CropIdentification.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install ipyleaflet"
      ],
      "metadata": {
        "id": "Hj59Nm6n9xwd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0201ef0-72ce-4d21-f976-c5cce84585ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting ipyleaflet\n",
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            "Installing collected packages: xyzservices, traittypes, jedi, ipyleaflet\n",
            "Successfully installed ipyleaflet-0.17.2 jedi-0.18.2 traittypes-0.2.1 xyzservices-2023.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install odc-stac"
      ],
      "metadata": {
        "id": "BmhrYQ3M925r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a95b273-ddbd-4c98-8471-8525be4ef67c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting odc-stac\n",
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            "Requirement already satisfied: locket in /usr/local/lib/python3.10/dist-packages (from partd>=0.3.10->dask[array]->odc-stac) (1.0.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7.0->pystac<2,>=1.0.0->odc-stac) (1.16.0)\n",
            "Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.10/dist-packages (from snuggs>=1.4.1->rasterio!=1.3.0,!=1.3.1,>=1.0.0->odc-stac) (3.0.9)\n",
            "Installing collected packages: snuggs, pyproj, cligj, click-plugins, affine, rasterio, odc-geo, odc-stac\n",
            "Successfully installed affine-2.4.0 click-plugins-1.1.1 cligj-0.7.2 odc-geo-0.3.3 odc-stac-0.3.5 pyproj-3.5.0 rasterio-1.3.6 snuggs-1.4.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install planetary-computer"
      ],
      "metadata": {
        "id": "VrkJelx8-2Nb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52de41e1-6c11-423d-a65f-1400828248b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting planetary-computer\n",
            "  Downloading planetary_computer-0.5.1-py3-none-any.whl (17 kB)\n",
            "Requirement already satisfied: pytz>=2020.5 in /usr/local/lib/python3.10/dist-packages (from planetary-computer) (2022.7.1)\n",
            "Requirement already satisfied: requests>=2.25.1 in /usr/local/lib/python3.10/dist-packages (from planetary-computer) (2.27.1)\n",
            "Requirement already satisfied: pydantic[dotenv]>=1.7.3 in /usr/local/lib/python3.10/dist-packages (from planetary-computer) (1.10.7)\n",
            "Collecting pystac>=1.0.0\n",
            "  Downloading pystac-1.7.3-py3-none-any.whl (150 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m150.0/150.0 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pystac-client>=0.2.0\n",
            "  Downloading pystac_client-0.6.1-py3-none-any.whl (30 kB)\n",
            "Requirement already satisfied: click>=7.1 in /usr/local/lib/python3.10/dist-packages (from planetary-computer) (8.1.3)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic[dotenv]>=1.7.3->planetary-computer) (4.5.0)\n",
            "Collecting python-dotenv>=0.10.4\n",
            "  Downloading python_dotenv-1.0.0-py3-none-any.whl (19 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.7.0 in /usr/local/lib/python3.10/dist-packages (from pystac>=1.0.0->planetary-computer) (2.8.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25.1->planetary-computer) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25.1->planetary-computer) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25.1->planetary-computer) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.25.1->planetary-computer) (2.0.12)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7.0->pystac>=1.0.0->planetary-computer) (1.16.0)\n",
            "Installing collected packages: python-dotenv, pystac, pystac-client, planetary-computer\n",
            "Successfully installed planetary-computer-0.5.1 pystac-1.7.3 pystac-client-0.6.1 python-dotenv-1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Rich"
      ],
      "metadata": {
        "id": "-jVNPUZ--81q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec356c70-ee94-49fa-8762-cb588e47899a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: Rich in /usr/local/lib/python3.10/dist-packages (13.3.4)\n",
            "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from Rich) (2.2.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from Rich) (2.14.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py<3.0.0,>=2.2.0->Rich) (0.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m rich"
      ],
      "metadata": {
        "id": "89Ltq89l_C-e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d5a3147-7d74-48ae-d6b2-c7c322a91f99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[3m                                 Rich features                                  \u001b[0m\n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m   Colors   \u001b[0m\u001b[1;31m \u001b[0m✓ \u001b[1;32m4-bit color\u001b[0m                 \u001b[30;40m▄\u001b[0m\u001b[30;40m▄\u001b[0m\u001b[30;40m▄\u001b[0m\u001b[30;40m▄\u001b[0m\u001b[33;40m▄\u001b[0m\u001b[33;40m▄\u001b[0m\u001b[33;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[32;40m▄\u001b[0m\u001b[32;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[34;40m▄\u001b[0m\u001b[34;40m▄\u001b[0m\u001b[34;40m▄\u001b[0m\u001b[34;40m▄\u001b[0m\u001b[34;40m▄\u001b[0m\u001b[34;40m▄\u001b[0m\u001b[34;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[90;40m▄\u001b[0m\u001b[30;40m▄\u001b[0m\u001b[30;40m▄\u001b[0m\u001b[30;40m▄\u001b[0m\u001b[30;40m▄\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m✓ \u001b[1;34m8-bit color\u001b[0m                 \u001b[31;41m▄\u001b[0m\u001b[31;41m▄\u001b[0m\u001b[33;41m▄\u001b[0m\u001b[33;43m▄\u001b[0m\u001b[33;43m▄\u001b[0m\u001b[33;43m▄\u001b[0m\u001b[33;43m▄\u001b[0m\u001b[33;43m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[36;100m▄\u001b[0m\u001b[36;100m▄\u001b[0m\u001b[36;46m▄\u001b[0m\u001b[36;46m▄\u001b[0m\u001b[36;100m▄\u001b[0m\u001b[36;100m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[35;44m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[35;41m▄\u001b[0m\u001b[31;41m▄\u001b[0m\u001b[31;41m▄\u001b[0m\u001b[31;41m▄\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m✓ \u001b[1;35mTruecolor (16.7 million)\u001b[0m    \u001b[31;41m▄\u001b[0m\u001b[31;41m▄\u001b[0m\u001b[33;43m▄\u001b[0m\u001b[33;43m▄\u001b[0m\u001b[33;43m▄\u001b[0m\u001b[93;43m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;102m▄\u001b[0m\u001b[92;42m▄\u001b[0m\u001b[92;42m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[32;42m▄\u001b[0m\u001b[92;42m▄\u001b[0m\u001b[92;42m▄\u001b[0m\u001b[36;46m▄\u001b[0m\u001b[36;46m▄\u001b[0m\u001b[36;46m▄\u001b[0m\u001b[36;46m▄\u001b[0m\u001b[36;46m▄\u001b[0m\u001b[36;46m▄\u001b[0m\u001b[94;44m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[34;44m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[35;45m▄\u001b[0m\u001b[31;41m▄\u001b[0m\u001b[31;41m▄\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m✓ \u001b[1;33mDumb terminals\u001b[0m              \u001b[91;41m▄\u001b[0m\u001b[91;101m▄\u001b[0m\u001b[91;101m▄\u001b[0m\u001b[91;101m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[96;106m▄\u001b[0m\u001b[96;106m▄\u001b[0m\u001b[96;106m▄\u001b[0m\u001b[96;46m▄\u001b[0m\u001b[36;46m▄\u001b[0m\u001b[94;104m▄\u001b[0m\u001b[94;104m▄\u001b[0m\u001b[94;44m▄\u001b[0m\u001b[94;44m▄\u001b[0m\u001b[94;104m▄\u001b[0m\u001b[94;45m▄\u001b[0m\u001b[95;45m▄\u001b[0m\u001b[95;45m▄\u001b[0m\u001b[95;105m▄\u001b[0m\u001b[95;105m▄\u001b[0m\u001b[95;45m▄\u001b[0m\u001b[91;45m▄\u001b[0m\u001b[91;101m▄\u001b[0m\u001b[91;101m▄\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m✓ \u001b[1;36mAutomatic color conversion\u001b[0m  \u001b[91;101m▄\u001b[0m\u001b[91;101m▄\u001b[0m\u001b[37;101m▄\u001b[0m\u001b[93;101m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;103m▄\u001b[0m\u001b[93;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[92;102m▄\u001b[0m\u001b[96;102m▄\u001b[0m\u001b[96;106m▄\u001b[0m\u001b[96;106m▄\u001b[0m\u001b[96;106m▄\u001b[0m\u001b[96;106m▄\u001b[0m\u001b[96;106m▄\u001b[0m\u001b[96;106m▄\u001b[0m\u001b[37;104m▄\u001b[0m\u001b[94;104m▄\u001b[0m\u001b[94;104m▄\u001b[0m\u001b[94;104m▄\u001b[0m\u001b[94;104m▄\u001b[0m\u001b[95;104m▄\u001b[0m\u001b[95;105m▄\u001b[0m\u001b[95;105m▄\u001b[0m\u001b[95;105m▄\u001b[0m\u001b[95;105m▄\u001b[0m\u001b[95;105m▄\u001b[0m\u001b[95;101m▄\u001b[0m\u001b[91;101m▄\u001b[0m\u001b[91;101m▄\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m   Styles   \u001b[0m\u001b[1;31m \u001b[0mAll ansi styles: \u001b[1mbold\u001b[0m, \u001b[2mdim\u001b[0m, \u001b[3mitalic\u001b[0m, \u001b[4munderline\u001b[0m, \u001b[9mstrikethrough\u001b[0m,     \n",
