOOXv2wA9+AL/8ZaghzJkD554Lw4eH/dWs+FqlHS1DARjVtfeg7YUxY9l0zad55v0f5PgvfI4xX/kPRtx1B09/6KMUhg2DVLrfn5Uw01yImDRrJ7WI1EpnJ/zoR6HjOZeDP/9zeP3re4IhBttbwyS5kV172VFhf2H0GJ664TNsvezdTLr+KiZ++hq6jx/P9ovfSm7aSf3+PM2mjoeW2hAZrIpF+MY3YOZMuOuu8PPaa+Gtb401HAB2tA4lU+hmSP7wd4HInTyddV+7gzXfvAvLZRl3878y4jvfxrr6d4Ohdi3YFwvVIEQGo2XL4IMfDP0MZ54JF18cAqJOdrQMZUTXPqqd9brnnLls/vtPcNzd32fo/b+gbdXD7HjjJXSd/ryqzm9LJtieLRx5gaUi1SBEBpP16+Ftb4OXvCTMW/iv/4Lly+saDgDbW4cyKru37wPLeEsLOy98E8984MMU29sZu+gWRn77G1XVJoakE+ztVg2i1hQQIoPBvn1w3XUhCO68E66+Glavhne8AxL1/d+8EC2zMbKrfwFRkpsylac/+FF2v+Zchixfxvh//ida1qw+7DnD0wm6Ck53UR3VtaSAEBnIisUw83nmTLj+erjgAnjsMfj0p2Ho0IYUaVvrMNwSjOncc+Rvkkqx67UX8Mz7P4QnEoz9j5uYeO0CrKur4uHDMuFStjunZqZaUkCIDETusGQJvPCF8Pa3hyUw7r8fFi+GKVMaVqz1e7t5JBl1gG/Zwvq93Uf1frlpJ/H0h69i30vOZtyXvsApc15K20O/e9Zxw6NF+nbn1MxUSwoIkYHmvvtCH8O8eWEI6ze/GTqjX/ayRpcMgK3DR5HO5xh2NDWIMt7Swo43XsKaxd8jtXMHp8x9Ocf/y41hrajI8HRUg1A/RE0pIEQGgmIxTHJ7xSvCBLennoKvfAUeeQTe8pa69zMczuZR4xm3c0vVI5iqtec15/Lorx5k5+svZMKN1zPj9a+mZc1qVm7tYu3uHAB/2pll5dbKzVDSf83zWyUiz7Z3bwiC5z4X3vAGWLcObropdEBffvnBq6o2gT1tQ9nbNpQJ2zfH8v6FkaN44j9v4/GFt9LSsZqZrzqLsV++iUSxSEvC6Cqok7qWFBAyOLmHG9rs3BmaYbq7w7aBwD0soPc3fxP6Ft79bshkQmf0mjXwgQ+EezE0oadGhTWVJm7bFOvn7LzozTz2qxXsOfuVTPrkx5j+hjmMX99Bl5bbqKnm+vNDpBq5XGhi2bgxLE/9wAPh+YYN4Y5n+/dDV1dolilnFv7izmTCCJ9hw8KM4oceCktcT50K06aFx/HH17fZJpcL/Qjf/W4Ypvrkk+HGO5dcEgLiJS8J5W9ym0aNZ+j+PQyL+TajAN0TJrHuG99h5B3fYtLVH+HiN76CZe9fAB/7+9g/+1ihgJDm1tkJf/oTdHSEiV9PPQVbtvTUBszCxXzyZJg+Hc4+O9z+sq2t5/HrX4cLcD4fahLZbGi62bMHNm0KI3+291osuLU1jAYqBcaUKeFx4onh5/jxR74ktnsItIcegpUrw+J5998fgi2TCQvo3XBD6IQeMeLo/vvVUd4SPD1iHNM2P16/DzVjx5vfyp6Xv4oRV36As//1evb94odw261w2mn1K8cgpYCQw6tmhc/582v3eV1d4YL54x+Hi+Zvfxsu6gDjxoUgmD0bJk0Kj9Gj+26HnzPn8Pvnzw8X5yeeCG38jz8efpYev/lNuKlOuUQCRo0KtZAhQ+AFLwh3WhsxIiyhnUyGY/bvD2H0wAOwa1d4n+3bD75N58SJ8Nd/HZbdfs1rwl3c+quGK7EeqQ1DR5NPpZl4iP6H0V/7amyfnR8/gZULv8mDi7/Naz97Vfj3+MhHwoTBIUNi+9zBTgEhjdfR0bPa6M9+Fi6emUwIgg9/OPylf9JJ8U78am+HU08Nj0puuilc2LdtCz9Lz/fuhd274X//N2zbtevg88zCBSqRCOExZkyo6UyYEMJu8uRQy6llyDbI48eNw4pFjt/5TEM+f1gmya/Pu4hh587h1TddD//0T3DbbfD5z8Ob3jQgmuiajQJC6m///nCLy7vvDsHQ0RG2T58O73oXzJ0Lr3xluGhDU/x1TGtr+Et/4sTK+0sXePdwj+ZCIfSBtLaGC1MzfIeYrRt+PGN3bSVdyPd9cAyGRneW23bcKPjv/w59N1dcAW9+c6id3XRTGA0mVVNASPzcw/IPpUD4xS9CP0BbW7iT2d/9XQiF6dMbXdKjV+oIb7Lhp3Hbl2rh6SEjeP7Tv29YGZIJY0jK2FOaLPeyl8GKFSGcr74azjgjLHV+3XWD43etDo6t32Kpn127wozfH/0I7rknjMqB0ITzvvfB+eeHG9e0tja2nFITj46aBMCkrfEOb+3LsHSSXeXrMSWT4T7bl1wCn/1sqEUsXhz6fD75STjhhMYVdgBQQEhtFAqhQ7kUCMuWhW3DhoWO16uuCrWEqVMbXVKpsSLw23Encfy+nYzct7OhZRmWSbC5M09nvkhbqmyY8u2tje0AAAppSURBVKhRcOONobb6j/8It9wCt94Kl10GV14Jz3lO4wrdxDRRTo5MoRBG+9x7L1x4YRhhNHs2XHNNGIm0YEEYjbRtW7ib2Xveo3AYpH437iS2tQ3nJZsebXRRGNUShh4/eahFAidMgC9+McxEf+c7Qyf2qaeGWeo/+lH4vZYDVIOQ6uzbF5qJ1q0Lncpr1oQgADj55DBmf84cOOccGDu2sWWVutncfhw/O+F5nLRzMzN3PMWGBpfnuEyCpMG63d3MHHGY2eZTpsCXvwyf+hR86Utw881hrasTTwxLmLz97WH+yzFOASHPtm0bPPwwPPggfPvboaawZUvP/gkT4KyzYMaM0Nn3sY81rKjSOB1dxt3PPYtMLssL/7CMDd1Ht7R3LSTMGNOaZPWuLOf6EBJ9DW0dOzbcp/uqq+B73wsd2tdeGx5nnRX6LubNC8Osj0EKiGOVe1iWYvXqsCLoqlXh8cgj8PTTPceNGhX+2jr77J6ZxJp4dMzLJZLcf/pZdLa0cc5vf0prd7bRRTpgQnualdu66NiV45TD1SLKZTLhvt0XXxz+ILr99tCZfeWV4TF9Opx3Xs8Q7AbdjKneFBCDVT4fLvQbN4bH+vWwdm1oGlq7NjQVlc/mHTYsLE3w2tfC6aeHx5lnhrWBRCKb20ew4viT+dPIieQSKf7s0QcYs2d73yfW0bi2JMdlEvxq035OHp4hmejnBLmpU+GjHw2Pjo6e4dmLFoWmqGQyDJmdPbvn8ZznDMqhzbF+IzObC3wBSAJfcfcbe+1vAb4GvAjYBlzi7o9H+64CLgcKwN+6+z1xlrXpuYcZxdu2hcfWrQc/37q1ZwG7jRtDOPRerG7IkFBVPuWU8JfQSSeF/oPTTgvD/TTTVCIOFM1IuLM33UrHiAn8YfSJbBw2mnQhz6nbNzD+yQ7G7t7W6KI+S8KM10wawv+s28PSJ/fy+ilDsSP93Z4+Paye+4EPhD63++8Ps/0feCDUMG65JRyXTocm15kzQ1jMnBlq25MmhdnypUmfA0xsAWFmSeBm4BxgA7DczJa4+yNlh10O7HD36WZ2KfAZ4BIzOw24FDgdmAj8xMxOcff6DDFwDxfX0qM0K7aaR2kxuGw2LBBX6Xn5666ucOEvLR5X+tn7+bZtPWsS9WYWmoImTAi/kGecEWb8ltYrmjgx/JKOG6cQaBA/8NNwK/00itFr8kWc6FcPcBx3DmwDKHr45zMgnTAySSNpUHDIJ9MUEknylqCQSFDEwIxsMkVXMkPCnZQXMHc6Uxl2tbSzO9NOupCntdBNwotkk2k6Uy1saxvGxqGjyCXTB32H4ft286LVv+OkTevIFBrf33A4p4xo4ezxBe7fvJ+93UVeOr6NcW0pWpN25GHR2hoGYpTW9ioWQxPtAw+E5tlHH4U//hG+//2D7nYHhHW6xo8Pa4eNHBn+fy09Row4eHHJ9vaDn2cyPZMvez+SyZ7nMaw+HGcNYjbQ4e5rAcxsMTAPKA+IecB10fM7gX+38K83D1js7llgnZl1RO/365qXcsuWcPEsv8g3Qmn56dLPYcPCBf+UU8Lz0aPDY8yYZz8fMeLIVxaVmlr+TCe/3LTvoIu7A/7ivzj8iQ8fZTPNmW/o9ynJQp5CInnQHw2pfDdDu/YxZfMTtGU7cTMy+Rzjdm5h5N6dNb9LXFxWbu1iSMo4fWQLj+7M8q2OEGgGJCwE7AfPGH10H5JIhJrCzJkHb+/uDv0YGzaER/lS9Dt2hObehx4Ka3ft3Xt0ZSiZPTssKllj5jHdRMXM3gTMdfd3Ra/fDpzl7leUHfOH6JgN0es1wFmE0Fjm7l+Ptn8VuNvd7+z1GfOB0ipnM4HHYvky/TcG2NroQtSQvk9z0/dpbs3+faa4e8Wx6QO6V8XdFwJNtwqama1w91mNLket6Ps0N32f5jaQv0+cM6k3AuULnUyOtlU8xsxSwHGEzupqzhURkRjFGRDLgRlmNs3MMoRO5yW9jlkCXBY9fxPwUw9tXkuAS82sxcymATOAB2Isq4iI9BJbE5O7583sCuAewjDXRe6+ysxuAFa4+xLgq8BtUSf0dkKIEB13O6FDOw+8v24jmGqj6Zq9jpK+T3PT92luA/b7xNZJLSIiA5tWcxURkYoUECIiUpECIgZm9mEzczMbE702M7vJzDrM7Pdmdmajy1gNM/tUVN6VZvZjM5sYbR+o3+dzZvZoVOa7zGxE2b6rou/zmJmd18hyVsvMLjazVWZWNLNZvfYNuO8DYXmeqMwdZrag0eXpLzNbZGbPRHO8SttGmdm9ZrY6+jmykWXsDwVEjZnZCcC5wJNlm88njMSaQZjY9x8NKNqR+Jy7n+HuLwB+AFwTbR+o3+de4LnufgbwJ+AqgF5Lu8wFvhQtFdPs/gD8BfDL8o0D9fuULc9zPnAa8Jbouwwk/034b15uAXCfu88A7oteDwgKiNr7V+Cj9Cy/A2HpkK95sAwYYWYTGlK6fnD33WUvh9DznQbq9/mxu5cWyVlGmF8DZUu7uPs6oLS0S1Nz9z+6e6XVAwbk96FseR53zwGl5XkGDHf/JWFEZrl5wK3R81uBC+taqKOggKghM5sHbHT3h3rtmgSsL3u9IdrW9MzsH8xsPfCX9NQgBuz3KfPXwN3R88HwfcoN1O8zUMvdl+PdfVP0fDNwfCML0x8DeqmNRjCznwDjK+y6Gvg4oXlpwDjc93H377n71cDV0fLrVwDX1rWA/dTX94mOuZowv+Yb9Szbkajm+8jA4e5uZgNmboECop/cfU6l7Wb2PGAa8FC0nPBk4LdmNpsmXjrkUN+ngm8ASwkBMWC/j5m9A3g98BrvmQQ0YL/PITTt9+nDQC13X542swnuvilqin2m0QWqlpqYasTdH3b3ce4+1d2nEqrHZ7r7ZsLSIX8Vjf75M2BXWZWzaZnZjLKX84BHo+cD9fvMJfQPXeDu+8t2DbalXQbq96lmeZ6BqHxJocuAAVPzUw2iPpYCryV0Fu4H3tnY4lTtRjObSbiHzRPAe6LtA/X7/DvQAtwb1fKWuft7BurSLmZ2EfBFYCzwQzNb6e7nDdTvc6jleRpcrH4xs28BrwTGmNkGQo37