Remote_Sensing_data_for_Wildfires_Prediction.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-nV_lFIrGfk"
      },
      "source": [
        "# Wildfires Prediction Using Remote Sensing Data and Machine Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have implemented a methodology to build a remote sensing dataset related to the state of the crops (**NDVI**), meteorological conditions (**LST**), and the fire indicator \"**Thermal Anomalies**\" using the Google Earth Engine cloud platform. These data were acquired from MODIS (Moderate Resolution Imaging Spectroradiometer), a key instrument onboard the Terra and Aqua satellites."
      ],
      "metadata": {
        "id": "rWLSIUDrjUFI"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "<img src='https://raw.githubusercontent.com/jdbcode/G4G19/master/img/data-reduction.png' width='700px' alt='data-to-insight.png'>\n",
        "\n",
        "- [**Earth Engine**](https://earthengine.google.com/) - geospatial data access and processing\n",
        "- [**Pandas**](https://pandas.pydata.org/) - dataframe structure\n",
        "- [**Colab**](https://colab.research.google.com) - integrative Python environment \n",
        "\n",
        "<br><br><br><br><br><br>"
      ],
      "metadata": {
        "id": "Zz_i6R1zixPm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "In order to analyze our dataset, we chose to use the GEE platform for the preprocessing and extraction of data namely: NDVI, LST and Thermal Anomalies. Then we saved the data as a CSV file in Google Drive "
      ],
      "metadata": {
        "id": "vnvHF6rsqxnv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  1: Dataset description "
      ],
      "metadata": {
        "id": "Se1FBEpVjJID"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yb4H7lEgqeSz"
      },
      "source": [
        "\n",
        "Our dataset is composed of 1713 samples: 1327 instances of the class \"no_fire\" and 386 instances of the class \"fire\".\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2: Importing the necessary libraries"
      ],
      "metadata": {
        "id": "4ECDrWbjrbey"
      }
    },
    {
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      "execution_count": null,
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        "id": "M-tC5Hxr7axR",
        "outputId": "a26c8afd-ec9f-4003-ae8e-3970bb5953ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1d/5b/9f37c9e5be8210c06c48621ee5100c2d62144e1043f474ec0598b2cd62d4/rgf_python-2.3.0-py2.py3-none-win_amd64.whl#sha256=810dddc9d57f0168babcf3baf42ef02ce70c7a0e618a4a5bebf26638de70a42e (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/c5/d7/633d28ac0b98deb21ee92780e292026e684cd0ae7a22e2a88cac5caa8480/rgf_python-2.3.0.tar.gz#sha256=d9b83e79e38d5e50e313d3cea27af989a1f96cbb9176c4067e71aebbb77181a2 (from https://pypi.org/simple/rgf-python/), version: 2.3.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_10_x86_64, py2-none-macosx_10_11_x86_64, py2-none-macosx_10_12_x86_64, py2-none-macosx_10_13_x86_64, py2-none-macosx_10_6_x86_64, py2-none-macosx_10_7_x86_64, py2-none-macosx_10_8_x86_64, py2-none-macosx_10_9_x86_64, py3-none-macosx_10_10_x86_64, py3-none-macosx_10_11_x86_64, py3-none-macosx_10_12_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_6_x86_64, py3-none-macosx_10_7_x86_64, py3-none-macosx_10_8_x86_64, py3-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fc/74/bae2bad70302916f10954f6a0698f9dd4550b8adc10eecd30c79b3334fca/rgf_python-3.0.0-py2.py3-none-macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.whl#sha256=42b4a9454facf6b2caa2f113765eed2e905c5cac70e8c9307c5b071a67b7bd40 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/61/ee/d8e917ebe9b9c15e0034d17af8aff82f0afb476d2c2c97a92a45b26d6152/rgf_python-3.0.0-py2.py3-none-manylinux1_i686.whl#sha256=b7c2bad1e28ca129f40d9410d09e0a332d4f5c0c5efde7a0442afe79b9a02641 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/e9/68/d0fddc3068c9b973bed1f5f8a4f0a9b25248d133685183d5235315d0d085/rgf_python-3.0.0-py2.py3-none-manylinux1_x86_64.whl#sha256=5b68c9b9de491fe975887617340f2dd28781a69bbdecdaaf9c0a563718877241 (from https://pypi.org/simple/rgf-python/), version: 3.0.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/59/70/d9b5ce24c4a0a3923d95df38000b213c5e29b95601a20b8de87f69f4c582/rgf_python-3.0.0-py2.py3-none-win32.whl#sha256=2bd5f871e386ec72c13fc1e8273f17ef5972a2402b08a2f4e675cf31a0151964 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b5/09/75d7fa21efe3298da8613192e740265d3c526bff5b45631c2bb64d66f6e1/rgf_python-3.0.0-py2.py3-none-win_amd64.whl#sha256=faf3f6ab043ae9186b13505f5e629c06f5739d56f4237cb44d4f4884da39802f (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/49/3f/5e72ad095c44a80f9878f34e62c4a6d541aa36772453b3c06c66a3bb8c77/rgf_python-3.0.0.tar.gz#sha256=f82bf4b429787bcf0f19e60a90f9e1faadf9e984d24c961098bd68a9f1ae9351 (from https://pypi.org/simple/rgf-python/), version: 3.0.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_10_x86_64, py2-none-macosx_10_11_x86_64, py2-none-macosx_10_12_x86_64, py2-none-macosx_10_13_x86_64, py2-none-macosx_10_6_x86_64, py2-none-macosx_10_7_x86_64, py2-none-macosx_10_8_x86_64, py2-none-macosx_10_9_x86_64, py3-none-macosx_10_10_x86_64, py3-none-macosx_10_11_x86_64, py3-none-macosx_10_12_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_6_x86_64, py3-none-macosx_10_7_x86_64, py3-none-macosx_10_8_x86_64, py3-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/52/1a/b9dee7540ee1da9be3a4c5008f2c57b45b4585bb20e2bdce0834787d8043/rgf_python-3.1.0-py2.py3-none-macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.whl#sha256=d1c0163303b1ff773786d69e613d5bc99bc1cf364d2af6bea6f6075ee4bfdb1b (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7f/9b/f488ff1547129261bc5e8c248c4813c6b3f30d1b0696829c838f20223cb4/rgf_python-3.1.0-py2.py3-none-manylinux1_i686.whl#sha256=ceba72ba952211210ce486ddfb42130fa2eb4a19bb5d707f404f29ba0dc590eb (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/33/c4/7c73917d9a250f1d476b7f585e6b8955179e916f8410ed8f77418665aabf/rgf_python-3.1.0-py2.py3-none-manylinux1_x86_64.whl#sha256=23f525d874772da297cddef9fb664de18f9727c4b9a55a9f8ff4ab3f18ab2475 (from https://pypi.org/simple/rgf-python/), version: 3.1.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/7b/13/2460bc545d347aa6d9e5c8b87a49771812b3b8cb288e8cb401f1b881ada9/rgf_python-3.1.0-py2.py3-none-win32.whl#sha256=d8260578b36d546d44976fd0b4662d482bccf7e868cc254dc0e82eb08310d616 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/5e/9a96a8ff4da1a68935b32ffc8a1a7d8ebcff9ee999a4668ef7deeb326b2a/rgf_python-3.1.0-py2.py3-none-win_amd64.whl#sha256=32bc342444340fcf8d6119f140d6d0b946b0f9ea60d05f11a35bf4cd80bd6fe1 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/ae/22/ad7e7d0ff9304d757170e62403e792334ee432b43c6a246a14830b5e1a54/rgf_python-3.1.0.tar.gz#sha256=811c6d78dc6056884f3d225459a77195689bfb963c4cce42c2b5e060dde39826 (from https://pypi.org/simple/rgf-python/), version: 3.1.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_10_x86_64, py2-none-macosx_10_11_x86_64, py2-none-macosx_10_12_x86_64, py2-none-macosx_10_13_x86_64, py2-none-macosx_10_6_x86_64, py2-none-macosx_10_7_x86_64, py2-none-macosx_10_8_x86_64, py2-none-macosx_10_9_x86_64, py3-none-macosx_10_10_x86_64, py3-none-macosx_10_11_x86_64, py3-none-macosx_10_12_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_6_x86_64, py3-none-macosx_10_7_x86_64, py3-none-macosx_10_8_x86_64, py3-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/48/47/5468d6c7c636c3614a5ea6ae717596f65094b9845c9cd037cf2e7f403f05/rgf_python-3.2.0-py2.py3-none-macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.whl#sha256=e3548997362562bc8e208dec31b90cbea42697b4d5f5bdcc52f62c604aa59f7e (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/24/4d/015b4d8e8d0f673880921778b98155548e017e439ce778c0a3b7c070b573/rgf_python-3.2.0-py2.py3-none-manylinux1_i686.whl#sha256=c14e55c8d40fed6d5f4526bff2cd7b8597780c3ea62d942c2f8f7d7b1d40f3a7 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/fe/d1/4dec9c38a61ac6eb159fe5161b9e6f54a9255d2f0585f9f0ca9b31d0bf71/rgf_python-3.2.0-py2.py3-none-manylinux1_x86_64.whl#sha256=0995ab3038054b611b4fa97d2bdb87aac4a59310a0fb6b86b5e500beb9e94da1 (from https://pypi.org/simple/rgf-python/), version: 3.2.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/00/fc/bf212db2a1235b993f11dae289115180e2dcb023715adff2674bac85792d/rgf_python-3.2.0-py2.py3-none-win32.whl#sha256=35281bdffb527234fed60944dd39a6219e2d29951ddc005be8267fe6d0054bd9 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c8/ba/eaddd7d4afa2e2cf7ac08595be70853b99e312f2b8d02a339de4e60b83d4/rgf_python-3.2.0-py2.py3-none-win_amd64.whl#sha256=9f6f70286f868d6a0ab31ee4b8a429b7664f3fbc01a999267af43688ec1cff66 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/f4/b9/d25a51f0a51d07d6d9d0d67f9ffeb1a057bc2113e281573c8160ce45b7ea/rgf_python-3.2.0.tar.gz#sha256=9a9f497c3e3f558257af20c43e00738d84969fa646780d5dc0fea43e5192ebb8 (from https://pypi.org/simple/rgf-python/), version: 3.2.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_10_x86_64, py2-none-macosx_10_11_x86_64, py2-none-macosx_10_12_x86_64, py2-none-macosx_10_13_x86_64, py2-none-macosx_10_6_x86_64, py2-none-macosx_10_7_x86_64, py2-none-macosx_10_8_x86_64, py2-none-macosx_10_9_x86_64, py3-none-macosx_10_10_x86_64, py3-none-macosx_10_11_x86_64, py3-none-macosx_10_12_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_6_x86_64, py3-none-macosx_10_7_x86_64, py3-none-macosx_10_8_x86_64, py3-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e5/e4/57088e1fbd717d85a15bb431e9ce461640f9ebf7eca5c1944546d65c8f3d/rgf_python-3.3.0-py2.py3-none-macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.whl#sha256=bcbf2f60b118da9f0ad06c372d61a2447734d8f6c7155b35e9ebec9ebd09c871 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1a/d9/0cb591531e9d2aacfb72a54fc6e9c097f4e861865e288c2e46e4d4836524/rgf_python-3.3.0-py2.py3-none-manylinux1_i686.whl#sha256=aa35a91dd287efaaca86a30dc6aa7d180ad54330c4075edaeda6cb792464eead (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/b4/b4/bb6ba5695d41494bc4b145f1efcffbbce78946456ed6e8cb74c17b46b511/rgf_python-3.3.0-py2.py3-none-manylinux1_x86_64.whl#sha256=e0640fe0767925857eaa9b827e13625849b35d4833c5dbf58cf3e433dba237f1 (from https://pypi.org/simple/rgf-python/), version: 3.3.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/a7/b2/cc7a23b3f40394a2a6fb4ffaa06ca6fb82fc16c6983bd29e344f1b7d78d8/rgf_python-3.3.0-py2.py3-none-win32.whl#sha256=88598820bf6218a4126560fc40be5fe025e76ee513ee7efc8f1aa177514c511b (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0f/d9/f3cee0162c6dc3d24989e7966998e081c92b4e0b550b6a2a45baaef53729/rgf_python-3.3.0-py2.py3-none-win_amd64.whl#sha256=f437ffe3175a94a3947d9605103ea0ba5952b3dfc8fc5feecf9fb61030aecf6d (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/1e/55/2e09803c30b79b96b30d5ed1038de75e7b642a054f724aa258c2e2c1b963/rgf_python-3.3.0.tar.gz#sha256=7cae6e2153cdb98364b9b79e0fe691123bc0d2dd6aa33e231c64cac8d6d36734 (from https://pypi.org/simple/rgf-python/), version: 3.3.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_10_x86_64, py2-none-macosx_10_11_x86_64, py2-none-macosx_10_12_x86_64, py2-none-macosx_10_13_x86_64, py2-none-macosx_10_14_x86_64, py2-none-macosx_10_6_x86_64, py2-none-macosx_10_7_x86_64, py2-none-macosx_10_8_x86_64, py2-none-macosx_10_9_x86_64, py3-none-macosx_10_10_x86_64, py3-none-macosx_10_11_x86_64, py3-none-macosx_10_12_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_14_x86_64, py3-none-macosx_10_6_x86_64, py3-none-macosx_10_7_x86_64, py3-none-macosx_10_8_x86_64, py3-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/0a/20/f4a41716d768641098b5dcf606dc53de6deff67df741515f186bc91030a8/rgf_python-3.4.0-py2.py3-none-macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.macosx_10_14_x86_64.whl#sha256=9056cd0e0c1f0beb488dee8ea8675f109a64cfa9e229cb7fabb3c68a651ac946 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/85/5e/58401eed977a4a6327fa2ed901f7c524b03898a473dec4533cc34cfce4f1/rgf_python-3.4.0-py2.py3-none-manylinux1_i686.whl#sha256=18ac51ec04df5e924cd0d7d30a0f907fc8de0dba60436f5fa43a137271f18b8a (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/34/1f/ff796d528b5d6a7cb745fc0ac0bb5784c154ade62a3358f0be08a009e0ec/rgf_python-3.4.0-py2.py3-none-manylinux1_x86_64.whl#sha256=72b05a5b0c848191a70030cf9210ae2f234ae074d5c776c27977884b1ddca434 (from https://pypi.org/simple/rgf-python/), version: 3.4.