            "\u001b[1;31m              \u001b[0m\u001b[7mreverse\u001b[0m, and even \u001b[5mblink\u001b[0m.                                          \n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
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            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "\u001b[1;31m              \u001b[0m\u001b[32mLorem ipsum    \u001b[0m \u001b[33m  Lorem ipsum  \u001b[0m \u001b[34m     Lorem ipsum\u001b[0m \u001b[31mLorem\u001b[0m\u001b[31m      \u001b[0m\u001b[31mipsum\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32mdolor sit amet,\u001b[0m \u001b[33mdolor sit amet,\u001b[0m \u001b[34m dolor sit amet,\u001b[0m \u001b[31mdolor\u001b[0m\u001b[31m \u001b[0m\u001b[31msit\u001b[0m\u001b[31m  \u001b[0m\u001b[31mamet,\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32mconsectetur    \u001b[0m \u001b[33m  consectetur  \u001b[0m \u001b[34m     consectetur\u001b[0m \u001b[31mconsectetur\u001b[0m      \n",
            "\u001b[1;31m              \u001b[0m\u001b[32madipiscing     \u001b[0m \u001b[33m  adipiscing   \u001b[0m \u001b[34madipiscing elit.\u001b[0m \u001b[31madipiscing\u001b[0m\u001b[31m \u001b[0m\u001b[31melit.\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32melit. Quisque  \u001b[0m \u001b[33m elit. Quisque \u001b[0m \u001b[34mQuisque in metus\u001b[0m \u001b[31mQuisque\u001b[0m\u001b[31m \u001b[0m\u001b[31min\u001b[0m\u001b[31m \u001b[0m\u001b[31mmetus\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32min metus sed   \u001b[0m \u001b[33m in metus sed  \u001b[0m \u001b[34m      sed sapien\u001b[0m \u001b[31msed\u001b[0m\u001b[31m       \u001b[0m\u001b[31msapien\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32msapien         \u001b[0m \u001b[33m    sapien     \u001b[0m \u001b[34m       ultricies\u001b[0m \u001b[31multricies\u001b[0m        \n",
            "\u001b[1;31m              \u001b[0m\u001b[32multricies      \u001b[0m \u001b[33m   ultricies   \u001b[0m \u001b[34m    pretium a at\u001b[0m \u001b[31mpretium\u001b[0m\u001b[31m   \u001b[0m\u001b[31ma\u001b[0m\u001b[31m   \u001b[0m\u001b[31mat\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32mpretium a at   \u001b[0m \u001b[33m pretium a at  \u001b[0m \u001b[34m justo. Maecenas\u001b[0m \u001b[31mjusto.\u001b[0m\u001b[31m  \u001b[0m\u001b[31mMaecenas\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32mjusto. Maecenas\u001b[0m \u001b[33mjusto. Maecenas\u001b[0m \u001b[34m luctus velit et\u001b[0m \u001b[31mluctus\u001b[0m\u001b[31m \u001b[0m\u001b[31mvelit\u001b[0m\u001b[31m  \u001b[0m\u001b[31met\u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32mluctus velit et\u001b[0m \u001b[33mluctus velit et\u001b[0m \u001b[34m auctor maximus.\u001b[0m \u001b[31mauctor maximus.\u001b[0m  \n",
            "\u001b[1;31m              \u001b[0m\u001b[32mauctor maximus.\u001b[0m \u001b[33mauctor maximus.\u001b[0m                                   \n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m   Asian    \u001b[0m\u001b[1;31m \u001b[0m🇨🇳  该库支持中文，日文和韩文文本！                                \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m  language  \u001b[0m\u001b[1;31m \u001b[0m🇯🇵                                                                \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m  support   \u001b[0m\u001b[1;31m \u001b[0mライブラリは中国語、日本語、韓国語のテキストをサポートしています  \n",
            "\u001b[1;31m              \u001b[0m🇰🇷  이 라이브러리는 중국어, 일본어 및 한국어 텍스트를 지원합니다  \n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m   Markup   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1;35mRich\u001b[0m supports a simple \u001b[3mbbcode\u001b[0m-like \u001b[1mmarkup\u001b[0m for \u001b[33mcolor\u001b[0m, \u001b[4mstyle\u001b[0m, and   \n",
            "\u001b[1;31m              \u001b[0memoji! 👍 🍎 🐜 🐻 🥖 🚌                                          \n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m   Tables   \u001b[0m\u001b[1;31m \u001b[0m\u001b[1m \u001b[0m\u001b[1;32mDate\u001b[0m\u001b[1m        \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1;34mTitle\u001b[0m\u001b[1m      \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1;36mProduction Budget\u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m    \u001b[0m\u001b[1;35mBox Office\u001b[0m\u001b[1m \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m───────────────────────────────────────────────────────────────── \n",
            "\u001b[1;31m              \u001b[0m\u001b[32m \u001b[0m\u001b[32mDec 20, 2019\u001b[0m\u001b[32m \u001b[0m \u001b[34m \u001b[0m\u001b[34mStar Wars: \u001b[0m\u001b[34m \u001b[0m \u001b[36m \u001b[0m\u001b[36m     $275,000,000\u001b[0m\u001b[36m \u001b[0m \u001b[35m \u001b[0m\u001b[35m  $375,126,118\u001b[0m\u001b[35m \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32m              \u001b[0m \u001b[34m \u001b[0m\u001b[34mThe Rise of\u001b[0m\u001b[34m \u001b[0m \u001b[36m                   \u001b[0m \u001b[35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32m              \u001b[0m \u001b[34m \u001b[0m\u001b[34mSkywalker  \u001b[0m\u001b[34m \u001b[0m \u001b[36m                   \u001b[0m \u001b[35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[2;32m \u001b[0m\u001b[2;32mMay 25, 2018\u001b[0m\u001b[2;32m \u001b[0m \u001b[2;34m \u001b[0m\u001b[1;2;34mSolo\u001b[0m\u001b[2;34m: A    \u001b[0m\u001b[2;34m \u001b[0m \u001b[2;36m \u001b[0m\u001b[2;36m     $275,000,000\u001b[0m\u001b[2;36m \u001b[0m \u001b[2;35m \u001b[0m\u001b[2;35m  $393,151,347\u001b[0m\u001b[2;35m \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[2;32m              \u001b[0m \u001b[2;34m \u001b[0m\u001b[2;34mStar Wars  \u001b[0m\u001b[2;34m \u001b[0m \u001b[2;36m                   \u001b[0m \u001b[2;35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[2;32m              \u001b[0m \u001b[2;34m \u001b[0m\u001b[2;34mStory      \u001b[0m\u001b[2;34m \u001b[0m \u001b[2;36m                   \u001b[0m \u001b[2;35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32m \u001b[0m\u001b[32mDec 15, 2017\u001b[0m\u001b[32m \u001b[0m \u001b[34m \u001b[0m\u001b[34mStar Wars  \u001b[0m\u001b[34m \u001b[0m \u001b[36m \u001b[0m\u001b[36m     $262,000,000\u001b[0m\u001b[36m \u001b[0m \u001b[35m \u001b[0m\u001b[1;35m$1,332,539,889\u001b[0m\u001b[35m \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32m              \u001b[0m \u001b[34m \u001b[0m\u001b[34mEp. VIII:  \u001b[0m\u001b[34m \u001b[0m \u001b[36m                   \u001b[0m \u001b[35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32m              \u001b[0m \u001b[34m \u001b[0m\u001b[34mThe Last   \u001b[0m\u001b[34m \u001b[0m \u001b[36m                   \u001b[0m \u001b[35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[32m              \u001b[0m \u001b[34m \u001b[0m\u001b[34mJedi       \u001b[0m\u001b[34m \u001b[0m \u001b[36m                   \u001b[0m \u001b[35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[2;32m \u001b[0m\u001b[2;32mMay 19, 