date29.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled8.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOcaJwIxEuqd1hEPOiBYnWM",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/freezer2019/Analytics/blob/main/date29.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QK9zZdipSkOm",
        "outputId": "5d0d1906-9012-4502-dc46-0bbe0b963a00"
      },
      "source": [
        "pip install hyperopt==0.2.3"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting hyperopt==0.2.3\n",
            "  Downloading hyperopt-0.2.3-py3-none-any.whl (1.9 MB)\n",
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            "Collecting networkx==2.2\n",
            "  Downloading networkx-2.2.zip (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 49.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3) (1.3.0)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx==2.2->hyperopt==0.2.3) (4.4.2)\n",
            "Building wheels for collected packages: networkx\n",
            "  Building wheel for networkx (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for networkx: filename=networkx-2.2-py2.py3-none-any.whl size=1526921 sha256=ca80d1971f4a9956d1990bed7c079458eb4336773ae23ebb469f7d499f2e25c9\n",
            "  Stored in directory: /root/.cache/pip/wheels/49/fb/7f/02c31ca537b34e1073844b733832e4c3a94071d8edda2c0faa\n",
            "Successfully built networkx\n",
            "Installing collected packages: networkx, hyperopt\n",
            "  Attempting uninstall: networkx\n",
            "    Found existing installation: networkx 2.6.3\n",
            "    Uninstalling networkx-2.6.3:\n",
            "      Successfully uninstalled networkx-2.6.3\n",
            "  Attempting uninstall: hyperopt\n",
            "    Found existing installation: hyperopt 0.1.2\n",
            "    Uninstalling hyperopt-0.1.2:\n",
            "      Successfully uninstalled hyperopt-0.1.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed hyperopt-0.2.3 networkx-2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "paKWAKKpxISe",
        "outputId": "9a174bc1-fa6a-4e09-a35a-d6bef60feba8"
      },
      "source": [
        "!pip install geemap"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->geemap) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->geemap) (1.3.2)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.6.0->ipyleaflet>=0.14.0->geemap) (0.8.4)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.6.0->ipyleaflet>=0.14.0->geemap) (1.5.0)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.6.0->ipyleaflet>=0.14.0->geemap) (0.3)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.6.0->ipyleaflet>=0.14.0->geemap) (0.5.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.6.0->ipyleaflet>=0.14.0->geemap) (0.7.1)\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.6.0->ipyleaflet>=0.14.0->geemap) (0.5.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from sankee->geemap) (4.4.1)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->sankee->geemap) (1.3.3)\n",
            "Collecting jupyter-server<2.0.0,>=0.3.0\n",
            "  Downloading jupyter_server-1.11.0-py3-none-any.whl (393 kB)\n",
            "\u001b[K     |████████████████████████████████| 393 kB 57.4 MB/s \n",
            "\u001b[?25hCollecting jupyter-client\n",
            "  Downloading jupyter_client-6.1.12-py3-none-any.whl (112 kB)\n",
            "\u001b[K     |████████████████████████████████| 112 kB 54.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: nbclient<0.6,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from voila->geemap) (0.5.4)\n",
            "Collecting nbconvert\n",
            "  Downloading nbconvert-6.2.0-py3-none-any.whl (553 kB)\n",
            "\u001b[K     |████████████████████████████████| 553 kB 40.1 MB/s \n",
            "\u001b[?25hCollecting anyio<4,>=3.1.0\n",
            "  Downloading anyio-3.3.2-py3-none-any.whl (78 kB)\n",
            "\u001b[K     |████████████████████████████████| 78 kB 5.7 MB/s \n",
            "\u001b[?25hCollecting requests-unixsocket\n",
            "  Downloading requests_unixsocket-0.2.0-py2.py3-none-any.whl (11 kB)\n",
            "Collecting tornado>=4.0\n",
            "  Downloading tornado-6.1-cp37-cp37m-manylinux2010_x86_64.whl (428 kB)\n",
            "\u001b[K     |████████████████████████████████| 428 kB 49.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: argon2-cffi in /usr/local/lib/python3.7/dist-packages (from jupyter-server<2.0.0,>=0.3.0->voila->geemap) (21.1.0)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.7/dist-packages (from jupyter-server<2.0.0,>=0.3.0->voila->geemap) (0.11.0)\n",