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/15/8b/28cbab179a42974b74d95e1794f68645ebb793f8bb39bcc1037ff413bafe/rgf_python-3.4.0-py2.py3-none-win32.whl#sha256=3c471fe405ce3b375ca8cfbdc714f661375408308532737febdbd0853818c060 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/bf/f7/ca6f8ea8c1d4b36be04527235b53e0666b6d239fca57343c2c2ff2763203/rgf_python-3.4.0-py2.py3-none-win_amd64.whl#sha256=c9777befd8459d4548598580221eb4d72d8caf5f05215baaa75d9ad28165e918 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/ca/09/158f9c995f0264f1328c61f49871d934caa6b2dd779c02c622fe130fcfc2/rgf_python-3.4.0.tar.gz#sha256=69d83aeb36c2e075402db06457ea19a39fa83415dbcf958e22eac1f177c00b92 (from https://pypi.org/simple/rgf-python/), version: 3.4.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_10_x86_64, py2-none-macosx_10_11_x86_64, py2-none-macosx_10_12_x86_64, py2-none-macosx_10_13_x86_64, py2-none-macosx_10_14_x86_64, py2-none-macosx_10_6_x86_64, py2-none-macosx_10_7_x86_64, py2-none-macosx_10_8_x86_64, py2-none-macosx_10_9_x86_64, py3-none-macosx_10_10_x86_64, py3-none-macosx_10_11_x86_64, py3-none-macosx_10_12_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_14_x86_64, py3-none-macosx_10_6_x86_64, py3-none-macosx_10_7_x86_64, py3-none-macosx_10_8_x86_64, py3-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/9b/82/c9079fde6bfb5e3875fb970ecb96fe3a9dafa44be35224cf876633e4025e/rgf_python-3.5.0-py2.py3-none-macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.macosx_10_14_x86_64.whl#sha256=a24bf7bf373dfd8a7d08fe99cbdc8613fcb25b9755267952b7a6e92017521b16 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/c4/d2/1d963ee18f183e9d0784b1b18d7de969489491a54387030bd631ca1e3ae4/rgf_python-3.5.0-py2.py3-none-manylinux1_i686.whl#sha256=de85a35374dc8a91c24992b4aa9e47c71990bd560e7ffe3172f2fd5d9401106e (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/27/b4/4b6cc5448e49c9a13bba46da351e5bf9149486996466e2f8e546bee98afb/rgf_python-3.5.0-py2.py3-none-manylinux1_x86_64.whl#sha256=38a4f296de1745665f6063eff01b8b07c727c0120676d090f751fe19528cb064 (from https://pypi.org/simple/rgf-python/), version: 3.5.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/eb/c9/c85db732fec74bedc00d317bfc5021a7430c2d467a4b3b23a5e7095b3a66/rgf_python-3.5.0-py2.py3-none-win32.whl#sha256=ea4b1ba6d9ee8f2f77be7a7a9ec90de6f80e5845b129b8796118254e370aee6f (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/e3/0b/d7157d140bc9ad58799957d9c99c6f5b867c1878c48ff9153437454b3c7e/rgf_python-3.5.0-py2.py3-none-win_amd64.whl#sha256=0fab659e58bc75299dd07312d8acb91cca1470612723c006475b420cafc948b1 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/2f/be/07e7295c0378ed88ac320835e4dd58b83596d74ecc5feb7ec824749d3875/rgf_python-3.5.0.tar.gz#sha256=0d97086da9d092de5307b603de2ad2250f883f77634cae29b0484e386e139fbd (from https://pypi.org/simple/rgf-python/), version: 3.5.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_10_x86_64, py2-none-macosx_10_11_x86_64, py2-none-macosx_10_12_x86_64, py2-none-macosx_10_13_x86_64, py2-none-macosx_10_14_x86_64, py2-none-macosx_10_6_x86_64, py2-none-macosx_10_7_x86_64, py2-none-macosx_10_8_x86_64, py2-none-macosx_10_9_x86_64, py3-none-macosx_10_10_x86_64, py3-none-macosx_10_11_x86_64, py3-none-macosx_10_12_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_14_x86_64, py3-none-macosx_10_6_x86_64, py3-none-macosx_10_7_x86_64, py3-none-macosx_10_8_x86_64, py3-none-macosx_10_9_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5a/eb/dc56ba6728d7ddba6c708ade14d5e6c501a1893def32544be302eb1a48ce/rgf_python-3.6.0-py2.py3-none-macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.macosx_10_14_x86_64.whl#sha256=9963d1d65ce64ede8c07d7ab1752d18bf0aed7689594538e7119fbcdb6561947 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/d1/1e/ddb208e3d8c0f1fbe1947de4dd6b1a81a0a05da88a660f11a096c518bc2c/rgf_python-3.6.0-py2.py3-none-manylinux1_i686.whl#sha256=618d0e1ac7f67c3b1b1f3d7f5d91d216ceb0e6a2c8a811b7a46daf6333094cc0 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/95/7c/41f50b22c5a40614ffe8b3bfb857dbe7b09b843692697443d7811773f0a4/rgf_python-3.6.0-py2.py3-none-manylinux1_x86_64.whl#sha256=0a3012d8c8b4517632f5098386e8754928cf9fc6e765f9d7a3e74837d447031f (from https://pypi.org/simple/rgf-python/), version: 3.6.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5f/ce/bac7ffd6ae708b17bd8d4c7e69b63efa21242cbf8c60c57002e6e710b89a/rgf_python-3.6.0-py2.py3-none-win32.whl#sha256=57bf7331891f9de430ed5902aedf2cb5bc1ca6df8aff899b74369ab2de6fe827 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/af/5d/7d8a78d8f9ba4e7de5d28cf830c2de147ff2858808004679629a8544f1db/rgf_python-3.6.0-py2.py3-none-win_amd64.whl#sha256=5e5da0afb67bf94b3df1f149f1a9761bd77d8c34704a4ca7b30476a3a12096f3 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/3b/ff/fec333f5da1fce8d88cebb77df2c86df0e98681390734196833ebc4a1241/rgf_python-3.6.0.tar.gz#sha256=a766cafa1bd9a0cb043b6b9f393510fab47461f4f267b6c1071c95f9fb6a54d7 (from https://pypi.org/simple/rgf-python/), version: 3.6.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_13_x86_64, py2-none-macosx_10_14_x86_64, py2-none-macosx_10_15_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_14_x86_64, py3-none-macosx_10_15_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/14/3a/51fcca1a16c1f0ce866df62ef19a5c782b0e9cef16b76bc545eeb4ae78c1/rgf_python-3.7.0-py2.py3-none-macosx_10_13_x86_64.macosx_10_14_x86_64.macosx_10_15_x86_64.whl#sha256=9d2a25edef026fc57c55de752016cb893b2644c7953bd1dcb9de365202ddceed (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b9/bd/591f9818ac126c215c5620684a35480ae841132a9143d2de5a1dae7baa84/rgf_python-3.7.0-py2.py3-none-manylinux1_i686.whl#sha256=8911a4782a471cd7b202d56286144e4aa07607122e640ff109e3feb0b378914a (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/7e/38/452ac435c3a1fac1cbff2c84211be2b386aaa6c7bdc248c1212ffa174c5f/rgf_python-3.7.0-py2.py3-none-manylinux1_x86_64.whl#sha256=6e39fbae07cb07698973c04066e4f7c33f80c7469feb468de49805c8f5fc2dda (from https://pypi.org/simple/rgf-python/), version: 3.7.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/5e/37/035521054606eb1946dc9e45821ec7b0a32e51756f9bdc27a6780b4a906c/rgf_python-3.7.0-py2.py3-none-win32.whl#sha256=218399509a4cd1f4d50505a084e57fcb4f990a16004e54c1932e5ad453c279ac (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/22/a6/5bc0b4a23ed82cd821718b3b8bfb83d4bd89a84f5b304b75ba4016bd1c7e/rgf_python-3.7.0-py2.py3-none-win_amd64.whl#sha256=744668cf82bfb953f4d50aee74a2801221cd23e997cb278c5f27ceaabba65270 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/21/88/1d9ce017ac2014a5a42c0065d75286b8bd51c5d826ad2b1638d976ab602f/rgf_python-3.7.0.tar.gz#sha256=cedd292ebe5a2663182efed90f87657b886dc0789f97ef6273656451807ce019 (from https://pypi.org/simple/rgf-python/), version: 3.7.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_13_x86_64, py2-none-macosx_10_14_x86_64, py2-none-macosx_10_15_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_14_x86_64, py3-none-macosx_10_15_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/59/1f/b16acebb8c30a2ded722468b737d2bb493014a82619a7e614b8367926b1a/rgf_python-3.8.0-py2.py3-none-macosx_10_13_x86_64.macosx_10_14_x86_64.macosx_10_15_x86_64.whl#sha256=add508ed76264d989f9f648b25b51b9bd019fd13b9841d7bd4ebd8082de013d7 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/07/ee/b75af1b37359c40ec1af76858b40aa9503e42a0ed7de77a3cfc5314710fd/rgf_python-3.8.0-py2.py3-none-manylinux1_i686.whl#sha256=97c2a1ca4ca085170d5237909e62930e507ec747fea701f3777e437ce759464c (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/88/f4/8fad3f0f3183b016720fd96b9b6833913188a2bbf4bf28fd757815fef5fe/rgf_python-3.8.0-py2.py3-none-manylinux1_x86_64.whl#sha256=a4eda377a7833c8ff3483675c7d1841429884e59753db33050b422245e60915d (from https://pypi.org/simple/rgf-python/), version: 3.8.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/89/fd/f1b2f9b55feaafebc894e442097154689fcae9868e09c4cd0641f5ae0e5a/rgf_python-3.8.0-py2.py3-none-win32.whl#sha256=f26a25d2da4a08afc9adc0095ef2ce3d8f307118f4a3bb74ae2ca69a8573c596 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ec/92/ca06adb5daeaf47070341ee42b3a7ad6cc355a40983ae1e5679dd34a649e/rgf_python-3.8.0-py2.py3-none-win_amd64.whl#sha256=10c815a24f53922ea8683bfbab80f18c0c7008c533938e075841f4be1bdeab28 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/1e/8b/0b74bf58341be6944301148b326369a231f185c89359bd2ce02176b075ea/rgf_python-3.8.0.tar.gz#sha256=436cad547c9d32aa0db6dcce0b942aa0eab83bd4a8c510ab1d90451a673caf87 (from https://pypi.org/simple/rgf-python/), version: 3.8.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-macosx_10_13_x86_64, py2-none-macosx_10_14_x86_64, py2-none-macosx_10_15_x86_64, py3-none-macosx_10_13_x86_64, py3-none-macosx_10_14_x86_64, py3-none-macosx_10_15_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/75/ab/c8c510fc432f7b8c5f2b222217d4c85b5f853646988be9d70afad8bc063c/rgf_python-3.9.0-py2.py3-none-macosx_10_13_x86_64.macosx_10_14_x86_64.macosx_10_15_x86_64.whl#sha256=0ef7a7f892d7b13458b9e2c06ba3f54750d3b140fa92f6c9ccb7a806ef586cd1 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-manylinux1_i686, py3-none-manylinux1_i686) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/43/c7/a39b858d70c8e873b65aa46324421c8738a047a923f921811190952def54/rgf_python-3.9.0-py2.py3-none-manylinux1_i686.whl#sha256=7be8d44a42035a087333d4cc2ae4ad47ce3a0ab77085a5e3d6c0b84205872f26 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/7d/b5/9ba527ce20a1a5a6c279318be856aa4996e0d1b59035d466173198ffd468/rgf_python-3.9.0-py2.py3-none-manylinux1_x86_64.whl#sha256=abda5d1d6389f8e6096376df357a97fd191a16bb173eebf5fccd871781ebf420 (from https://pypi.org/simple/rgf-python/), version: 3.9.0\n",
            "  Skipping link: none of the wheel's tags (py2-none-win32, py3-none-win32) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/b9/2d/b42d6d06aa2edecf971c76c9460a2e19bf8648215d4a5b7d269aab8607de/rgf_python-3.9.0-py2.py3-none-win32.whl#sha256=0ba5d1c941c14e3aad457fbd51bc6924d9908cf7fadcb5bec38ffea1f8e3a4e8 (from https://pypi.org/simple/rgf-python/)\n",
            "  Skipping link: none of the wheel's tags (py2-none-win_amd64, py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ca/fa/22e2bd72d988690d05002fc2eb706c50b0f09ae069c6af138b9703b4d58d/rgf_python-3.9.0-py2.py3-none-win_amd64.whl#sha256=286a3b4b099d1be7a16120659a9894ec9e20ebc10e3d13fd552039217397ab13 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/79/78/60e9e2eb2ad5535bb022d3ba362f62cd832dc3e49101ba324753b1ad3941/rgf_python-3.9.0.tar.gz#sha256=bf828cd7eeb0b26114e4d911478b52341ef9e01a5f9134fb3a7bc8ba5c359bed (from https://pypi.org/simple/rgf-python/), version: 3.9.0\n",
            "  Skipping link: none of the wheel's tags (py3-none-macosx_10_14_x86_64, py3-none-macosx_10_15_x86_64, py3-none-macosx_11_0_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/8d/28/5c727da964daa346d7d58eb9e6d065a77acd24833c4df9f152cd245ce8b4/rgf_python-3.10.0-py3-none-macosx_10_14_x86_64.macosx_10_15_x86_64.macosx_11_0_x86_64.whl#sha256=536709087580dceec3dbadb974f04a70ce69eb20b892a5265b2c6a8ee0d59266 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/05/be/1d5eda35782f9721bc0ac17a5a119b168260f4aa5ec2471cdef14f31598b/rgf_python-3.10.0-py3-none-manylinux1_x86_64.whl#sha256=6b04b71ab16e2d970cfdace44680d501d768a508d4f4ab4d8e5f945d1c9fd9dd (from https://pypi.org/simple/rgf-python/), version: 3.10.0\n",
            "  Skipping link: none of the wheel's tags (py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/97/c3/780eb4707f7e8fe37d7633984d30ed51f9569b56648db82b4ef1d55d0c36/rgf_python-3.10.0-py3-none-win_amd64.whl#sha256=ecf855d83d5e7ea013ac05271a621eb7533c59a6d2ba924a0e85570997694835 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/cc/bc/cc5261f586f03aa3d01b2c3f9f42d5cab824e7332b5bea234f4ed9a9dbf7/rgf_python-3.10.0.tar.gz#sha256=ebf5a58af20e43dfc13b1742585688525168d8549e6ec09ec74a1674f308c151 (from https://pypi.org/simple/rgf-python/), version: 3.10.0\n",
            "  Skipping link: none of the wheel's tags (py3-none-macosx_10_14_x86_64, py3-none-macosx_10_15_x86_64, py3-none-macosx_11_0_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/dc/b9/dac1762939f3c43e8ff560b98de5a0c65841bf4051e87dae83acecf928e0/rgf_python-3.11.0-py3-none-macosx_10_14_x86_64.macosx_10_15_x86_64.macosx_11_0_x86_64.whl#sha256=052fcadefd2774054c7423a5d5fd8d37361043e1b23790c85c0997898f1c6416 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/8d/57/36cbc924eb17537c4bd72d2515f7911e32d8a40b27285ee3721e5b1c87cb/rgf_python-3.11.0-py3-none-manylinux1_x86_64.whl#sha256=350da0dd006ea925c128457d8d425e48b53e12cf55020413e340ebe048a166a0 (from https://pypi.org/simple/rgf-python/), version: 3.11.0\n",