1999\u001b[0m\u001b[2;32m \u001b[0m \u001b[2;34m \u001b[0m\u001b[2;34mStar Wars  \u001b[0m\u001b[2;34m \u001b[0m \u001b[2;36m \u001b[0m\u001b[2;36m     $115,000,000\u001b[0m\u001b[2;36m \u001b[0m \u001b[2;35m \u001b[0m\u001b[2;35m$1,027,044,677\u001b[0m\u001b[2;35m \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[2;32m              \u001b[0m \u001b[2;34m \u001b[0m\u001b[2;34mEp. \u001b[0m\u001b[1;2;34mI\u001b[0m\u001b[2;34m: \u001b[0m\u001b[2;3;34mThe \u001b[0m\u001b[2;34m \u001b[0m \u001b[2;36m                   \u001b[0m \u001b[2;35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[2;32m              \u001b[0m \u001b[2;34m \u001b[0m\u001b[2;3;34mphantom \u001b[0m\u001b[2;34m   \u001b[0m\u001b[2;34m \u001b[0m \u001b[2;36m                   \u001b[0m \u001b[2;35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m\u001b[2;32m              \u001b[0m \u001b[2;34m \u001b[0m\u001b[2;3;34mMenace\u001b[0m\u001b[2;34m     \u001b[0m\u001b[2;34m \u001b[0m \u001b[2;36m                   \u001b[0m \u001b[2;35m                \u001b[0m \n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m   Syntax   \u001b[0m\u001b[1;31m \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m 1 \u001b[0m\u001b[96;40mdef\u001b[0m\u001b[97;40m \u001b[0m\u001b[92;40miter_last\u001b[0m\u001b[97;40m(\u001b[0m\u001b[97;40mvalues\u001b[0m\u001b[97;40m:\u001b[0m\u001b[97;40m \u001b[0m\u001b[97;40mItera\u001b[0m  \u001b[1m{\u001b[0m                               \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31mhighlighting\u001b[0m\u001b[1;31m \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m 2 \u001b[0m\u001b[2;90;40m│   \u001b[0m\u001b[93;40m\"\"\"Iterate and generate\u001b[0m  \u001b[2;32m│   \u001b[0m\u001b[32m'foo'\u001b[0m: \u001b[1m[\u001b[0m                    \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m     &      \u001b[0m\u001b[1;31m \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m 3 \u001b[0m\u001b[2;90;40m│   \u001b[0m\u001b[97;40miter_values\u001b[0m\u001b[97;40m \u001b[0m\u001b[91;40m=\u001b[0m\u001b[97;40m \u001b[0m\u001b[97;40miter\u001b[0m\u001b[97;40m(\u001b[0m\u001b[97;40mvalu\u001b[0m  \u001b[2;32m│   │   \u001b[0m\u001b[1;36m3.1427\u001b[0m,                 \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m   pretty   \u001b[0m\u001b[1;31m \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m 4 \u001b[0m\u001b[2;90;40m│   \u001b[0m\u001b[96;40mtry\u001b[0m\u001b[97;40m:\u001b[0m\u001b[40m                   \u001b[0m  \u001b[2;32m│   │   \u001b[0m\u001b[1m(\u001b[0m                       \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m  printing  \u001b[0m\u001b[1;31m \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m 5 \u001b[0m\u001b[2;90;40m│   │   \u001b[0m\u001b[97;40mprevious_value\u001b[0m\u001b[97;40m \u001b[0m\u001b[91;40m=\u001b[0m\u001b[97;40m \u001b[0m\u001b[97;40mne\u001b[0m  \u001b[2;32m│   │   │   \u001b[0m\u001b[32m'Paul Atreides'\u001b[0m,    \n",
            "\u001b[1;31m              \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m 6 \u001b[0m\u001b[2;90;40m│   \u001b[0m\u001b[96;40mexcept\u001b[0m\u001b[97;40m \u001b[0m\u001b[92;40mStopIteration\u001b[0m\u001b[97;40m:\u001b[0m\u001b[40m  \u001b[0m  \u001b[2;32m│   │   │   \u001b[0m\u001b[32m'Vladimir \u001b[0m          \n",
            "\u001b[1;31m              \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m 7 \u001b[0m\u001b[2;90;40m│   │   \u001b[0m\u001b[96;40mreturn\u001b[0m\u001b[40m             \u001b[0m  \u001b[32mHarkonnen'\u001b[0m,                     \n",
            "\u001b[1;31m              \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m 8 \u001b[0m\u001b[2;90;40m│   \u001b[0m\u001b[96;40mfor\u001b[0m\u001b[97;40m \u001b[0m\u001b[97;40mvalue\u001b[0m\u001b[97;40m \u001b[0m\u001b[91;40min\u001b[0m\u001b[97;40m \u001b[0m\u001b[97;40miter_value\u001b[0m  \u001b[2;32m│   │   │   \u001b[0m\u001b[32m'Thufir Hawat'\u001b[0m      \n",
            "\u001b[1;31m              \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m 9 \u001b[0m\u001b[2;90;40m│   │   \u001b[0m\u001b[96;40myield\u001b[0m\u001b[97;40m \u001b[0m\u001b[96;40mFalse\u001b[0m\u001b[97;40m,\u001b[0m\u001b[97;40m \u001b[0m\u001b[97;40mprevio\u001b[0m  \u001b[2;32m│   │   \u001b[0m\u001b[1m)\u001b[0m                       \n",
            "\u001b[1;31m              \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m10 \u001b[0m\u001b[2;90;40m│   │   \u001b[0m\u001b[97;40mprevious_value\u001b[0m\u001b[97;40m \u001b[0m\u001b[91;40m=\u001b[0m\u001b[97;40m \u001b[0m\u001b[97;40mva\u001b[0m  \u001b[2;32m│   \u001b[0m\u001b[1m]\u001b[0m,                          \n",
            "\u001b[1;31m              \u001b[0m\u001b[40m  \u001b[0m\u001b[2;40m11 \u001b[0m\u001b[2;90;40m│   \u001b[0m\u001b[96;40myield\u001b[0m\u001b[97;40m \u001b[0m\u001b[96;40mTrue\u001b[0m\u001b[97;40m,\u001b[0m\u001b[97;40m \u001b[0m\u001b[97;40mprevious_va\u001b[0m  \u001b[2;32m│   \u001b[0m\u001b[32m'atomic'\u001b[0m: \u001b[1m(\u001b[0m                 \n",
            "\u001b[1;31m              \u001b[0m                                  \u001b[2;32m│   │   \u001b[0m\u001b[3;91mFalse\u001b[0m,                  \n",
            "\u001b[1;31m              \u001b[0m                                  \u001b[2;32m│   │   \u001b[0m\u001b[3;92mTrue\u001b[0m,                   \n",
            "\u001b[1;31m              \u001b[0m                                  \u001b[2;32m│   │   \u001b[0m\u001b[3;35mNone\u001b[0m                    \n",
            "\u001b[1;31m              \u001b[0m                                  \u001b[2;32m│   \u001b[0m\u001b[1m)\u001b[0m                           \n",
            "\u001b[1;31m              \u001b[0m                                  \u001b[1m}\u001b[0m                               \n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m  Markdown  \u001b[0m\u001b[1;31m \u001b[0m\u001b[36m# Markdown\u001b[0m                        ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ \n",
            "\u001b[1;31m              \u001b[0m                                  ┃          \u001b[1mMarkdown\u001b[0m           ┃ \n",
            "\u001b[1;31m              \u001b[0m\u001b[36mSupports much of the *markdown* \u001b[0m  ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ \n",
            "\u001b[1;31m              \u001b[0m\u001b[36m__syntax__!\u001b[0m                                                       \n",
            "\u001b[1;31m              \u001b[0m                                  Supports much of the \u001b[3mmarkdown\u001b[0m   \n",
            "\u001b[1;31m              \u001b[0m\u001b[36m- Headers\u001b[0m                         \u001b[1msyntax\u001b[0m!                         \n",
            "\u001b[1;31m              \u001b[0m\u001b[36m- Basic formatting: **bold**, \u001b[0m                                    \n",
            "\u001b[1;31m              \u001b[0m\u001b[36m*italic*, `code`\u001b[0m                  \u001b[1;33m • \u001b[0mHeaders                      \n",
            "\u001b[1;31m              \u001b[0m\u001b[36m- Block quotes\u001b[0m                    \u001b[1;33m • \u001b[0mBasic formatting: \u001b[1mbold\u001b[0m,      \n",
            "\u001b[1;31m              \u001b[0m\u001b[36m- Lists, and more...\u001b[0m              \u001b[1;33m   \u001b[0m\u001b[3mitalic\u001b[0m, \u001b[1;36;40mcode\u001b[0m                 \n",
            "\u001b[1;31m              \u001b[0m\u001b[36m    \u001b[0m                              \u001b[1;33m • \u001b[0mBlock quotes                 \n",
            "\u001b[1;31m              \u001b[0m                                  \u001b[1;33m • \u001b[0mLists, and more...           \n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "\u001b[1;31m \u001b[0m\u001b[1;31m   +more!   \u001b[0m\u001b[1;31m \u001b[0mProgress bars, columns, styled logging handler, tracebacks,       \n",
