Conjoint_Triad__Calculation_peptide_shannon_entropy.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Conjoint_Triad _Calculation_peptide_shannon entropy.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOoonPLPSjJm0JpWY7nlda0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mukherjeesutanu/peptide_machine_learning/blob/main/Conjoint_Triad__Calculation_peptide_shannon_entropy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAdG3jDZFBY0",
        "outputId": "32e0ce5a-9082-4f4a-9ecb-0671427bdc12"
      },
      "source": [
        "#INSTALL MINICONDA\n",
        "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
        "! chmod +x Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
        "! bash ./Miniconda3-py37_4.8.2-Linux-x86_64.sh -b -f -p /usr/local\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-12 11:18:14--  https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8303, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 85055499 (81M) [application/x-sh]\n",
            "Saving to: ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’\n",
            "\n",
            "Miniconda3-py37_4.8 100%[===================>]  81.12M  76.7MB/s    in 1.1s    \n",
            "\n",
            "2021-08-12 11:18:15 (76.7 MB/s) - ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’ saved [85055499/85055499]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - _libgcc_mutex==0.1=main\n",
            "    - asn1crypto==1.3.0=py37_0\n",
            "    - ca-certificates==2020.1.1=0\n",
            "    - certifi==2019.11.28=py37_0\n",
            "    - cffi==1.14.0=py37h2e261b9_0\n",
            "    - chardet==3.0.4=py37_1003\n",
            "    - conda-package-handling==1.6.0=py37h7b6447c_0\n",
            "    - conda==4.8.2=py37_0\n",
            "    - cryptography==2.8=py37h1ba5d50_0\n",
            "    - idna==2.8=py37_0\n",
            "    - ld_impl_linux-64==2.33.1=h53a641e_7\n",
            "    - libedit==3.1.20181209=hc058e9b_0\n",
            "    - libffi==3.2.1=hd88cf55_4\n",
            "    - libgcc-ng==9.1.0=hdf63c60_0\n",
            "    - libstdcxx-ng==9.1.0=hdf63c60_0\n",
            "    - ncurses==6.2=he6710b0_0\n",
            "    - openssl==1.1.1d=h7b6447c_4\n",
            "    - pip==20.0.2=py37_1\n",
            "    - pycosat==0.6.3=py37h7b6447c_0\n",
            "    - pycparser==2.19=py37_0\n",
            "    - pyopenssl==19.1.0=py37_0\n",
            "    - pysocks==1.7.1=py37_0\n",
            "    - python==3.7.6=h0371630_2\n",
            "    - readline==7.0=h7b6447c_5\n",
            "    - requests==2.22.0=py37_1\n",
            "    - ruamel_yaml==0.15.87=py37h7b6447c_0\n",
            "    - setuptools==45.2.0=py37_0\n",
            "    - six==1.14.0=py37_0\n",
            "    - sqlite==3.31.1=h7b6447c_0\n",
            "    - tk==8.6.8=hbc83047_0\n",
            "    - tqdm==4.42.1=py_0\n",
            "    - urllib3==1.25.8=py37_0\n",
            "    - wheel==0.34.2=py37_0\n",
            "    - xz==5.2.4=h14c3975_4\n",
            "    - yaml==0.1.7=had09818_2\n",
            "    - zlib==1.2.11=h7b6447c_3\n",
            "\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main\n",
            "  asn1crypto         pkgs/main/linux-64::asn1crypto-1.3.0-py37_0\n",
            "  ca-certificates    pkgs/main/linux-64::ca-certificates-2020.1.1-0\n",
            "  certifi            pkgs/main/linux-64::certifi-2019.11.28-py37_0\n",
            "  cffi               pkgs/main/linux-64::cffi-1.14.0-py37h2e261b9_0\n",
            "  chardet            pkgs/main/linux-64::chardet-3.0.4-py37_1003\n",
            "  conda              pkgs/main/linux-64::conda-4.8.2-py37_0\n",
            "  conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.6.0-py37h7b6447c_0\n",
            "  cryptography       pkgs/main/linux-64::cryptography-2.8-py37h1ba5d50_0\n",
            "  idna               pkgs/main/linux-64::idna-2.8-py37_0\n",
            "  ld_impl_linux-64   pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7\n",
            "  libedit            pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0\n",
            "  libffi             pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4\n",
            "  libgcc-ng          pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n",
            "  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n",
            "  ncurses            pkgs/main/linux-64::ncurses-6.2-he6710b0_0\n",
            "  openssl            pkgs/main/linux-64::openssl-1.1.1d-h7b6447c_4\n",
            "  pip                pkgs/main/linux-64::pip-20.0.2-py37_1\n",
            "  pycosat            pkgs/main/linux-64::pycosat-0.6.3-py37h7b6447c_0\n",
            "  pycparser          pkgs/main/linux-64::pycparser-2.19-py37_0\n",
            "  pyopenssl          pkgs/main/linux-64::pyopenssl-19.1.0-py37_0\n",
            "  pysocks            pkgs/main/linux-64::pysocks-1.7.1-py37_0\n",
            "  python             pkgs/main/linux-64::python-3.7.6-h0371630_2\n",
            "  readline           pkgs/main/linux-64::readline-7.0-h7b6447c_5\n",
            "  requests           pkgs/main/linux-64::requests-2.22.0-py37_1\n",
            "  ruamel_yaml        pkgs/main/linux-64::ruamel_yaml-0.15.87-py37h7b6447c_0\n",
            "  setuptools         pkgs/main/linux-64::setuptools-45.2.0-py37_0\n",
            "  six                pkgs/main/linux-64::six-1.14.0-py37_0\n",
            "  sqlite             pkgs/main/linux-64::sqlite-3.31.1-h7b6447c_0\n",
            "  tk                 pkgs/main/linux-64::tk-8.6.8-hbc83047_0\n",
            "  tqdm               pkgs/main/noarch::tqdm-4.42.1-py_0\n",
            "  urllib3            pkgs/main/linux-64::urllib3-1.25.8-py37_0\n",
            "  wheel              pkgs/main/linux-64::wheel-0.34.2-py37_0\n",
            "  xz                 pkgs/main/linux-64::xz-5.2.4-h14c3975_4\n",
            "  yaml               pkgs/main/linux-64::yaml-0.1.7-had09818_2\n",
            "  zlib               pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n",
            "\n",
            "\n",
            "Preparing transaction: / \b\b- \b\b\\ \b\bdone\n",
            "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Miniconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Miniconda3: /usr/local\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ev5QpSjDGAF2",
        "outputId": "74aaf0a6-7ea2-4b73-bba9-5696729ef1b2"
      },
      "source": [
        "#INSTALL PFEATURE\n",
        "\n",
        "! wget https://github.com/raghavagps/Pfeature/raw/master/PyLib/Pfeature.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-12 11:21:27--  https://github.com/raghavagps/Pfeature/raw/master/PyLib/Pfeature.zip\n",
            "Resolving github.com (github.com)... 140.82.113.4\n",
            "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/raghavagps/Pfeature/master/PyLib/Pfeature.zip [following]\n",
            "--2021-08-12 11:21:27--  https://raw.githubusercontent.com/raghavagps/Pfeature/master/PyLib/Pfeature.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 511222 (499K) [application/zip]\n",
            "Saving to: ‘Pfeature.zip.1’\n",
            "\n",
            "Pfeature.zip.1      100%[===================>] 499.24K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2021-08-12 11:21:27 (21.2 MB/s) - ‘Pfeature.zip.1’ saved [511222/511222]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwENtmFKGB8E",
        "outputId": "6887f14f-3486-463b-b75a-dad4d2c6f523"
      },
      "source": [
        "! unzip Pfeature.zip"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  Pfeature.zip\n",