            "  Skipping link: none of the wheel's tags (py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/fe/61/b0bb0645d4294a39b4118d7bd74c9b6e4fa4d5596d731b4c1584a2b76bc3/rgf_python-3.11.0-py3-none-win_amd64.whl#sha256=38d8a9952d3157d32cd23044aea86f9462358a3d8cd21d3216f65923065b840a (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/75/33/ba4abb5655d0653463216036747496174cf97a6ba87c5e092bd96735b10b/rgf_python-3.11.0.tar.gz#sha256=54b89cc9d388e49f30b6973ec4f1131b26b57a9da357ea91cb2866f904ccf33e (from https://pypi.org/simple/rgf-python/), version: 3.11.0\n",
            "  Skipping link: none of the wheel's tags (py3-none-macosx_10_15_x86_64, py3-none-macosx_11_6_x86_64, py3-none-macosx_12_0_x86_64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/ce/7e/c43ac7b0ae984ab859837c26e8bee7729e855389974b2a1166304427ebd7/rgf_python-3.12.0-py3-none-macosx_10_15_x86_64.macosx_11_6_x86_64.macosx_12_0_x86_64.whl#sha256=112838cc99cd312253cdd33c2ad42332dc5c697a9a2117e9694da9f44fba3249 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/ad/14/4c5c69054ef1fc2a76b2824a8fc272541ccd17cef417d077e74fe6725dfb/rgf_python-3.12.0-py3-none-manylinux1_x86_64.whl#sha256=7b6d0a1f9e9463b056d234e3a181178f3f19d721dc2d1cc180c600f1a9f7a83e (from https://pypi.org/simple/rgf-python/), version: 3.12.0\n",
            "  Skipping link: none of the wheel's tags (py3-none-win_amd64) are compatible (run pip debug --verbose to show compatible tags): https://files.pythonhosted.org/packages/1b/0b/41ab9b6be4c04cf95d17e4688bb7308bbe1c00d786647ce21b2d3934a3cb/rgf_python-3.12.0-py3-none-win_amd64.whl#sha256=4ede4a61f0a478bbc8f3c326a31a099cc654702ede7e3f2099329b1cdca9ee17 (from https://pypi.org/simple/rgf-python/)\n",
            "  Found link https://files.pythonhosted.org/packages/8e/76/f54da5d5adb09db12ea4dbf28ee038f7b6978ac04d006f8e0561974abf05/rgf_python-3.12.0.tar.gz#sha256=256885634e94412820c1d95ee8f765207d663d5b64c4bb4cf62d361698a32acc (from https://pypi.org/simple/rgf-python/), version: 3.12.0\n",
            "Fetching project page and analyzing links: https://us-python.pkg.dev/colab-wheels/public/simple/rgf-python/\n",
            "Getting page https://us-python.pkg.dev/colab-wheels/public/simple/rgf-python/\n",
            "Found index url https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking up \"https://us-python.pkg.dev/colab-wheels/public/simple/rgf-python/\" in the cache\n",
            "Request header has \"max_age\" as 0, cache bypassed\n",
            "Starting new HTTPS connection (1): us-python.pkg.dev:443\n",
            "https://us-python.pkg.dev:443 \"GET /colab-wheels/public/simple/rgf-python/ HTTP/1.1\" 404 32\n",
            "Status code 404 not in (200, 203, 300, 301)\n",
            "Could not fetch URL https://us-python.pkg.dev/colab-wheels/public/simple/rgf-python/: 404 Client Error: Not Found for url: https://us-python.pkg.dev/colab-wheels/public/simple/rgf-python/ - skipping\n",
            "Skipping link: not a file: https://us-python.pkg.dev/colab-wheels/public/simple/rgf-python/\n",
            "Skipping link: not a file: https://pypi.org/simple/rgf-python/\n",
            "Given no hashes to check 44 links for project 'rgf-python': discarding no candidates\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from rgf_python) (1.0.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from rgf_python) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->rgf_python) (3.1.0)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->rgf_python) (1.21.6)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->rgf_python) (1.4.1)\n",
            "Created temporary directory: /tmp/pip-unpack-5ygp8h99\n",
            "Value for scheme.platlib does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/lib/python3.7/dist-packages\n",
            "sysconfig: /usr/lib/python3.7/site-packages\n",
            "Value for scheme.purelib does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/lib/python3.7/dist-packages\n",
            "sysconfig: /usr/lib/python3.7/site-packages\n",
            "Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/include/python3.7/UNKNOWN\n",
            "sysconfig: /usr/include/python3.7m/UNKNOWN\n",
            "Value for scheme.scripts does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local/bin\n",
            "sysconfig: /usr/bin\n",
            "Value for scheme.data does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
            "distutils: /usr/local\n",
            "sysconfig: /usr\n",
            "Additional context:\n",
            "user = False\n",
            "home = None\n",
            "root = None\n",
            "prefix = None\n",
            "Removed build tracker: '/tmp/pip-req-tracker-lc4qi00c'\n"
          ]
        }
      ],
      "source": [
        "!pip install rgf_python -v -U"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iYIbIjSk-MF7"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "import gc\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')\n",
        "pd.set_option('display.max_columns',100)\n",
        "pd.set_option('display.max_rows', 500)\n",
        "from importlib import reload\n",
        "from pprint import pprint\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import roc_auc_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import plot_confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NDfYaSuY91vI"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from IPython.core.interactiveshell import InteractiveShell\n",
        "import random\n",
        "InteractiveShell.ast_node_interactivity = \"all\"\n",
        "import os\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import glob\n",
        "from tqdm import tqdm\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import plotly.figure_factory as ff\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oty6zxIO31jY"
      },
      "outputs": [],
      "source": [
        "import pandas as pd, numpy as np, os\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.model_selection import cross_val_score, validation_curve, train_test_split, GridSearchCV \n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
        "from sklearn.experimental import enable_hist_gradient_boosting\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import BaggingClassifier, GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier,HistGradientBoostingClassifier\n",
        "import xgboost as xgb\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from rgf.sklearn import RGFClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OseqO3Px8DmN",
        "outputId": "d1de07cb-242b-44d6-cfeb-be788f7db1c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3:  Importing the dataset"
      ],
      "metadata": {
        "id": "ZK8hehi5ricd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4TIt5_1o88P_"
      },
      "outputs": [],
      "source": [
        "#import wildfire data set \n",
        "folder_path = '/content/drive/MyDrive/WildFires-Dossier/'\n",
        "df = pd.read_csv(f'{folder_path}WildFires_DataSet.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data exploration**"
      ],
      "metadata": {
        "id": "3O2L91qor5nh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "t_USto919dtY",
        "outputId": "de5000f7-a560-44bd-c388-65df1008f919"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       NDVI           LST  BURNED_AREA    CLASS\n",
              "0  0.506782  14584.272727     4.692308  no_fire\n",
              "1  0.522150  14655.833333     5.000000  no_fire\n",
              "2  0.682284  14780.000000     5.000000     fire\n",
              "3  0.120046  13298.500000     3.500000  no_fire\n",
              "4  0.568734  14743.000000     5.000000  no_fire"
            ],
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              "      <td>fire</td>\n",
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              "      <td>no_fire</td>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "#snapshot of the data\n",
        "df.head()"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "C7qqnYJE9jJt",
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "              NDVI           LST  BURNED_AREA\n",
              "count  1713.000000   1713.000000  1713.000000\n",
              "mean      0.555665  14622.802073     4.674973\n",
              "std       0.092847    366.927861     0.583791\n",
              "min       0.030735  13137.000000     3.000000\n",
              "25%       0.501276  14406.000000     4.521951\n",
              "50%       0.565181  14645.750000     4.920635\n",
              "75%       0.620987  14881.300000     5.000000\n",
              "max       0.781723  15611.570513     9.000000"
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              "      <td>1713.000000</td>\n",
              "      <td>1713.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.555665</td>\n",
              "      <td>14622.802073</td>\n",
              "      <td>4.674973</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
              "      <td>0.092847</td>\n",
              "      <td>366.927861</td>\n",
              "      <td>0.583791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.030735</td>\n",
              "      <td>13137.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.501276</td>\n",
              "      <td>14406.000000</td>\n",
              "      <td>4.521951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.565181</td>\n",
              "      <td>14645.750000</td>\n",
              "      <td>4.920635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.620987</td>\n",
              "      <td>14881.300000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.781723</td>\n",
              "      <td>15611.570513</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3d79052-6b45-4a68-a769-9b81a95a9fd3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c3d79052-6b45-4a68-a769-9b81a95a9fd3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c3d79052-6b45-4a68-a769-9b81a95a9fd3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAjgP5Dmri6K",
        "outputId": "2be839b7-ef81-432e-d0f9-d6bc1750f4b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1713 entries, 0 to 1712\n",
            "Data columns (total 4 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   NDVI         1713 non-null   float64\n",
            " 1   LST          1713 non-null   float64\n",
            " 2   BURNED_AREA  1713 non-null   float64\n",
            " 3   CLASS        1713 non-null   object \n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 53.7+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Good news, we don’t have any null values in this dataset"
      ],
      "metadata": {
        "id": "HtKhdxnPsA9l"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IyVjIyr29oWJ"
      },
      "outputs": [],
      "source": [
        "# split the dataset into train features and target varaible\n",
        "X = df.drop('CLASS', axis=1)\n",
        "y = df['CLASS']"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5: Data balancing"
      ],
      "metadata": {
        "id": "vRWHAVetsOCe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "5aWcaX-l-G9s",
        "outputId": "bd7fdc1f-8a04-428c-8bfc-7ee82af13c4b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '\\nwildfires prediction \\n 0: no_fire | 1: fire \\n')"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.0, 1327.0, '1327 (77.5%)')"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(1.0, 386.0, '386 (22.5%)')"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ax = sns.countplot(x='CLASS', data = df,palette = 'plasma')\n",
        "\n",
        "plt.title(\"\\nwildfires prediction \\n 0: no_fire | 1: fire \\n\")\n",
        "\n",
        "plt.margins(0.05, 0.1)\n",
        "\n",
        "for p in ax.patches:\n",
        "  x=p.get_bbox().get_points()[:,0]\n",
        "  y=p.get_bbox().get_points()[1,1]\n",
        "  ax.annotate('{} ({:.1f}%)'.format(int(y),100.*y/len(df)), (x.mean(), y), \n",
        "          ha='center', va='bottom') \n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EMsa1A98-2m2"
      },
      "source": [
        "We can notice that there is class imbalance.Class imbalance is a common problem in machine learning, especially in classification problems. Imbalance data can significantly affect the accuracy of our model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTd_LLlPdF1X",
        "outputId": "86a068f2-b678-4bd3-8fe8-5363c2e4a0bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "class 0: (1327, 4)\n",
            "class 1: (386, 4)\n"
          ]