RuB2M7uc8P/RmxtXwv7RUhsiIlKRmphERKQiBYSIiFSkgBARkYoUECIiUpECQkREKlJAiNSImf2s98qpZvZBM/svM/tttCruKjN7z6HeQ6SZaJirSI2Y2XzgJe7+zrJtywiT837j7lkzG0pYhfWl7v5Ug4oqUhXVIERq507gddEsYMxsKjAR+JW7Z6NjWtD/dzJA6BdVpEbcfTthSYvzo02XArdHC7SdYGa/J6xW+hnVHmQgUECI1Na3CMFA9PNbAO6+PrpR0XTgMjMbMEs+y7FLASFSW98DXhPdhrXd3R8s3xnVHP4A/HkjCifSHwoIkRpy973Az4BFRLUHM5tsZm3R85HA2UClO8GJNBWt5ipSe98C7qKnqelU4F+iG8UY8M/u/nCjCidSLQ1zFRGRitTEJCIiFSkgRESkIgWEiIhUpIAQEZGKFBAiIlKRAkJERCpSQIiISEX/D/X6cDVLj9lSAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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sfsHTS4Enh7mmyLiVyu8zVEmL4MA2EwoCCcFws4ZONbOOIscNGPL+eO6eMbNlwF0E+xJ9z93Xmtl1wBp3XwEsM7MLgD5gD/Cesj+ByDjRGw22oK5k+miyYL8hkWobMgjcfUQby7n7KoI1B4XHri14/JGRXF9kPOnfeTRZwayhhLqGJESlLigTkRE6MEZQQddQLGJETV1DEo5ybl4vIqXq6IDf/AYmTIBXvhJiMVIj6BoC3cRewqMgEKm2X/8aliyBXbuC5yefDCtWFHQNVRYEiYi2mZBwqGtIpJo2boSLL4ajj4Z774VbboEXXoDzziO3Zy+xbIaYV3Yj+mRUq4slHGoRiFRLdzfceCPMmQO//z1MmhQcX7AAXvUqTvz653n0G7dVfPlkNMLuVGWtCZGhqEUgUi133gl798Kttx4MAYDTT4cvfIG2Rx7ixat+VPHlExGjL4e2mZCqUxCIVMOePUFX0FlnwRlnHH7+Ax9g14KTWfSdr0Cm9NtUFtKdyiQsCgKRarj//uAX/GWXFT8fifDI0n+iZdtWePDBit5Ci8okLAoCkZHq6YHf/Q5OOw2mTRu02FMvP5+9CxYGs4py5Q8YJ3QTewmJgkBkpG6/Hbq64LzzhiyWiifYfMWVsH07PP542W+jriEJi4JAZKRuvhmmTg1mBw3C3emNxtn78nOhtTVYbFYmbTwnYVEQiIxEezvcfXcwQGw2aLFU1sGMZIRgQPmxx2DnYTu8DylqRjwCvdnK1iGIDEZBIDISP/oRZLPFZwoV6M3/Fd+Q7QuCIJuF28pfU9AQjRy4lki1KAhERuKWW2DhQph52F1YD9H/yzuZ6YPZs2HWLLjpprLfriFqCgKpOgWBSKW2b4cHHoC3vW3IbiGAVL4758CN6886C/74R1i/vqy3DIJAXUNSXQoCkUrdeSe4D752oMCBrqH+nUf7u5J+/OOy3rIhFqEvB1mtJZAqUhCIVGrlSjjmmGD9wDAOjhHkWwSTJ8OZZ8JPf1rWWzbkZw6pe0iqSZvOiVQim4W77oIrrzzQLbS5M/hrf9fO3sOKP90RBMDOfT105Lehjl50GTOu/wxrH90As+aU9LYHgyBHc1x/x0l16DtJpBJPPRXcfOaSS0oqnnHHPEe84F4E+y69AoCJq35e8tsmo8GPrNYSSDWpRSBHjuXLDz5eurTyMqX4298gHocLLhiy2NSbvgvApvmnE58+h8Ih5dTx8+k58UVMWvkzdi794GGvAdj17vcecj11DUkY1CIQqcQTT8DLXx6sEi5BOhYnUeQWlfsuvYLmhx4gurO9pOvEIv2LyhQEUj0KApFydXfD5s3D7i1UKB1PkMikDzu+75LLsVyOiXetKvlayWhEU0ilqkINAjNbbGbrzWyjmV1T5PxHzWydmf3VzO4xs2PDrI9IVTz5ZDBttJwgiCVI9B0eBD0vOY30jJm03l16EDREjd6MWgRSPaEFgZlFgRuAi4GFwNvNbOGAYn8GFrn7S4DbgS+GVR+Rqlm/PhgfOPvskl+SjsWJF+kawoyOCxYz4f57sVSqpGtpdbFUW5gtgjOBje6+yd3TwK3AFYUF3P0+d+/OP30ImBVifUSqY/16OO44SCZLfklfrHjXEEDHhYuJdnXS/NADJV2rMRohnXMtKpOqCTMIZgKbC55vyR8bzHuBX4ZYH5GR6+yELVvgxBPLetlgg8UAna86j1wySevdpX37N8WDmUPdGY0TSHWMicFiM3snsAj40iDnl5rZGjNb095e2uwKkVBs2BB8LSMIspEI2Whs0BZBrrmZznPOpfXuO0u6XlMs+LHt0jiBVEmYQbAVmF3wfFb+2CHM7ALgU8Dl7l60k9Tdl7v7Indf1NbWFkplRUqyfj0kEjB3bskvSccSAEUHi/t1XLiYhk0bSTy1cdjrNeeDQC0CqZYwg2A1MN/M5plZAlgCrCgsYGYvBb5NEAI7QqyLSHVs2ADHHw+x0tdipmNxgEG7hgA6LrwYgIkldA/FIkYyYnQpCKRKQgsCd88Ay4C7gMeBH7r7WjO7zswuzxf7EtAC/MjMHjWzFYNcTqT2duyA55+vYHwg3yIYpGsIID13Hr3zT2TCPXeVdM2muNHdpyCQ6gh1iwl3XwWsGnDs2oLHQ6/PFxlL+u8zXMFAMQzdIoCge2jajd+i43WX4MPMSGqORdjRky2rHiKDGRODxSLjwn33BVNGjy1v3WMpYwQQdA9F0mkaNjwx7DWbYsEU0j5NIZUqUBCIlOq++2D+fIhGy3pZOh4EQXyIriGAzrPOITuhlYbH1w57TQ0YSzUpCERK8cILwUZzCxaU/dJUPOjmSQ4TBMTj7H/N+UEQ+NB/6fevJejSOIFUgYJApBT33x98LXN8AIIWQbwvTWSYX+4QdA/FOvYR37plyHIH1xIoCGTkFAQipbjvvmDL6dmzhy87QCqWJNlX2j5CHee/DoDGYbqHomY0xYxOtQikChQEIqW47z4499yyxwcAUonE8N1CeZmjppOafSwNjz82bNnWeJSOtIJARk5BIDKczZth48aytp0uVE6LAKD3RSeTeO5ZIp37hyzXmojQk3V61T0kI6QgEBnOvfcGX88/v6KXp+IJksNMHS3Us/BkzJ2GJ9YNWW5C/ub1Wk8gI6UgEBnOPffAtGlwyikVvTwVL69F0DdzNtnWVhrX/m3Icq2J4Md3e0+monqJ9FMQiAzFPWgRvPa1ECn/xyVrETKx+LCLyQ4RidBz8ik0PLEO6+0dtFgyGiEZMQWBjJiCQGQoGzbA1q0Vdwv1LyYrp0UA0HPyS4ik07T87v4hy01IRNjerSCQkVEQiAzlnnuCr699bUUvP7CYrMwg6J2/gFwyycQ7fzFkudZ4hF29WTLaakJGQEEgMpR774U5c4KtpyuQOtAiKKNrCCAWp/fEhUy8cyXkBp8VNDERJQfsUPeQjICCQGQwuVywfuD888GsokscbBGUGQRAz4tPIb5jG01/XjNomYnJ4Ed4a5eCQCqnIBAZzJ/+BLt3V9wtBJCKVTZGAMF6Ao9GmfjLwbuHGqIRWuMRnu8aeotrkaEoCEQGs2pV0BK46KKKL5FK9G84V34Q5Jqa6Tz7FbTetXLIcsc0x3heA8YyAgoCkcGsXAlnnQUjuE92KpYkms0QHaKffygdi19P4xPrSDy9adAyM5pi7EvntBOpVExBIFLMjh2wejVccsmILpOOJyrqFuq37+LLAJh4588HLTOzObgD2vPd6h6SyigIRIr55S+DxWSXXjqiy/TGkxUNFPdLHzuXnpNPYeLP7xi0zPSmGBHgeQ0YS4UUBCLFrFwJxxwDL33piC4z0hYBwN7L30zL6oeIb91c9Hw8YhzVGFMQSMUUBCIDdXcHA8WXXVbxtNF+5e4zVMyeN7wFgEk/+/GgZWY0x3ihO0OuhJvfiAykIBAZaOVK6OqCJUtGfKnUCLuGANLHHU/3S17KpDtuH7TMjOYY6Zyzq1c7kUr5YmFe3MwWA18DosCN7v75AefPBb4KvARY4u6Df6eLjJZbbw3uRrZ+PTz5JCxdWtbLp970XQD6ojHS576ZplT3kOVKuVZ69hwmrfwZR33lC2SnTju00Ec/yIym/IBxV4a2xlB/rOUIFFqLwMyiwA3AxcBC4O1mtnBAseeAq4Cbw6qHSFk6OoJuoUWLKtpttFB3sgmA5t7iQVDWtU47HYCmv/y56PnJyQgNUdPMIalImF1DZwIb3X2Tu6eBW4ErCgu4+zPu/ldAE6BlbLjjDujtDYJghLoagiAYrEVQjuyUqaTmHEvTo48UPW9mzGjWgLFUJswgmAkUTnPYkj8mMnYtXw7z58O8eSO+VNeBFkHXiK8FQasgsXULsfbtRc/PaIrT3pslldXfVVKecTFYbGZLzWyNma1pb2+vdXXkSPXXv8IDD8DVV4+4Wwigu6EJy+VoTA9+c5ly9Jx6Om5G05+Kb0I3qzkYG9AGdFKuMINgKzC74Pms/LGyuftyd1/k7ovaRrDcX2RIX/saNDTAVVdV5XJdySYa0z1EqjSlMztpMqkTFtC85o9Ft6ae0RzHgC2dGieQ8oQZBKuB+WY2z8wSwBJgRYjvJ1K5Z5+Fm26C978fpkypyiW7G5qqMlBcqOuMs4nt3kXy6acOO5eIGkc3xdisnUilTKEFgbtngGXAXcDjwA/dfa2ZXWdmlwOY2RlmtgW4Evi2ma0Nqz4iQ/rc54LFY//8z1W7ZFdDc9WDoOeUU8klG2he/VDR87PyA8a6Y5mUI9QxAndf5e4L3P14d//3/LFr3X1F/vFqd5/l7s3uPtXdTw6zPiJFrVkD3/0uLFsGs2ZV5ZIOdCcbaUpVZ6D4wHUTCbpPO53Gv/wZSx2+Ynl2S5yswzZtSy1lGBeDxSKh6e2F970PjjoKPvvZ6l020UAuEq16iwCg64yziKTTNBZZUzCrJVhYtlnjBFIGBYHUL3f44AfhL3+B730vWE1cJdVcQzBQeu5x9LUdRctDDxx2rikWoa0hytP7FQRSOgWB1KdcDj760SAAPvWpEd93YKCuKq4qPowZnee8iuSzTxPfcviOpPMnJtjc2Ud3RusJpDQKAqk/7e3w9a/DV78KH/4wXH991d+is7EFgOYQWgQQdA/l4nFaHvzdYecWTEriwJP7RrbZndQP7U4l9WH/fvjpT4O1Ak88AfE4fPvbwXTREW41Xczulsk093SRyITTReONTXSffgZNf1oNe/fCpEkHzk1vjDIxEWHD3hSnTm0I5f3lyKIgkCNXJgN33gk/+AGsWBEMDE+bFtyM/jWvKXtX0XLsbp3C1P27Q7s+QOcrzqXl4QfZ+o3v0H71hw45NzkZZVNHH6t39BCPDB50p01TUIi6huRI1NcHX/5ysF/QZZfBvffCe98bbB/xuc/BG95wyF/Q1dYbT9DZ2MLUjl2hvQdA38xZpOYex7QbvxmEXoEZTTEcrTKW0igI5Mjy+OPwr/8KH/sYLFgAP/kJPP88fOMbcM45oXQDDbR7QrAyOewWAUDHeReQfPYZJv3s0Ft5tCaiTElGeaazT3ctk2EpCOTIkMsFYwBf/SrEYnD33XDPPfDGNwbjAaNo14Qp4M6U/XtCf6/ehS+m58QXMf3r/xFMhy0wb0KcVNa1NbUMS0Eg419vL7ztbcF4wCtfCZ/+NFxwQc2qs6t1Cq3dHcSzo/ALOBJhx4c+RuO6x2i9+5eHnJrWEGVCPMJTHWm1CmRICgIZ3zIZeMtb4Pbb4cor4V3vgkSiZtVxghbB1I7wu4X67XnTW0nPnsP0r37xkFaBmbFgYoKerPPcCMYKtnVnWL2jhz+19yhQjlAKAhm/3OEf/zG42fy3vlXTVkC/3S2T6U02Mn3vKN43Ix5n+0c+QfPqh2n91apDTrU1xpjaEOWpjjTpbPm/xLd1Z/j++r3cs7WLX23p4hfPdioMjkAKAhm/rrsObrwx6Aq6+upa1waAzUfNwnI5Zu2s6NYbFdv1jvfQe/x8jrn+M5DNHnLupIkJMjlYtyeFl/FLPJtzVj67n+ZYhA+cPJlXH9PEuj0p7tzcWdZ1ZOxTEMj4dOONwSZxV10VBMIY4MBzbbOYvncHycwor+qNx3nhU/9G4/rHmXLzTYecmpCIcsLEBNt6MrxQxq6kD+/oob03y0VzmmlNRHn50U2cM72Rv+5K8Uh7de66JmODgkDGn1/8ImgBLF4c3GN4FKaElmJfUyv7m1qZ3b6lNu//+jfQeebLmfG5zxDdtfOQc/MmxJmUiPDYnhR7UtlBrnBQV1+Oh7b3sGBigvkTkweOv+qYJuZPTHDP1i7W7z18G2wZn7SyWMaXhx+Gt74VTjsNfvSjUZ8aOpRnp88Bd2aPcrfQAWZs+b//yYmvPZsZ//YpNn/92wdORcx46bQGHt7RwyPtPbysrZHJySiP7iz+l/3a3b305ZzpjbHDyhzbEqc7k+Nnz+znTfOMEybWbnBeqkMtAhk/nnwSXv96OOaYYIC4paXWNTogYxGenHE8M3c9X7Wb1Vei90Uns+MfP8LUW25iwj2/OuRcMhrhjLZGElHjjzt62NzZV7Svf2dvhs1dGWa3xGmOH/4rIhYxrjyulbaGKD/e1DFomMj4oSCQ8eGFF4KuIAjWC0yfXq/JjxoAAAn2SURBVNv6DLBu6mxSiQZO2ryh1lVh2yc+Rc/CFzNn2fuIbXvhkHONsQhnH9XElGSUtXtSrG7vZV/6YFdRRzrLozt7aYlHWDDEX/oNsQjvmD+ReRPi3Lm5kz9sC2eXVRkdCgIZ+3buDKaG7tgRtATmz691jQ7h7qyefgKTOvcyfe+OWlcHb2zkmeU3Ee3qZO4/vOewW1omosaitgYWTk7Skc7yh+09/GF7N4+09/Dg9h4iZpw+rYHYEJvVQdDCeMvxrSycnOQ3L3QrDMYxBYGMbbt3B7uFbtoEP/85nHlmrWt0mD/t7KW9aSILn3uCsTFsDakTX8TmL99Ay4O/Y86HlwZbcBQwM+a0xHn1jGbmT0wQMejK5JjbEucVRzfSFCvtV0PEjNcf23IgDNbuVjfReKTBYhm7nnsu6A566im4445g6+gxZk8qy/3Pd3Hc3m3M3f5sratziD1veTvx57cy4/rPkEs2sPnLNwT7MBWIR4zjWxMc31r5gG/EjEvntNDZl2PVc50konbITCMZ+xQEMjbdfXewXURvL/zqV/DqV9e6RofZl85y28Z9RMxY/Myf2VfrChWx40MfI9Lby9Ff+nfiO7bz7De/S3bK1BFft9gA8fyJCfals/xk035OmpRidkucSJGpvboHwtgTateQmS02s/VmttHMrilyPmlmt+XPP2xmc8Osj4wDO3cGN5S/6CKYOhUefHBMhsBzLVP57w376Mk6bz2+lda+nlpXqTgztv3zp9n8H9+g5bf3ctK5i5i48meH7VRaDfGIcUZbI1Maojy+N83vt3WzqSNNV5/unTzWhRYEZhYFbgAuBhYCbzezhQOKvRfY4+4nAF8BvhBWfWSMe+IJ+PjH4YQTgltILlsGq1fDwoHfMrXTl3M2t0zl5/Nexs0nnUvU4B0nTGRm89hZyzCYXe9+L0/e9TsyU6Yy76olzL/oVUy+7X+IdHZW9X1iEWPRtAZeOrWBeMTYsC/N77Z180A+FHoyCoWxKMyuoTOBje6+CcDMbgWuANYVlLkC+Gz+8e3AN8zMXBuZjFuF/3U+4Csd+/EdO2DHDmzHDnh6E/boX7A/PIht3IjHYuTe+CZyn/4M9uKTMYNI/no5IOfB/jc5h6w7WeeQx9Y4EcMxd6w3gwGGkXWnL+dkctA3cTp9kSh9kRh97T2kc8G5dNbpy0E65+zszRA5/hz6onEy0Sjpv+4i48F5XvRqYpk+TtqygVlnn84L3cG2DVPHwZ3Aek45lfX3PsSU2/6H6V/7Escuex+5j36A7lNPp/vMs0kddwLp2cfSd/QxZFtayLVMINsyoexFe2bG9KYY05ti9GRybO/JsK07w4Z9aTbsS7NuT4rpTTEmJiLEI0bUjHjEiEWCVkUiYiSiwbF4xA4MwJuR/z8N3uPg4+ArB87bwbIDXiPFhRkEM4HNBc+3AGcNVsbdM2a2D5gK7KTKVu/o4bcvdFXtetWMqmqnXsnXK7HgYb/QK/ShCxbSvPvQXTk7px3FtpNO5am3vJ8nz7uEzrajIQv8pYLbPL74/IOPH99bvMyCVxx8vOXg90PUIGrBL6OoGclojEQ2TVMqQ/aoaUQtmHY548H7OWb3NuLZDLvOeVn5day1WIzdf/cedr/j3TQ//CCtd62k5eEHmbb8BiJ9xcNs/b0P0XPKqRW9XWMswtwJCeZOSNDVl2NHT4advVme3JcmVcFuqCN1eDgEwTFenD+rmVOnVn+MxcL649vM3gIsdvf35Z+/CzjL3ZcVlHksX2ZL/vlT+TI7B1xrKdB/p/ETgfWhVLp00wghrGpIn2fsOpI+C+jz1NKx7t5W7ESYLYKtwOyC57Pyx4qV2WJmMWAicNifgu6+HFgeUj3LZmZr3H1RretRLfo8Y9eR9FlAn2esCnPW0GpgvpnNM7MEsARYMaDMCuA9+cdvAe7V+ICIyOgKrUWQ7/NfBtwFRIHvuftaM7sOWOPuK4DvAj8ws43AboKwEBGRURTqgjJ3XwWsGnDs2oLHvcCVYdYhJGOmm6pK9HnGriPps4A+z5gU2mCxiIiMD9p0TkSkzikISmRmV5rZWjPLmdmiguNzzazHzB7N//uvWtazVIN9nvy5T+a3/VhvZhfVqo6VMrPPmtnWgv+TS2pdp0oMt0XLeGNmz5jZ3/L/J2tqXZ9ymdn3zGxHftp7/7EpZna3mT2Z/zq5lnWslIKgdI8BbwJ+W+TcU+5+Wv7f1aNcr0oV/Tz5bUCWACcDi4Fv5rcLGW++UvB/smr44mNLiVu0jEfn5f9PxuOUy/9H8DNR6BrgHnefD9yTfz7uKAhK5O6Pu3utF7JVzRCf5wrgVndPufvTwEaC7UJkdB3YosXd00D/Fi1SI+7+W4LZjYWuAL6ff/x94A2jWqkqURBUxzwz+7OZ/cbMXlXryoxQsa1BZtaoLiOxzMz+mm/Oj8fm+pHy/1DIgV+Z2SP53QKOBNPdvf9+oNuAsXUP1RLpfgQFzOzXwNFFTn3K3X82yMteAOa4+y4zexlwh5md7O4doVW0RBV+nnFhqM8GfAu4nuAXz/XAfwB/P3q1k0G80t23mtlRwN1m9kT+r+wjgru7mY3LaZgKggLufkEFr0kBqfzjR/L7JS0Aaj4YVsnnobStQWqu1M9mZt8BfhFydcIwLv4fyuHuW/Nfd5jZTwm6v8Z7EGw3s2Pc/QUzOwao/U2rK6CuoREys7b+wVQzOw6YD2yqba1GZAWwJH/ToHkEn+ePNa5TWfI/kP3eSDAwPt6UskXLuGFmzWY2of8x8DrG5//LQIXb5LwHGJctbbUISmRmbwT+E2gDVprZo+5+EXAucJ2Z9RFsm3+1uw8cUBpzBvs8+W1Afkhw34gM8EF3z9ayrhX4opmdRtA19AzwD7WtTvkG26KlxtUaienAT/P3BIgBN7v7nbWtUnnM7BbgNcA0M9sC/CvweeCHZvZe4FngrbWrYeW0slhEpM6pa0hEpM4pCERE6pyCQESkzikIRETqnIJARKTOKQhEymRm9w3cldXM/reZfcvM5pjZr8zscTNbZ2Zza1NLkdIpCETKdwuH31Z1Sf74TcCX3P1FBCtnx+VKU6kvCgKR8t0OXJpf8Uv+r/4ZwC4g5u53A7h7p7t316qSIqVSEIiUKb9y/I8E9wqAoDXwQ4LtOPaa2U/yu9F+aZzey0HqjIJApDKF3UP93UIx4FXAx4EzgOOAq2pROZFyKAhEKvMz4HwzOx1ocvdHCO4Z8Gj+ZjIZ4A7g9FpWUqQUCgKRCrh7J3Af8D2C1gAEO4ZOMrO2/PPXEmzeJzKmKQhEKncLcGr+K/ldWj8O3GNmfwMM+E7tqidSGu0+KiJS59QiEBGpcwoCEZE6pyAQEalzCgIRkTqnIBARqXMKAhGROqcgEBGpcwoCEZE69/8BaSr95t9MNj0AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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jhXqzoLqC0sZqXho/m7qsvJTFYUxvLBGYIUVVeb26nZIsL1N72YfYbQJcUllORizCM1PmE0EgFILWVojFUhqbMYncH1NnTBJtbQ5TF4zy4Ym5Sa8N7Oy0dSUAsQ4ueOwBdP16Iitew9ewHwD1egmNm0D7B2ZTeO6ZkJub1FiNSWSJwAwpK2vbyfEJMwtTv+5P1oZ1FD77NP7qvQQLhlGx8DzyszPICwfxNjaQ+V4FhUuXwLIX4KKL4IILwGd/kib57LfODBn7g1Heawpz+qhsvJ7U9Q1IsJ1hT/+BnPK3CJeMoO7qT9Fywkn8c8GHaMsMcMmKFwmEnH2S/Xt2M+ql5+GZZ2DVKvjsZ2H48JTFbtKT9RGYIWNlXTsegbnFqZv07qutZuSPvk/22+U0nn8Re790B+1z5+H1CAvXv07E62P5zJPfH1IaHj3G+fD/7Gehtha+/W3Yti1l8Zv0ZInADAkd0Rhr93UwszCTHH9qfq39u6oY8ZMf4GltpfYzN9F04SWHNPUUtDVTtmUVe4tGsWrKCYdePHcufOUrkJUFP/gBvPdekqM36czVvxgRuVBENolIhYjc3sX5z4rIWhFZLSL/EJFZbsZjhq61+zsIxZSyktTUBjK2b2XEgz9C/X5qbv4iHVOP67LclD2VTK/azIYJM9g4rlOZkSPhttugoAB++lNYuzYJkRvjYiIQES9wP3ARMAu4qosP+sdUdbaqngjcC9znVjxm6FJVVta2MzbHx+ic5M8kzqh8j5Kf/YRoTi41N36BSMmIbssKcNKW1Yyv2cnKaXPZPGbqoQWGDYNbboGMDLj4Yqe5yBiXuVkjWABUqGqlqoaAx4HLEguoauLKpjlgs/HNkatoClHfEWNeSSDpr+2r3kvJwz8jWlBIzY3/QbSoqNdrPCgL17/B2LpdrJg+j1WdZx4PHw433ugkgauugmjUpeiNcbiZCMYCOxOeV8WPHUJEbhSR93BqBJ/v6kYislhEykWkvNa+IZlO3qppJ9/vYXphcieQeZqaKPnFA6jPR+2nP0csv6DP13o1xgffXc7Yul28OGkuq0smHVpgwgR44AF46SW4s8ttvo3pNynvLFbV+1V1CvCfwNe6KfOQqpapallJSUlyAzQD2u7WMDtbIpSNCPT7NpQ98bS0UPKrB/G0tlB7/WeJDi8+4nscSAZTGvbywqSTWFM86dAC110HN9wA//3f8Nxz/RO4MV1wMxHsAsYnPB8XP9adx4GPuhiPGYLeqmkn0yucMDyJE8giESZ++hr8u6rYd811hMdPPOpbeTXGxyreYHLDXp4vPYkNRZ0qzT/5CZx4Ilx/Pezbd4yBG9M1NxPBCmCaiJSKSAawCFiSWEBEEvf0uwTY4mI8Zohp6IiyqSHE3OFZZHqTVLlVZdyXb6Fg2QvUX34lwVnHH/MtfRrj4xVvMK65judKy9iVM+zgyaws+O1vYf9++HyXLafGHDPXZharakREbgJeBLzAw6q6TkTuBspVdQlwk4icB4SBeuDf3IrHDG6r64KHHVtf3wFAlk+6PO+GET/8HsWPPkz1LbcRmnD0NYHODiSDR2aexZ+mnsp1614i58DJOXPga1+Du+6CT3wCLrushzsZc+REdXAN1CkrK9Py8vJUh2EOeOgh9+69ePH7P3b+oA9FlVd3NjKxZienbnyry8v3ffL6fg1n2B9+z8TPXcf+Kxax44GHGf7ow/1y3/G5B4e81gTy+e2ss5nQXMcnzpx1cOG8UAgWLIDqali3DvowOsmYRCKyUlXLujqX8s5iY47G1uYQUY+XmTs2JuX1cl97hfG3fIbm089k549+Di51TI9ob+LcHe+wtWAkbycmv4wM+PWvnSGld9zhymub9GWJwAw6wUiM7S1hSvduo7CtqfcLjlHW+ncp/bcr6ZgyjW2/eRzNcHeY6tzarZQ2VvPq7jaaQglzCObOhZtvdmphVis2/cgSgRl0KppCqMKcbe+6/lr+nduZvOgyojk5VP7+aaIFhf3+Gjtbwoc8qlrCzFm/gogqf3iviVW17ayuC7K6Lsg7N99OuGQkrYs/a5vbmH5jicAMKg0dUapaI0zM9ZMbbHP1tXw11Uy54sN42tqofPwZwuMmuPp6iXKDrUzLz6A2GKW6/WCtIJZfwO7/+m9yVq2EX/0qafGYoc0SgRk0Yqqsq+8g0ytMLXC3ecbT1MjkKy8jY88uKh/7E8EPzHb19boyMc9Pvt/DhvoOwrGDgzrqL19Ey6mnO6uV7t+f9LjM0GOJwAwaFU0hmsMxZhZm4nNx4xlv/X6mXHEJgY3r2Prrx2lbcKprr9UTjwgfKMqkI6ZUNIYOnhCh6jv3QX09fOtbKYnNDC2WCMygUNMeobIpzLgcH6Oy3dtYz1dTzdSPfojAurVs/fXvaT73Atdeqy8KMryMy/GxoyVMS/hgn0DwA7OdJSh++lOoqEhhhGYosERgBryGjijv7AuS7/e4uhdxxtZKpl56PhnbKql87GmaLvywa691JKYVZOAR2NTQceiJu+92hpXefthWH8YcEUsEZkALx5Q/bW1CBE4sznJtL+L8/1vK9PNOw7evjsonn6XlzHNceZ2jken1MCXecVwXjBw8MXo0fPnL8Mc/wj/+kboAzaBnicAMWKrK/+1soaY9ypyiLLJ9Lvy6hsOM+s5/MflfL6djYimbly2n9eTT+v91jtGkPD8Br7CxIcQhqwHceiuMGeP8a8NJzVGyRGAGrDX7Oli7v4PTRgUoCfR/v0DO8n8w/fyFjLrvHvZd9Um2/OVvhCZO6vfX6Q8eEaYXZtISjlHVmlAryMlxNrx/6y144onUBWgGNfd63Yw5EqrQ0AC7dsHu3bS0BdFlb3BltINJowqpzsonOqyIjkmldEyZRmhiae/37Eo0St6rf6PkwR+T/8oyQmPGUvnIkzRd9JH+/f+4YGTAS2GGh4qmEGMSO8w/+Un40Y+c4aQf+5izYqkxR8ASgUmtqipnuYS333YWVIvLzAowI5BNZk4AaW9nZH09ktD0oR4P0WFFhItLiJSMcB7FJUQLCogFsollBfDuq8Pb0oJ3fx1ZmzaS++Zy8v/6PP7qvYRLRrL769+k9obPodnZqfifHzER4bjCTN6qaWd7S5h5I+Jbc3o88D//A+ee6ySE//zP1AZqBh1LBCY1tmyBZ5+FTZucBdymT4czz0THj+e5hZeyYcIMrpkx7P3N6NfUtOGt30/mtkoyK98j870t5L/0Ir66WjK3VeLp6DjsJcZ99UuHPI8UFNLywbNouOxyGi/6CJqZxM1s+klRppeSLC+VTSGCkRhZB/pNzjkHPvxhZzez664D28nPHAFLBCa5amrgscdgwwbIz4crroBTToG8PADWlExi3aRZnLPjHUbPSxi54/EQHV5M2/Bi2uYtACA8Jr6blyqe5mb8tTV4WprxtLXiCQZpOe2DxHLziBQW0jH1ODomTwWvN9n/4353XEEG/6xu542ads4ak3PwxL33wuzZzr4F99+fsvjM4ONqIhCRC4Ef4WxM80tVvafT+S8CNwARoBa4TlW3uxmTSZFYDF55Bf70J/D5nARw5pnOOPi4powAL42fw6TGauZXVwB9HMIpQiw/n478/EMO9/d+BANFXoaXMdk+ymvamVeSRZ4/ntxmzoTPfAZ+/nO46SbnuTF94NqoIRHxAvcDFwGzgKtEZFanYquAMlWdAzwF3OtWPCaFWlqctusnnnCagL7xDTj//EOSAMCyCXNQ4MJtq0jeNvSD09SCDGLAP/e0H3rirruckUS33ZaKsMwg5ebw0QVAhapWqmoIZ3P6Q/bYU9WXVfXAEpJv4Gxwb4aSPXvgnnucZRCuvtr5pjps2GHFKgpGsXnYWBbu2UhhyN1VRYeCbJ+HE4dnsWZfkP3BhD0LSkqcbS3/8hdYtix1AZpBxc1EMBbYmfC8Kn6sO9cDz7sYj0m2DRucJNDRAV/8Inzwg13u7BX2ePnrxBMY3t7Egr1bUhDo4LRwVDY+D/x9T+uhJ26+GUpLnUlm0WjXFxuTYEB0FovI1UAZcGY35xcDiwEmTEjemvDmGKxdCz/7GYwaBTfe+P4euztbwocVXV06g8bMHM5b9Td2Nx9cZXNfkjakH6xy/B7mjwiwfG87e9siBxfjy8qC737X2ej+N7+B64dmX4npP27WCHYB4xOej4sfO4SInAd8FbhUVQ8fAwio6kOqWqaqZSU2LG7gW70aHnzQWfrgi1/scaP1xuw8NkyYTunebYxsqE1ikEPDySMCBLzCK7s71QquuAJOO81pJmpuTk1wZtBwMxGsAKaJSKmIZACLgCWJBURkLvBznCRQ42IsJlnWrHFGrUyYAF/4gtNx2Q0FVhw3D180ykkVq5MX4xCS6fVw6qhstjWH2dZ86J4F3Hcf7N3rDCs1pgeuJQJVjQA3AS8CG4AnVXWdiNwtIpfGi30PyAX+ICKrRWRJN7czg0FFBfziF04SuOUW6GXG7raRE6keNpITKt8hK9xlZdD0wUnFWeT7Pby6u+3QBelOPhmuugq+/33YubP7G5i05+qic6q6VFWPU9Upqvrt+LE7VXVJ/OfzVHWkqp4Yf1za8x3NgLV7tzOJqajIGRkUCPRYPOTz8/bUExnetI+puyuTFOTQ5PMIC0dns6ctwubEncwAvvMdZx2nO+5ITXBmULDVR82xq6+HH/8Y/H74/OffnyXck9Wls+nwZ7Bg00o8aK/lTc9mF2UyPNPLa3vaiCXWCiZOdPppfvc7ePPN1AVoBjRLBObYhELwwAPQ1uYMWywu7vWSmoJitoydynG7KihqqU9CkEOfR4QPjs6mLhhl3f5OzWxf+YrTcf/v/27DSU2XLBGYo6fqDE/cuRNuuAHGj+/1kojHy+szFpAbbOWEyrXux5hGphdmMCrg47W9bURiCbWCvDz44Q9h1SpnNJcxnVgiMEfv7rth5UpnDfw5c/p0yaopJ9ASyOWUjW/hj0Z6v8D0mYhw5phsmkIxVneeg3HFFXDBBfDVrzojiYxJYInAHJ3nnnPWtTnlFOcDpg+25Zewedw0ZlRttjkDLpmU52dCrp/l1W2Eogm1AhH46U8hGIQvfan7G5i0NCBmFptBprISrrkG5s6Ff/3XLpeN6Kzd62fppHnktzZZk1A/Oexbf9yYbB87WsI8u62ZKQUJC/sNG8+om29l1P98x5ltfPbZSYrUDHRWIzBHpr3daWYAeOqpw1YQ7UoMWDJlAS3+LE7b8Ca+mHVYuqkw08uIgJetzaFDawVA9S23OesQ3Xij09FvDJYIzJG6+Wan0/HRR2Hy5D5d8o+xs9haMJILdqxmePN+lwM0ANPyM4gobG0+9MNeAwGniWjDBmc9ImOwRGCOxK9+5Ty+9jVnW8Q+2Fw4muVjZjCndhsn1G5zNz7zvgOb12xvCROMxA49efHFsGgRfPOb8M47qQnQDChyyJT0QaCsrEzLy8tTHUb6eOgh598dO5xvkNOmOZPGPL1/h6jLyuPRmWcxrKOFqze8ik9jXa4+miq97WA2/JFfJSkSd7Rk5fDsyRcxec9WTt688v3j43P9zmZBd90FhYXOPIOutvBcvNjdAA/8bnXH7ddPMyKyUlXLujpnNQLTu9ZWZyG5vDynk7EPSaDVl8kfjjsNn0b5WMUb+DTW6zWmf+UGW5m6u5L3Rk+mOZDb6WSu09G/cye88EJqAjQDhiUC07NYDH79a2cZicWL+7R8RFg8/HHaKbT6Mrliy+sUhNp7vca44/jt6/FojDWlxx9+cu5cmD/f2c1sx47kB2cGDEsEpmcvvOBsMvMv/9KnzmEF/jK5jN05RXxkazmjW20JiVQKhILMqNrM9pET2Zd3+BahLFoE+fnOqrFB2wgoXVkiMN1btgyWLIEFC+Css/p0yd/HzmJj0TjOrnqX6fW73Y3P9Mms7RvJ6mjnrePKiNFpzkduLlx3HdTWwu9/n5oATcpZIjBd27nTWct+9Ghn0/k+TBp7p3gir4+ZwQk1W23v4QEkIxpmXsUq9ucXsWXslMMLHHccXHIJvPGG8zBpx2YWm8MFg3D55c6m8zffDJmZhxXpPPpnb+EI/jZxLqP272XW+hVUDbLRaEPdxJqdVI7ew+rJc5i3vjV+ZSkAABDySURBVI5hHZ22trz4Yti0CR57zFk8cOzY1ARqUsLVGoGIXCgim0SkQkRu7+L8GSLytohEROQKN2MxR+Dzn4cVK+CRR5zN53vRmJ3Ha8cvJL+9mQ++uxyPJYEBR4BTNq7Ao8qzk+cf3kTk9cKnP+1sKPTAA87wUpM2XEsEIuIF7gcuAmYBV4nIrE7FdgDXAo+5FYc5Qr/4hfO44w746Ed7LR70Z/LKnDPwaIyz3vk7GdGBM0/AHCq7o535m8vZnVvEy+O7GEVUUACf/Sw0NDhj/CO2Omy6cLNGsACoUNVKVQ0BjwOXJRZQ1W2q+g7OcjQm1d56y9lm8oILnCWmexH1eHh19um0Z2Rx5trXyA22JSFIcywm1exkXnUFK0ZN453hEw4vUFrq9Alt2uTsO221u7TgZiIYCyTumF0VP3bERGSxiJSLSHltrS1f7IqaGqdfYMwYp524q5mmCRR4fcYC6gqKOW3DmxQ32RpCg8U5O9cysbGG50tPYlPhmMMLnHoqnH++00T07