            "   creating: Pfeature/\n",
            "  inflating: __MACOSX/._Pfeature     \n",
            "  inflating: Pfeature/PKG-INFO       \n",
            "  inflating: __MACOSX/Pfeature/._PKG-INFO  \n",
            "  inflating: Pfeature/.DS_Store      \n",
            "  inflating: __MACOSX/Pfeature/._.DS_Store  \n",
            "  inflating: Pfeature/README         \n",
            "  inflating: __MACOSX/Pfeature/._README  \n",
            "  inflating: Pfeature/setup.py       \n",
            "  inflating: __MACOSX/Pfeature/._setup.py  \n",
            "  inflating: Pfeature/Functions_Tables.pdf  \n",
            "  inflating: __MACOSX/Pfeature/._Functions_Tables.pdf  \n",
            "   creating: Pfeature/build/\n",
            "  inflating: __MACOSX/Pfeature/._build  \n",
            "  inflating: Pfeature/Pfeature_Descriptors.pdf  \n",
            "  inflating: __MACOSX/Pfeature/._Pfeature_Descriptors.pdf  \n",
            "   creating: Pfeature/Pfeature/\n",
            "  inflating: __MACOSX/Pfeature/._Pfeature  \n",
            "   creating: Pfeature/build/lib/\n",
            "  inflating: __MACOSX/Pfeature/build/._lib  \n",
            "  inflating: Pfeature/Pfeature/bonds.csv  \n",
            "  inflating: Pfeature/Pfeature/pfeature.py  \n",
            "  inflating: Pfeature/Pfeature/AAIndexNames.csv  \n",
            "  inflating: Pfeature/Pfeature/aaindices.csv  \n",
            "  inflating: Pfeature/Pfeature/atom.csv  \n",
            "  inflating: Pfeature/Pfeature/PhysicoChemical.csv  \n",
            "  inflating: Pfeature/Pfeature/__init__.py  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/.___init__.py  \n",
            "  inflating: Pfeature/Pfeature/z_aaindex.csv  \n",
            "  inflating: Pfeature/Pfeature/aaindex.csv  \n",
            "  inflating: Pfeature/Pfeature/bin_di.csv  \n",
            "  inflating: Pfeature/Pfeature/Grantham.csv  \n",
            "  inflating: Pfeature/Pfeature/can_pat.csv  \n",
            "  inflating: Pfeature/Pfeature/aa_attr_group.csv  \n",
            "  inflating: Pfeature/Pfeature/Schneider-Wrede.csv  \n",
            "  inflating: Pfeature/Pfeature/data  \n",
            "  inflating: Pfeature/Pfeature/aaind.txt  \n",
            "   creating: Pfeature/build/lib/Pfeature/\n",
            "  inflating: Pfeature/build/lib/Pfeature/bonds.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/pfeature.py  \n",
            "  inflating: Pfeature/build/lib/Pfeature/AAIndexNames.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/aaindices.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/atom.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/PhysicoChemical.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/__init__.py  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/.___init__.py  \n",
            "  inflating: Pfeature/build/lib/Pfeature/z_aaindex.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/aaindex.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/bin_di.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/Grantham.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/can_pat.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/aa_attr_group.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/Schneider-Wrede.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/data  \n",
            "  inflating: Pfeature/build/lib/Pfeature/aaind.txt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QChDiXcGLnN",
        "outputId": "9e77567c-9bcf-4b3a-8989-dcd1d1e68e75"
      },
      "source": [
        "% cd Pfeature"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Pfeature\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25YuJiRnGPpj",
        "outputId": "221911ce-b80d-45b9-d77a-a70a67205fdb"
      },
      "source": [
        "! python setup.py install"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/distutils/dist.py:274: UserWarning: Unknown distribution option: 'zip_safe'\n",
            "  warnings.warn(msg)\n",
            "running install\n",
            "running build\n",
            "running build_py\n",
            "copying Pfeature/pfeature.py -> build/lib/Pfeature\n",
            "copying Pfeature/__init__.py -> build/lib/Pfeature\n",
            "running install_lib\n",
            "creating /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/bin_di.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/PhysicoChemical.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/pfeature.py -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/data -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/AAIndexNames.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/atom.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/__init__.py -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/bonds.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/can_pat.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/aa_attr_group.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/aaindices.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/z_aaindex.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/Grantham.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/Schneider-Wrede.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/aaind.txt -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "copying build/lib/Pfeature/aaindex.csv -> /usr/local/lib/python3.7/site-packages/Pfeature\n",
            "byte-compiling /usr/local/lib/python3.7/site-packages/Pfeature/pfeature.py to pfeature.cpython-37.pyc\n",
            "byte-compiling /usr/local/lib/python3.7/site-packages/Pfeature/__init__.py to __init__.cpython-37.pyc\n",
            "running install_egg_info\n",
            "Writing /usr/local/lib/python3.7/site-packages/Pfeature-1.0-py3.7.egg-info\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9Wjwj-XGRw6",
        "outputId": "e666cb9e-2866-4a82-a85e-6111bd9c2c74"
      },
      "source": [
        "#INSTALL CD-HIT\n",
        "\n",
        "\n",
        "\n",
        "! conda install -c bioconda cd-hit -y"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Solving environment: | \b\b/ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - cd-hit\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    ca-certificates-2021.7.5   |       h06a4308_1         113 KB\n",
            "    cd-hit-4.8.1               |       hdbcaa40_0         221 KB  bioconda\n",
            "    certifi-2021.5.30          |   py37h06a4308_0         139 KB\n",
            "    conda-4.10.3               |   py37h06a4308_0         2.9 MB\n",
            "    openssl-1.1.1k             |       h27cfd23_0         2.5 MB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         5.9 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  cd-hit             bioconda/linux-64::cd-hit-4.8.1-hdbcaa40_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                                2020.1.1-0 --> 2021.7.5-h06a4308_1\n",
            "  certifi                                 2019.11.28-py37_0 --> 2021.5.30-py37h06a4308_0\n",
            "  conda                                        4.8.2-py37_0 --> 4.10.3-py37h06a4308_0\n",
            "  openssl                                 1.1.1d-h7b6447c_4 --> 1.1.1k-h27cfd23_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "cd-hit-4.8.1         | 221 KB    | : 100% 1.0/1 [00:00<00:00,  4.34s/it]                \n",
            "ca-certificates-2021 | 113 KB    | : 100% 1.0/1 [00:00<00:00,  5.04it/s]                \n",
            "certifi-2021.5.30    | 139 KB    | : 100% 1.0/1 [00:00<00:00, 11.52it/s]\n",
            "openssl-1.1.1k       | 2.5 MB    | : 100% 1.0/1 [00:00<00:00,  1.95it/s]                \n",