        }
      ],
      "source": [
        "# class count\n",
        "class_count_0, class_count_1 = df['CLASS'].value_counts()\n",
        "\n",
        "# Separate class\n",
        "class_0 = df[df['CLASS'] == 'no_fire']\n",
        "class_1 = df[df['CLASS'] == 'fire']# print the shape of the class\n",
        "print('class 0:', class_0.shape)\n",
        "print('class 1:', class_1.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qtHG8CASdG9y"
      },
      "outputs": [],
      "source": [
        "# split the dataset into train features and target varaible\n",
        "X = df.drop('CLASS', axis=1)\n",
        "y = df['CLASS']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hvy2aTcRC--j"
      },
      "outputs": [],
      "source": [
        "#output\n",
        "y = df[\"CLASS\"]\n",
        "\n",
        "#replace the categories with numerical values\n",
        "y.replace({\"no_fire\":0, \"fire\":1}, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We balanced our dataset using the oversampling technique via the SMOT method   "
      ],
      "metadata": {
        "id": "bh_ESkqIsgi9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yh_xB8eydRb6",
        "outputId": "f76f5589-60d0-4990-a097-80d240475b90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original dataset shape Counter({0: 1327, 1: 386})\n",
            "Resampled dataset shape Counter({0: 1327, 1: 1327})\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter\n",
        "from imblearn.over_sampling import SMOTE \n",
        "print('Original dataset shape %s' % Counter(y))\n",
        "sm = SMOTE(random_state=42)\n",
        "X_res, y_res = sm.fit_resample(X, y)\n",
        "print('Resampled dataset shape %s' % Counter(y_res))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "2muZwZ5mdXZg",
        "outputId": "fee70045-95a5-407f-baea-327e4fc54f75"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total class of fire and no_fire: no_fire    386\n",
            "fire       386\n",
            "Name: CLASS, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f471dc83850>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEgCAYAAABPSzOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAX0ElEQVR4nO3de7SkVX3m8e8jIKAiDXJkYTfSCBiCJrbYIhnMCgGNgImNawziOIqK6eig0URUcJylJmI0Y8RbBtMGFB0jotGAt4wIqMtkEBtE5KJjKyDdNnBELo0XFPzNH7VbiuacPuf0uZTs/n7WqnXe2nu/9f6qTp3nvLXrfatSVUiS+vKAURcgSZp7hrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOGuGUnywSRv3oL1rk3ylPmoaaaSPDPJ9UnuSPL4CfoPSfLd1n90ks8nOW4UtU4lSSXZdwG391tJLkuyIclfJHlfkv+xUNvX9G076gI0t5JcC7y4qr44H+M78XbgZVV1ziT9fw28t6re1a7/68KUdb/wGuDCqlo26kK0ee65a2u0F3DlLPp/LcnWtoPkY3M/Ybh3JMmHgUcCn25TCq9p7c9IcmWSW5N8KclvTzH+40luSHJbkq8kecwMavizJFe3l+1XJTlwgjEHJfm/rZ71Sd6b5IGtL0lOTXJTktuTfCvJY1vfUe02NyRZl+TESWp4QJLXJ7mu3c6HkuycZPskdwDbAN9M8r0J1v0e8Kihx2T79pi9uPW/IMm/txpvBt7Yxrw9yQ+S3NimKnacpLZ9klyQ5OYkP0rykSSLhvqvTXJiksvb4/+xJDsM9b+6PWY/TPKiKX4XX0ryN63eDUm+kGS3of4Jnxebub0LgD8E3tsem0cPT9MlOTTJ2iSvTXID8IH2uzgpyffafT47ya6b247mSFV56egCXAs8Zej6o4GfAE8FtmPwsnoN8MCJxre2FwE7AdsD7wQuG+r7IPDmSbb9p8A64IlAgH2BvTbdDvAE4GAG04JLgauBV7a+pwGXAIvabfw2sEfrWw/8flveBThwkjpe1O7jo4CHAJ8EPjzUX8C+M3gMv8Rg6grgBcBdwMtb/TsCpwLnAru2x+3TwN9Octv7tt/F9sAY8BXgnZts+2LgEe32rgZe0vqOAG4EHgs8GPjnzd2XVvf32nNgx3b9rdN5Xmzmsfn1Y7Hp8wE4tD02b2v3b0fgFcBFwJLW9o/AR0f9d7I1XNxz79+zgc9W1XlV9UsG8807Av9pshWq6oyq2lBVdwJvBB6XZOdpbOvFwN9V1ddrYE1VXTfB7V9SVRdV1V1VdS2DP/g/aN2/ZBCQ+wOpqqurav1Q3wFJHlpVt1TVpZPU8VzgHVX1/aq6AzgZOHYOpwl+WFXvqaq7gJ8DK4G/rKofV9UG4C3AsROt2B6T86rqzqoaB97BPfd9o3dX1Q+r6scM/lFsnN8+BvhAVV1RVT9h8LuZygeq6v9V1c+As4dua8bPi2n6FfCGdv9+BrwE+O9VtXbo+fQsp2zmn+Hev0cAvw7YqvoVcD2weKLBSbZJ8tb2Mvp2BnuSALtNNH4TezLYU9ys9nL+M23q53YGYbhbq+8C4L3APwA3JVmV5KFt1f8MHAVcl+TLSX5vkk3c6z635W2B3adxH6bj+qHlMeBBwCVteuNW4N9a+30k2T3JWW1a6Xbgf3Pfx/aGoeWfMnj1AYP7Nbzt+/zjnMDmbmvaz4sZGK+qnw9d3wv41NBjczVwN3P3u9AkDPf+bPoZzj9k8AcGDOa0GYTwuknG/xdgBfAUYGcG0yYwmCKZyvXAPtMYdxrwbWC/qnoo8Lrh26+qd1fVE4ADGEwfvLq1f72qVgAPZ3AEy9mT3P697jOD9xXuYjClMReGH7MfAT8DHlNVi9pl56p6yCTrvqWt/zvtvv9XpvfYwmBaas+h64+cYd3DpnpebKlNn0/XA0cOPTaLqmqHqprtdjQFw70/NzKYa97obODpSQ5Psh3wKuBO4D8mGb9T67+ZwR7pW2aw7X8CTkzyhPbG6L5J9ppg3E7A7cAdSfYHXrqxI8kTkzyp1foTBtMev0rywCTPTbJzm0a4ncEUwEQ+Cvxlkr2TPKTdh4+1aZQ51fZ43w+cmuTh7T4sTvK0SVbZCbgDuC3JYto/rmk6G3hBkgOSPAh4wyxKn+p5MVfeB5yy8XmQZCzJijnehiZguPfnb4HXt5fBJ1bVdxjsHb6HwV7mnwB/UlW/mGg88CEGL9fXAVcxeDNsWqrq48ApDN7o28Bg73qiIyNOZPAKYQODYPzYUN9DW9strY6bgf/Z+p4HXNumM17CYG59ImcAH2bwZuU1DP5BvHy692MLvJbBm5EXtdq+CPzWJGPfBBwI3AZ8lsGbvdNSVZ9n8Ab3BW17F2xpwdN4XsyVdzF4s/kLSTYweD49aY63oQmkym9ikqTeuOcuSR3ycCRJv5bkkQym4yZyQFX9YCHr0ZZzWkaSOjTtPfck2wCrgXVV9cdJ9gbOAh7G4IzC51XVL5Jsz+BNuScweDPs2e1ElUnttttutXTp0i27B5K0lbrkkkt+VFUTnlMxk2mZVzA4AWHjCSVvA06tqrOSvA84nsHxy8cDt1TVvkmObeOevbkbXrp0KatXr55BKZKkJJOeyDatN1STLAGezuA45o0nPBwGfKINORM4ui2vaNdp/Ye38ZKkBTLdo2XeyeCDhTaeNPIw4Nahk0LWcs9py4tpp0i3/tva+HtJsjLJ6iSrx8fHt7B8SdJEpgz3JH8M3FRVl8zlhqtqVVUtr6rlY2MTThlJkrbQdObcDwGekeQoYAcGc+7vAhYl2bbtnS/hns+kWMfgMyrWtk9+25nBG6uSpAUy5Z57VZ1cVUuqaimDjzG9oKqeC1wIPKsNOw7Y+JVl57brtP4LyuMtJWlBzeYM1dcCf5VkDYM59dNb++nAw1r7XwEnza5ESdJMzegM1ar6EoNvYqGqvg8cNMGYnzP4Rh5J0oj42TKS1CHDXZI65AeHzcDSkz476hK6cu1bnz7qErrhc3Nu9fDcdM9dkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDk0Z7kl2SHJxkm8muTLJm1r7B5Nck+SydlnW2pPk3UnWJLk8yYHzfSckSfc2nS/ruBM4rKruSLId8NUkn299r66qT2wy/khgv3Z5EnBa+ylJWiBT7rnXwB3t6nbtUptZZQXwobbeRcCiJHvMvlRJ0nRNa849yTZJLgNuAs6rqq+1rlPa1MupSbZvbYuB64dWX9vaNr3NlUlWJ1k9Pj4+i7sgSdrUtMK9qu6uqmXAEuCgJI8FTgb2B54I7Aq8diYbrqpVVbW8qpaPjY3NsGxJ0ubM6GiZqroVuBA4oqrWt6mXO4EPAAe1YeuAPYdWW9LaJEkLZDpHy4wlWdSWdwSeCnx74zx6kgBHA1e0Vc4Fnt+OmjkYuK2q1s9L9ZKkCU3naJk9gDOTbMPgn8HZVfWZJBckGQMCXAa8pI3/HHAUsAb4KfDCuS9bkrQ5U4Z7VV0OPH6C9sMmGV/ACbMvTZK0pTxDVZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtSh6bzBdk7JLk4yTeTXJnkTa197yRfS7ImyceSPLC1b9+ur2n9S+f3LkiSNjWdPfc7gcOq6nHAMuCIJAcDbwNOrap9gVuA49v444FbWvupbZwkaQFNGe41cEe7ul27FHAY8InWfiZwdFte0a7T+g9PkjmrWJI0pWnNuSfZJsllwE3AecD3gFur6q42ZC2wuC0vBq4HaP23AQ+b4DZXJlmdZPX4+Pjs7oUk6V6mFe5VdXdVLQOWAAcB+892w1W1qqqWV9XysbGx2d6cJGnIjI6WqapbgQuB3wMWJdm2dS0B1rXldcCeAK1/Z+DmOalWkjQt0zlaZizJora8I/BU4GoGIf+sNuw44Jy2fG67Tuu/oKpqLouWJG3etlMPYQ/gzCTbMPhncHZVfSbJVcBZSd4MfAM4vY0/HfhwkjXAj4Fj56FuSdJmTBnuVXU58PgJ2r/PYP590/afA386J9VJkraIZ6hKUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SerQdL4ge88kFya5KsmVSV7R2t+YZF2Sy9rlqKF1Tk6yJsl3kjxtPu+AJOm+pvMF2XcBr6qqS5PsBFyS5LzWd2pVvX14cJIDGHwp9mOARwBfTPLoqrp7LguXJE1uyj33qlpfVZe25Q3A1cDizayyAjirqu6sqmuANUzwRdqSpPkzozn3JEuBxwNfa00vS3J5kjOS7NLaFgPXD622lgn+GSRZmWR1ktXj4+MzLlySNLlph3uShwD/Aryyqm4HTgP2AZYB64G/n8mGq2pVVS2vquVjY2MzWVWSNIVphXuS7RgE+0eq6pMAVXVjVd1dVb8C3s89Uy/rgD2HVl/S2iRJC2Q6R8sEOB24uqreMdS+x9CwZwJXtOVzgWOTbJ9kb2A/4OK5K1mSNJXpHC1zCPA84FtJLmttrwOek2QZUMC1wJ8DVNWVSc4GrmJwpM0JHikjSQtrynCvqq8CmaDrc5tZ5xTglFnUJUmaBc9QlaQOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA5N5ztU90xyYZKrklyZ5BWtfdck5yX5bvu5S2tPkncnWZPk8iQHzvedkCTd23T23O8CXlVVBwAHAyckOQA4CTi/qvYDzm/XAY5k8KXY+wErgdPmvGpJ0mZNGe5Vtb6qLm3LG4CrgcXACuDMNuxM4Oi2vAL4UA1cBCxKssecVy5JmtSM5tyTLAUeD3wN2L2q1reuG4Dd2/Ji4Pqh1da2NknSApl2uCd5CPAvwCur6vbhvqoqoGay4SQrk6xOsnp8fHwmq0qSpjCtcE+yHYNg/0hVfbI137hxuqX9vKm1rwP2HFp9SWu7l6paVVXLq2r52NjYltYvSZrAdI6WCXA6cHVVvWOo61zguLZ8HHDOUPvz21EzBwO3DU3fSJIWwLbTGHMI8DzgW0kua22vA94KnJ3keOA64JjW9zngKGAN8FPghXNasSRpSlOGe1V9Fcgk3YdPML6AE2ZZlyRpFjxDVZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtSh6bzBdlnJLkpyRVDbW9Msi7JZe1y1FDfyUnWJPlOkqfNV+GSpMlNZ8/9g8ARE7SfWlXL2uVzAEkOAI4FHtPW+V9JtpmrYiVJ0zNluFfVV4AfT/P2VgBnVdWdVXUNsAY4aBb1SZK2wGzm3F+W5PI2bbNLa1sMXD80Zm1ru48kK5OsTrJ6fHx8FmVIkja1peF+GrAPsAxYD/z9TG+gqlZV1fKqWj42NraFZUiSJrJF4V5VN1bV3VX1K+D93DP1sg7Yc2joktYmSVpAWxTuSfYYuvpMYOORNOcCxybZPsnewH7AxbMrUZI0U9tONSDJR4FDgd2SrAXeAByaZBlQwLXAnwNU1ZVJzgauAu4CTqiqu+endEnSZKYM96p6zgTNp29m/CnAKbMpSpI0O56hKkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ1OGe5IzktyU5Iqhtl2TnJfku+3nLq09Sd6dZE2Sy5McOJ/FS5ImNp099w8CR2zSdhJwflXtB5zfrgMcCezXLiuB0+amTEnSTEwZ7lX1FeDHmzSvAM5sy2cCRw+1f6gGLgIWJdljroqVJE3Pls65715V69vyDcDubXkxcP3QuLWt7T6SrEyyOsnq8fHxLSxDkjSRWb+hWlUF1Bast6qqllfV8rGxsdmWIUkasqXhfuPG6Zb286bWvg7Yc2jcktYmSVpAWxru5wLHteXjgHOG2p/fjpo5GLhtaPpGkrRAtp1qQJKPAocCuyVZC7wBeCtwdpLjgeuAY9rwzwFHAWuAnwIvnIeaJUlTmDLcq+o5k3QdPsHYAk6YbVGSpNnxDFVJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR2a8mv2NifJtcAG4G7grqpanmRX4GPAUuBa4JiqumV2ZUqSZmIu9tz/sKqWVdXydv0k4Pyq2g84v12XJC2g+ZiWWQGc2ZbPBI6eh21IkjZjtuFewBeSXJJkZWvbvarWt+UbgN0nWjHJyiSrk6weHx+fZRmSpGGzmnMHnlxV65I8HDgvybeHO6uqktREK1bVKmAVwPLlyyccI0naMrPac6+qde3nTcCngIOAG5PsAdB+3jTbIiVJM7PF4Z7kwUl22rgM/BFwBXAucFwbdhxwzmyLlCTNzGymZXYHPpVk4+38c1X9W5KvA2cnOR64Djhm9mVKkmZii8O9qr4PPG6C9puBw2dTlCRpdjxDVZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtSh+Yt3JMckeQ7SdYkOWm+tiNJuq95Cfck2wD/ABwJHAA8J8kB87EtSdJ9zdee+0HAmqr6flX9AjgLWDFP25IkbWLbebrdxcD1Q9fXAk8aHpBkJbCyXb0jyXfmqZat0W7Aj0ZdxFTytlFXoBHwuTm39pqsY77CfUpVtQpYNart9yzJ6qpaPuo6pE353Fw48zUtsw7Yc+j6ktYmSVoA8xXuXwf2S7J3kgcCxwLnztO2JEmbmJdpmaq6K8nLgP8DbAOcUVVXzse2NCGnu/SbyufmAklVjboGSdIc8wxVSeqQ4S5JHTLcJalDhrskdchw70iSvZI8pS3vmGSnUdckJXl0kvOTXNGu/26S14+6rt4Z7p1I8mfAJ4B/bE1LgH8dXUXSr70fOBn4JUBVXc7g3BfNI8O9HycAhwC3A1TVd4GHj7QiaeBBVXXxJm13jaSSrYjh3o872ydwApBkW8CTGPSb4EdJ9qE9H5M8C1g/2pL6N7IPDtOc+3KS1wE7Jnkq8N+AT4+4JgkGrypXAfsnWQdcAzx3tCX1zzNUO5EkwIuBPwLC4KMf/qn8BWuE2hf3vK2qTkzyYOABVbVh1HVtDQz3DrQ/oCurav9R1yJtKslFVXXwqOvY2jgt04Gqurt9X+0jq+oHo65H2sQ3kpwLfBz4ycbGqvrk6Erqn+Hej12AK5NczL3/gJ4xupIkAHYAbgYOG2orwHCfR07LdCLJH0zUXlVfXuhaJI2e4S5pXiR5TVX9XZL3MMFhuVX1FyMoa6thuN/PJflqVT05yQbu/QcUoKrqoSMqTVu5JDdX1cOSvBK4ZdP+qjpzBGVtNZxzv/97PkBV+Tky+k1zY5JHAC8EDmWww6EF4p77/VySS6rqCUnOr6rDR12PtFGSlzM4me5RwLrhLgavKh81ksK2Eob7/VySbzA4xOylwKmb9lfVOxa8KGlIktOq6qWjrmNr42fL3P8dC9zNYIptpwku0kgZ7KPhnnsnkhxZVZ/fTP9xvoElbT0M961Ekkur6sBR1yFpYTgts/XwSAVpK2K4bz18iSZtRQz3rYd77tJWxHDfevz7qAuQtHB8Q7UTSXYG3gj8fmv6MvDXVXXbyIqSNDLuuffjDAZfjn1Mu9wOfGCkFUkaGffcO5HksqpaNlWbpK2De+79+FmSJ2+8kuQQ4GcjrEfSCLnn3okky4AzgZ1b0y3AcVV1+eiqkjQqhnsnkmwPPAvYB1gE3Mbgk/f+eqSFSRoJP8+9H+cAtwKXcu+PV5W0FXLPvRNJrqiqx466Dkm/GXxDtR//keR3Rl2EpN8M7rl3IslVwL7ANcCd3PNtN7870sIkjYTh3okke03UXlXXLXQtkkbPcJekDjnnLkkdMtwlqUOGuyR1yHCXpA79f8ShFOsMgsKVAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "class_0_under = class_0.sample(class_count_1)\n",
        "\n",
        "test_under = pd.concat([class_0_under, class_1], axis=0)\n",
        "\n",
        "print(\"total class of fire and no_fire:\",test_under['CLASS'].value_counts())# plot the count after under-sampeling\n",
        "test_under['CLASS'].value_counts().plot(kind='bar', title='total class of fire and no_fire')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U9wcv7nTdi93"
      },
      "outputs": [],
      "source": [
        "# split the tdata\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6: Application of Machine Learning algorithms "
      ],
      "metadata": {
        "id": "99A-tesxs80c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In our simulation, we used eleven of the most popular data mining algorithms: Neural Networks,SVM, Bagging Classifier, Extremely Randomised Trees, Adaboost Classifier, Gradient Boosting, Extreme Gradient Boosting (XGBOOST), Random Forest, Nearest Neighbour, Gaussian Naive Bayes and Stochastic Gradientdescent. "
      ],
      "metadata": {
        "id": "uCZ_8YoxVAuj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mZ4pCMlSDmjI"
      },
      "outputs": [],
      "source": [
        "#Scaler\n",
        "scaler = StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from tensorflow.keras.optimizers import Adam # - Works\n"
      ],
      "metadata": {
        "id": "gd0GaM4MVCa7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_clf = XGBClassifier(learning_rate= 0.1,subsample = 0.8,max_depth = 12)"
      ],
      "metadata": {
        "id": "c6UTsEzgVRMh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rjQnQZTUVW3b",
        "outputId": "954a10f9-6aa7-49b0-9987-670848ebd1c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(max_depth=12, subsample=0.8)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the accuracy score\n",
        "print(xgb_clf.score(X_test, y_test))\n",
        "xgb_score= xgb_clf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwFZAcHXVbCl",
        "outputId": "4a9fcccc-525f-45a9-ed12-ace3cede487d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8469259723964868\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute ROC AUC Score\n",
        "prediction_probability = xgb_clf.predict_proba(X_test)\n",
        "print(roc_auc_score(y_test, prediction_probability[ : , 1]))\n",
        "xgb_roc_auc_score = roc_auc_score(y_test, prediction_probability[ : ,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKGWN0AAVfm3",