W8nP0CTdINi1JCqPqSqZapaVlJSkupwhp5g0GkGqquDP/0Jhg/v9ZI1pbPZPnIiJ763hgm1VUkI0vQXryofr3iD0a31PDNlARUFXfQDffzjzlLjX/+6M6vcDGluJoJdQOLehePix8xAEovBtdfC6687G5zPndvrJatKSlk3aRZTd7/HrB0b3Y/R9LvMWIRPbF7OiPZGnp56MpX5Iw4t4PE4Cwxecomz1/Fj1o03lLmZCFYA00SkVEQygEXAEhdfzxyNu+6CJ56Ae+5xmoZ6saVgFP838UTG1O1m/uaVnceemEEkKxrmyk3/YHh7M3+cdurhycDvhyefhDPOcPoNHn44NYEa17mWCFQ1AtwEvAhsAJ5U1XUicreIXAogIvNFpAr4F+DnIrLOrXhMFx55xFmK+Lrr4Mtf7rX47pxhPDNlASPbGjh9/es2THQICETDXHVIMhh5aIHsbFi61OkzuP56Zy8DM+S4OqFMVZcCSzsduzPh5xU4TUYm2V59FW64wdmu8MEHe505XJ+Zw1PTTiUn3MG/bF7Oftt4ftA6fCnwMGe8/TIvnXgWT007hTPW/pOx+/ew7/2tMD3Iw09ywo3/5kwwrKpyOpF7GVBgBo9B0Vls+tnKlfCRj8CUKfDHP/a63WSzP4snjluIIly5+Z/kRDqSFKhJlsxIiHNXv0JBaxN/n72QXcNHH3JeMzPhD39w5hl897vwsY9Bc3OKojX9zRJButmwAS68EIqK4K9/hWFdrEiZoNmfxWMzzqDNn8kVW5ZT1GEzToeqA8mgsKWRvx+/kJr2TrU+v9+pPd5/v9NcdMoptsPZEGGJIJ28957T1uv1OiuLjuu5Va45FOWxGWfQ6s/kyk3/ZKwtKT3kZUZCnLPGSQar6oLUdk4GAJ/7HLz4Iuzb5+xncN99zugzM2hZIkgXGzc6oz/a252awNSpPRbfH4zyv1saE5KATRhLF5mRMOeseYU8v4e3u0sG557r7FNx0UVw661wzjnw7rvJD9b0C1t9NB2sXQvnned0CL/6Khx/cJ2Z1XWHb0ayLxhh9T7n+DmrXyHWtP+QKeJm6MuMhCkrCbCitp1VdUFOKsk6vFBJCTz9tLOD3W23wYknOkuUfOMbvTY5moHFagRD3d//Dmee6bTvdkoCXdnZEqa8Nkimx8OpI7Nt6Yg0luEV5pcEyInXDLY1hQ4vJOIMP9682Vm99Mc/hkmT4M47Yb/97gwWlgiGskcfdWoCI0bAa6/B9OndFo3ElHf2BVlX30FRlpeTRwbI9tmvR7o7kAyyfR6eqmxiY303I8aGD3c6klevdvqhvvlNJyHcfDOss+lBA539pQ9F0Sh87WvwyU/C6ac7y0eUlnZbvDEUZXl1G7vbIkzJ9zOvOAu/x+YMG0eGV1hQEmBkto8/b2vmH3vaiHU3mXDOHHjqKWc00aWXOstZH3+883v44IPO4oZmwLFEMNTs3u3UAr79bWcm6AsvdNteG44pmxs6eKO6nZjCgpIA0woy8fRhW0qTXjK8wlVTC/jAsEz+sbeNx7Y0si/Yw6TC2bOdtauqquDee50RRp/7nLP16dlnO3MRduyw0UYDhHUWDyV/+Qt86lPQ2up04F17bbdFtzWHeHFnC/UdMcZk+5hRmEmG1xKA6Z7PI3xkUh6T8vws29XKrzY2MLc4iwUjAhRkdDPLuKTE6Uj+0pecUUVPPAHPPgu3xzcszMuDGTOcyY2TJztDmm3GctJZIhgKamqcTUQef9yphj/xBMzqvBmcY18wwiu729jSGGJYpof5JVkMz7JfA9N3s4dnMTk/g1f3tLKqNsjbtUFmDctkXkkWo7N9SFc1ShGnljB7NnzrW7Bnj7ML3vr1ziY4K1Y45fx+p29h8mSn36GsDCZM6HUJFHNs7BNgMAuFnLXi77rL2WP2rrucb1pdbDbfGIry+t521uwL4vcIZ4zOZv6IAOv223IRpm86DzUek+1nWIaXbc1hNjZ0sK6+g4BXGJXtY0TAR0GGB48IJxZ3MfR09GhnA5xTT3V2QauvdyY8VlY6j2XLnElr4CSEefMOfUycaMmhH1kiGIwiEedb/9e/Dlu3Om2u998PM2ceVrS2PcIb1e2sr+9ABE4qyWLhyGyy/dY9ZI5dwOdh5rBMphZkUN0eYW9bhG3NYbY2hxGgIMNDTXuEoiwvRZnOIz+eIN4n4ix5UlTkzFQGCIedn1euPPj43vcO7qNsyaFfWSIYTBobnc1Cfvxj2L4dTjgBnn8ePvShQ/4AOqIxNjWEWLs/yM6WCH4PzCtx2nLzu2vLNeYY+D3CuBw/43L8hKJKfUeU+lCUho4o7+7vIBQ7OMrIK1CY6aVo6ikMC7ZQFGyhqKOFYcEWcsNBZ48Lvx8WLHAeBwSDzmiknpLDSSc5zUmWHI6IJYKBLhx2loT43e/gz392log44wz44Q+d4XkeD6rK8j1t1AUj1AWj7O+IElXI9gnTCjIYn+MnwytUNoWBzksQG9O/MrzCyGwfI7OdjxdVJRRTWiNKWzhGayRGWyRGjT+H9/JHEkvoHPZFwuS1t5DX1szY3a0UZnopzPBQmOklLzMTT1fJYe1aKC/vOTnMm3cwQVhyOIwlgoFG1WkjfeUV59v+smVOTaCoCK69lvCnrqPh+BOpaY9Qvaed6rYI1e0RglHnG1fAK4zN8TM620dhhqfrjjtjkkhEyPQKmV4oyjz4oT986QvEENoyAzRn59EcyKMpO4/m7Fz25w2jqrqdxMGlXoHiLC8jAz5GZPsYGfBREsgga/78g01KcDA5HEgM5eXw/e8fTA5FRQdrDMcf74xamj4dcnOT84YMQK4mAhG5EPgR4AV+qar3dDqfCTwCzAP2AVeq6jY3YxpQWlth40Z0/Xpi6zcQe/ddPG++ibfWmXQTGj2GfZd8jKqzL2TLKWezHx8t4RhsbACcP4ySgI/phRmEY84fic0GNoOJByW3o43cjjZG11cfcq72musIRpW2SIz2iNIaidEcirGhoYN3EgY5BLxCfoaHPL+XvAwPc4uzKDhpHpk9JYeVKw9NDgBjxzpJYdo0p9YwYcLBx5gx4Bu635td+5+JiBe4HzgfqAJWiMgSVV2fUOx6oF5Vp4rIIuC7wJVuxdQdVUXBeWjiv/r+z1GFqCrRSIxoMEgs2IF2dBCLPzQUIhbsINoRItYeJNbUBI2NSEMDNDXiaWrCt6+OjOq9ZO3dTaB6D1kNzlosAuDz0TiulD0nn8WuOfPZdcICaqfORETI8ArZHqHA62F0wEfAJ+T5PeT4PTb5ywxZHhGyfXLYlxtVpSOmNIViNIed5NAUjlLdHgVgVXx0U6ZXyPF5CPiELK8QGDGD7I/MIvDRTxLweQhEQgS2byXw3maytmzGv2Ujvi2b8T75JNJpnST1eJw5EcXFSHExFBc7z4uKnLkQubkHHzk5zr/Z2U5fR1cPn+/Q557UfoFzM8UtACpUtRJARB4HLgMSE8FlwF3xn58Cfioiotr/m+GuqGnn73taO33QO48jMXvJ77nkrs8f8etH/Bl0FBTSWjKaxtHj2XviAtpGjaFlynG0TZ1OR+kUfFnOpK4sjzDDI8zxCj7BmneMSSDifLBnBTyMCBw8HokpLeEY7VGnBhGMKqGoEow4SSMcixCOKdHEP/qMsTBzLMw8+5DX8Le3krd3N/l7q8jfW0XBniqy99eRXb+P7IZ9ZG9/h0DDfrKa6vEc4+zoirMu4pkfPtKnsueNy+WE4V0Mxz1G4sJnrnNjkSuAC1X1hvjza4CTVfWmhDLvxstUxZ+/Fy9T1+lei4HF8afTgU2uBA3FQF2vpdKDvRcH2XtxkL0XBw2292KiqpZ0dWJQNHqp6kPAQ26/joiUq2qZ268zGNh7cZC9FwfZe3HQUHov3GyY2gWMT3g+Ln6syzIi4gMKcDqNjTHGJImbiWAFME1ESkUkA1gELOlUZgnwb/GfrwD+5kb/gDHGmO651jSkqhERuQl4EWf46MOquk5E7gbKVXUJ8CvgURGpAPbjJItUcr35aRCx9+Igey8OsvfioCHzXrjWWWyMMWZwsNlHxhiT5iwRGGNMmrNE0A0RuVVEVESKUx1LqojI90Rko4i8IyJPi0hhqmNKNhG5UEQ2iUiFiNye6nhSRUTGi8jLIrJeRNaJyC2pjimVRMQrIqtE5LlUx9IfLBF0QUTGAxcAO1IdS4r9FTheVecAm4GvpDiepEpYJuUiYBZwlYh0vfXb0BcBblXVWcApwI1p/F4A3AJsSHUQ/cUSQdd+AHyZI1+BYkhR1f9T1QOrcr2BMxcknby/TIqqhoADy6SkHVXdo6pvx39uxvkQHJvaqFJDRMYBlwC/THUs/cUSQScichmwS1XXpDqWAeY64PlUB5FkY4GdCc+rSNMPv0QiMgmYC7yZ2khS5oc4XxSPbZGhAWRQLDHR30RkGTCqi1NfBe7AaRZKCz29F6r6TLzMV3GaBv43mbGZgUdEcoE/Av+hqk2pjifZROTDQI2qrhSRs1IdT39Jy0Sgqud1dVxEZgOlwJr4ip/jgLdFZIGq7k1iiEnT3XtxgIhcC3wYODcNZ333ZZmUtCEifpwk8L+q+qdUx5MiC4FLReRiIAvIF5HfqerVKY7rmNiEsh6IyDagrPNqqOkivrHQfcCZqlqb6niSLb7+1WbgXJwEsAL4f6q6LqWBpYA434x+C+xX1f9IdTwDQbxG8CVV/XCqYzlW1kdgevJTIA/4q4isFpGfpTqgZIp3lB9YJmUD8GQ6JoG4hcA1wDnx34XV8W/FZgiwGoExxqQ5qxEYY0yas0RgjDFpzhKBMcakOUsExhiT5iwRGGNMmrNEYEwfxFfe/FCnY/8hIg+KyAsi0tB5JUoR+ZWIrImv3vpUfFauMQOOJQJj+ub3HL6V6qL48e/hjLHv7AuqekJ89dYdOHMSjBlwLBEY0zdPAZeISAa8v/DaGOA1VX0JaO58wYG1eOKzcgOk+Wq2ZuCyRGBMH6jqfuAtnL0JwKkNPNnb+ksi8mtgLzAD+ImrQRpzlCwRGNN3ic1DB5qFeqSqn8KpOWwArnQvNGOOniUCY/ruGeBcETkJyFbVlX25SFWjOJvaXO5mcMYcLUsExvSRqrYALwMP00ttQBxTD/wMXApsdD1IY46CLTpnzBEQkY8CTwMzVXVj/NhrOH0AucA+4Hqc/Z5fA/IBAdYA/56Om7mYgc8SgTHGpDlrGjLGmDRnicAYY9KcJQJjjElzlgiMMSbNWSIwxpg0Z4nAGGPSnCUCY4xJc/8fxGdeL7jGqzkAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Zg8qixFNOQyz7FdBE+vupfNzMvkFwEftzgD8A9x7IG5tZAcF1kL/S17HuvsTd57v7/JqamgN5W5Gc2a8gcOfgL3wGSybZedlVEO29NzdVVk79ossprNtG5YPB5cLHpFc2bYjrwjWSXX8/ldcCdcDLwKcJLkh/XR/P2QJMzdiekt7XaRRwFPCYmb0BnAws1YCxjFStiRTl+xgEVcuWMuovK3j7uhtJjuvfj6DYrMNpmX8io/76GJFd9RRFjLKo0aggkB7096yhFPBfwD+5+8XufnM/ZhmvBGaa2QwzKwIWAXvmHrh7o7uPc/fp7j4deBpY6O61+/VvIjKEuTttCad0HwaKrb2dSd++lrbZR7Ljyk/u0/s1LvggAFUPBL2uVUURGuLqGpLseg0CC9xgZjuAdcC69NXJru/rhd09AVwNLAfWAHe6+yozu9HMFg5E8SLDRXvScaAs0v8WQfWvb6H4zTfY8j9/2GeXUFfJMWPZfepplD33LMWvraeqqIBY0mlPKAyku74+lV8iOFvoBHcf6+5jgZOAU8zsS329uLsvc/dZ7n6ou383ve96d+82K9ndT1drQEaq1vQXcH9bBBaLMf6mH9P8nlNpPu3M/XrP3aefBQURxv38JkYXdY4TKAiku76C4HLgEnd/vXOHu28ELgOuCLMwkZGkNRH0pPZ3Wekxd95G0dtbeefLPc7D7FNqVCUt8+Yz9ve3MqalAQONE0hWfX0qC919R9ed7l4HaDaKSD+17WkR9CMIUikm/J//Tcvc4/e7NdCp+bQziLS2UvObX1JZVECjWgSSRV+fyvh+PiYiGd5tEfTdNVSybg3Fb2yk7rNfCNYROgAdB01m9/tOp/q3v2RU5N1lLkQy9RUEx5pZU5bbbtAy5yL9tS8tgoon/0LH+Ik0XjAwV4Otv/QKit98g2nPP0U85cSTmmEse+v1U+nuEXevzHIb5e7qGhLpp9ZEisICKOzjugCRnTsoWbuanVf8I160f1co66rh/AtJjqrk0LtvA6BFZw5JFwOzMLqI9Ko14f0aKK54+kkwY+flHx+w9/ayMnZ9+KNMWPYnilqa1T0k3SgIRAZBWzLVd7dQKkVZ7bO0HzGHjklTBvT96xddRqStlSNW3KcgkG4UBCKDoDXhlEV67xYqeXUt0aZGWuafNPDvP/8k4lOmMueRe9U1JN0oCEQGQVui7xZBWe0zJEvLaDvyqIEvwIyGD32Yqf+9go6duwb+9WVYUxCIDIK2hPd66qi1t1H68ku0zj0eouGch9Fw4UeIdMSZuuJ+OiL7tmSFjGwKApGQJVJOPNX7YHHpqpcpSHTQOu+E0OponXcCbZOncsRDS2kqqwztfWT4URCIhKy1H3MIyv72Aomq0cSnTQ+vEDPqz1/I9Gcep7lAZ3/LuxQEIiFrS88q7nHBubY2Stauoe3YuVAQ7p9ky4ILiMZjlGxYG+r7yPCijkKRAfLijuyX3d7RngBga0tiTyhkmvrM81QnE7QeNy/U+gDaTj6FWMUoxrzwHMw4L/T3k+FBLQKRkHUu6dDTrOKyvz1PYvQY4gdPD70WLypi8ylnMrH2SdDF7CUt1CAwswVmts7MNphZt/V0zewzZvaymb1oZk+Y2Zww6xHJhXgq+MItyhIEkcYGStatDbqFDnCBuf5658wFlO+sg02bBuX9ZOgLLQjMLALcBJwHzAEuyfJFf7u7H+3uxwE/JLiYvciI0pHqbBF0f6zygfuwZILWY8PvFupUf8YHcDMSL78yaO8pQ1uYLYITgQ3uvtHd48AdwF7LKbp7U8ZmOaC2qow48aRTWACW5Rd/1f33BmcLHTxt0OqJTqhh61HH468oCCQQZhBMBjLbnpvT+/ZiZp8zs9cIWgSfD7EekZyIpzxrt5DFYox67BHa5xw5aN1CEFwlbcP7zqFw42vQ2Dho7ytDV84Hi939Jnc/FPgacF22Y8xssZnVmlltXV3d4BYocoA6UlCUZZ2h8qeeINLSTNucEJaU6EVZtICNp54dbKxZM6jvLUNTmEGwBZiasT0lva8ndwAXZXvA3Ze4+3x3n19TUzOAJYqEr6cWQdWDy0iVlhKbefig1lNYYDRMn0msaoyCQIBwg2AlMNPMZphZEbAIWJp5gJnNzNi8AFgfYj0iORGMEXQJAncqH1zG7vedgRcOzAVo9kVFvJV3jj85CAKdRpr3QgsCd08AVwPLgTXAne6+ysxuNLOF6cOuNrNVZvYi8GXgyrDqEckFd6cjS4ug+NW1FL/5Bk0fyM2kroq2ZjaefEYwRrB1a05qkKEj1JnF7r4MWNZl3/UZ978Q5vuL5FrCg1PhMscIqn/zC0Y9+hAQzCNIjh7T7XnVv/lFqHVVtLWw5r1ncQYMv+6hJUv23l68ODd1jCA5HywWGck6ZxV3bRGUrn6F+OQpWUNgMFS0t9A06WCSEycOvyCQAacgEAlRPNV9eYmClmaK3tg46GcLZapobwag5ejjYP16iMdzVovknoJAJESds4ozu4ZK1q7B3GnPYRCUt7UAsHPeSRCLwVNP5awWyT0FgUiIsnUNla5+meSoUcSnHJyrsiiPtWLubJ13crD09UMP5awWyT0FgUiIunUNdXQE1x6YfVTo1x7oTYE7lfFWdo4dD9Onw8MP56wWyT0FgUiI4imnwCCaDoKKZ5+ioL0tp91CnapiLTQWl8Ps2bByJezSRe3zlYJAJEQdyb3nEFQuvw+PRGmfdUQOqwqMjrXSUFQOc+ZAKgUrVuS6JMkRBYFIiLouL1H54P20HzYTLy7OYVWBqlgLLUUldBxyKFRUqHsojykIREIUTzmF6TOGil9bT8lr64dEtxAELQKAxrJKOO00eOSRHFckuaIgEAlR5vISlQ8Gk+xzOX8gU1U8OIW0obgczjoLXn1VVy3LUwoCkRDFkplBcD9ts48kObY6x1UFRrcHQdBYXAZnp5elVqsgLykIREKSSDlJh+KIEWlsoOLpJ2k6JzeLzGVTnogRTSaCFsFRR8H48RonyFMKApGQxNKTyYojxqhHH8ISiZytNpqNAVXx1qBFYBZ0Dz3yiJalzkMKApGQZAZB5UP3kxhbTcv8k3Jc1d72nEIKQRC88w6sXp3bomTQKQhEQhJLpQAoJkXlw8tpOvtciERyXNXeOieVufu74wTqHso7CgKRkHS2CKqfX0l0V/2QGh/oVBVrIRYtpD3pMG0aHHqoBozzUKhBYGYLzGydmW0ws2uzPP5lM1ttZi+Z2SNmNi3MekQGUyzpFADVD9+PR6M0nXlOrkvqZs9cgnjQeuHss+GxxyCRyF1RMuhCCwIziwA3AecBc4BLzGxOl8NeAOa7+zHAXcAPw6pHZLDFkh6MDzy4jOb3nEqqsirXJXUzOpaeSxBLBjvOOgt27w7WHpK8EWaL4ERgg7tvdPc4cAdwYeYB7r7C3VvTm08DU0KsR2RQxZJOzZbXKV23hsYFH8x1OVl1TiprjKeD4IwzgjOINE6QV8IMgslA5jTFzel9PfkEcH+2B8xssZnVmlltXV3dAJYoEp5Y0jn08eUANJ17QY6rya4kmaAkEaehs2to3Dg47jgFQZ4ZEoPFZnYZMB/4t2yPu/sSd5/v7vNramoGtziR/dSeTDH9sQdom3MU8WnTc11Oj6piLe92DUEwTvDUU9DSkruiZFCFGQRbgKkZ21PS+/ZiZmcD3wQWunssxHpEBk0y5UQbdlFT+xSNQ7Q10Gl0rIWGeJcg6OiAv/41d0XJoAozCFYCM81shpkVAYuApZkHmNlc4OcEIbA9xFpEBlUs5Rzy5CMUJJM0LRjaQVDd3kxDLEUifTU1Tj0Viop0GmkeCS0I3D0BXA0sB9YAd7r7KjO70cwWpg/7N6AC+IOZvWhmS3t4OZFhJZZ0Zj5+P7GaCbQed3yuy+lVTWsjDuxsT7cKysrgve/VOEEeiYb54u6+DFjWZd/1GffPDvP9RXIl3trOIf/9KPUXXZzTaxP3R01bEwB17QkmlKW/Es46C771LairA43LjXhD+xMqMkyNeuovFLc003Tu0DxtNNOYWDMRg7q2jHGCc9KT3x56KDdFyaBSEIiEYNyD99NRUkrbaWfkupQ+RdypLolQ15Yxm3j+/OBU0vuzntEtI4yCQGSgpVJMeuQ+Nr3n9KC/fRioKYlS157RIohE4Nxz4YEHggvby4imIBAZYGXPr6Tina1sWnBh3wcPETWlEXZ3pGhPZHzpn38+7NgBtbW5K0wGhYJAZIBV/eluktFCdg7B1UZ7UlMSDBLv1Sr4wAeC5SaWLevhWTJSKAhEBpI7o++9h9dPPp2iMWNyXU2/1ZQG10nYljlOMG4cnHSSxgnygIJAZACVvVBL8ZZNrD1nIWWFw+fPa1RhAaMKC9jS3LH3A+edF6xEqjW+RrTh80kVGQaq7r2HVGEh609bQHnUcl1Ov5kZU8qjbGpJBFcr63T++cE1jJcvz11xErpQJ5SJ5JPqX99C9e2/Yfsxx5MsK+eg235FmFFQ/ZtfDOhxUyoKWdMQpzGeYnRxBJYsCc4YqqyEn/wEWlth8eIDKVmGKLUIRAZI0aa3iNbvZONp5zKqrTnUEAjDlPJCADa3ZHQPFRTAscfCK69APJ6jyiRsCgKRAVL2/Eo8GmX12Rcxqm13rsvZZzWlEYoLjM3NXS5TOW8exGKwZk1uCpPQKQhEBkJHB2Uv1NJ65DHsHD+JytbhFwQFZkwuj+7dIgCYNSuYGPf887kpTEKnIBAZCMuXE2luZsv7z8YLChjXuDPXFe2XqRWF7GhP0tKRMbEsGoVjjoGXXgquUyAjjoJAZCDceivJ8gpeO/VscKemaUeuK9ov00YF4wRvdj2NdO7cYLB4xYocVCVhUxCIHKiGBvjTn2g9bh7bqg9idEsjRYnh+ct5YlmU4ojxxu4uA8Nz5kBxMfzxj7kpTEIVahCY2QIzW2dmG8zs2iyPv9/MnjezhJldHGYtIqG56y6IxWg+4WR2VFYzvmH4Tr4qMOPgikLe2N2BZz5QVARHHQX33AOJRE9Pl2EqtHkEZhYBbgLOATYDK81sqbuvzjjsLeAq4Kth1SESuptvhtmz2Tb7WBLRQmoah0cQbMro/tm5o33P/ahBUzzFmkQRo9rfvYB96dHzGPfcc7x2130cumj4LKgnfQuzRXAisMHdN7p7HLgD2OvT4+5vuPtLgNa5leGpthaefRY++1m2jRkPMKxbBADV6QXo3hkzYa/9bbPnkBgzlrF/uD0XZUmIwgyCycCmjO3N6X37zMwWm1mtmdXWac0TGUr+4z+gvByuuII3JxzMmN27KIu39/28Iaw8ahRHjHfG7h0ERAtpuOhiqpYthaam3BQnoRgWg8XuvsTd57v7/BpdP1WGivp6uP12uOwytheVUz9qLIe8/XquqzpgZkZ1cYRtoyfsPU4A1P/9pRS0twfjIjJihBkEW4CpGdtT0vtERob//E9ob4fPfpaXdrZTkEoyffubua5qQFSXRIgVFbOrYvRe+1uPP5H2mYcH6xDJiBFmEKwEZprZDDMrAhYBS0N8P5HBk0wG3UKnnELi6GNYtSvGlB1bKekYGevxVJcE1yfoOk6AGTuu+hQ88ww891wOKpMwhBYE7p4ArgaWA2uAO919lZndaGYLAczsBDPbDHwU+LmZrQqrHpEBdc89sGEDfP7zrNkVoy3hHLb1tVxXNWBKIgVUtjR1DwJg1z98LFhy4j/+IweVSRhCHSNw92XuPsvdD3X376b3Xe/uS9P3V7r7FHcvd/dqdz8yzHpEBoQ7fO97MHMm/nd/x8q6NmpKIkzctS3XlQ2oibu2sX10DUnb+2siWTUaPvaxYHykvj5H1clAGhaDxSJDykMPBQuw/fM/82Zbiu1tSeaPLx12y073ZeKubSQjUXZUVnd/8POfh7Y2+L//d/ALkwGnIBDZF+7w7