            "\u001b[1;31m              \u001b[0metc...                                                            \n",
            "\u001b[1;31m              \u001b[0m                                                                  \n",
            "rendered in 113.0ms (cold cache)\n",
            "rendered in 36.6ms (warm cache)\n",
            "\u001b[94m╭─\u001b[0m\u001b[94m───────────────────────\u001b[0m\u001b[94m \u001b[0m\u001b[1;31mThanks for trying out Rich!\u001b[0m\u001b[94m \u001b[0m\u001b[94m────────────────────────\u001b[0m\u001b[94m─╮\u001b[0m\n",
            "\u001b[94m│\u001b[0m                                                                              \u001b[94m│\u001b[0m\n",
            "\u001b[94m│\u001b[0m  We hope you enjoy using Rich!   \u001b[32mTextualize\u001b[0m\u001b[32m \u001b[0m\u001b]8;id=680611;https://github.com/textualize\u001b\\\u001b[4;34mhttps://github.com/textualize\u001b[0m\u001b]8;;\u001b\\    \u001b[94m│\u001b[0m\n",
            "\u001b[94m│\u001b[0m                                  \u001b[32m           \u001b[0m                                 \u001b[94m│\u001b[0m\n",
            "\u001b[94m│\u001b[0m  Rich is maintained with \u001b[31m❤\u001b[0m by    \u001b[32m   Twitter\u001b[0m\u001b[32m \u001b[0m\u001b]8;id=159151;https://twitter.com/willmcgugan\u001b\\\u001b[4;34mhttps://twitter.com/willmcgugan\u001b[0m\u001b]8;;\u001b\\  \u001b[94m│\u001b[0m\n",
            "\u001b[94m│\u001b[0m  \u001b]8;id=821280;https://www.textualize.io\u001b\\Textualize.io\u001b]8;;\u001b\\                                                               \u001b[94m│\u001b[0m\n",
            "\u001b[94m│\u001b[0m                                                                              \u001b[94m│\u001b[0m\n",
            "\u001b[94m│\u001b[0m  - Will McGugan                                                              \u001b[94m│\u001b[0m\n",
            "\u001b[94m│\u001b[0m                                                                              \u001b[94m│\u001b[0m\n",
            "\u001b[94m╰──────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIttq9sL-ggE",
        "outputId": "1bd1ed3b-fd18-4de3-eac0-21954b8f2233"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading the dependencies"
      ],
      "metadata": {
        "id": "KtAp-ZrP_WkQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import odc\n",
        "from odc import stac\n",
        "# Supress Warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Visualization\n",
        "import ipyleaflet\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import Image\n",
        "import seaborn as sns\n",
        "\n",
        "# Data Science\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Feature Engineering\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Machine Learning\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import f1_score, accuracy_score,classification_report,confusion_matrix\n",
        "\n",
        "# Planetary Computer Tools\n",
        "import pystac\n",
        "import pystac_client\n",
        "# import odc-stac\n",
        "from pystac_client import Client\n",
        "from pystac.extensions.eo import EOExtension as eo\n",
        "from odc.stac import stac_load\n",
        "import planetary_computer as pc\n",
        "pc.settings.set_subscription_key('8037994b42c1450e9a4daca46c196464')\n",
        "\n",
        "# Others\n",
        "import requests\n",
        "import rich.table\n",
        "from itertools import cycle\n",
        "from tqdm import tqdm\n",
        "tqdm.pandas()"
      ],
      "metadata": {
        "id": "MyRXwC8v_bSr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crop_presence_data = pd.read_csv(\"/content/drive/MyDrive/Crop_Location_Data_20221201.csv\")\n",
        "crop_presence_data.head()"
      ],
      "metadata": {
        "colab": {
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        "id": "XINEyK0AADy2",
        "outputId": "84959d16-d3be-43b0-88fa-8a68d59fdf5e"
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      "execution_count": null,
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            "text/plain": [
              "                     Latitude and Longitude Class of Land\n",
              "0   (10.323727047081501, 105.2516346045924)          Rice\n",
              "1  (10.322364360592521, 105.27843410554115)          Rice\n",
              "2  (10.321455902933202, 105.25254306225168)          Rice\n",
              "3  (10.324181275911162, 105.25118037576274)          Rice\n",
              "4  (10.324635504740822, 105.27389181724476)          Rice"
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              "      <th>Latitude and Longitude</th>\n",
              "      <th>Class of Land</th>\n",
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              "      <td>Rice</td>\n",
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              "      <td>Rice</td>\n",
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              "      <td>Rice</td>\n",
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              "      <td>Rice</td>\n",
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              "      <td>(10.324635504740822, 105.27389181724476)</td>\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_sentinel_data(latlong, time_slice, assets):\n",
        "  '''\n",
        "  Returns VV and VH values for a given latitude and longitude \n",
        "  Attributes:\n",
        "  latlong - A tuple with 2 elements - latitude and longitude\n",
        "  time_slice - Timeframe for which the VV and VH values have to be extracted\n",
        "  assets - A list of bands to be extracted\n",
        "  '''\n",
        "  latlong = latlong.replace('(','').replace(')','').replace(' ','').split(',')\n",
        "  for i in range(2):\n",
        "    latlong[i] = float(latlong[i])\n",
        "\n",
        "  time_of_interest = time_slice\n",
        "  box_size_deg = 0.0004 # Surrounding box in degrees, yields approximately 5x5 pixel region\n",
        "  min_lon = latlong[1] - box_size_deg / 2\n",
        "  min_lat = latlong[0] - box_size_deg / 2\n",
        "  max_lon = latlong[1] + box_size_deg / 2\n",
        "  max_lat = latlong[0] + box_size_deg / 2\n",
        "  bbox_of_interest = (min_lon, min_lat, max_lon, max_lat)\n",
        "\n",
        "  catalog = pystac_client.Client.open(\n",
        "      \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n",
        "  )\n",
        "  search = catalog.search(\n",
        "      collections=[\"sentinel-1-rtc\"], bbox=bbox_of_interest, datetime=time_of_interest\n",
        "  )\n",
        "  items = list(search.get_all_items())\n",
        "  bands_of_interest = assets\n",
        "  resolution = 10  # meters per pixel \n",
        "  scale = resolution / 111320.0\n",
        "  data = stac_load([items[0]], bands = bands_of_interest, patch_url=pc.sign,crs=\"EPSG:4326\",resolution=scale,bbox=bbox_of_interest)\n",
        "  vh = data[\"vh\"].astype(\"float\").values.tolist()[0][0]\n",
        "  vv = data[\"vv\"].astype(\"float\").values.tolist()[0][0]\n",
        "\n",
        "  mean = data.mean(dim=['latitude','longitude']).compute()\n",
        "  dop = (mean.vv / (mean.vv + mean.vh))\n",
        "  m = 1 - dop\n",
        "  rvi = (np.sqrt(dop))*((4*mean.vh)/(mean.vv + mean.vh))\n",
        "  return vh[0],vv[0]"
      ],
      "metadata": {
        "id": "IvRM_d5AAP9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "time_slice = '2020-03-20/2021-03-21'\n",
        "assests = ['vh','vv']\n",
        "vh_vv = []\n",
        "for coordinates in tqdm(crop_presence_data['Latitude and Longitude']):\n",