        "outputId": "52d64dc4-8718-441b-c245-a25f367071ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9063999697698731\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Computef1_score\n",
        "prediction = xgb_clf.predict(X_test)\n",
        "print(f1_score(y_test, prediction, pos_label= 1))\n",
        "xgb_f1_score = f1_score(y_test, prediction, pos_label= 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yoxd9GJBVlTy",
        "outputId": "94645510-8944-48f1-991a-2345a080ba11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8463476070528968\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate recall\n",
        "recall = recall_score(y_test, prediction, labels=[1,0], average='micro')\n",
        "print('Recall: %.3f' % recall)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTNRgySEVvnM",
        "outputId": "0c02dade-fcd1-42e3-9e53-04ec60682ead"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall: 0.847\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "confusion_matrix ( y_test , prediction)\n",
        "\n",
        "tn, fp, fn, tp = confusion_matrix ( y_test , prediction).ravel()\n",
        "conf_matrix = pd.DataFrame(\n",
        "    {\n",
        "        'Predicted Fire': [tp, fp],\n",
        "        'Predicted Not Fire': [fn, tn]\n",
        "    }, index=['Fire', 'Not fire'])\n",
        "plot_confusion_matrix(xgb_clf, X_test, y_test)  \n",
        "plt.show()\n",
        "conf_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "_TUnAozHbfhi",
        "outputId": "598541ab-ce5a-49bb-cd74-3afc05ac3f3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[339,  64],\n",
              "       [ 58, 336]])"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f471dc75650>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAAEGCAYAAADxD4m3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaaUlEQVR4nO3de7xVVb338c+Xzf0iF0FFwEBFPWiJhPdHHzVN1HrQjibVKSs7WF7ylmbn6Yh5Hs3zVOJJ0/J21PKSHuuEhpKZhpY3QESBUBKUmyAXQQQ27L1/5485Ny4V9l6TvRdrrbm/79drvlhzrLnm/K294ccYc8wxhiICM7M8alfuAMzMSsUJzsxyywnOzHLLCc7McssJzsxyq325AyjUt09NDB7UodxhWAavvtyt3CFYBhviPTbGBrXkHMcf3S1WrKwv6tipM2onRcSollyvJSoqwQ0e1IHnJw0qdxiWwaghB5c7BMvg2dpHWnyOFSvreX7SbkUdW9P/tb4tvmALVFSCM7PKF0ADDeUOoyhOcGaWSRBsiuKaqOXmBGdmmbkGZ2a5FAT1VTLE0wnOzDJrwAnOzHIogHonODPLK9fgzCyXAtjke3BmlkdBuIlqZjkVUF8d+c0JzsyySUYyVAcnODPLSNTTovH6240TnJllknQyOMGZWQ4lz8E5wZlZTjW4BmdmeeQanJnlViDqq2S1Ayc4M8vMTVQzy6VAbIyacodRFCc4M8skedDXTVQzyyl3MphZLkWI+nANzsxyqsE1ODPLo6SToTpSR3VEaWYVo5o6GaojSjOrKPWhoramSOos6XlJL0maKekHafkQSc9Jmivp15I6puWd0v256fuDm4vTCc7MMmkcyVDM1oxa4JiI2B8YDoySdAjw78D4iNgTWAWcmR5/JrAqLR+fHtckJzgzy6wh2hW1NSUSa9PdDukWwDHAf6XldwInp69Hp/uk739KUpPVRCc4M8skGWzfKjU4JNVImg4sAx4D/g68ExF16SELgQHp6wHAAoD0/dXAjk2d350MZpZJIDYVP1Srr6QpBfs3R8TNm88VUQ8Ml9QL+C2wT+tF6gRnZhlFkOVB3+URMbL5c8Y7kp4ADgV6SWqf1tIGAovSwxYBg4CFktoDPYEVTZ3XTVQzy0g0FLk1eRapX1pzQ1IX4DhgNvAEcGp62BnA79LXE9J90vf/FNH0Aq2uwZlZJkGmGlxT+gN3SqohqWzdHxEPS5oF3Cfp/wEvArelx98G/FLSXGAlMKa5CzjBmVlmrTHhZUTMAA7YQvnrwEFbKN8AnJblGk5wZpZJIE94aWb5lCwbWB2pozqiNLMK4oWfzSynApodpVApnODMLDPX4MwslyLkGpyZ5VPSyeBVtcwsl7wmg5nlVNLJ4HtwZpZTrTGSYXtwgjOzTDySwcxyrVoWnXGCM7NMImBTgxOcmeVQ0kR1gjOznPJIhjZi4wZx8ef2ZNPGdtTXwREnreYrl7zFtRcN4tUZXSFgwO61fOe6N+nSrYGlCztw7UW7sXpFe3r0qufS69+g366byv012rRuPeq44N/nMXiv9UTA+EuHMPvFHgB87htLGPt/F/D5EQewZlWHMkdaGfyYSErSKOA/gBrg1oi4ppTXK4cOnYL//8Df6dKtgbpNcNHJQznwmDWc9YNFdOvRAMAvrtiVCbf35fTzlnHLlQM49tSVHPf5VUx/ujv/+cP+XHr9m2X+Fm3bN8e9wdQ/9+Sqs4fSvkMDnTonv7e+/Wv55BGrWbqoY5kjrDTV00QtWZTpNMQ/A04AhgFfkDSsVNcrFwm6dEv+QdRtEvWbhMTm5BYBtRva0Vijf+PVTux/eLIU5P6Hr+WZST3LErcluvao4+MHvcujv+4HQN2mdrz3bvL//ln/+ia3XrNbUmWxD2iNNRm2h1Km4YOAuRHxekRsBO4jWbg1d+rr4VvH7s3pn9iPA458l31GrAPgxxcMYsz++7JgbidGf/1tAHYftoG/PJIktb880pN1a2tYs7I6xvXl0S4Da1m9sgMX/2geNzz8ChdcM49OXeo55LhVrHirI/Nmdy13iBUn6UWtKWort1ImuM2LtKYKF3DdTNJYSVMkTXl7RX0Jwymdmhq46Y9zuHvqLOZM78r8v3UG4DvXLeCeF2ey29Ba/jyhNwBjL1/Ey8905+zj9uLlZ7rTt/9G2pX/70GbVdM+2HPf93j47p049zP7sWFdO758wSLGnL2Yu8Z/5K+r8f6DvsVs5Vb2hnRE3BwRIyNiZL8dq/tfevee9ex/2FpeeKLH5rKaGjhq9CqenpjU2nbcpY7Lb5vPjY+9ylcvW7L5c1Yey5d0ZPlbHZkzvTsATz3Shz32XccuA2u5aeIr3PnUdPruspEbHppJ774byxxt5XAT9f1FWhsVLuCaG++sqGHt6iQx164X0yb3YNAetSyal9yYjoBnJvVk0B61AKxeUUNDcnuO+67fiU+fvrIscVti1fKOvL2kIwN3Xw/AAYet5u8zuzLmwBGcccRwzjhiOMvf6si5n92XVcvd2QDv96JWQw2ulL2oLwBDJQ0hSWxjgC+W8HplsXJpB358/m40NIiGBjjys+9w0LFruPjkPVm3toYI2H3Yes67ZiEAM57pzu0/3BUp+PjB73HO1QvL/A3sxnEf49Lxf6dDx2DJm5249pLdyx1SxauWXlQ1szB0y04unQhcR/KYyO0RcVVTx4/cv3M8P2lQU4dYhRk15OByh2AZPFv7CGsaVrSoatV7n53imNtPbf5A4DeH3zQ1Ika25HotUdLn4CJiIjCxlNcws+2vEpqfxfBIBjPLxCMZzCzXnODMLJc84aWZ5VolPONWDCc4M8skAuqqZMLL6ojSzCpKazzoK2mQpCckzZI0U9L5afkVkhZJmp5uJxZ85nuS5kqaI+n45uJ0Dc7MMmnFe3B1wMURMU1SD2CqpMfS98ZHxI8LD05nIxoD7AvsCvxR0l4RsdWxjq7BmVlmESpqa/ocsSQipqWv3wVms4UJOQqMBu6LiNqImAfMJZm1aKuc4MwsswyD7fs2zhaUbmO3dD5Jg4EDgOfSonMlzZB0u6TeaVlRMxQVchPVzDKJyPQc3PLmhmpJ6g48CFwQEWsk3QT8G8kzxf8G/AT4+rbE6gRnZhmJ+lbqRZXUgSS53R0RvwGIiKUF798CPJzuZp6hyE1UM8usNe7BSRJwGzA7Iq4tKO9fcNgpwCvp6wnAGEmd0lmKhgLPN3UN1+DMLJNWHIt6OPBl4GVJ09OyfyFZv2V4eqn5wFkAETFT0v3ALJIe2HOa6kEFJzgzyyqS+3AtPk3E07DFIRFbnYEonXKtyWnXCjnBmVlmHqplZrkUrdjJUGpOcGaWWQknAm9VTnBmlllzPaSVwgnOzDKJcIIzsxzzhJdmllu+B2dmuRSIBveimlleVUkFzgnOzDJyJ4OZ5VqVVOGc4Mwss6qvwUm6nibydER8uyQRmVlFC6ChocoTHDBlu0VhZtUjgGqvwUXEnYX7krpGxLrSh2Rmla5anoNr9mEWSYdKmgX8Ld3fX9KNJY/MzCpXFLmVWTFP610HHA+sAIiIl4AjSxmUmVWy4qYrr4SOiKJ6USNiQTJ9+mZNThNsZjlXAbWzYhST4BZIOgyIdAWc80kWaDWztiggqqQXtZgm6jeBc0gWWF0MDE/3zazNUpFbeTVbg4uI5cCXtkMsZlYtqqSJWkwv6u6SHpL0tqRlkn4nafftEZyZVagc9aLeA9wP9Ad2BR4A7i1lUGZWwRof9C1mK7NiElzXiPhlRNSl26+AzqUOzMwqV0RxW7k1NRa1T/ryEUmXAfeR5O7TaWJhVjNrA6qkF7WpToapJAmt8ZucVfBeAN8rVVBmVtlUAbWzYjQ1FnXI9gzEzKpEhXQgFKOokQyS9gOGUXDvLSLuKlVQZlbJKqMDoRjNJjhJ44CjSBLcROAE4GnACc6sraqSGlwxvainAp8C3oqIrwH7Az1LGpWZVbaGIrcyKybBrY+IBqBO0g7AMmBQacMys4rVSs/BSRok6QlJsyTNlHR+Wt5H0mOSXkv/7J2WS9JPJc2VNEPSiOZCLSbBTZHUC7iFpGd1GvBMEZ8zs5xSFLc1ow64OCKGAYcA50gaBlwGPB4RQ4HH031Ibo8NTbexwE3NXaCYsahnpy9/LulRYIeImNFs6GaWX61wDy4ilgBL0tfvSppNMqnHaJL7/gB3Ak8C303L74qIAJ6V1EtS//Q8W9TUg75brf5JGhER07J9HTNrg/pKKlzf5eaIuPnDB0kaDBwAPAfsXJC03gJ2Tl8PABYUfGxhWpY9wQE/aeK9AI5p4v1t8uqMrhy/6/DWPq2V0MOL/lruECyDw0atbZXzZHjQd3lEjGzyXFJ34EHggohYUzi5bkSEtO2PFTf1oO/R23pSM8uxoNWGaqWT6D4I3B0Rv0mLlzY2PSX1J+nYBFjEBzs4B6ZlW1VMJ4OZ2Qe1wnRJSqpqtwGzI+LagrcmAGekr88AfldQ/pW0N/UQYHVT99/AK9ub2TZopbGohwNfBl6WND0t+xfgGuB+SWcCbwCfT9+bCJwIzAXWAV9r7gJOcGaWXev0oj7N1uc1/9QWjg8yLpdQzIy+kvRPki5P93eTdFCWi5hZzuRoRt8bgUOBL6T77wI/K1lEZlbRin3ItxKmVCqmiXpwRIyQ9CJARKyS1LHEcZlZJcvBhJeNNkmqIa1wSupHRQyjNbNyqYTaWTGKaaL+FPgtsJOkq0imSrq6pFGZWWWrkntwxYxFvVvSVJJeDQEnR4RXtjdrqyrk/loxipnwcjeSZ04eKiyLiDdLGZiZVbC8JDjg97y/+ExnYAgwB9i3hHGZWQVTldyFL6aJ+vHC/XSWkbO3criZWcXIPJIhIqZJOrgUwZhZlchLE1XSRQW77YARwOKSRWRmlS1PnQxAj4LXdST35B4sTThmVhXykODSB3x7RMR3tlM8ZlYNqj3BSWofEXWSDt+eAZlZZRP56EV9nuR+23RJE4AHgPca3yyYfdPM2pKc3YPrDKwgWYOh8Xm4AJzgzNqqHCS4ndIe1Fd4P7E1qpKvZ2YlUSUZoKkEVwN0Z8szblbJ1zOzUshDE3VJRFy53SIxs+qRgwRXHTPamdn2FfnoRf3Iog9mZkD11+AiYuX2DMTMqkce7sGZmW2ZE5yZ5VKFTEdeDCc4M8tEuIlqZjnmBGdm+eUEZ2a55QRnZrlURbOJFLPws5nZB7XSws+Sbpe0TNIrBWVXSFokaXq6nVjw3vckzZU0R9LxzZ3fCc7MMlNDcVsR7gBGbaF8fEQMT7eJAJKGAWNIliwdBdyYzjq+VU5wZpaZoritORExGSh21NRo4L6IqI2IecBc4KCmPuAEZ2bZFNs8bdl9unMlzUibsL3TsgHAgoJjFqZlW+UEZ2bZFZ/g+kqaUrCNLeLsNwF7AMOBJcBPtjVM96KaWSYZRzIsj4iRWc4fEUs3X0u6BXg43V0EDCo4dGBatlWuwZlZZmqIorZtOrfUv2D3FJJlEwAmAGMkdZI0BBhKsjjWVrkGZ2bZtOJge0n3AkeRNGUXAuOAoyQNT68yHzgLICJmSrofmEWyCP05EVHf1Pmd4Mwss9Z60DcivrCF4tuaOP4q4Kpiz+8EZ2bZVclIBic4M8usWoZqOcGZWXZOcGaWSzlZVcvM7CM8o6+Z5VtUR4ZzgjOzzFyDa6PufG4W69fW0NAA9XXivBP2Yvd91/PtaxbSsXMD9XXihu8NZM70ruUOtc3auEF89x/3ZlOtaKgXh5+0ii99Zwn/cfHHeO2l5Pey65BaLrxuPl26JTebnprQm3uu7Y8EQ4at55KfzSvnVygvr6qVTGQHfAZYFhH7leo6lejS0/Zgzcr3f7Tf+P5ifnXtzkx5YgcOPGYNZ35/MZeeumcZI2zbOnQKrr7/Vbp0a6BuE1x6yj588ug1/PMVC+jaI0lot1wxkIf/sx+nnbuURa934oEbduFH/z2H7r3qeWe56wXV0slQyrGod7DliezanAjo1iMZUdJth3pWLu1Q5ojaNonNNbO6OlG/SUixOblFwMYN7ZCS4yfd05eTvrqM7r2S32GvvnVlibuStOKElyVVsv+KImKypMGlOn/FCnH1va9DwO9/uSOP3L0jP798AFff+zr/fPkSpODC/zO03FG2efX1cMGof2DJ/E6c9NW32XvEOgCuu/BjTPlTTwYN3cCZ45Kpxxa/3hmAS0bvTUM9fPHiJXzy6DVli73sAncyFCudH2osQGeq/77URSfvyYq3OtBzx01cc9/rLJjbiSM+s5pfjNuVpyf24sjPvsNF1y7gstP3KHeobVpNDVz/2GzWrq7hqjP3YP7fOjN4nw1cMP4N6uvhF98fxFMT+nDc6Suor4PF8zrxw/+aw/IlHbnsc3tzw+Oz6N6zyXHeuVYtnQxlny4pIm6OiJERMbIDncodTouteCtpfq5e0YG/PNqTfQ5Yx3GnreTpiT0BmPxQT/Yavq6cIVqB7j3r+cTh7zLtyZ6by2pq4MjRq/jr73sBsGP/TRz86dW07wC77LaRXXffwOJ51f93tUVKP6Nvqyh7gsuTTl3q6dKtfvPrT/7vd5n/t86sWNqBTxz6HgDD/9da/+Mos9Ur2rN2dbJWSe168eLkHgwoSFoR8NwfejJwzw0AHDrqHV7+a4/ksytrWPx6Z3bZrbY8wVeAxgd9W2NNhlIrexM1T3r3q2PcbfMBqGkfPPHb3kx5cgfWX9KOb125mJqaYGNtO667ZGB5A23jVi7twPgLBtPQAA0N4ojPruLAY1fz3VP2Zt3aGiJgyLB1nPPDNwEYcdQapv15B7511DDa1cDX/nUhO/Rpu81TYtsns9zeFCW6WVg4kR2wFBgXEVud5wlgB/WJg/WpksRjpfHwoqnlDsEyOGzUIqa+VKuWnKNHr4FxwJHnF3XsUw9dOjXrlOWtqZS9qFuayM7McqASmp/FcBPVzLIJoEqaqE5wZpZddeQ3Jzgzy85NVDPLrWrpRXWCM7NsKuQh3mI4wZlZJsmDvtWR4ZzgzCy7CpgppBhOcGaWmWtwZpZPvgdnZvlVPWNRneDMLDs3Uc0sl7zws5nlWpXU4DzhpZll10oz+kq6XdIySa8UlPWR9Jik19I/e6flkvRTSXMlzZA0ornzO8GZWWZqaChqK8IdfHT1vcuAxyNiKPB4ug9wAjA03cYCNzV3cic4M8smSB70LWZr7lQRk4GVHyoeDdyZvr4TOLmg/K5IPAv0ktS/qfP7HpyZZSIiy4O+fSVNKdi/OSJubuYzO0fEkvT1W8DO6esBwIKC4xamZUvYCic4M8uu+AS3vCVTlkdESNs+OZObqGaWXURx27ZZ2tj0TP9clpYvAgYVHDcwLdsqJzgzy6YV78FtxQTgjPT1GcDvCsq/kvamHgKsLmjKbpGbqGaWWZE9pM2fp2D1PUkLgXHANcD9ks4E3gA+nx4+ETgRmAusA77W3Pmd4MwsoxY1Pz94pq2vvveR9UMjWeP0nCznd4Izs2yCqhnJ4ARnZtl5LKqZ5ZUnvDSz/HKCM7NcioD66mijOsGZWXauwZlZbjnBmVkuBeA1GcwsnwLC9+DMLI8CdzKYWY75HpyZ5ZYTnJnlU+sNti81JzgzyyaAVpouqdSc4MwsO9fgzCyfPFTLzPIqIPwcnJnllkcymFlu+R6cmeVShHtRzSzHXIMzs3wKor6+3EEUxQnOzLLxdElmlmt+TMTM8iiAcA3OzHIpPOGlmeVYtXQyKCqou1fS28Ab5Y6jBPoCy8sdhGWS19/ZxyKiX0tOIOlRkp9PMZZHxKiWXK8lKirB5ZWkKRExstxxWPH8O8uHduUOwMysVJzgzCy3nOC2j5vLHYBl5t9ZDvgenJnllmtwZpZbTnBmlltOcCUkaZSkOZLmSrqs3PFY8yTdLmmZpFfKHYu1nBNciUiqAX4GnAAMA74gaVh5o7Ii3AGU7cFUa11OcKVzEDA3Il6PiI3AfcDoMsdkzYiIycDKcsdhrcMJrnQGAAsK9hemZWa2nTjBmVluOcGVziJgUMH+wLTMzLYTJ7jSeQEYKmmIpI7AGGBCmWMya1Oc4EokIuqAc4FJwGzg/oiYWd6orDmS7gWeAfaWtFDSmeWOybadh2qZWW65BmdmueUEZ2a55QRnZrnlBGdmueUEZ2a55QRXRSTVS5ou6RVJD0jq2oJz3SHp1PT1rU1NBCDpKEmHbcM15kv6yOpLWyv/0DFrM17rCknfyRqj5ZsTXHVZHxHDI2I/YCPwzcI3JW3TOrcR8Y2ImNXEIUcBmROcWbk5wVWvp4A909rVU5ImALMk1Uj6kaQXJM2QdBaAEjek89P9Edip8USSnpQ0Mn09StI0SS9JelzSYJJEemFaezxCUj9JD6bXeEHS4elnd5T0B0kzJd0KqLkvIem/JU1NPzP2Q++NT8sfl9QvLdtD0qPpZ56StE9r/DAtn7yyfRVKa2onAI+mRSOA/SJiXpokVkfEgZI6AX+R9AfgAGBvkrnpdgZmAbd/6Lz9gFuAI9Nz9YmIlZJ+DqyNiB+nx90DjI+IpyXtRjJa4x+AccDTEXGlpJOAYkYBfD29RhfgBUkPRsQKoBswJSIulHR5eu5zSRaD+WZEvCbpYOBG4Jht+DFaG+AEV126SJqevn4KuI2k6fh8RMxLyz8NfKLx/hrQExgKHAncGxH1wGJJf9rC+Q8BJjeeKyK2Ni/ascAwaXMFbQdJ3dNrfC797O8lrSriO31b0inp60FprCuABuDXafmvgN+k1zgMeKDg2p2KuIa1UU5w1WV9RAwvLEj/ob9XWAScFxGTPnTcia0YRzvgkIjYsIVYiibpKJJkeWhErJP0JNB5K4dHet13PvwzMNsa34PLn0nAtyR1AJC0l6RuwGTg9PQeXX/g6C189lngSElD0s/2ScvfBXoUHPcH4LzGHUmNCWcy8MW07ASgdzOx9gRWpcltH5IaZKN2QGMt9IskTd81wDxJp6XXkKT9m7mGtWFOcPlzK8n9tWnpwim/IKmp/xZ4LX3vLpIZMz4gIt4GxpI0B1/i/SbiQ8ApjZ0MwLeBkWknxize7839AUmCnEnSVH2zmVgfBdpLmg1cQ5JgG70HHJR+h2OAK9PyLwFnpvHNxNPAWxM8m4iZ5ZZrcGaWW05wZpZbTnBmlltOcGaWW05wZpZbTnBmlltOcGaWW/8DpWM2GG3WPEgAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Predicted Fire  Predicted Not Fire\n",
              "Fire                 336                  58\n",
              "Not fire              64                 339"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-616ec115-0f98-4962-ad97-1e4a211846ba\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Predicted Fire</th>\n",
              "      <th>Predicted Not Fire</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fire</th>\n",
              "      <td>336</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Not fire</th>\n",
              "      <td>64</td>\n",
              "      <td>339</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-616ec115-0f98-4962-ad97-1e4a211846ba')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-616ec115-0f98-4962-ad97-1e4a211846ba button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-616ec115-0f98-4962-ad97-1e4a211846ba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier(criterion = \"gini\",n_estimators = 50 )\n",
        "model.fit(X_train, y_train)\n",
        "model.score(X_test, y_test)\n",
        "rf_score= model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Nz-khdBdKXq",
        "outputId": "835506da-fb89-4dbb-f024-2a11d00b890d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=50)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8506900878293601"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute ROC AUC Score\n",
        "prediction_probability = model.predict_proba(X_test)\n",
        "print(roc_auc_score(y_test, prediction_probability[ : , 1]))\n",
        "rf_roc_auc_score = roc_auc_score(y_test, prediction_probability[ : , 1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tKqGZPFdYZx",