            "conda-4.10.3         | 2.9 MB    | : 100% 1.0/1 [00:00<00:00,  4.43it/s]                \n",
            "Preparing transaction: \\ \b\bdone\n",
            "Verifying transaction: / \b\b- \b\bdone\n",
            "Executing transaction: | \b\bdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZ_PFnKEGwOG",
        "outputId": "d925fed7-2945-4381-dba1-9b8f68e3112c"
      },
      "source": [
        "#LOAD ANTI-VIRAL PEPTIDE DATASET\n",
        "\n",
        "! wget https://raw.githubusercontent.com/dataprofessor/AMP/main/train_po.fasta\n",
        "\n",
        "\n",
        "\n",
        "! wget https://raw.githubusercontent.com/dataprofessor/AMP/main/train_ne.fasta"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-12 11:41:02--  https://raw.githubusercontent.com/dataprofessor/AMP/main/train_po.fasta\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 52619 (51K) [text/plain]\n",
            "Saving to: ‘train_po.fasta.1’\n",
            "\n",
            "\rtrain_po.fasta.1      0%[                    ]       0  --.-KB/s               \rtrain_po.fasta.1    100%[===================>]  51.39K  --.-KB/s    in 0.006s  \n",
            "\n",
            "2021-08-12 11:41:02 (7.95 MB/s) - ‘train_po.fasta.1’ saved [52619/52619]\n",
            "\n",
            "--2021-08-12 11:41:02--  https://raw.githubusercontent.com/dataprofessor/AMP/main/train_ne.fasta\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 57206 (56K) [text/plain]\n",
            "Saving to: ‘train_ne.fasta’\n",
            "\n",
            "train_ne.fasta      100%[===================>]  55.87K  --.-KB/s    in 0.006s  \n",
            "\n",
            "2021-08-12 11:41:02 (8.90 MB/s) - ‘train_ne.fasta’ saved [57206/57206]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRr_WoynKgI-",
        "outputId": "d2f209d3-fb22-4264-c12b-e14d000691c8"
      },
      "source": [
        "#Remove redundant sequences using CD-HIT\n",
        "\n",
        "! cd-hit -i train_po.fasta -o train_po_cdhit.txt -c 0.99"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "================================================================\n",
            "Program: CD-HIT, V4.8.1, Mar 01 2019, 14:14:47\n",
            "Command: cd-hit -i train_po.fasta -o train_po_cdhit.txt -c\n",
            "         0.99\n",
            "\n",
            "Started: Thu Aug 12 11:42:07 2021\n",
            "================================================================\n",
            "                            Output                              \n",
            "----------------------------------------------------------------\n",
            "total seq: 1445\n",
            "longest and shortest : 30 and 11\n",
            "Total letters: 30071\n",
            "Sequences have been sorted\n",
            "\n",
            "Approximated minimal memory consumption:\n",
            "Sequence        : 0M\n",
            "Buffer          : 1 X 10M = 10M\n",
            "Table           : 1 X 65M = 65M\n",
            "Miscellaneous   : 0M\n",
            "Total           : 76M\n",
            "\n",
            "Table limit with the given memory limit:\n",
            "Max number of representatives: 4000000\n",
            "Max number of word counting entries: 90485873\n",
            "\n",
            "\rcomparing sequences from          0  to       1445\n",
            ".\n",
            "     1445  finished       1337  clusters\n",
            "\n",
            "Approximated maximum memory consumption: 76M\n",
            "writing new database\n",
            "writing clustering information\n",
            "program completed !\n",
            "\n",
            "Total CPU time 0.07\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBp0lqx1K2Sv",
        "outputId": "6f7862de-b672-4990-b96d-151d8368c3bb"
      },
      "source": [
        "! cd-hit -i train_ne.fasta -o train_ne_cdhit.txt -c 0.99"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "================================================================\n",
            "Program: CD-HIT, V4.8.1, Mar 01 2019, 14:14:47\n",
            "Command: cd-hit -i train_ne.fasta -o train_ne_cdhit.txt -c\n",
            "         0.99\n",
            "\n",
            "Started: Thu Aug 12 11:43:53 2021\n",
            "================================================================\n",
            "                            Output                              \n",
            "----------------------------------------------------------------\n",
            "total seq: 1445\n",
            "longest and shortest : 30 and 11\n",
            "Total letters: 30071\n",
            "Sequences have been sorted\n",
            "\n",
            "Approximated minimal memory consumption:\n",
            "Sequence        : 0M\n",
            "Buffer          : 1 X 10M = 10M\n",
            "Table           : 1 X 65M = 65M\n",
            "Miscellaneous   : 0M\n",
            "Total           : 76M\n",
            "\n",
            "Table limit with the given memory limit:\n",
            "Max number of representatives: 4000000\n",
            "Max number of word counting entries: 90485331\n",
            "\n",
            "\rcomparing sequences from          0  to       1445\n",
            ".\n",
            "     1445  finished       1422  clusters\n",
            "\n",
            "Approximated maximum memory consumption: 76M\n",
            "writing new database\n",
            "writing clustering information\n",
            "program completed !\n",
            "\n",
            "Total CPU time 0.06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQmq3tsqLQP9",
        "outputId": "fa43508a-40bc-4ae1-adb9-33fd4f95f199"
      },
      "source": [
        "! ls -l"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 704\n",
            "drwxr-xr-x 3 root root   4096 Oct 23  2019 build\n",
            "-rw-r--r-- 1 root root  86079 Sep 29  2020 Functions_Tables.pdf\n",
            "drwxr-xr-x 2 root root   4096 Sep 29  2020 Pfeature\n",
            "-rw-r--r-- 1 root root 221146 Sep 29  2020 Pfeature_Descriptors.pdf\n",
            "-rw-r--r-- 1 root root    971 Sep 28  2020 PKG-INFO\n",
            "-rw-r--r-- 1 root root   1265 Sep 29  2020 README\n",
            "-rw-r--r-- 1 root root   1267 Sep 29  2020 setup.py\n",
            "-rw-r--r-- 1 root root  54341 Aug 12 11:43 train_ne_cdhit.txt\n",
            "-rw-r--r-- 1 root root  61425 Aug 12 11:43 train_ne_cdhit.txt.clstr\n",
            "-rw-r--r-- 1 root root  57206 Aug 12 11:41 train_ne.fasta\n",
            "-rw-r--r-- 1 root root  47229 Aug 12 11:42 train_po_cdhit.txt\n",
            "-rw-r--r-- 1 root root  56665 Aug 12 11:42 train_po_cdhit.txt.clstr\n",
            "-rw-r--r-- 1 root root  52619 Aug 12 11:40 train_po.fasta\n",
            "-rw-r--r-- 1 root root  52619 Aug 12 11:41 train_po.fasta.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5DjCCrdLWSz",
        "outputId": "2bba5cd2-d3a7-4e72-d661-f07099082fab"
      },
      "source": [
        "! grep \">\" train_po_cdhit.txt | wc -l"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1337\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OT6npAt1LaIr",
        "outputId": "1410a741-3e65-4adb-951b-23a435e87138"
      },
      "source": [
        "! grep \">\" train_po.fasta | wc -l"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1529\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMB99kvILeej",
        "outputId": "8d269294-7e2d-423f-8f5a-ba409608c814"
      },
      "source": [
        "! grep \">\" train_ne.fasta | wc -l"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1529\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDDZgY2XLg3I",