        "    vh_vv.append(get_sentinel_data(coordinates,time_slice,assests))\n",
        "vh_vv_data = pd.DataFrame(vh_vv,columns =['vh','vv'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTFoKsBWDs37",
        "outputId": "82d8adc5-0a50-4b9d-fc7d-ecd95502581c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 600/600 [06:54<00:00,  1.45it/s]\n"
          ]
        }
      ]
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      "cell_type": "code",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         vh        vv\n",
              "0  0.028296  0.112560\n",
              "1  0.051894  0.048721\n",
              "2  0.033304  0.081855\n",
              "3  0.010928  0.024132\n",
              "4  0.012968  0.015349"
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              "      <th></th>\n",
              "      <th>vh</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "def combine_two_datasets(dataset1,dataset2):\n",
        "    data = pd.concat([dataset1,dataset2], axis=1)\n",
        "    return data"
      ],
      "metadata": {
        "id": "uwAvlOK8F7Z8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crop_data = combine_two_datasets(crop_presence_data,vh_vv_data)\n",
        "crop_data.head()"
      ],
      "metadata": {
        "id": "YUoM2ZSEGE0I",
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            "text/plain": [
              "                     Latitude and Longitude Class of Land        vh        vv\n",
              "0   (10.323727047081501, 105.2516346045924)          Rice  0.028296  0.112560\n",
              "1  (10.322364360592521, 105.27843410554115)          Rice  0.051894  0.048721\n",
              "2  (10.321455902933202, 105.25254306225168)          Rice  0.033304  0.081855\n",
              "3  (10.324181275911162, 105.25118037576274)          Rice  0.010928  0.024132\n",
              "4  (10.324635504740822, 105.27389181724476)          Rice  0.012968  0.015349"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Latitude and Longitude</th>\n",
              "      <th>Class of Land</th>\n",
              "      <th>vh</th>\n",
              "      <th>vv</th>\n",
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              "      <th>0</th>\n",
              "      <td>(10.323727047081501, 105.2516346045924)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.028296</td>\n",
              "      <td>0.112560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(10.322364360592521, 105.27843410554115)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.051894</td>\n",
              "      <td>0.048721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(10.321455902933202, 105.25254306225168)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.033304</td>\n",
              "      <td>0.081855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(10.324181275911162, 105.25118037576274)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.010928</td>\n",
              "      <td>0.024132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(10.324635504740822, 105.27389181724476)</td>\n",
              "      <td>Rice</td>\n",
              "      <td>0.012968</td>\n",
              "      <td>0.015349</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5712532-0d36-4e41-a0d4-32872a44a007')\"\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-f5712532-0d36-4e41-a0d4-32872a44a007 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-f5712532-0d36-4e41-a0d4-32872a44a007');\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": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MODEL"
      ],
      "metadata": {
        "id": "2hJFEzOJNNdH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.decomposition import PCA\n",
        "X = crop_data.drop(columns=['Class of Land']).values\n",
        "y = crop_data ['Class of Land'].values\n",
        "crop_data = crop_data[['vh','vv','Class of Land']]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,stratify=y,random_state=40)\n",
        "\n",
        "# poly = PolynomialFeatures(degree = 3, interaction_only=True)\n",
        "# X_train = poly.fit_transform(X_train)\n",
        "# X_test = poly.fit_transform(X_test)\n",
        "\n",
        "sc = RobustScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n"
      ],
      "metadata": {
        "id": "TyU4xzEMNSRG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TRAINING"
      ],
      "metadata": {
        "id": "0PBxY0MxadZD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
        "model = GradientBoostingClassifier(n_estimators=150, learning_rate=0.1, max_depth=1, random_state=0)\n",
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "f51jJzDTahlM",
        "outputId": "315ceb9b-4db4-4b1f-a1a8-3366578e573e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(max_depth=1, n_estimators=150, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(max_depth=1, n_estimators=150, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(max_depth=1, n_estimators=150, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insample_predictions = model.predict(X_train)\n",
        "print(\"Insample Accuracy {0:.2f}%\".format(100*accuracy_score(insample_predictions,y_train)))\n",
        "print(classification_report(insample_predictions,y_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_ta4hk3eBFX",
        "outputId": "4e68e592-1f78-42ae-d0d1-5f556b956761"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Insample Accuracy 81.67%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.84      0.80      0.82       221\n",
            "        Rice       0.79      0.83      0.81       199\n",
            "\n",
            "    accuracy                           0.82       420\n",
            "   macro avg       0.82      0.82      0.82       420\n",
            "weighted avg       0.82      0.82      0.82       420\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(true_value,predicted_value,title,labels):\n",
        "    \n",
        "    '''Plots a confusion matrix.\n",
        "    Attributes:\n",
        "    true_value - The ground truth value for comparision.\n",
        "    predicted_value - The values predicted by the model.\n",
        "    title - Title of the plot.\n",
        "    labels - The x and y labels of the plot.'''\n",
        "   \n",
        "    cm = confusion_matrix(true_value,predicted_value)\n",
        "    ax= plt.subplot()\n",
        "    sns.heatmap(cm, annot=True, fmt='g', ax=ax, cmap='Blues');\n",
        "    ax.set_xlabel('Predicted labels');\n",
        "    ax.set_ylabel('True labels'); \n",
        "    ax.set_title(title); \n",
        "    ax.xaxis.set_ticklabels(labels); \n",
        "    ax.yaxis.set_ticklabels(labels);"
      ],
      "metadata": {
        "id": "mpnbEVVkeLeh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_train,insample_predictions,\"Model Level 1: Gradient Boosting Method In-Sample Results\",['Rice', 'Non Rice'])"
      ],
      "metadata": {
        "id": "RiuniJOeeUmY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TESTING"
      ],
      "metadata": {
        "id": "eFQh8HucerGC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outsample_predictions = model.predict(X_test)\n",
        "print(\"Accuracy {0:.2f}%\".format(100*accuracy_score(outsample_predictions, y_test)))\n",
        "print(classification_report(y_test, outsample_predictions))\n",
        "#print(outsample_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gRNO5aJeuvq",
        "outputId": "b861ac26-603e-45f2-dfa7-e6ee7fb4e562"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 88.89%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.88      0.90      0.89        90\n",
            "        Rice       0.90      0.88      0.89        90\n",