        "outputId": "28c61a2d-94d4-4ec0-8a82-d73786dea8ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.911973649406104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = model.predict(X_test)\n",
        "print(f1_score(y_test, prediction, pos_label= 1))\n",
        "rf_f1_score= f1_score(y_test, prediction, pos_label= 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ea_4Aea7deLj",
        "outputId": "3e9ce779-5160-4da5-d4b9-c74a67a1acf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8514357053682895\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate recall\n",
        "recall = recall_score(y_test, prediction, labels=[1,0], average='micro')\n",
        "print('Recall: %.3f' % recall)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmeX2kNAdor8",
        "outputId": "5b3da3ca-79d9-43cf-d6ed-98aa916c202b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall: 0.851\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "confusion_matrix ( y_test , prediction)\n",
        "\n",
        "tn, fp, fn, tp = confusion_matrix ( y_test , prediction).ravel()\n",
        "conf_matrix = pd.DataFrame(\n",
        "    {\n",
        "        'Predicted Fire': [tp, fp],\n",
        "        'Predicted Not Fire': [fn, tn]\n",
        "    }, index=['Fire', 'Not fire'])\n",
        "plot_confusion_matrix(model, X_test, y_test)  \n",
        "plt.show()\n",
        "conf_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "JvjgDTIedwap",
        "outputId": "d71471d5-dab7-47ea-e1cc-4e84c1769600"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[337,  66],\n",
              "       [ 53, 341]])"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f471d9a0050>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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              "          Predicted Fire  Predicted Not Fire\n",
              "Fire                 341                  53\n",
              "Not fire              66                 337"
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-af77003e-1bb7-4cd4-b292-d3408b7604b5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-af77003e-1bb7-4cd4-b292-d3408b7604b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "MONyywVMPneF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "msQtzn6jE0q5"
      },
      "source": [
        "**SVM WITH BALANCED CLASS**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHFiqzF0Euf-",
        "outputId": "df7a495c-c538-491a-f814-ae5f69186579"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0, estimator=SVC(random_state=5), n_jobs=-1,\n",
              "             param_grid={'C': [0.1, 1, 10, 100],\n",
              "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         'kernel': ['rbf', 'poly', 'sigmoid']},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'C': 100, 'gamma': 1, 'kernel': 'rbf'}\n",
            "SVC(C=100, gamma=1, random_state=5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.71      0.75       403\n",
            "           1       0.73      0.81      0.77       394\n",
            "\n",
            "    accuracy                           0.76       797\n",
            "   macro avg       0.76      0.76      0.76       797\n",
            "weighted avg       0.76      0.76      0.76       797\n",
            "\n",
            "[[288 115]\n",
            " [ 76 318]]\n"
          ]
        }
      ],
      "source": [
        "#### GRID SEARCH ON SVM \n",
        "clf = SVC(random_state=5)\n",
        "# defining parameter range \n",
        "param_grid = {'C': [0.1,1,10,100],  \n",
        "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001], \n",
        "              'kernel': ['rbf','poly','sigmoid']}  \n",
        "grid = GridSearchCV(clf, param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzlCrVsnuE8V"
      },
      "source": [
        "**The standard deviation here is used to understand the uncertainty of the model.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khPn6QieEzHA",
        "outputId": "213e954d-9979-47d1-f3c6-d1496730fcd9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.78717201 0.80174927 0.79008746 0.82163743 0.78070175]\n",
            "Accuracy: 0.796 (+/- 0.03)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import KFold\n",
        "#### Cross val score\n",
        "scores_SVM = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "print(scores_SVM)\n",
        "#print accuracy and standard deviation \n",
        "print(\"Accuracy: %0.3f (+/- %0.2f)\" % (scores_SVM.mean(), scores_SVM.std() * 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOVvv7iISXXK",
        "outputId": "9a4e2fe2-c864-48a0-9a9c-e24f5e44beeb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.78717201 0.80174927 0.79008746 0.82163743 0.78070175]\n",
            "ROC: 0.702 (+/- 0.03)\n"
          ]
        }
      ],
      "source": [
        "roc_auc_SVM = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5, scoring='roc_auc')\n",
        "print(scores_SVM)\n",
        "#print Roc and standard deviation \n",
        "print(\"ROC: %0.3f (+/- %0.2f)\" % (roc_auc_SVM.mean(), roc_auc_SVM.std() * 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3jJi4JwsUJ9W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3cefdbb2-81ef-484f-8470-e0e314f529fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.29126214 0.34615385 0.33333333 0.42990654 0.28571429]\n",
            "f1_score: 0.337 (+/- 0.10)\n"
          ]
        }
      ],
      "source": [
        "f1_score_SVM = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5, scoring='f1')\n",
        "print(f1_score_SVM)\n",
        "#print Roc and standard deviation \n",
        "print(\"f1_score: %0.3f (+/- %0.2f)\" % (f1_score_SVM.mean(), f1_score_SVM.std() * 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9tvbnW-dXdmk",
        "outputId": "96fa2eff-8f9c-4fb8-bb70-8b88357d6ac3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.19480519 0.23376623 0.23076923 0.2987013  0.19480519]\n",
            "recall: 0.231 (+/- 0.08)\n"
          ]
        }
      ],
      "source": [
        "recall_score_SVM = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5, scoring='recall')\n",
        "print(recall_score_SVM)\n",
        "#print Roc and standard deviation \n",
        "print(\"recall: %0.3f (+/- %0.2f)\" % (recall_score_SVM.mean(), recall_score_SVM.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgW5cMrEFYdN"
      },
      "source": [
        "**BAGGING CLASSIFER (Built on SVM with Balanced Class)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOq3PaPdFScw",
        "outputId": "c19fa937-143b-4c04-be76-2c11dbdb9545"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0,\n",
              "             estimator=BaggingClassifier(base_estimator=SVC(C=100, gamma=1,\n",
              "                                                            random_state=5),\n",
              "                                         n_jobs=-1, random_state=5),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'max_features': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
              "                         'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
              "                                          100]},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'max_features': 3, 'n_estimators': 80}\n",
            "BaggingClassifier(base_estimator=SVC(C=100, gamma=1, random_state=5),\n",
            "                  max_features=3, n_estimators=80, n_jobs=-1, random_state=5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.73      0.76       403\n",
            "           1       0.74      0.80      0.77       394\n",
            "\n",
            "    accuracy                           0.77       797\n",
            "   macro avg       0.77      0.77      0.77       797\n",
            "weighted avg       0.77      0.77      0.77       797\n",
            "\n",
            "[[294 109]\n",
            " [ 77 317]]\n"
          ]
        }
      ],
      "source": [
        "#grid search for bagging classifier using the SVM classifer obtained above \n",
        "\n",
        "bag = BaggingClassifier(grid.best_estimator_, n_jobs=-1, random_state=5)\n",
        "\n",
        "param_grid = {'n_estimators': list(range(0, 110, 10))[1:],  'max_features': list (range(1,21, 2))}  \n",
        "\n",
        "grid = GridSearchCV(bag, param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfnpPOPtGFV-",
        "outputId": "cfb71c4c-6366-40e1-ad48-88d31417d246"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7916 (+/- 0.0229)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "#bagging classifier (SVM)\n",
        "scores_BAG = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "#print accuracy and standard deviation\n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_BAG.mean(), scores_BAG.std() * 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fLCUSkMUyNG",
        "outputId": "4d3084de-981d-4069-d065-beb79092c886"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.7481203  0.7646226  0.73265602 0.69948542 0.71737319]\n",
            "ROC: 0.732 (+/- 0.05)\n"
          ]
        }
      ],
      "source": [
        "roc_auc_BAG = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5, scoring='roc_auc')\n",
        "print(roc_auc_BAG )\n",
        "#print Roc and standard deviation \n",
        "print(\"ROC: %0.3f (+/- %0.2f)\" % (roc_auc_BAG .mean(), roc_auc_BAG .std() * 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XaUdqjJYXHDu",
        "outputId": "5e9c2a34-8128-47c2-e365-45f863a09ca8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.26262626 0.35514019 0.32727273 0.39252336 0.26923077]\n",
            "f1_score: 0.321 (+/- 0.10)\n"
          ]
        }
      ],
      "source": [
        "f1_score_BAG = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5, scoring='f1')\n",
        "print(f1_score_BAG)\n",
        "#print Roc and standard deviation \n",
        "print(\"f1_score: %0.3f (+/- %0.2f)\" % (f1_score_BAG.mean(), f1_score_BAG.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6N96N4x9GyZm"
      },
      "source": [
        "**EXTRA TREES CLASSIFIER**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBam5MkCG6TD",
        "outputId": "baea749e-be30-4347-c0c0-8df4de377b08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 1000 candidates, totalling 5000 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0,\n",
              "             estimator=ExtraTreesClassifier(n_jobs=-1, random_state=5),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
              "                         'max_features': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
              "                         'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
              "                                          100]},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'max_depth': 17, 'max_features': 3, 'n_estimators': 40}\n",
            "ExtraTreesClassifier(max_depth=17, max_features=3, n_estimators=40, n_jobs=-1,\n",
            "                     random_state=5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.80      0.83       403\n",
            "           1       0.81      0.86      0.84       394\n",
            "\n",
            "    accuracy                           0.83       797\n",
            "   macro avg       0.83      0.83      0.83       797\n",
            "weighted avg       0.83      0.83      0.83       797\n",
            "\n",
            "[[323  80]\n",
            " [ 54 340]]\n"
          ]
        }
      ],
      "source": [
        "#### GRID SEARCH for extra trees classifier \n",
        "\n",
        "ext = ExtraTreesClassifier(n_jobs=-1, random_state=5)\n",
        "# defining parameter range \n",
        "param_grid = {'n_estimators': list(range(0, 110, 10))[1:],  'max_features': list (range(1,21, 2)), 'max_depth' :list(range(1,21, 2))}  \n",
        "  \n",
        "\n",
        "grid = GridSearchCV(ext, param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JMZwk0ElGEwE",
        "outputId": "9882b1e7-5d6d-4c85-f5f9-545a432a9ef3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8324 (+/- 0.0240)\n"
          ]
        }
      ],
      "source": [
        "#Accuracy for extra trees classifier \n",
        "scores_EXT = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "#print accuracy and standard deviation \n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_EXT.mean(), scores_EXT.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-UWCO1EjGK1I"
      },
      "source": [