W/D5Mlw+eWs3N5GWdQ4ckxxrisbcBMatmOe6n4aKQSzjBcuhJ/+FHYPv5VWZW8KApF9sXQpPP00fPvb1HuU15o6mDuuhGjBSGsPQFGig7G7d2UdJwDgG9+AXbvg5z8f3MJkwCkIRPormYRvfjNYn//jH2dlXRsRg3njSnNdWWgm1m9j56ixdESyrEZz0klwzjnwg2PFBhkAAA01SURBVB8EC+/JsKUgEOmvJUtg1Sr47ndpo4BX6ts5ckwx5YUj989o4q5teEEB20b3MJHzBz+AnTvhX/91cAuTATVyP8EiA2n79qAr5Mwz4SMfobaujY4UnDB+5LYGAGqadhBJJthSPSn7AXPnwlVXBWMFGzYMXmHuwSqoyeTgvecIFtrqoyIjyle/Ci0tcNNNxFPwXF07h1UVUVM6sv+EIqkUU+s289b4g5m/4YXsB33nO8F1Cj7xieDCNQUh/b7csAH+8Af41a9g69bg/wfADTcEV1D74Afhox+FST2ElvRILQKRvtx9N9x6K3zta3DEEbywo432pPOeCSO7NdDpkHfeIF5YxObqHtaMnDQpaBH85S/BPwfac8/BBRfAzJlBqywWg+OPD85a+tCHggB4+2344hdh2jS44gp4beRM7hsMCgKR3mzdCosXB188119Pa0eK/97WxoxRhUwuL8x1dYNiwq7tlLW3svGg6T0fdOWVwZfytdfCE08MzBtv3Rp0O82fDytXBr/8N20KBuw/9rEgHD74QfjFL+CVV2DdOvjc54LgnjMnqEWntvaLgkCkixd3tPPijnb+tqWRlgs/TLKtjTX/5xZebEzyx9ebiCedyeWFe47rvI1UBTgz3nmDt8dOpLmjh0uHmAWL8E2bBh/+8IH9Im9rC7qbZs6E3/0uaIlt2BDM35gypefnzZoFP/kJvPoqXHJJMJA9ezb86U/7X0ueUBCIZOPO1K9cTXnts7z1s1uIzTqCurYEW1oSTB9VSMUIPlMomyM2v0o0kWBtQwz3rotTp40dC3/+czCAe9ppwRlW+yKVCr74Dz8cvvUtOO88WLMGvv99qKzs/+tMmhSE0lNPBTVddBFcfHHQfSRZ5denWaQ/3Jn8za8y9ve/5e1/vo7GD32Y1kSKl+rbqSgs4LDKolxXOOhKOmIc/cYqdrQnqWvv5UydWbPg8ceDL/VTTw2+2HsKjk7usHx50AV06aVQXQ2PPRZc8+CQQ/a/6JNPDsYXvvvdIKBmzw4uK5rSBRG7UhCIZEokmPy1L1Fz87+z/TPXsO2r36ClI0VtXRvuMLe6hMgInEXcH7O2bKAiWsDL9e00xHoJg6OPhiefDH7ZX3ppMOnsvvuCazdkevNN+NnP4LjjYMGCYJbyrbcGX96nnTYwRRcWBgPML70UnOq6eDGccUYwniB7KAhEOu3YAQsXUvOrn7Ptn77I6m/+K2sa4jy1rZVECo6vKR3Rk8f6EvEUc8eVAHDXxibaEr38sp4xIxg0/tGPYPXqYFC3sjL4VX7ssVBTA9OnwzXXBOMLN98cfDlfdlk4p5/OmgWPPgq33BKEwrHHBi2F+Mi4fsSByt9PtUgn92ANoaOPxh9+mNobf8wdn76Op+vaeau5g7ElEU6eUMqY4kiuK8258sICjqsuYVcsye3rG2npafAYIBqFL30J3ngj+O/7xS/CkUe+O6D8k5/A2rXw4ovwyU9CUchdbmbBXIc1a4JTT6+7Ljgb7IEH+u6+GuFG9mwYkd64w2OP4d/5DvboozTMmsPdP76dusOPprqwgEOrokwojVKYp11BPakuifLRQyq5a2MTv17XwIemj2JqRS+n0hYVBaeWfuhDg1dkbyZOhDvvDMLp6quDQenjjoMvfxn+7u+gvDzXFQ66UFsEZrbAzNaZ2QYzuzbL48Vm9vv048+Y2fQw6xEhmYTnnsOvu47E7Dlw5pm0v/gSD37t+9z+u0c54vSTOH1SGfNrSplSXqgQ6MH0yiI+NquKSAHctr6R29c38uz2NjY2xWmIJXs+s2goWbgQ1q8P5iG0tQUT0SZMCOYo/Pa3sCV/LrEeWovAzCLATcA5wGZgpZktdffVGYd9Atjl7oeZ2SLgB8A/hFWTZNf5R5v5p+sZOxy6P+6Zx3rG/UABhhkUWPBrw8y6vU/KO29OMv3PFN1b6WZgsOfCLwmHjpSTSHn6n5BIJEju3o037Sa1uxnfvRt2NxHZtp3CzW9RvHkT5a+uoWrV3yhsbcELCtgy7z2suv7H7Lp4EcdOHs1Zo4uJFNiInhMwUDr/Gx0/rpQ3mzvY0tLBW80dex4vMCiPFgS3QqM8WkBRxCgsMI4eW0JnvlpGznaNXMu4Y5mPRov3OtY6u6esy/OyvGa3Y6KF2JUfh8uvxJ54goLf3UbBH/+I3X47AD51Kn7kUTD7CHzKFOygSfikg6CyEquogM5beTmW8S+TeX84CLNr6ERgg7tvBDCzO4ALgcwguBC4IX3/LuBnZmYews+Jldvb+OvbrQP9sjm11xdwly/tPfd7+TIfSc6/4fMcs/R3PT7eNnosjVNnsPaiS9l17PHsOO1siiaMZ0xxhEmFBSRS8HJ9bBArHhmiBcahlUUcWllEPOm0JFI0d6RoSaRo6UjR1JHknba9P3VPbWs7sDede8He268M0OUyRx8Jn/1X+PR3mPDqK0xb+QTj177M+A1rGLNiBYWxnn8g3HzXE+w85PBeX773oOv52MxMOXtyBcemB+wHkoXVhDOzi4EF7v7J9PblwEnufnXGMa+kj9mc3n4tfcyOLq+1GFic3jwcyOW5X+OAHX0elTuq78CovgOj+g5MmPVNc/es64kPi8Fid18CLMl1HQBmVuvu83NdR09U34FRfQdG9R2YXNUX5mDxFmBqxvaU9L6sx5hZFKgCdoZYk4iIdBFmEKwEZprZDDMrAhYBS7scsxS4Mn3/YuDRMMYHRESkZ6F1Dbl7wsyuBpYDEeCX7r7KzG4Eat19KfAL4FYz2wDUE4TFUDckuqh6ofoOjOo7MKrvwOSkvtAGi0VEZHjQEhMiInlOQSAikucUBPvBzI4zs6fN7EUzqzWzE3NdU1dmdo2ZrTWzVWb2w1zXk42ZfcXM3MzG5bqWTGb2b+n/di+Z2T1mNjrXNUHfS7bkkplNNbMVZrY6/Zn7Qq5rysbMImb2gpn9Ode1dGVmo83srvRnb42ZvWew3ltBsH9+CPyLux8HXJ/eHjLM7AyCWdvHuvuRwP/KcUndmNlU4APAW7muJYuHgKPc/RjgVeDrOa4nc8mW84A5wCVmNie3Ve0lAXzF3ecAJwOfG2L1dfoCsCbXRfTgp8AD7n4EcCyDWKeCYP840HntvCpgaw5ryeazwPfdPQbg7ttzXE82Pwb+mSG46oW7P+juifTm0wRzYHJtz5It7h4HOpdsGRLc/W13fz59fzfBl9jk3Fa1NzObAlwA3JLrWroysyrg/QRnUuLucXdvGKz3VxDsny8C/2Zmmwh+bef8F2MXs4D3pVd0fdzMTsh1QZnM7EJgi7v/Lde19MM/AvfnugiCL9VNGdubGWJftJ3SqwjPBZ7JbSXd/ITgx8dQvFblDKAO+FW66+oWMxu09bCHxRITuWBmDwMTszz0TeAs4Evu/kcz+3uCFD97CNUXBcYSNNFPAO40s0MGc7JeH/V9g6BbKGd6q8/d/5Q+5psEXR63DWZtw5mZVQB/BL7o7k25rqeTmX0Q2O7uz5nZ6bmuJ4soMA+4xt2fMbOfAtcC3xqMN9c8gv1gZo3AaHd3C9abbXT3yr6eN1jM7AHgB+6+Ir39GnCyu9fltjIws6OBR4DOpWCnEHStneju7+SssC7M7Crg08BZ7p7zZWvTA4c3uPu56e2vA7j793JaWAYzKwT+DCx39x/lup5MZvY94HKCYC8h6Nq9290vy2lhaWY2EXja3aent98HXOvuF/T6xAGirqH9sxXovLr2mcD6HNaSzX8BZwCY2SygiCGy4qK7v+zu4919evpDvxmYN8RCYAFBF8LCoRACaf1ZsiVn0j+IfgGsGWohAODuX3f3KenP3CKC5WyGRAgApD//m8yscy3rs9h7yf5QqWto/3wK+Gl6obx23l0ie6j4JfDL9DLfceBKreG0T34GFAMPpS8w8rS7fyaXBfW0ZEsua+riFIJf3C+b2Yvpfd9w92U5rGm4uQa4LR30G4GPD9Ybq2tIRCTPqWtIRCTPKQhERPKcgkBEJM8pCERE8pyCQEQkzykIRPohvbLmuV32fdHM7jezp9Irbr5kZv+Q8fht6dVCXzGzX6YnXIkMOQoCkf75Hd0vpboI+B5wRXqV1wXATzKWrb4NOAI4GigFPjlItYrsEwWBSP/cBVyQnuzTubDaJOCv7r4ewN23AtuBmvT2Mk8DnmVorGIq0o2CQKQf3L2e4Mv8vPSuRcCdmTO20xcoKgJey3xuukvocuCBwalWZN8oCET6L7N7aFF6GwAzOwi4Ffi4u3dd5vjfgb+4+18HpUqRfaQgEOm/PwFnmdk8oMzdnwMws0rgPoIlrJ/OfIKZfZugq+jLg12sSH9p0TmRfnL3ZjNbQbCo3+8A0mMG9wC/cfe7Mo83s08C5xIsZT0UL4YiAmjROZF9YmYXEXzxz3b3tWZ2GfArIHMl0Kvc/UUzSwBvArvT++929xsHt2KRvikIRETynMYIRETynIJARCTPKQhERPKcgkBEJM8pCERE8pyCQEQkzykIRETy3P8HFNiS6GkneDwAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 피처 선정\n",
        "  - 제외 : V8, V13, V15, V19, V20, V21, V22, V23, V24, V25, V26, V27, V28, V29\n",
        "  - 애매 : V30은 애매하다.\n",
        "  - 선정 : V1, V2, V3, V4, V5, V6, V7, V9, V10, V11, V12, V14, V16, V17, V18, V30"
      ],
      "metadata": {
        "id": "70ZnV-iHVBUc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 시각화를 통한 피처선정\n",
        "select_feature = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V9', 'V10', 'V11', 'V12', 'V14', 'V16', 'V17', 'V18', 'V30']\n",
        "select_feature_val = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V9', 'V10', 'V11', 'V12', 'V14', 'V16', 'V17', 'V18', 'V30', 'Class']"
      ],
      "metadata": {
        "id": "F-xRHDIe6xBj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. AutoEncoder 모델 생성\n",
        "- 원본 Input과 Decoding된 Output의 코사인 유사도를 비교하여 이상치를 탐지한다."