            "\n",
            "    accuracy                           0.89       180\n",
            "   macro avg       0.89      0.89      0.89       180\n",
            "weighted avg       0.89      0.89      0.89       180\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_test, outsample_predictions,\"Model Level 1: Gradient Boosting Method Out-Sample Results\",['Rice', 'Non Rice'])"
      ],
      "metadata": {
        "id": "G_tqYpTaeyWx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "1f66b683-caf8-4d62-913a-c09f3b58079b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN - TRAINING"
      ],
      "metadata": {
        "id": "oOwDoCsDrDeb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''from sklearn.neighbors import KNeighborsClassifier'''"
      ],
      "metadata": {
        "id": "Aq7JbtyXrI9H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''pipeline = Pipeline([\n",
        "    ('scaler', RobustScaler()),\n",
        "    ('knn', KNeighborsClassifier())\n",
        "])\n",
        "k = 25  \n",
        "pipeline.set_params(knn__n_neighbors=k)\n",
        "pipeline.fit(X_train, y_train)\n",
        "insample_prediction2 = pipeline.predict(X_train)\n",
        "print(\"Accuracy {0:.2f}%\".format(100*accuracy_score(insample_prediction2, y_train)))\n",
        "print(classification_report(insample_prediction2,y_train))'''"
      ],
      "metadata": {
        "id": "dJV9SfenreQc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN - TESTING"
      ],
      "metadata": {
        "id": "FSbY_lxPtHb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''outsample_prediction2 = pipeline.predict(X_test)\n",
        "print(\"Accuracy {0:.2f}%\".format(100*accuracy_score(outsample_prediction2, y_test)))\n",
        "print(classification_report(outsample_prediction2,y_test))'''"
      ],
      "metadata": {
        "id": "ojH0NbjwtLNR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SELECTING DIFFERENT MODELS"
      ],
      "metadata": {
        "id": "a042M7zfYv-E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import all_estimators\n",
        "def getClassifiers(classifier):\n",
        "    classifierModels = []\n",
        "    for name, ClassifierClass in all_estimators(type_filter=classifier):\n",
        "        try:\n",
        "            classifier = ClassifierClass()\n",
        "            classifierModels.append((name, classifier))\n",
        "        except Exception as e:\n",
        "            pass\n",
        "    return classifierModels\n",
        "\n",
        "classifierModels = getClassifiers('classifier')\n",
        "print(len(classifierModels))\n",
        "model1 = classifierModels[: 10]\n",
        "model2 = classifierModels[10: 20]\n",
        "model3 = classifierModels[20: 30]\n",
        "model4 = classifierModels[30: 34]"
      ],
      "metadata": {
        "id": "kBIn1RU2Lezk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d654eb5-12d5-43eb-9779-46c326c9d3c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, r2_score\n",
        "\n",
        "def evaluate(model, XTrain, yTrain, XTest, yTest):\n",
        "    model.fit(XTrain, yTrain)\n",
        "    yPred = model.predict(XTest)\n",
        "    accuracy = accuracy_score(yTest, yPred)\n",
        "    # f1 = f1_score(yTest, yPred)\n",
        "    print(f'Model Name = {type(model).__name__}, accuracy score = {accuracy}')\n",
        "    print(classification_report(y_test, yPred))"
      ],
      "metadata": {
        "id": "-W_fB-AATMyQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tryModels(models):\n",
        "    for model in models:\n",
        "        try:\n",
        "            print(f'Running {type(model[1]).__name__}')\n",
        "            ans = evaluate(model[1], X_train, y_train, X_test, y_test)\n",
        "            # print(classification_report(y_test, outsample_predictions))\n",
        "            print(ans)\n",
        "        except Exception as e:\n",
        "            pass"
      ],
      "metadata": {
        "id": "iTnc7HuMTfwK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tryModels(model1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KA-VpONUJyg",
        "outputId": "b30b024c-e07c-4f47-a963-51b76eb7f51a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running AdaBoostClassifier\n",
            "Model Name = AdaBoostClassifier, accuracy score = 0.8166666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.80      0.84      0.82        90\n",
            "        Rice       0.84      0.79      0.81        90\n",
            "\n",
            "    accuracy                           0.82       180\n",
            "   macro avg       0.82      0.82      0.82       180\n",
            "weighted avg       0.82      0.82      0.82       180\n",
            "\n",
            "None\n",
            "Running BaggingClassifier\n",
            "Model Name = BaggingClassifier, accuracy score = 0.8555555555555555\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.83      0.89      0.86        90\n",
            "        Rice       0.88      0.82      0.85        90\n",
            "\n",
            "    accuracy                           0.86       180\n",
            "   macro avg       0.86      0.86      0.86       180\n",
            "weighted avg       0.86      0.86      0.86       180\n",
            "\n",
            "None\n",
            "Running BernoulliNB\n",
            "Model Name = BernoulliNB, accuracy score = 0.6722222222222223\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.70      0.61      0.65        90\n",
            "        Rice       0.65      0.73      0.69        90\n",
            "\n",
            "    accuracy                           0.67       180\n",
            "   macro avg       0.67      0.67      0.67       180\n",
            "weighted avg       0.67      0.67      0.67       180\n",
            "\n",
            "None\n",
            "Running CalibratedClassifierCV\n",
            "Model Name = CalibratedClassifierCV, accuracy score = 0.6444444444444445\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.69      0.53      0.60        90\n",
            "        Rice       0.62      0.76      0.68        90\n",
            "\n",
            "    accuracy                           0.64       180\n",
            "   macro avg       0.65      0.64      0.64       180\n",
            "weighted avg       0.65      0.64      0.64       180\n",
            "\n",
            "None\n",
            "Running CategoricalNB\n",
            "Running ComplementNB\n",
            "Running DecisionTreeClassifier\n",
            "Model Name = DecisionTreeClassifier, accuracy score = 0.7555555555555555\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.76      0.74      0.75        90\n",
            "        Rice       0.75      0.77      0.76        90\n",
            "\n",
            "    accuracy                           0.76       180\n",
            "   macro avg       0.76      0.76      0.76       180\n",
            "weighted avg       0.76      0.76      0.76       180\n",
            "\n",
            "None\n",
            "Running DummyClassifier\n",
            "Model Name = DummyClassifier, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.50      1.00      0.67        90\n",
            "        Rice       0.00      0.00      0.00        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n",
            "Running ExtraTreeClassifier\n",
            "Model Name = ExtraTreeClassifier, accuracy score = 0.8222222222222222\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.80      0.87      0.83        90\n",
            "        Rice       0.85      0.78      0.81        90\n",
            "\n",
            "    accuracy                           0.82       180\n",