        "outputId": "d45635f0-70bb-469e-d274-eea2aea5ce49"
      },
      "source": [
        "! grep \">\" train_ne_cdhit.txt | wc -l"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1422\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVBCqEt0Lkre"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "bal_kU7kLxW_",
        "outputId": "dc41f6d3-d4ce-4cc4-f5a6-585464a2d3f6"
      },
      "source": [
        "# Conjoint Triad Calculation (CTC)\n",
        "\n",
        "from Pfeature.pfeature import ctc_wp\n",
        "\n",
        "def ctc(input):\n",
        "  a = input.rstrip('txt')\n",
        "  output = a + 'ctc.csv'\n",
        "  df_out = ctc_wp(input, output)\n",
        "  df_in = pd.read_csv(output)\n",
        "  return df_in\n",
        "\n",
        "ctc('train_po_cdhit.txt')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1332</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1333</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.500</td>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
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              "      <td>...</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1337 rows × 343 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      CTC_111  CTC_112  CTC_113  CTC_114  ...  CTC_774  CTC_775  CTC_776  CTC_777\n",
              "0         0.0      1.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1         0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      1.0\n",
              "2         0.0      1.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "3         0.0      1.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "4         0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "...       ...      ...      ...      ...  ...      ...      ...      ...      ...\n",
              "1332      0.5      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1333      0.0      0.0      0.0      0.5  ...      0.0      0.0      0.0      0.0\n",
              "1334      0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1335      0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1336      0.0      0.0      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "\n",
              "[1337 rows x 343 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "V3cyR_3PMfBi",
        "outputId": "4c6abbcf-6afa-437d-e2c9-6e2c4a34ce0d"
      },
      "source": [
        "#Calculate feature for both positive and negative classes + combines the two classes + merge with class labels\n",
        "\n",
        "pos = 'train_po_cdhit.txt'\n",
        "neg = 'train_ne_cdhit.txt'\n",
        "\n",
        "def feature_calc(po, ne, feature_name):\n",
        "  # Calculate feature\n",
        "  po_feature = feature_name(po)\n",
        "  ne_feature = feature_name(ne)\n",
        "  # Create class labels\n",
        "  po_class = pd.Series(['positive' for i in range(len(po_feature))])\n",
        "  ne_class = pd.Series(['negative' for i in range(len(ne_feature))])\n",
        "  # Combine po and ne\n",
        "  po_ne_class = pd.concat([po_class, ne_class], axis=0)\n",
        "  po_ne_class.name = 'class'\n",
        "  po_ne_feature = pd.concat([po_feature, ne_feature], axis=0)\n",
        "  # Combine feature and class\n",
        "  df = pd.concat([po_ne_feature, po_ne_class], axis=1)\n",
        "  return df\n",
        "\n",
        "feature = feature_calc(pos, neg, ctc) # CTC\n",
        "feature"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CTC_111</th>\n",
              "      <th>CTC_112</th>\n",
              "      <th>CTC_113</th>\n",
              "      <th>CTC_114</th>\n",
              "      <th>CTC_115</th>\n",
              "      <th>CTC_116</th>\n",
              "      <th>CTC_117</th>\n",
              "      <th>CTC_121</th>\n",
              "      <th>CTC_122</th>\n",
              "      <th>CTC_123</th>\n",
              "      <th>CTC_124</th>\n",
              "      <th>CTC_125</th>\n",
              "      <th>CTC_126</th>\n",
              "      <th>CTC_127</th>\n",
              "      <th>CTC_131</th>\n",
              "      <th>CTC_132</th>\n",
              "      <th>CTC_133</th>\n",
              "      <th>CTC_134</th>\n",
              "      <th>CTC_135</th>\n",
              "      <th>CTC_136</th>\n",
              "      <th>CTC_137</th>\n",
              "      <th>CTC_141</th>\n",
              "      <th>CTC_142</th>\n",
              "      <th>CTC_143</th>\n",
              "      <th>CTC_144</th>\n",
              "      <th>CTC_145</th>\n",
              "      <th>CTC_146</th>\n",
              "      <th>CTC_147</th>\n",
              "      <th>CTC_151</th>\n",
              "      <th>CTC_152</th>\n",
              "      <th>CTC_153</th>\n",
              "      <th>CTC_154</th>\n",
              "      <th>CTC_155</th>\n",
              "      <th>CTC_156</th>\n",
              "      <th>CTC_157</th>\n",
              "      <th>CTC_161</th>\n",
              "      <th>CTC_162</th>\n",
              "      <th>CTC_163</th>\n",
              "      <th>CTC_164</th>\n",
              "      <th>CTC_165</th>\n",
              "      <th>...</th>\n",
              "      <th>CTC_724</th>\n",
              "      <th>CTC_725</th>\n",
              "      <th>CTC_726</th>\n",
              "      <th>CTC_727</th>\n",
              "      <th>CTC_731</th>\n",
              "      <th>CTC_732</th>\n",
              "      <th>CTC_733</th>\n",
              "      <th>CTC_734</th>\n",
              "      <th>CTC_735</th>\n",
              "      <th>CTC_736</th>\n",
              "      <th>CTC_737</th>\n",
              "      <th>CTC_741</th>\n",
              "      <th>CTC_742</th>\n",
              "      <th>CTC_743</th>\n",
              "      <th>CTC_744</th>\n",
              "      <th>CTC_745</th>\n",
              "      <th>CTC_746</th>\n",
              "      <th>CTC_747</th>\n",
              "      <th>CTC_751</th>\n",
              "      <th>CTC_752</th>\n",
              "      <th>CTC_753</th>\n",
              "      <th>CTC_754</th>\n",
              "      <th>CTC_755</th>\n",
              "      <th>CTC_756</th>\n",
              "      <th>CTC_757</th>\n",
              "      <th>CTC_761</th>\n",
              "      <th>CTC_762</th>\n",
              "      <th>CTC_763</th>\n",
              "      <th>CTC_764</th>\n",
              "      <th>CTC_765</th>\n",
              "      <th>CTC_766</th>\n",
              "      <th>CTC_767</th>\n",
              "      <th>CTC_771</th>\n",
              "      <th>CTC_772</th>\n",
              "      <th>CTC_773</th>\n",
              "      <th>CTC_774</th>\n",
              "      <th>CTC_775</th>\n",
              "      <th>CTC_776</th>\n",
              "      <th>CTC_777</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1417</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1418</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1419</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1420</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1421</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2759 rows × 344 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      CTC_111  CTC_112  CTC_113  CTC_114  ...  CTC_775  CTC_776  CTC_777     class\n",
              "0         0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0  positive\n",
              "1         0.0    0.000      0.0      0.0  ...      0.0      0.0      1.0  positive\n",
              "2         0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0  positive\n",
              "3         0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0  positive\n",