            "Collecting websocket-client\n",
            "  Downloading websocket_client-1.2.1-py2.py3-none-any.whl (52 kB)\n",
            "\u001b[K     |████████████████████████████████| 52 kB 1.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from anyio<4,>=3.1.0->jupyter-server<2.0.0,>=0.3.0->voila->geemap) (3.7.4.3)\n",
            "Collecting sniffio>=1.1\n",
            "  Downloading sniffio-1.2.0-py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.7/dist-packages (from nbclient<0.6,>=0.4.0->voila->geemap) (1.5.1)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets<8,>=7.6.0->ipyleaflet>=0.14.0->geemap) (0.1.2)\n",
            "Requirement already satisfied: cffi>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from argon2-cffi->jupyter-server<2.0.0,>=0.3.0->voila->geemap) (1.14.6)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0.0->argon2-cffi->jupyter-server<2.0.0,>=0.3.0->voila->geemap) (2.20)\n",
            "Building wheels for collected packages: pyshp, ipynb-py-convert, pycrs\n",
            "  Building wheel for pyshp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyshp: filename=pyshp-2.1.3-py3-none-any.whl size=37325 sha256=32f38e29f54b457aed1e7aab8c3fd3baba2e6bcaf8e9ee722bf63cddbd111134\n",
            "  Stored in directory: /root/.cache/pip/wheels/43/f8/87/53c8cd41545ba20e536ea29a8fcb5431b5f477ca50d5dffbbe\n",
            "  Building wheel for ipynb-py-convert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ipynb-py-convert: filename=ipynb_py_convert-0.4.6-py3-none-any.whl size=4639 sha256=269d5834319573a01dac746e6b6256864bdc1c20d35475d31490b6b14b2d3d97\n",
            "  Stored in directory: /root/.cache/pip/wheels/af/31/a9/761b134adbbca3c92d491eff3bad785c0a0c0079695d6f0504\n",
            "  Building wheel for pycrs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pycrs: filename=PyCRS-1.0.2-py3-none-any.whl size=32704 sha256=15a4262afadf6961a222be1e75611caacf03f50327fdc5c1c48817a3d7874b1d\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/ce/32/1ec0aba6b9770681a423e82f0274c57d09ad2c20c2864901f9\n",
            "Successfully built pyshp ipynb-py-convert pycrs\n",
            "Installing collected packages: tornado, jupyter-client, nbconvert, sniffio, websocket-client, soupsieve, requests-unixsocket, anyio, whitebox, traittypes, ratelim, logzero, jupyter-server, ipytree, ipyfilechooser, beautifulsoup4, whiteboxgui, voila, sankee, python-box, pyshp, pycrs, mss, ipynb-py-convert, ipyleaflet, ipyevents, geojson, geocoder, geeadd, folium, ffmpeg-python, colour, bqplot, geemap\n",
            "  Attempting uninstall: tornado\n",
            "    Found existing installation: tornado 5.1.1\n",
            "    Uninstalling tornado-5.1.1:\n",
            "      Successfully uninstalled tornado-5.1.1\n",
            "  Attempting uninstall: jupyter-client\n",
            "    Found existing installation: jupyter-client 5.3.5\n",
            "    Uninstalling jupyter-client-5.3.5:\n",
            "      Successfully uninstalled jupyter-client-5.3.5\n",
            "  Attempting uninstall: nbconvert\n",
            "    Found existing installation: nbconvert 5.6.1\n",
            "    Uninstalling nbconvert-5.6.1:\n",
            "      Successfully uninstalled nbconvert-5.6.1\n",
            "  Attempting uninstall: beautifulsoup4\n",
            "    Found existing installation: beautifulsoup4 4.6.3\n",
            "    Uninstalling beautifulsoup4-4.6.3:\n",
            "      Successfully uninstalled beautifulsoup4-4.6.3\n",
            "  Attempting uninstall: folium\n",
            "    Found existing installation: folium 0.8.3\n",
            "    Uninstalling folium-0.8.3:\n",
            "      Successfully uninstalled folium-0.8.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0; python_version >= \"3.0\", but you have tornado 6.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.12.1 which is incompatible.\u001b[0m\n",
            "Successfully installed anyio-3.3.2 beautifulsoup4-4.10.0 bqplot-0.12.30 colour-0.1.5 ffmpeg-python-0.2.0 folium-0.12.1 geeadd-0.5.5 geemap-0.9.1 geocoder-1.38.1 geojson-2.5.0 ipyevents-2.0.1 ipyfilechooser-0.6.0 ipyleaflet-0.14.0 ipynb-py-convert-0.4.6 ipytree-0.2.1 jupyter-client-6.1.12 jupyter-server-1.11.0 logzero-1.7.0 mss-6.1.0 nbconvert-6.2.0 pycrs-1.0.2 pyshp-2.1.3 python-box-5.4.1 ratelim-0.1.6 requests-unixsocket-0.2.0 sankee-0.0.5 sniffio-1.2.0 soupsieve-2.2.1 tornado-6.1 traittypes-0.2.1 voila-0.2.15 websocket-client-1.2.1 whitebox-2.0.3 whiteboxgui-0.6.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "jupyter_client",
                  "tornado"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1sEhVgAw-85"