            "   macro avg       0.82      0.82      0.82       180\n",
            "weighted avg       0.82      0.82      0.82       180\n",
            "\n",
            "None\n",
            "Running ExtraTreesClassifier\n",
            "Model Name = ExtraTreesClassifier, accuracy score = 0.8611111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.86      0.87      0.86        90\n",
            "        Rice       0.87      0.86      0.86        90\n",
            "\n",
            "    accuracy                           0.86       180\n",
            "   macro avg       0.86      0.86      0.86       180\n",
            "weighted avg       0.86      0.86      0.86       180\n",
            "\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tryModels(model2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHxYSdHMURMa",
        "outputId": "3e048c24-7324-4295-f9ff-b235348318e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running GaussianNB\n",
            "Model Name = GaussianNB, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.00      0.00      0.00        90\n",
            "        Rice       0.50      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n",
            "Running GaussianProcessClassifier\n",
            "Model Name = GaussianProcessClassifier, accuracy score = 0.9\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.91      0.89      0.90        90\n",
            "        Rice       0.89      0.91      0.90        90\n",
            "\n",
            "    accuracy                           0.90       180\n",
            "   macro avg       0.90      0.90      0.90       180\n",
            "weighted avg       0.90      0.90      0.90       180\n",
            "\n",
            "None\n",
            "Running GradientBoostingClassifier\n",
            "Model Name = GradientBoostingClassifier, accuracy score = 0.85\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.84      0.87      0.85        90\n",
            "        Rice       0.86      0.83      0.85        90\n",
            "\n",
            "    accuracy                           0.85       180\n",
            "   macro avg       0.85      0.85      0.85       180\n",
            "weighted avg       0.85      0.85      0.85       180\n",
            "\n",
            "None\n",
            "Running HistGradientBoostingClassifier\n",
            "Model Name = HistGradientBoostingClassifier, accuracy score = 0.8388888888888889\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.81      0.89      0.85        90\n",
            "        Rice       0.88      0.79      0.83        90\n",
            "\n",
            "    accuracy                           0.84       180\n",
            "   macro avg       0.84      0.84      0.84       180\n",
            "weighted avg       0.84      0.84      0.84       180\n",
            "\n",
            "None\n",
            "Running KNeighborsClassifier\n",
            "Model Name = KNeighborsClassifier, accuracy score = 0.8777777777777778\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.85      0.91      0.88        90\n",
            "        Rice       0.90      0.84      0.87        90\n",
            "\n",
            "    accuracy                           0.88       180\n",
            "   macro avg       0.88      0.88      0.88       180\n",
            "weighted avg       0.88      0.88      0.88       180\n",
            "\n",
            "None\n",
            "Running LabelPropagation\n",
            "Model Name = LabelPropagation, accuracy score = 0.8611111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.85      0.88      0.86        90\n",
            "        Rice       0.87      0.84      0.86        90\n",
            "\n",
            "    accuracy                           0.86       180\n",
            "   macro avg       0.86      0.86      0.86       180\n",
            "weighted avg       0.86      0.86      0.86       180\n",
            "\n",
            "None\n",
            "Running LabelSpreading\n",
            "Model Name = LabelSpreading, accuracy score = 0.8555555555555555\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.84      0.88      0.86        90\n",
            "        Rice       0.87      0.83      0.85        90\n",
            "\n",
            "    accuracy                           0.86       180\n",
            "   macro avg       0.86      0.86      0.86       180\n",
            "weighted avg       0.86      0.86      0.86       180\n",
            "\n",
            "None\n",
            "Running LinearDiscriminantAnalysis\n",
            "Model Name = LinearDiscriminantAnalysis, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.00      0.00      0.00        90\n",
            "        Rice       0.50      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n",
            "Running LinearSVC\n",
            "Model Name = LinearSVC, accuracy score = 0.6166666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.66      0.49      0.56        90\n",
            "        Rice       0.59      0.74      0.66        90\n",
            "\n",
            "    accuracy                           0.62       180\n",
            "   macro avg       0.62      0.62      0.61       180\n",
            "weighted avg       0.62      0.62      0.61       180\n",
            "\n",
            "None\n",
            "Running LogisticRegression\n",
            "Model Name = LogisticRegression, accuracy score = 0.6111111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.65      0.49      0.56        90\n",
            "        Rice       0.59      0.73      0.65        90\n",
            "\n",
            "    accuracy                           0.61       180\n",
            "   macro avg       0.62      0.61      0.61       180\n",
            "weighted avg       0.62      0.61      0.61       180\n",
            "\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tryModels(model3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Thwntd5CUVA1",
        "outputId": "f44c89fb-c9cf-4491-ed6b-00d137ad317a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running LogisticRegressionCV\n",
            "Model Name = LogisticRegressionCV, accuracy score = 0.6111111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.65      0.49      0.56        90\n",
            "        Rice       0.59      0.73      0.65        90\n",
            "\n",
            "    accuracy                           0.61       180\n",
            "   macro avg       0.62      0.61      0.61       180\n",
            "weighted avg       0.62      0.61      0.61       180\n",
            "\n",
            "None\n",
            "Running MLPClassifier\n",
            "Model Name = MLPClassifier, accuracy score = 0.7888888888888889\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.87      0.68      0.76        90\n",
            "        Rice       0.74      0.90      0.81        90\n",
            "\n",
            "    accuracy                           0.79       180\n",
            "   macro avg       0.80      0.79      0.79       180\n",
            "weighted avg       0.80      0.79      0.79       180\n",
            "\n",
            "None\n",
            "Running MultinomialNB\n",
            "Running NearestCentroid\n",
            "Model Name = NearestCentroid, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.00      0.00      0.00        90\n",
            "        Rice       0.50      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n",
            "Running NuSVC\n",
            "Running PassiveAggressiveClassifier\n",
            "Model Name = PassiveAggressiveClassifier, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.50      0.91      0.65        90\n",