              "4         0.0    0.000      0.0      0.0  ...      0.0      0.0      0.0  positive\n",
              "...       ...      ...      ...      ...  ...      ...      ...      ...       ...\n",
              "1417      0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0  negative\n",
              "1418      0.0    0.500      0.0      0.0  ...      0.0      0.0      0.0  negative\n",
              "1419      0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0  negative\n",
              "1420      0.0    0.333      0.0      0.0  ...      0.0      0.0      0.0  negative\n",
              "1421      0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0  negative\n",
              "\n",
              "[2759 rows x 344 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YhxH5y6NVl6"
      },
      "source": [
        "# Assigns the features to X and class label to Y\n",
        "X = feature.drop('class', axis=1)\n",
        "y = feature['class'].copy()"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsIC34PPNX82"
      },
      "source": [
        "# Encoding the Y class label\n",
        "y = y.map({\"positive\": 1, \"negative\": 0})"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7O0KgEfOOaU",
        "outputId": "a3caa3b4-d4d7-41df-c0b6-5e98616ec5a7"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2759, 343)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "oVMRe9S_OWz9",
        "outputId": "3a633011-ed14-4e75-ddc4-2cad833cf44a"
      },
      "source": [
        "# Feature selection (Variance threshold)\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "\n",
        "fs = VarianceThreshold(threshold=0.0001)\n",
        "fs.fit_transform(X)\n",
        "#X2.shape\n",
        "X2 = X.loc[:, fs.get_support()]\n",
        "X2"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CTC_111</th>\n",
              "      <th>CTC_112</th>\n",
              "      <th>CTC_113</th>\n",
              "      <th>CTC_114</th>\n",
              "      <th>CTC_115</th>\n",
              "      <th>CTC_116</th>\n",
              "      <th>CTC_117</th>\n",
              "      <th>CTC_121</th>\n",
              "      <th>CTC_122</th>\n",
              "      <th>CTC_123</th>\n",
              "      <th>CTC_124</th>\n",
              "      <th>CTC_125</th>\n",
              "      <th>CTC_126</th>\n",
              "      <th>CTC_127</th>\n",
              "      <th>CTC_131</th>\n",
              "      <th>CTC_132</th>\n",
              "      <th>CTC_133</th>\n",
              "      <th>CTC_134</th>\n",
              "      <th>CTC_135</th>\n",
              "      <th>CTC_136</th>\n",
              "      <th>CTC_137</th>\n",
              "      <th>CTC_141</th>\n",
              "      <th>CTC_142</th>\n",
              "      <th>CTC_143</th>\n",
              "      <th>CTC_144</th>\n",
              "      <th>CTC_145</th>\n",
              "      <th>CTC_146</th>\n",
              "      <th>CTC_147</th>\n",
              "      <th>CTC_151</th>\n",
              "      <th>CTC_152</th>\n",
              "      <th>CTC_153</th>\n",
              "      <th>CTC_154</th>\n",
              "      <th>CTC_155</th>\n",
              "      <th>CTC_156</th>\n",
              "      <th>CTC_157</th>\n",
              "      <th>CTC_161</th>\n",
              "      <th>CTC_162</th>\n",
              "      <th>CTC_163</th>\n",
              "      <th>CTC_164</th>\n",
              "      <th>CTC_165</th>\n",
              "      <th>...</th>\n",
              "      <th>CTC_721</th>\n",
              "      <th>CTC_722</th>\n",
              "      <th>CTC_723</th>\n",
              "      <th>CTC_724</th>\n",
              "      <th>CTC_725</th>\n",
              "      <th>CTC_727</th>\n",
              "      <th>CTC_731</th>\n",
              "      <th>CTC_732</th>\n",
              "      <th>CTC_733</th>\n",
              "      <th>CTC_734</th>\n",
              "      <th>CTC_735</th>\n",
              "      <th>CTC_736</th>\n",
              "      <th>CTC_737</th>\n",
              "      <th>CTC_741</th>\n",
              "      <th>CTC_742</th>\n",
              "      <th>CTC_743</th>\n",
              "      <th>CTC_744</th>\n",
              "      <th>CTC_745</th>\n",
              "      <th>CTC_746</th>\n",
              "      <th>CTC_747</th>\n",
              "      <th>CTC_751</th>\n",
              "      <th>CTC_752</th>\n",
              "      <th>CTC_753</th>\n",
              "      <th>CTC_754</th>\n",
              "      <th>CTC_755</th>\n",
              "      <th>CTC_756</th>\n",
              "      <th>CTC_757</th>\n",
              "      <th>CTC_761</th>\n",
              "      <th>CTC_762</th>\n",
              "      <th>CTC_763</th>\n",
              "      <th>CTC_764</th>\n",
              "      <th>CTC_765</th>\n",
              "      <th>CTC_766</th>\n",
              "      <th>CTC_767</th>\n",
              "      <th>CTC_771</th>\n",
              "      <th>CTC_772</th>\n",
              "      <th>CTC_773</th>\n",
              "      <th>CTC_774</th>\n",
              "      <th>CTC_775</th>\n",
              "      <th>CTC_777</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1417</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2759 rows × 339 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      CTC_111  CTC_112  CTC_113  CTC_114  ...  CTC_773  CTC_774  CTC_775  CTC_777\n",
              "0         0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1         0.0    0.000      0.0      0.0  ...      1.0      0.0      0.0      1.0\n",
              "2         0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "3         0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "4         0.0    0.000      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "...       ...      ...      ...      ...  ...      ...      ...      ...      ...\n",
              "1417      0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1418      0.0    0.500      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1419      0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1420      0.0    0.333      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "1421      0.0    1.000      0.0      0.0  ...      0.0      0.0      0.0      0.0\n",
              "\n",
              "[2759 rows x 339 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkEg0FkcOu1F"
      },
      "source": [
        "# Data split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X2, y, test_size=0.1, random_state =42, stratify=y)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e0Zj6C9xO1rx",
        "outputId": "3620a3a8-0ff0-443b-ab64-b596d4a1f98f"
      },
      "source": [
        "#Quickly compare >30 ML algorithms\n",
        "\n",
        "! pip install lazypredict"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting lazypredict\n",
            "  Downloading lazypredict-0.2.9-py2.py3-none-any.whl (12 kB)\n",
            "Collecting click==7.1.2\n",
            "  Downloading click-7.1.2-py2.py3-none-any.whl (82 kB)\n",
            "\u001b[K     |████████████████████████████████| 82 kB 1.2 MB/s \n",
            "\u001b[?25hCollecting xgboost==1.1.1\n",
            "  Downloading xgboost-1.1.1-py3-none-manylinux2010_x86_64.whl (127.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 127.6 MB 20 kB/s \n",
            "\u001b[?25hCollecting lightgbm==2.3.1\n",
            "  Downloading lightgbm-2.3.1-py2.py3-none-manylinux1_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 43.8 MB/s \n",
            "\u001b[?25hCollecting PyYAML==5.3.1\n",