      },
      "source": [
        "import ee\n",
        "import geemap\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "import datetime as dt\n",
        "from numpy import log as ln\n",
        "from sklearn.model_selection import train_test_split\n",
        "from google.colab import drive "
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YpTSehm2xC8J",
        "outputId": "1cb3f519-ae61-4c90-d2ce-4116364fd5aa"
      },
      "source": [
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSWeHdaKykuY"
      },
      "source": [
        "capteurdata11 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/mars.csv\", sep=\";\")\n",
        "capteurdata12 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/mai.csv\", sep=\";\")\n",
        "capteurdata13 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/juin.csv\", sep=\";\")\n",
        "capteurdata14 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/juillet.csv\", sep=\";\")\n",
        "capteurdata15 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/aout.csv\", sep=\";\")\n",
        "capteurdata16 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/septembre.csv\", sep=\";\")\n",
        "capteurdata17 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/octobre.csv\", sep=\";\")\n",
        "capteurdata18 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/novembre.csv\", sep=\";\")\n",
        "capteurdata19 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/decembre.csv\", sep=\";\")\n",
        "capteurdata110 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/janvier2021.csv\", sep=\";\")\n",
        "capteurdata111 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/fevrier2021.csv\", sep=\";\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpyylwmyyy8N"
      },
      "source": [
        "capteurdata21 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/mars.csv\", sep=\";\")\n",
        "capteurdata22 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/mai.csv\", sep=\";\")\n",
        "capteurdata23 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/juin.csv\", sep=\";\")\n",
        "capteurdata24 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/juillet.csv\", sep=\";\")\n",
        "capteurdata25 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/aout.csv\", sep=\";\")\n",
        "capteurdata26 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/septembre.csv\", sep=\";\")\n",
        "capteurdata27 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/octobre.csv\", sep=\";\")\n",
        "capteurdata28 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/novembre.csv\", sep=\";\")\n",
        "capteurdata29 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/decembre.csv\", sep=\";\")\n",
        "capteurdata210 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/janvier2021.csv\", sep=\";\")\n",
        "capteurdata211 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/fevrier2021.csv\", sep=\";\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3okha5K1y4mW"
      },
      "source": [
        "capteurdata31 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/mars.csv\", sep=\";\")\n",
        "capteurdata32 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/mai.csv\", sep=\";\")\n",
        "capteurdata33 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/juin.csv\", sep=\";\")\n",
        "capteurdata34 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/juillet.csv\", sep=\";\")\n",
        "capteurdata35 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/aout.csv\", sep=\";\")\n",
        "capteurdata36 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/septembre.csv\", sep=\";\")\n",
        "capteurdata37 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/octobre.csv\", sep=\";\")\n",
        "capteurdata38 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/novembre.csv\", sep=\";\")\n",
        "capteurdata39 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/decembre.csv\", sep=\";\")\n",
        "capteurdata310 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/janvier2021.csv\", sep=\";\")\n",
        "capteurdata311 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/fevrier2021.csv\", sep=\";\")"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1s2qVa9y9Aw"
      },
      "source": [
        "datasetcapteursite1 = pd.concat([capteurdata11,capteurdata12, capteurdata13, capteurdata14, capteurdata15, capteurdata16, \n",
        "                            capteurdata17, capteurdata18, capteurdata19, capteurdata110, capteurdata111],ignore_index=True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9yVagiey_ur"