            "        Rice       0.50      0.09      0.15        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.50      0.50      0.40       180\n",
            "weighted avg       0.50      0.50      0.40       180\n",
            "\n",
            "None\n",
            "Running Perceptron\n",
            "Model Name = Perceptron, accuracy score = 0.34444444444444444\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.36      0.39      0.37        90\n",
            "        Rice       0.33      0.30      0.31        90\n",
            "\n",
            "    accuracy                           0.34       180\n",
            "   macro avg       0.34      0.34      0.34       180\n",
            "weighted avg       0.34      0.34      0.34       180\n",
            "\n",
            "None\n",
            "Running QuadraticDiscriminantAnalysis\n",
            "Model Name = QuadraticDiscriminantAnalysis, accuracy score = 0.5166666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       1.00      0.03      0.06        90\n",
            "        Rice       0.51      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.52       180\n",
            "   macro avg       0.75      0.52      0.37       180\n",
            "weighted avg       0.75      0.52      0.37       180\n",
            "\n",
            "None\n",
            "Running RadiusNeighborsClassifier\n",
            "Running RandomForestClassifier\n",
            "Model Name = RandomForestClassifier, accuracy score = 0.8611111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.84      0.90      0.87        90\n",
            "        Rice       0.89      0.82      0.86        90\n",
            "\n",
            "    accuracy                           0.86       180\n",
            "   macro avg       0.86      0.86      0.86       180\n",
            "weighted avg       0.86      0.86      0.86       180\n",
            "\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tryModels(model4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yWzsqc9XUYbt",
        "outputId": "f0d2cfe6-1e9a-4067-b845-1eff23cb5c8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running RidgeClassifier\n",
            "Model Name = RidgeClassifier, accuracy score = 0.6111111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.65      0.49      0.56        90\n",
            "        Rice       0.59      0.73      0.65        90\n",
            "\n",
            "    accuracy                           0.61       180\n",
            "   macro avg       0.62      0.61      0.61       180\n",
            "weighted avg       0.62      0.61      0.61       180\n",
            "\n",
            "None\n",
            "Running RidgeClassifierCV\n",
            "Model Name = RidgeClassifierCV, accuracy score = 0.6111111111111112\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.65      0.49      0.56        90\n",
            "        Rice       0.59      0.73      0.65        90\n",
            "\n",
            "    accuracy                           0.61       180\n",
            "   macro avg       0.62      0.61      0.61       180\n",
            "weighted avg       0.62      0.61      0.61       180\n",
            "\n",
            "None\n",
            "Running SGDClassifier\n",
            "Model Name = SGDClassifier, accuracy score = 0.3277777777777778\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.35      0.40      0.37        90\n",
            "        Rice       0.30      0.26      0.28        90\n",
            "\n",
            "    accuracy                           0.33       180\n",
            "   macro avg       0.32      0.33      0.32       180\n",
            "weighted avg       0.32      0.33      0.32       180\n",
            "\n",
            "None\n",
            "Running SVC\n",
            "Model Name = SVC, accuracy score = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.00      0.00      0.00        90\n",
            "        Rice       0.50      1.00      0.67        90\n",
            "\n",
            "    accuracy                           0.50       180\n",
            "   macro avg       0.25      0.50      0.33       180\n",
            "weighted avg       0.25      0.50      0.33       180\n",
            "\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.semi_supervised import LabelPropagation\n",
        "model0 = LabelPropagation(kernel='knn', n_neighbors=7, max_iter=1000)\n",
        "model0.fit(XTrain,yTrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "lfswx3O7VB95",
        "outputId": "116645df-0637-4f1d-929f-5deed4ee6626"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelPropagation(kernel='knn')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelPropagation(kernel=&#x27;knn&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelPropagation</label><div class=\"sk-toggleable__content\"><pre>LabelPropagation(kernel=&#x27;knn&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "insample_predictions0 = model0.predict(XTest)\n",
        "print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(insample_predictions0,yTest)))\n",
        "print(classification_report(insample_predictions0,yTest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66vb5TrtWCm4",
        "outputId": "097177a4-316b-434e-801d-880aefeccfcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outsample Accuracy 88.33%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.92      0.86      0.89        97\n",
            "        Rice       0.84      0.92      0.88        83\n",
            "\n",
            "    accuracy                           0.88       180\n",
            "   macro avg       0.88      0.89      0.88       180\n",
            "weighted avg       0.89      0.88      0.88       180\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf_model = RandomForestClassifier(n_estimators=150, max_depth=1, random_state=0)\n",
        "rf_model.fit(XTrain, yTrain)\n",
        "predicted_labels2 = rf_model.predict(XTest)\n",
        "print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(predicted_labels2,yTest)))\n",
        "print(classification_report(predicted_labels2,yTest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggu7MyEMXQDF",
        "outputId": "015f16f3-ffbb-4c4b-cd31-4d89d3d653cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outsample Accuracy 82.22%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.74      0.88      0.81        76\n",
            "        Rice       0.90      0.78      0.84       104\n",
            "\n",
            "    accuracy                           0.82       180\n",
            "   macro avg       0.82      0.83      0.82       180\n",
            "weighted avg       0.83      0.82      0.82       180\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn_model = KNeighborsClassifier(n_neighbors=7)\n",
        "knn_model.fit(XTrain, yTrain)\n",
        "predicted_labels3 = knn_model.predict(XTest)\n",
        "print(\"Outsample Accuracy {0:.2f}%\".format(100*accuracy_score(predicted_labels3,yTest)))\n",
        "print(classification_report(predicted_labels3,yTest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylTfArA3YKNN",
        "outputId": "868879de-61cb-4dc1-8855-8d067b795b7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outsample Accuracy 88.33%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Non Rice       0.92      0.86      0.89        97\n",
            "        Rice       0.84      0.92      0.88        83\n",
            "\n",
            "    accuracy                           0.88       180\n",
            "   macro avg       0.88      0.89      0.88       180\n",
            "weighted avg       0.89      0.88      0.88       180\n",
            "\n"
          ]
        }
      ]
    }
  ]
}