        "**ADABOOST CLASSIFIER**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecZdHEzaGRX8",
        "outputId": "a3463658-8793-41b9-c6db-a155d649bf41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0,\n",
              "             estimator=AdaBoostClassifier(base_estimator=DecisionTreeClassifier(class_weight='balanced',\n",
              "                                                                                max_depth=1),\n",
              "                                          random_state=5),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'learning_rate': [0.2, 0.4, 0.6, 0.8, 1],\n",
              "                         'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
              "                                          100]},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'learning_rate': 1, 'n_estimators': 80}\n",
            "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(class_weight='balanced',\n",
            "                                                         max_depth=1),\n",
            "                   learning_rate=1, n_estimators=80, random_state=5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.74      0.75       403\n",
            "           1       0.74      0.76      0.75       394\n",
            "\n",
            "    accuracy                           0.75       797\n",
            "   macro avg       0.75      0.75      0.75       797\n",
            "weighted avg       0.75      0.75      0.75       797\n",
            "\n",
            "[[300 103]\n",
            " [ 95 299]]\n"
          ]
        }
      ],
      "source": [
        "#### GRID SEARCH for Ada bosst classifier \n",
        "\n",
        "adb = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1, class_weight='balanced'), random_state=5)\n",
        "# defining parameter range \n",
        "param_grid = {'n_estimators': list(range(0, 110, 10))[1:],  'learning_rate': [0.2, 0.4, 0.6, 0.8, 1]}  \n",
        "  \n",
        "\n",
        "grid = GridSearchCV(adb, param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_sjHlEiGWPN",
        "outputId": "5fc58705-707a-424e-a06f-536b21f2ce51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6889 (+/- 0.0527)\n"
          ]
        }
      ],
      "source": [
        "#Adaboost classifier \n",
        "scores_ADB = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "\n",
        "#print accuracy and standard deviation\n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_ADB.mean(), scores_ADB.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBfHoDMvGeP-"
      },
      "source": [
        "**GRADIENT BOOSTING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIy3lfClGm2o",
        "outputId": "1b717c92-7c1b-4f84-818c-e45ae1486fee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 1000 candidates, totalling 5000 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0,\n",
              "             estimator=GradientBoostingClassifier(random_state=5,\n",
              "                                                  subsample=0.289),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
              "                         'max_features': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
              "                         'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
              "                                          100]},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'max_depth': 11, 'max_features': 1, 'n_estimators': 90}\n",
            "GradientBoostingClassifier(max_depth=11, max_features=1, n_estimators=90,\n",
            "                           random_state=5, subsample=0.289)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.84      0.85       403\n",
            "           1       0.84      0.86      0.85       394\n",
            "\n",
            "    accuracy                           0.85       797\n",
            "   macro avg       0.85      0.85      0.85       797\n",
            "weighted avg       0.85      0.85      0.85       797\n",
            "\n",
            "[[340  63]\n",
            " [ 54 340]]\n"
          ]
        }
      ],
      "source": [
        "#Gradient Boosting \n",
        "gb_clf2 = GradientBoostingClassifier( subsample=0.289, random_state=5)\n",
        "# defining parameter range \n",
        "param_grid = {'n_estimators': list(range(0, 110, 10))[1:],  'max_features': list (range(1,21, 2)), 'max_depth' :list(range(1,21, 2))}  \n",
        "  \n",
        "\n",
        "grid = GridSearchCV(gb_clf2, param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QbjvJDjiGutR",
        "outputId": "55627e64-8fc5-4458-a3ac-1d1d3460a5e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8342 (+/- 0.0178)\n"
          ]
        }
      ],
      "source": [
        "#Gradient boosting classfier \n",
        "scores_GB = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "\n",
        "#print accuracy and standard deviation\n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_GB.mean(), scores_GB.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-FuCfT_G684"
      },
      "source": [
        "**XGBOOST**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMcD2aXsG863",
        "outputId": "8b1ffae7-e544-43dc-d096-84dd6edb26da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 1000 candidates, totalling 5000 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0, estimator=XGBClassifier(random_state=5),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'max_depth': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
              "                         'max_features': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
              "                         'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
              "                                          100]},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'max_depth': 17, 'max_features': 1, 'n_estimators': 80}\n",
            "XGBClassifier(max_depth=17, max_features=1, n_estimators=80, random_state=5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.83      0.85       403\n",
            "           1       0.84      0.87      0.85       394\n",
            "\n",
            "    accuracy                           0.85       797\n",
            "   macro avg       0.85      0.85      0.85       797\n",
            "weighted avg       0.85      0.85      0.85       797\n",
            "\n",
            "[[336  67]\n",
            " [ 51 343]]\n"
          ]
        }
      ],
      "source": [
        "xg_clf = xgb.XGBClassifier(random_state=5)\n",
        "\n",
        "param_grid = {'n_estimators': list(range(0, 110, 10))[1:] ,  'max_features': list (range(1,21, 2)), 'max_depth':list(range(1,21, 2))}  \n",
        "  \n",
        "\n",
        "grid = GridSearchCV(xg_clf , param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xePRNnwvHAy_",
        "outputId": "95dc232c-aa67-4705-90be-8c1eb85239a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8243 (+/- 0.0436)\n"
          ]
        }
      ],
      "source": [
        "#Extreme Gradient Boosting classifier \n",
        "scores_XGB = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "\n",
        "#print accuracy and standard deviation\n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_XGB.mean(), scores_XGB.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ub3ZaI7fHGru"
      },
      "source": [
        "**RANDOM FOREST**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CgKVpnLXHMA0",
        "outputId": "67ee8d5a-6be2-4b9f-e472-985ffec351bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 1000 candidates, totalling 5000 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0,\n",
              "             estimator=RandomForestClassifier(random_state=5), n_jobs=-1,\n",
              "             param_grid={'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18, 20],\n",
              "                         'max_features': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],\n",
              "                         'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
              "                                          100]},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'max_depth': 20, 'max_features': 3, 'n_estimators': 50}\n",
            "RandomForestClassifier(max_depth=20, max_features=3, n_estimators=50,\n",
            "                       random_state=5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.83      0.84       403\n",
            "           1       0.83      0.86      0.84       394\n",
            "\n",
            "    accuracy                           0.84       797\n",
            "   macro avg       0.84      0.84      0.84       797\n",
            "weighted avg       0.84      0.84      0.84       797\n",
            "\n",
            "[[336  67]\n",
            " [ 57 337]]\n"
          ]
        }
      ],
      "source": [
        "#Random Forest\n",
        "\n",
        "rf_clf2 = RandomForestClassifier(random_state=5)\n",
        "\n",
        "\n",
        "param_grid = {'n_estimators': list(range(0, 110, 10))[1:], 'max_features': list (range(1,21, 2)), 'max_depth' :list(range(0,21, 2))[1:]}  \n",
        "  \n",
        "\n",
        "grid = GridSearchCV(rf_clf2 , param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dcxAD0j8HQ4C",
        "outputId": "3152e7f5-fad0-40a9-8626-43ff0afec00d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8289 (+/- 0.0338)\n"
          ]
        }
      ],
      "source": [
        "#Random Forest classfier \n",
        "scores_RF = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "\n",
        "#print accuracy and standard deviation\n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_RF.mean(), scores_RF.std() * 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAbDPnSIFdHv",
        "outputId": "56293146-80f1-4a4e-9f3b-72ded3e63510"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.83090379 0.85131195 0.8425656  0.8128655  0.80701754]\n",
            "ROC: 0.817 (+/- 0.06)\n"
          ]
        }
      ],
      "source": [
        "roc_auc_RF = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5, scoring='roc_auc')\n",
        "print(scores_RF)\n",
        "#print Roc and standard deviation \n",
        "print(\"ROC: %0.3f (+/- %0.2f)\" % (roc_auc_RF.mean(), roc_auc_RF.std() * 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bx_2Gea4bhNU",
        "outputId": "d733f4f7-9620-4af1-f930-1468e9462df4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.57971014 0.61068702 0.57142857 0.52238806 0.51470588]\n",
            "f1_score: 0.560 (+/- 0.07)\n"
          ]
        }
      ],
      "source": [
        "f1_score_RF = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5, scoring='f1')\n",
        "print(f1_score_RF)\n",
        "#print Roc and standard deviation \n",
        "print(\"f1_score: %0.3f (+/- %0.2f)\" % (f1_score_RF.mean(), f1_score_RF.std() * 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHPZ9nYjbsnt",
        "outputId": "5d4c7453-1636-400e-c556-1ee8e1446998"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.51948052 0.51948052 0.46153846 0.45454545 0.45454545]\n",
            "recall: 0.482 (+/- 0.06)\n"
          ]
        }
      ],
      "source": [
        "recall_score_RF = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5, scoring='recall')\n",
        "print(recall_score_RF)\n",
        "#print Roc and standard deviation \n",
        "print(\"recall: %0.3f (+/- %0.2f)\" % (recall_score_RF.mean(), recall_score_RF.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUiv6Lp4HWmE"
      },
      "source": [
        "**NEURAL NETWORK**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r15CGp5bHcvT",
        "outputId": "499e3b5b-7804-4718-fb6a-96f2f7fe46ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0,\n",
              "             estimator=MLPClassifier(alpha=1, hidden_layer_sizes=30,\n",
              "                                     random_state=5),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'hidden_layer_sizes': [5, 10, 15, 20, 25, 30, 35, 40,\n",
              "                                                45, 50]},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'hidden_layer_sizes': 50}\n",
            "MLPClassifier(alpha=1, hidden_layer_sizes=50, random_state=5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.62      0.67       403\n",
            "           1       0.66      0.75      0.70       394\n",
            "\n",
            "    accuracy                           0.69       797\n",