      },
      "source": [
        "datasetcapteursite2 = pd.concat([capteurdata21,capteurdata22, capteurdata23, capteurdata24, capteurdata25, capteurdata26, \n",
        "                            capteurdata27, capteurdata28, capteurdata29, capteurdata210, capteurdata211],ignore_index=True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItnY-q50zB2R"
      },
      "source": [
        "datasetcapteursite3 = pd.concat([capteurdata31,capteurdata32, capteurdata33, capteurdata34, capteurdata35, capteurdata36, \n",
        "                            capteurdata37, capteurdata38, capteurdata39, capteurdata310, capteurdata311],ignore_index=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMdmKtwzzOo4"
      },
      "source": [
        "landsatdatasite1 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/indexes_calcul/datasetsite1Landsat8.csv\")\n",
        "landsatdatasite2 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/indexes_calcul/datasetsite2Landsat8.csv\")\n",
        "landsatdatasite3 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/indexes_calcul/datasetsite3Landsat8.csv\")"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8KTg65bOKHG"
      },
      "source": [
        "sentineldatasite1 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site1/indexes_calculsentinel/dataset1sentinel.csv\")\n",
        "sentineldatasite2 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site2/indexes_calculsentinel/dataset2sentinel.csv\")\n",
        "sentineldatasite3 = pd.read_csv(\"/content/drive/MyDrive/stage_agriedge/site3/indexes_calculsentinel/dataset3sentinel.csv\")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGciy9hgVWpS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "outputId": "d4830131-07c5-420b-f9d6-060ba400250e"
      },
      "source": [
        "weatherdata = pd.read_excel('/content/drive/MyDrive/stage_agriedge/weatherdata.xlsx', index_col=0)  \n",
        "weatherdata.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Date time</th>\n",
              "      <th>Maximum Temperature</th>\n",
              "      <th>Minimum Temperature</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Wind Chill</th>\n",
              "      <th>Heat Index</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>Snow</th>\n",
              "      <th>Snow Depth</th>\n",
              "      <th>Wind Speed</th>\n",
              "      <th>Wind Direction</th>\n",
              "      <th>Wind Gust</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Cloud Cover</th>\n",
              "      <th>Relative Humidity</th>\n",
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              "    <tr>\n",
              "      <th>Name</th>\n",
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              "  <tbody>\n",
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              "      <th>بن جرير, مراكش - آسفي, المغرب</th>\n",
              "      <td>04/01/2020 00:00:00</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>72.03</td>\n",
              "      <td>Overcast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>بن جرير, مراكش - آسفي, المغرب</th>\n",
              "      <td>04/01/2020 01:00:00</td>\n",
              "      <td>13.9</td>\n",
              "      <td>13.9</td>\n",
              "      <td>13.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.9</td>\n",
              "      <td>275.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>74.3</td>\n",
              "      <td>81.33</td>\n",
              "      <td>Partially cloudy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>بن جرير, مراكش - آسفي, المغرب</th>\n",
              "      <td>04/01/2020 02:00:00</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.4</td>\n",
              "      <td>250.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>82.01</td>\n",
              "      <td>Partially cloudy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>بن جرير, مراكش - آسفي, المغرب</th>\n",
              "      <td>04/01/2020 03:00:00</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>82.01</td>\n",
              "      <td>Partially cloudy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>بن جرير, مراكش - آسفي, المغرب</th>\n",
              "      <td>04/01/2020 04:00:00</td>\n",
              "      <td>12.9</td>\n",
              "      <td>12.9</td>\n",
              "      <td>12.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.6</td>\n",
              "      <td>300.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>82.80</td>\n",