            "  Downloading PyYAML-5.3.1.tar.gz (269 kB)\n",
            "\u001b[K     |████████████████████████████████| 269 kB 69.0 MB/s \n",
            "\u001b[?25hCollecting scipy==1.5.4\n",
            "  Downloading scipy-1.5.4-cp37-cp37m-manylinux1_x86_64.whl (25.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 25.9 MB 928 bytes/s \n",
            "\u001b[?25hCollecting six==1.15.0\n",
            "  Downloading six-1.15.0-py2.py3-none-any.whl (10 kB)\n",
            "Collecting tqdm==4.56.0\n",
            "  Downloading tqdm-4.56.0-py2.py3-none-any.whl (72 kB)\n",
            "\u001b[K     |████████████████████████████████| 72 kB 1.2 MB/s \n",
            "\u001b[?25hCollecting joblib==1.0.0\n",
            "  Downloading joblib-1.0.0-py3-none-any.whl (302 kB)\n",
            "\u001b[K     |████████████████████████████████| 302 kB 67.9 MB/s \n",
            "\u001b[?25hCollecting numpy==1.19.1\n",
            "  Downloading numpy-1.19.1-cp37-cp37m-manylinux2010_x86_64.whl (14.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 14.5 MB 7.9 kB/s \n",
            "\u001b[?25hCollecting pytest==5.4.3\n",
            "  Downloading pytest-5.4.3-py3-none-any.whl (248 kB)\n",
            "\u001b[K     |████████████████████████████████| 248 kB 53.8 MB/s \n",
            "\u001b[?25hCollecting scikit-learn==0.23.1\n",
            "  Downloading scikit_learn-0.23.1-cp37-cp37m-manylinux1_x86_64.whl (6.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.8 MB 54.4 MB/s \n",
            "\u001b[?25hCollecting pandas==1.0.5\n",
            "  Downloading pandas-1.0.5-cp37-cp37m-manylinux1_x86_64.whl (10.1 MB)\n",
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            "Building wheels for collected packages: PyYAML\n",
            "  Building wheel for PyYAML (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyYAML: filename=PyYAML-5.3.1-cp37-cp37m-linux_x86_64.whl size=393702 sha256=f4653fc5a489362c05280da20bdd533523325423c55e8a8f23c8152dd5046f38\n",
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            "Installing collected packages: click, numpy, scipy, xgboost, joblib, threadpoolctl, scikit-learn, lightgbm, PyYAML, six, tqdm, pyparsing, packaging, more-itertools, typing-extensions, zipp, importlib-metadata, pluggy, wcwidth, py, attrs, pytest, pytz, python-dateutil, pandas, lazypredict\n",
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            "    Found existing installation: six 1.14.0\n",
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            "      Successfully uninstalled six-1.14.0\n",
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            "Successfully installed PyYAML-5.3.1 attrs-21.2.0 click-7.1.2 importlib-metadata-4.6.3 joblib-1.0.0 lazypredict-0.2.9 lightgbm-2.3.1 more-itertools-8.8.0 numpy-1.19.1 packaging-21.0 pandas-1.0.5 pluggy-0.13.1 py-1.10.0 pyparsing-2.4.7 pytest-5.4.3 python-dateutil-2.8.2 pytz-2021.1 scikit-learn-0.23.1 scipy-1.5.4 six-1.15.0 threadpoolctl-2.2.0 tqdm-4.56.0 typing-extensions-3.10.0.0 wcwidth-0.2.5 xgboost-1.1.1 zipp-3.5.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pyparsing",
                  "six",
                  "wcwidth"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w50TqIUTO8aI",
        "outputId": "55768472-49e6-42b0-bdff-a26752b61aa0"
      },
      "source": [
        "# Import libraries\n",
        "import lazypredict\n",
        "from lazypredict.Supervised import LazyClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import matthews_corrcoef\n",
        "\n",
        "# Load dataset\n",
        "X = feature.drop('class', axis=1)\n",
        "y = feature['class'].copy()\n",
        "\n",
        "# Data split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state =42, stratify=y)\n",
        "\n",
        "# Defines and builds the lazyclassifier\n",
        "clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=matthews_corrcoef)\n",
        "models_train,predictions_train = clf.fit(X_train, X_train, y_train, y_train)\n",
        "#models_test,predictions_test = clf.fit(X_train, X_test, y_train, y_test)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:31<00:00,  1.10s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "TYv9xH4XPS0i",
        "outputId": "37ebf869-6475-4986-e00c-04ed31376462"
      },
      "source": [
        "# Prints the model performance (Training set)\n",
        "models_train"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>matthews_corrcoef</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.61</td>\n",
              "      <td>0.60</td>\n",
              "      <td>None</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>None</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>None</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.61</td>\n",
              "      <td>0.59</td>\n",
              "      <td>None</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.61</td>\n",
              "      <td>0.59</td>\n",
              "      <td>None</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.61</td>\n",
              "      <td>0.59</td>\n",
              "      <td>None</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.61</td>\n",
              "      <td>0.59</td>\n",
              "      <td>None</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.61</td>\n",
              "      <td>0.59</td>\n",
              "      <td>None</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.57</td>\n",
              "      <td>None</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.15</td>\n",
              "      <td>3.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.57</td>\n",
              "      <td>None</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.14</td>\n",
              "      <td>2.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.56</td>\n",
              "      <td>None</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CategoricalNB</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.56</td>\n",
              "      <td>None</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.56</td>\n",
              "      <td>None</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.56</td>\n",
              "      <td>None</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.56</td>\n",
              "      <td>None</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.56</td>\n",
              "      <td>None</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.56</td>\n",
              "      <td>None</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.15</td>\n",
              "      <td>4.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.56</td>\n",
              "      <td>0.56</td>\n",
              "      <td>None</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.55</td>\n",
              "      <td>None</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.55</td>\n",
              "      <td>None</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.55</td>\n",
              "      <td>None</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.53</td>\n",
              "      <td>0.54</td>\n",
              "      <td>None</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.07</td>\n",
              "      <td>3.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.51</td>\n",
              "      <td>0.53</td>\n",
              "      <td>None</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.52</td>\n",
              "      <td>0.52</td>\n",
              "      <td>None</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.52</td>\n",
              "      <td>0.52</td>\n",
              "      <td>None</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.52</td>\n",