            "   macro avg       0.69      0.69      0.69       797\n",
            "weighted avg       0.69      0.69      0.69       797\n",
            "\n",
            "[[251 152]\n",
            " [ 98 296]]\n"
          ]
        }
      ],
      "source": [
        "ml_clf2 = MLPClassifier(hidden_layer_sizes=30\n",
        "                        , activation='relu', solver='adam', alpha=1, random_state=5,)\n",
        "param_grid = {'hidden_layer_sizes': list(range(0, 51, 5))[1:]}  \n",
        "  \n",
        "\n",
        "grid = GridSearchCV(ml_clf2 , param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDgiXuLfHid5",
        "outputId": "be27932b-984e-493e-c81d-63b78bf06902"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7828 (+/- 0.0150)\n"
          ]
        }
      ],
      "source": [
        "#MLP Classifier\n",
        "scores_NN = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "\n",
        "#print accuracy and standard deviation\n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_NN.mean(), scores_NN.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXoutlsIHoLe"
      },
      "source": [
        "**NEAREST NEIGHBOUR**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vc9mhMjLHt1D",
        "outputId": "d05f23b3-091e-42f6-c0e3-4930a96ac913"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 7 candidates, totalling 35 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0,\n",
              "             estimator=KNeighborsClassifier(algorithm='brute', n_neighbors=3),\n",
              "             n_jobs=-1, param_grid={'n_neighbors': [1, 3, 5, 7, 9, 11, 13]},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_neighbors': 1}\n",
            "KNeighborsClassifier(algorithm='brute', n_neighbors=1)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.77      0.78       403\n",
            "           1       0.77      0.79      0.78       394\n",
            "\n",
            "    accuracy                           0.78       797\n",
            "   macro avg       0.78      0.78      0.78       797\n",
            "weighted avg       0.78      0.78      0.78       797\n",
            "\n",
            "[[310  93]\n",
            " [ 81 313]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='brute', n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.74      0.76       403\n",
            "           1       0.75      0.78      0.76       394\n",
            "\n",
            "    accuracy                           0.76       797\n",
            "   macro avg       0.76      0.76      0.76       797\n",
            "weighted avg       0.76      0.76      0.76       797\n",
            "\n",
            "0.7603513174404015\n"
          ]
        }
      ],
      "source": [
        "neigh = KNeighborsClassifier(n_neighbors=3, algorithm=\"brute\")\n",
        "param_grid = {'n_neighbors': list(range(1, 15, 2))}  \n",
        "\n",
        "grid = GridSearchCV(neigh , param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) \n",
        "neigh.fit(scaler.fit_transform(X_train), y_train)\n",
        "predictions = neigh.predict(scaler.transform(X_test))\n",
        "\n",
        "print(classification_report(y_test, predictions)) \n",
        "print(accuracy_score(y_test, predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OESao0ooHyIp",
        "outputId": "639f9c63-5161-4d0f-a266-8c54c3bedf9a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7770 (+/- 0.0266)\n"
          ]
        }
      ],
      "source": [
        "#Nearest NEighbour Classifier\n",
        "scores_NNeigh = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "\n",
        "#print accuracy and standard deviation\n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_NNeigh.mean(), scores_NNeigh.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQkNA_sqH1zT"
      },
      "source": [
        "**GAUSSIAN NAIVE BAYES**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4eRc-vJH8do",
        "outputId": "a08b7986-43b7-442f-bd29-dce516b57970"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7776 (+/- 0.0128)\n"
          ]
        }
      ],
      "source": [
        "#NAive bayes classfier\n",
        "gnb = GaussianNB()\n",
        "scores_NB = cross_val_score(gnb, scaler.fit_transform(X), y, cv=5)\n",
        "\n",
        "#print accuracy and standard deviation\n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_NB.mean(), scores_NB.std() * 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQZuPVLqIGKy"
      },
      "source": [
        "**STOCHASTIC GRADIENT**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6A0jqezIAIf",
        "outputId": "dc29b2ae-cc19-4ad6-f09e-4a1c6e3e54cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=0,\n",
              "             estimator=SGDClassifier(class_weight='balanced', random_state=5),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'loss': ['hinge', 'log', 'modified_huber',\n",
              "                                  'squared_hinge', 'perceptron', 'squared_loss',\n",
              "                                  'huber', 'epsilon_insensitive',\n",
              "                                  'squared_epsilon_insensitive']},\n",
              "             verbose=3)"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'loss': 'huber'}\n",
            "SGDClassifier(class_weight='balanced', loss='huber', random_state=5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.39      0.51       403\n",
            "           1       0.58      0.87      0.70       394\n",
            "\n",
            "    accuracy                           0.62       797\n",
            "   macro avg       0.67      0.63      0.60       797\n",
            "weighted avg       0.67      0.62      0.60       797\n",
            "\n",
            "[[156 247]\n",
            " [ 52 342]]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='brute', n_neighbors=3)"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.74      0.76       403\n",
            "           1       0.75      0.78      0.76       394\n",
            "\n",
            "    accuracy                           0.76       797\n",
            "   macro avg       0.76      0.76      0.76       797\n",
            "weighted avg       0.76      0.76      0.76       797\n",
            "\n"
          ]
        }
      ],
      "source": [
        "param_grid = {'loss': ['hinge', 'log', 'modified_huber',\n",
        "    'squared_hinge', 'perceptron', 'squared_loss',\n",
        "    'huber', 'epsilon_insensitive', 'squared_epsilon_insensitive']}\n",
        "sgc = SGDClassifier(random_state=5, class_weight='balanced')\n",
        "grid = GridSearchCV(sgc , param_grid, refit = True, cv = 5, verbose = 3,\n",
        "                    error_score=0,\n",
        "                    n_jobs=-1) \n",
        "  \n",
        "# fitting the model for grid search \n",
        "grid.fit(scaler.fit_transform(X_train), y_train) \n",
        "\n",
        "#### Best Grid search parameter fitting\n",
        "\n",
        "# print best parameter after tuning \n",
        "print(grid.best_params_) \n",
        "  \n",
        "# print how our model looks after hyper-parameter tuning \n",
        "print(grid.best_estimator_) \n",
        "\n",
        "grid_predictions = grid.predict(scaler.transform(X_test)) \n",
        "  \n",
        "# print classification report \n",
        "print(classification_report(y_test, grid_predictions)) \n",
        "print(confusion_matrix(y_test, grid_predictions)) \n",
        "neigh.fit(scaler.fit_transform(X_train), y_train)\n",
        "predictions = neigh.predict(scaler.transform(X_test))\n",
        "print(classification_report(y_test, predictions)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jWEbzVT_IMW-",
        "outputId": "c7541271-fd80-44b6-d539-62c4d889829b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.6094 (+/- 0.0949)\n"
          ]
        }
      ],
      "source": [
        "#Stochastic Gradient boosting classifier \n",
        "scores_SGD = cross_val_score(grid.best_estimator_, scaler.fit_transform(X), y, cv=5)\n",
        "\n",
        "#print accuracy and standard deviation\n",
        "print(\"Accuracy: %0.4f (+/- %0.4f)\" % (scores_SGD.mean(), scores_SGD.std() * 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NB1LzU8KMeTe"
      },
      "outputs": [],
      "source": [
        "#Creating plot elelments\n",
        "models = ['SVM with bal', 'BAG_SVM wit bal', 'EXT with bal', 'ADB with bal', 'GB', 'XGB', 'RF', 'NN', 'NNEIGH', 'NB', 'SGD with bal']\n",
        "x_pos = np.arange(len(models))\n",
        "mean = [scores_SVM.mean(), scores_BAG.mean(), scores_EXT.mean(), scores_ADB.mean(), scores_GB.mean(), scores_XGB.mean(), scores_RF.mean(),scores_NN.mean(), scores_NNeigh.mean(), scores_NB.mean(), scores_SGD.mean() ]\n",
        "std = [scores_SVM.std(), scores_BAG.std(), scores_EXT.std(), scores_ADB.std(), scores_GB.std(), scores_XGB.std(), scores_RF.std(),scores_NN.std(), scores_NNeigh.std(), scores_NB.std(), scores_SGD.std() ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "kM6WVCjSMoRD",
        "outputId": "fb4f36b6-b69f-43ff-df7c-d11bb5470874"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<BarContainer object of 11 artists>"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy')"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.axis.XTick at 0x7f8bddd6d350>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddd6de50>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddae22d0>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddcb9b10>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddcd1090>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddcd1910>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddada6d0>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddadacd0>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddf8aa50>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddf8a550>,\n",
              " <matplotlib.axis.XTick at 0x7f8bddb3f450>]"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "[Text(0, 0, 'SVM with bal'),\n",
              " Text(0, 0, 'BAG_SVM wit bal'),\n",
              " Text(0, 0, 'EXT with bal'),\n",
              " Text(0, 0, 'ADB with bal'),\n",
              " Text(0, 0, 'GB'),\n",
              " Text(0, 0, 'XGB'),\n",
              " Text(0, 0, 'RF'),\n",
              " Text(0, 0, 'NN'),\n",
              " Text(0, 0, 'NNEIGH'),\n",
              " Text(0, 0, 'NB'),\n",
              " Text(0, 0, 'SGD with bal')]"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy and standard deviation of classifiers in predicting wildfires')"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#plot with class weight balance \n",
        "fig, ax = plt.subplots(figsize =(10,5))\n",
        "ax.bar(x_pos, mean, yerr=std, align='center', alpha=0.5, ecolor='red', capsize=18)\n",
        "ax.set_ylabel('Accuracy')\n",
        "ax.set_xticks(x_pos)\n",
        "ax.set_xticklabels(models, rotation=45)\n",
        "ax.set_title('Accuracy and standard deviation of classifiers in predicting wildfires')\n",
        "ax.yaxis.grid(True)\n",
        "# Save the figure and show\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"accuracy_1\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnDv0UfyM17s"
      },
      "source": [
        "SVM = Support Vector Machine with Balanced Class Weight.\n",
        "\n",
        "BAG_SVM = bagging Classifiers (of SVMs with Balanced Class Weight).\n",
        "\n",
        "EXT = Extra Trees Classifier with Balanced Class Weight\n",
        "\n",
        "ADB = Adabost Classifier with Balanced Class Weight\n",
        "\n",
        "GB = Gradient Boosting\n",
        "\n",
        "XGB = Extreme Gradient Boosting\n",
        "\n",
        "RF = Random Forest\n",
        "\n",
        "NN = Neural Network\n",
        "\n",
        "NNEIGH = Nearest Neighbour\n",
        "\n",
        "SGD = Stochastic Gradient Descent with Balanced Class Weight"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The best accuracy among the eleven models is obtained by using the Random Forest and Extreme Gradient Boosting (XGBOOST) classifiers.\n",
        "Extreme Gradient Boosting (XGBOOST).\n",
        "\n",
        "The accuracy is 85.06% for the Random Forest model and 84.7% for XGBoost respectively."
      ],
      "metadata": {
        "id": "Li58jNM5rk3W"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
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  "nbformat_minor": 0
}