              "      <td>Partially cloudy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Date time  ...        Conditions\n",
              "Name                                                ...                  \n",
              "بن جرير, مراكش - آسفي, المغرب  04/01/2020 00:00:00  ...          Overcast\n",
              "بن جرير, مراكش - آسفي, المغرب  04/01/2020 01:00:00  ...  Partially cloudy\n",
              "بن جرير, مراكش - آسفي, المغرب  04/01/2020 02:00:00  ...  Partially cloudy\n",
              "بن جرير, مراكش - آسفي, المغرب  04/01/2020 03:00:00  ...  Partially cloudy\n",
              "بن جرير, مراكش - آسفي, المغرب  04/01/2020 04:00:00  ...  Partially cloudy\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MwoIi_jVeZ6"
      },
      "source": [
        "date = []\n",
        "hour = []\n",
        "date = [x[:10] for x in weatherdata[\"Date time\"]]\n",
        "hour = [x[11:19] for x in weatherdata[\"Date time\"]]\n",
        "date = [x.replace('/', '-') for x in date]\n",
        "date = [ x[6:] + '-' + x[:2] + '-' + x[3:5] for x in date ]\n",
        "weatherdata[\"Date\"] = pd.to_datetime(date, format='%Y-%m-%d', errors='ignore')\n",
        "weatherdata['Hour'] = hour"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "dEm9zE-EXSmz",
        "outputId": "6c02f0c9-e0b2-42d7-891d-7703a60cfcb7"
      },
      "source": [
        "weatherdata.head(3)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date time</th>\n",
              "      <th>Maximum Temperature</th>\n",
              "      <th>Minimum Temperature</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Wind Chill</th>\n",
              "      <th>Heat Index</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>Snow</th>\n",
              "      <th>Snow Depth</th>\n",
              "      <th>Wind Speed</th>\n",
              "      <th>Wind Direction</th>\n",
              "      <th>Wind Gust</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Cloud Cover</th>\n",
              "      <th>Relative Humidity</th>\n",
              "      <th>Conditions</th>\n",
              "      <th>Date</th>\n",
              "      <th>Hour</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>بن جرير, مراكش - آسفي, المغرب</th>\n",
              "      <td>04/01/2020 00:00:00</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>72.03</td>\n",
              "      <td>Overcast</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>بن جرير, مراكش - آسفي, المغرب</th>\n",
              "      <td>04/01/2020 01:00:00</td>\n",
              "      <td>13.9</td>\n",
              "      <td>13.9</td>\n",
              "      <td>13.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.9</td>\n",
              "      <td>275.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>74.3</td>\n",
              "      <td>81.33</td>\n",
              "      <td>Partially cloudy</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>01:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>بن جرير, مراكش - آسفي, المغرب</th>\n",
              "      <td>04/01/2020 02:00:00</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.4</td>\n",
              "      <td>250.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>82.01</td>\n",
              "      <td>Partially cloudy</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>02:00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Date time  ...      Hour\n",
              "Name                                                ...          \n",
              "بن جرير, مراكش - آسفي, المغرب  04/01/2020 00:00:00  ...  00:00:00\n",
              "بن جرير, مراكش - آسفي, المغرب  04/01/2020 01:00:00  ...  01:00:00\n",
              "بن جرير, مراكش - آسفي, المغرب  04/01/2020 02:00:00  ...  02:00:00\n",
              "\n",
              "[3 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xOPX6ukkjR1d",
        "outputId": "7a7e4787-784a-4fde-92f7-4b5425bd0501"
      },
      "source": [
        "weatherdata[\"Precipitation\"].unique"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Series.unique of Name\n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "                                ... \n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "بن جرير, مراكش - آسفي, المغرب    0.0\n",
              "Name: Precipitation, Length: 8737, dtype: float64>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOmaH-8Xxhm2"
      },
      "source": [
        "column_names = ['Hourtime', 'Date', 'Air_temperature', 'Precipitation', 'BLUE_BAND', 'GREEN_BAND', 'RED_BAND', 'NIR_BAND', 'SWIR1_BAND', 'SWIR2_BAND', 'NDVI', 'NDWI', 'SAVI', 'Leaf_Area_index', 'Volumetric_Soil_Moisture']"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KOP06o4yj4w"