              "      <td>0.52</td>\n",
              "      <td>None</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.49</td>\n",
              "      <td>0.49</td>\n",
              "      <td>None</td>\n",
              "      <td>0.49</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.45</td>\n",
              "      <td>0.44</td>\n",
              "      <td>None</td>\n",
              "      <td>0.44</td>\n",
              "      <td>-0.11</td>\n",
              "      <td>7.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Accuracy  ...  Time Taken\n",
              "Model                                    ...            \n",
              "RandomForestClassifier             0.61  ...        1.48\n",
              "LGBMClassifier                     0.60  ...        0.64\n",
              "BaggingClassifier                  0.60  ...        0.73\n",
              "LabelSpreading                     0.61  ...        0.60\n",
              "DecisionTreeClassifier             0.61  ...        0.20\n",
              "ExtraTreeClassifier                0.61  ...        0.08\n",
              "LabelPropagation                   0.61  ...        0.52\n",
              "ExtraTreesClassifier               0.61  ...        1.56\n",
              "KNeighborsClassifier               0.57  ...        3.26\n",
              "XGBClassifier                      0.57  ...        2.29\n",
              "QuadraticDiscriminantAnalysis      0.55  ...        0.30\n",
              "CategoricalNB                      0.57  ...        0.15\n",
              "RidgeClassifierCV                  0.56  ...        0.24\n",
              "RidgeClassifier                    0.56  ...        0.16\n",
              "LogisticRegression                 0.56  ...        0.23\n",
              "LinearDiscriminantAnalysis         0.56  ...        0.33\n",
              "SVC                                0.57  ...        4.66\n",
              "LinearSVC                          0.56  ...        1.78\n",
              "AdaBoostClassifier                 0.55  ...        0.57\n",
              "NearestCentroid                    0.55  ...        0.10\n",
              "BernoulliNB                        0.55  ...        0.11\n",
              "NuSVC                              0.53  ...        3.68\n",
              "GaussianNB                         0.51  ...        0.08\n",
              "PassiveAggressiveClassifier        0.52  ...        0.12\n",
              "SGDClassifier                      0.52  ...        0.31\n",
              "Perceptron                         0.52  ...        0.11\n",
              "DummyClassifier                    0.49  ...        0.06\n",
              "CalibratedClassifierCV             0.45  ...        7.10\n",
              "\n",
              "[28 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "v2zq5d9TPXnt",
        "outputId": "9b92a975-bf87-4a11-ba73-bdcf6d9b0b73"
      },
      "source": [
        "\n",
        "\n",
        "# Prints the model performance (Test set)\n",
        "models_test\n",
        "\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-2ada96743e48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Prints the model performance (Test set)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodels_test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'models_test' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "32-B1iw9QWL6",
        "outputId": "324adf28-25f0-4a7f-f880-61783ac3d532"
      },
      "source": [
        "# Plot of Accuracy\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=models_train.index, x=\"Accuracy\", data=models_train)\n",
        "ax.set(xlim=(0, 1))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "M-XRFoobQed8",
        "outputId": "d2f27139-0a83-4737-ce24-7f3c1f931903"
      },
      "source": [
        "# Plot of MCC\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=models_train.index, x=\"matthews_corrcoef\", data=models_train)\n",
        "ax.set(xlim=(0, 1))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rq7wSiYQWyqf",
        "outputId": "9afba859-73a0-4026-bfab-75cdc0549dbd"
      },
      "source": [
        "# Build random forest model\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators=500)\n",
        "\n",
        "rf.fit(X_train, y_train)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=500,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5QVVXkbW2F5"
      },
      "source": [
        "y_train_pred = rf.predict(X_train)\n",
        "y_test_pred = rf.predict(X_test)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r622ZGE5W5uA",
        "outputId": "5fab1da9-17ef-4429-c01e-94d1ada45da0"
      },
      "source": [
        "feature['class']"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       positive\n",
              "1       positive\n",
              "2       positive\n",
              "3       positive\n",
              "4       positive\n",
              "          ...   \n",
              "1417    negative\n",
              "1418    negative\n",
              "1419    negative\n",
              "1420    negative\n",
              "1421    negative\n",
              "Name: class, Length: 2759, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BptEDCNFW72A",
        "outputId": "83245cf5-841e-4c7d-ca27-ec20e27d160d"
      },
      "source": [
        "# Simplest and quickest way to obtain the model performance (Accuracy)\n",
        "rf.score(X_test,y_test)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13768115942028986"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CAbisNmVW_Nh",
        "outputId": "90c74f97-74d2-41d3-a4ff-fe6a46116e52"
      },
      "source": [
        "# Accuracy\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy_score(y_test, y_test_pred)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13768115942028986"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGNaNBiaXBsZ",
        "outputId": "3cad5576-091e-49a6-eafc-a93f51fe9b4c"
      },
      "source": [
        "# Matthew Correlation Coefficient\n",
        "from sklearn.metrics import matthews_corrcoef\n",
        "\n",
        "matthews_corrcoef(y_test, y_test_pred)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.727872154700112"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xm9w1Op8XE28",
        "outputId": "f0e602d4-2ee4-40d6-85a0-ff006cfe68cd"
      },
      "source": [
        "# Confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "confusion_matrix(y_test, y_test_pred)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 50, 235],\n",
              "       [241,  26]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0dGzvhJXIUT"
      },
      "source": [
        "# Classification report\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "model_report = classification_report(y_train, y_train_pred, target_names=['positive','negative'])\n",
        "\n",
        "f = open('model_report.txt','w')\n",
        "f.writelines(model_report) \n",
        "f.close()"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "RSHO70PeXLOi",
        "outputId": "eb89d1d6-44e1-449c-bcdd-5396b8df5004"
      },
      "source": [
        "# ROC curve\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "\n",
        "plot_roc_curve(rf, X_test, y_test)  \n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "KFtu7nb9XNkX",
        "outputId": "e7957e25-da0a-4478-c861-00fe326c7fd6"
      },
      "source": [
        "plot_roc_curve(rf, X_train, y_train)  \n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "40P9YdR0XRqb",
        "outputId": "3317c9d3-ada6-438d-b192-575307fb94ab"
      },