      ],
      "metadata": {
        "id": "Lh92Ru4XLNBZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) AutoEncoder 모델 생성을 위한 패키지 로드"
      ],
      "metadata": {
        "id": "JZQK6sZzLg0P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from tqdm.auto import tqdm\n",
        "from sklearn.metrics import f1_score"
      ],
      "metadata": {
        "id": "TTjhcXlGHNzS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2) gpu 설정"
      ],
      "metadata": {
        "id": "zJG5rVumLmuf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')"
      ],
      "metadata": {
        "id": "iPoFXl5_LpZ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3) 하이퍼 파라미터 변수 정의"
      ],
      "metadata": {
        "id": "mJCQt_PbLs60"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 400 # 학습횟수\n",
        "LR = 1e-2 # 학습률\n",
        "BS = 16384 # ??\n",
        "SEED = 41 # 랜덤값 고정"
      ],
      "metadata": {
        "id": "yg_aGRhQLqVA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4) Random Seed 고정"
      ],
      "metadata": {
        "id": "nrmEksrML0sZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def seed_everything(seed):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = True\n",
        "\n",
        "seed_everything(SEED) # Seed 고정"
      ],
      "metadata": {
        "id": "lluIHUufL4WS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5) DataSet 생성"
      ],
      "metadata": {
        "id": "pKLCw_m5L6d6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MyDataset(Dataset):\n",
        "    def __init__(self, df, eval_mode):\n",
        "        self.df = df\n",
        "        self.eval_mode = eval_mode\n",
        "        if self.eval_mode:\n",
        "            self.labels = self.df['Class'].values\n",
        "            self.df = self.df.drop(columns=['Class']).values\n",
        "        else:\n",
        "            self.df = self.df.values\n",
        "        \n",
        "    def __getitem__(self, index):\n",
        "        if self.eval_mode:\n",
        "            self.x = self.df[index]\n",
        "            self.y = self.labels[index]\n",
        "            return torch.Tensor(self.x), self.y\n",
        "        else:\n",
        "            self.x = self.df[index]\n",
        "            return torch.Tensor(self.x)\n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.df)"
      ],
      "metadata": {
        "id": "ybkAERkjL9kq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = train_df[select_feature]\n",
        "val_df = val_df[select_feature_val]\n",
        "\n",
        "train_dataset = MyDataset(df=train_df, eval_mode=False)\n",
        "train_loader = DataLoader(train_dataset, batch_size=BS, shuffle=True, num_workers=6)\n",
        "\n",
        "val_dataset = MyDataset(df=val_df, eval_mode=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=BS, shuffle=False, num_workers=6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9u2jnGSMBKc",
        "outputId": "8880bdf8-e45b-484b-bd53-bb90d232299b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 6 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6) 1D AutoEncoder 모델 정의"
      ],
      "metadata": {
        "id": "ISoZhZ5xN4pc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class AutoEncoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(AutoEncoder, self).__init__()\n",
        "        self.Encoder = nn.Sequential(\n",
        "            nn.Linear(16,64),\n",
        "            nn.BatchNorm1d(64),\n",
        "            nn.LeakyReLU(),\n",
        "            nn.Linear(64,128),\n",
        "            nn.BatchNorm1d(128),\n",
        "            nn.LeakyReLU(),\n",
        "        )\n",
        "        self.Decoder = nn.Sequential(\n",
        "            nn.Linear(128,64),\n",
        "            nn.BatchNorm1d(64),\n",
        "            nn.LeakyReLU(),\n",
        "            nn.Linear(64,16),\n",
        "        )\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.Encoder(x)\n",
        "        x = self.Decoder(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "F0IG3FxBN4KZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. 학습"
      ],
      "metadata": {
        "id": "Nfa_rTpvOHol"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) 학습 클래스 정의"
      ],
      "metadata": {
        "id": "Q9roqrrXONUb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Trainer():\n",
        "    def __init__(self, model, optimizer, train_loader, val_loader, scheduler, device):\n",
        "        self.model = model\n",
        "        self.optimizer = optimizer\n",
        "        self.train_loader = train_loader\n",
        "        self.val_loader = val_loader\n",
        "        self.scheduler = scheduler\n",
        "        self.device = device\n",
        "        # Loss Function\n",
        "        self.criterion = nn.L1Loss().to(self.device)\n",
        "        \n",
        "    def fit(self, ):\n",
        "        self.model.to(self.device)\n",
        "        best_score = 0\n",
        "        for epoch in range(EPOCHS):\n",
        "            self.model.train()\n",
        "            train_loss = []\n",
        "            for x in iter(self.train_loader):\n",
        "                x = x.float().to(self.device)\n",
        "                self.optimizer.zero_grad()\n",
        "\n",
        "                _x = self.model(x)\n",
        "                loss = self.criterion(x, _x)\n",
        "\n",
        "                loss.backward()\n",
        "                self.optimizer.step()\n",
        "\n",
        "                train_loss.append(loss.item())\n",
        "\n",
        "            score = self.validation(self.model, 0.95)\n",
        "            print(f'Epoch : [{epoch}] Train loss : [{np.mean(train_loss)}] Val Score : [{score}])')\n",
        "\n",
        "            if self.scheduler is not None:\n",
        "                self.scheduler.step(score)\n",
        "\n",
        "            if best_score < score:\n",
        "                best_score = score\n",
        "                torch.save(model.module.state_dict(), path + 'best_model.pth', _use_new_zipfile_serialization=False)\n",
        "    \n",
        "    def validation(self, eval_model, thr):\n",
        "        cos = nn.CosineSimilarity(dim=1, eps=1e-6)\n",
        "        eval_model.eval()\n",
        "        pred = []\n",
        "        true = []\n",
        "        with torch.no_grad():\n",
        "            for x, y in iter(self.val_loader):\n",
        "                x = x.float().to(self.device)\n",
        "\n",
        "                _x = self.model(x)\n",
        "                diff = cos(x, _x).cpu().tolist()\n",
        "                batch_pred = np.where(np.array(diff)<thr, 1,0).tolist()\n",
        "                pred += batch_pred\n",
        "                true += y.tolist()\n",
        "\n",
        "        return f1_score(true, pred, average='macro')"
      ],
      "metadata": {
        "id": "sZnGoOoiOKXl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2) 모델 학습"
      ],
      "metadata": {
        "id": "aW_J00taOPhM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = nn.DataParallel(AutoEncoder())\n",
        "model.eval()\n",
        "optimizer = torch.optim.Adam(params = model.parameters(), lr = LR)\n",
        "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=10, threshold_mode='abs', min_lr=1e-8, verbose=True)\n",
        "\n",
        "trainer = Trainer(model, optimizer, train_loader, val_loader, scheduler, device)\n",
        "trainer.fit()"
      ],
      "metadata": {
        "id": "bl8OSt6BOQz0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. 예측"
      ],
      "metadata": {
        "id": "XpzyrDe2OWQB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoEncoder()\n",
        "model.load_state_dict(torch.load(path + 'best_model.pth'))\n",
        "model = nn.DataParallel(model)\n",
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oRtNXgMSOYY8",
        "outputId": "0d71b6d0-ca3e-4879-bcb7-d91fe2c39c38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataParallel(\n",
              "  (module): AutoEncoder(\n",
              "    (Encoder): Sequential(\n",
              "      (0): Linear(in_features=16, out_features=64, bias=True)\n",
              "      (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): LeakyReLU(negative_slope=0.01)\n",
              "      (3): Linear(in_features=64, out_features=128, bias=True)\n",
              "      (4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): LeakyReLU(negative_slope=0.01)\n",
              "    )\n",
              "    (Decoder): Sequential(\n",
              "      (0): Linear(in_features=128, out_features=64, bias=True)\n",
              "      (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): LeakyReLU(negative_slope=0.01)\n",
              "      (3): Linear(in_features=64, out_features=16, bias=True)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = test_df[select_feature]"
      ],
      "metadata": {
        "id": "vTtXQ74UOoK8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = MyDataset(test_df, False)\n",
        "test_loader = DataLoader(test_dataset, batch_size=BS, shuffle=False, num_workers=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tx_KiJb4OzIb",
        "outputId": "947171b6-5a44-4336-f573-cd3feab70138"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 6 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def prediction(model, thr, test_loader, device):\n",
        "    model.to(device)\n",
        "    model.eval()\n",
        "    cos = nn.CosineSimilarity(dim=1, eps=1e-6)\n",
        "    pred = []\n",
        "    with torch.no_grad():\n",
        "        for x in iter(test_loader):\n",
        "            x = x.float().to(device)\n",
        "            \n",
        "            _x = model(x)\n",
        "            \n",
        "            diff = cos(x, _x).cpu().tolist()\n",
        "            batch_pred = np.where(np.array(diff)<thr, 1,0).tolist()\n",
        "            pred += batch_pred\n",
        "    return pred"
      ],
      "metadata": {
        "id": "hBSsc0xTOzpf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds = prediction(model, 0.95, test_loader, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "koFvX2wJO1Mg",
        "outputId": "0ce17222-a8dc-4795-894a-dae2fbef7f7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 6 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submit = pd.read_csv(path + 'sample_submission.csv')\n",
        "submit['Class'] = preds\n",
        "submit.to_csv(path + 'submit_2.csv', index=False)"
      ],
      "metadata": {
        "id": "byDsr4qTO3OM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}