      "source": [
        "# Display Dataframe of the dataset after feature selection (variance threshold)\n",
        "X2"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CTC_111</th>\n",
              "      <th>CTC_112</th>\n",
              "      <th>CTC_113</th>\n",
              "      <th>CTC_114</th>\n",
              "      <th>CTC_115</th>\n",
              "      <th>CTC_116</th>\n",
              "      <th>CTC_117</th>\n",
              "      <th>CTC_121</th>\n",
              "      <th>CTC_122</th>\n",
              "      <th>CTC_123</th>\n",
              "      <th>CTC_124</th>\n",
              "      <th>CTC_125</th>\n",
              "      <th>CTC_126</th>\n",
              "      <th>CTC_127</th>\n",
              "      <th>CTC_131</th>\n",
              "      <th>CTC_132</th>\n",
              "      <th>CTC_133</th>\n",
              "      <th>CTC_134</th>\n",
              "      <th>CTC_135</th>\n",
              "      <th>CTC_136</th>\n",
              "      <th>CTC_137</th>\n",
              "      <th>CTC_141</th>\n",
              "      <th>CTC_142</th>\n",
              "      <th>CTC_143</th>\n",
              "      <th>CTC_144</th>\n",
              "      <th>CTC_145</th>\n",
              "      <th>CTC_146</th>\n",
              "      <th>CTC_147</th>\n",
              "      <th>CTC_151</th>\n",
              "      <th>CTC_152</th>\n",
              "      <th>CTC_153</th>\n",
              "      <th>CTC_154</th>\n",
              "      <th>CTC_155</th>\n",
              "      <th>CTC_156</th>\n",
              "      <th>CTC_157</th>\n",
              "      <th>CTC_161</th>\n",
              "      <th>CTC_162</th>\n",
              "      <th>CTC_163</th>\n",
              "      <th>CTC_164</th>\n",
              "      <th>CTC_165</th>\n",
              "      <th>...</th>\n",
              "      <th>CTC_721</th>\n",
              "      <th>CTC_722</th>\n",
              "      <th>CTC_723</th>\n",
              "      <th>CTC_724</th>\n",
              "      <th>CTC_725</th>\n",
              "      <th>CTC_727</th>\n",
              "      <th>CTC_731</th>\n",
              "      <th>CTC_732</th>\n",
              "      <th>CTC_733</th>\n",
              "      <th>CTC_734</th>\n",
              "      <th>CTC_735</th>\n",
              "      <th>CTC_736</th>\n",
              "      <th>CTC_737</th>\n",
              "      <th>CTC_741</th>\n",
              "      <th>CTC_742</th>\n",
              "      <th>CTC_743</th>\n",
              "      <th>CTC_744</th>\n",
              "      <th>CTC_745</th>\n",
              "      <th>CTC_746</th>\n",
              "      <th>CTC_747</th>\n",
              "      <th>CTC_751</th>\n",
              "      <th>CTC_752</th>\n",
              "      <th>CTC_753</th>\n",
              "      <th>CTC_754</th>\n",
              "      <th>CTC_755</th>\n",
              "      <th>CTC_756</th>\n",
              "      <th>CTC_757</th>\n",
              "      <th>CTC_761</th>\n",
              "      <th>CTC_762</th>\n",
              "      <th>CTC_763</th>\n",
              "      <th>CTC_764</th>\n",
              "      <th>CTC_765</th>\n",
              "      <th>CTC_766</th>\n",
              "      <th>CTC_767</th>\n",
              "      <th>CTC_771</th>\n",
              "      <th>CTC_772</th>\n",
              "      <th>CTC_773</th>\n",
              "      <th>CTC_774</th>\n",
              "      <th>CTC_775</th>\n",
              "      <th>CTC_777</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1417</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1418</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1419</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1420</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1421</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2759 rows × 339 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      CTC_111  CTC_112  CTC_113  CTC_114  ...  CTC_773  CTC_774  CTC_775  CTC_777\n",
              "0        0.00     1.00     0.00     0.00  ...     0.00     0.00     0.00     0.00\n",
              "1        0.00     0.00     0.00     0.00  ...     1.00     0.00     0.00     1.00\n",
              "2        0.00     1.00     0.00     0.00  ...     0.00     0.00     0.00     0.00\n",
              "3        0.00     1.00     0.00     0.00  ...     0.00     0.00     0.00     0.00\n",
              "4        0.00     0.00     0.00     0.00  ...     0.00     0.00     0.00     0.00\n",
              "...       ...      ...      ...      ...  ...      ...      ...      ...      ...\n",
              "1417     0.00     1.00     0.00     0.00  ...     0.00     0.00     0.00     0.00\n",
              "1418     0.00     0.50     0.00     0.00  ...     0.00     0.00     0.00     0.00\n",
              "1419     0.00     1.00     0.00     0.00  ...     0.00     0.00     0.00     0.00\n",
              "1420     0.00     0.33     0.00     0.00  ...     0.00     0.00     0.00     0.00\n",
              "1421     0.00     1.00     0.00     0.00  ...     0.00     0.00     0.00     0.00\n",
              "\n",
              "[2759 rows x 339 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4h7ff7tXWQm"
      },
      "source": [
        "# Retrieve feature importance from the RF model\n",
        "importance = pd.Series(rf.feature_importances_, name = 'Gini')\n",
        "\n",
        "# Retrieve feature names\n",
        "feature_names = pd.Series(X2.columns, name = 'Feature')"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ufUSJC4HXZr-",
        "outputId": "d60c4e5f-6d54-44d4-8f88-6504ee0de885"
      },
      "source": [
        "# Combine feature names and Gini values into a Dataframe\n",
        "df = pd.concat([feature_names, importance], axis=1, names=['Feature', 'Gini'])\n",
        "df"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Gini</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CTC_111</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CTC_112</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CTC_113</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CTC_114</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CTC_115</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>CTC_777</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>343 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Feature  Gini\n",
              "0    CTC_111  0.01\n",
              "1    CTC_112  0.01\n",
              "2    CTC_113  0.01\n",
              "3    CTC_114  0.01\n",
              "4    CTC_115  0.01\n",
              "..       ...   ...\n",
              "338  CTC_777  0.00\n",
              "339      NaN  0.00\n",
              "340      NaN  0.00\n",
              "341      NaN  0.00\n",
              "342      NaN  0.00\n",
              "\n",
              "[343 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "cbE-jECeXcK5",
        "outputId": "e64813e0-0172-4ee1-be46-1435e2e25a30"
      },
      "source": [
        "# Plot of feature importance\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "df_sorted = df.sort_values('Gini', ascending=False)[:20] # Sort by Gini in descending order; Showing only the top 20 results\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(x = 'Gini', y = 'Feature', data = df_sorted)\n",
        "plt.xlabel(\"Feature Importance\")"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Feature Importance')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oIttG-JXfSJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGVBzqJgXmCC"
      },
      "source": [
        "Reference: YouTube Tutorial of DataProfessor"
      ]
    }
  ]
}