      },
      "source": [
        "Datasetintermediairesite1 = pd.DataFrame(columns = column_names)\n",
        "Datasetintermediairesite2 = pd.DataFrame(columns = column_names)\n",
        "Datasetintermediairesite3 = pd.DataFrame(columns = column_names)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gL9TltlA3Avm",
        "outputId": "94885c90-df52-4da2-d342-5535b0710ab9"
      },
      "source": [
        "sentineldatasite1.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>system:index</th>\n",
              "      <th>NDVI</th>\n",
              "      <th>NDWI</th>\n",
              "      <th>SAVI</th>\n",
              "      <th>RED</th>\n",
              "      <th>BLUE</th>\n",
              "      <th>GREEN</th>\n",
              "      <th>NIR</th>\n",
              "      <th>SWIR1</th>\n",
              "      <th>SWIR2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20200406T110621_20200406T111909_T29SPR</td>\n",
              "      <td>0.341724</td>\n",
              "      <td>-0.452421</td>\n",
              "      <td>0.512537</td>\n",
              "      <td>1726.0</td>\n",
              "      <td>792.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>3514.0</td>\n",
              "      <td>3840.0</td>\n",
              "      <td>3048.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20200501T110619_20200501T111347_T29SPR</td>\n",
              "      <td>0.353303</td>\n",
              "      <td>-0.454786</td>\n",
              "      <td>0.529901</td>\n",
              "      <td>1576.0</td>\n",
              "      <td>753.0</td>\n",
              "      <td>1236.0</td>\n",
              "      <td>3292.0</td>\n",
              "      <td>3565.0</td>\n",
              "      <td>2870.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20200506T110621_20200506T112111_T29SPR</td>\n",
              "      <td>0.353986</td>\n",
              "      <td>-0.456319</td>\n",
              "      <td>0.530928</td>\n",
              "      <td>1688.0</td>\n",
              "      <td>805.0</td>\n",
              "      <td>1324.0</td>\n",
              "      <td>3542.0</td>\n",
              "      <td>3686.0</td>\n",
              "      <td>2996.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20200511T110619_20200511T111534_T29SPR</td>\n",
              "      <td>0.229318</td>\n",
              "      <td>-0.394263</td>\n",
              "      <td>0.343941</td>\n",
              "      <td>1844.0</td>\n",
              "      <td>868.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>3008.0</td>\n",
              "      <td>4073.0</td>\n",
              "      <td>3426.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20200521T110619_20200521T111355_T29SPR</td>\n",
              "      <td>0.186961</td>\n",
              "      <td>-0.369803</td>\n",
              "      <td>0.280416</td>\n",
              "      <td>2164.0</td>\n",
              "      <td>976.0</td>\n",
              "      <td>1442.0</td>\n",
              "      <td>3168.0</td>\n",
              "      <td>4349.0</td>\n",
              "      <td>3736.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             system:index      NDVI  ...   SWIR1   SWIR2\n",
              "0  20200406T110621_20200406T111909_T29SPR  0.341724  ...  3840.0  3048.0\n",
              "1  20200501T110619_20200501T111347_T29SPR  0.353303  ...  3565.0  2870.0\n",
              "2  20200506T110621_20200506T112111_T29SPR  0.353986  ...  3686.0  2996.0\n",
              "3  20200511T110619_20200511T111534_T29SPR  0.229318  ...  4073.0  3426.0\n",
              "4  20200521T110619_20200521T111355_T29SPR  0.186961  ...  4349.0  3736.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Yu8JB42P4rD"
      },
      "source": [
        "sentineldatasite1[\"Date\"] = [x[:8] for x in sentineldatasite1[\"system:index\"]]\n",
        "sentineldatasite1[\"Hour\"] = [x[9:13] for x in sentineldatasite1[\"system:index\"]]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBBhsMTPQwug"
      },
      "source": [
        "sentineldatasite2[\"Date\"] = [x[:8] for x in sentineldatasite2[\"system:index\"]]\n",
        "sentineldatasite2[\"Hour\"] = [x[9:13] for x in sentineldatasite2[\"system:index\"]]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmRMc1e7Tqfj"
      },
      "source": [
        "sentineldatasite3[\"Date\"] = [x[:8] for x in sentineldatasite3[\"system:index\"]]\n",
        "sentineldatasite3[\"Hour\"] = [x[9:13] for x in sentineldatasite3[\"system:index\"]]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_jbH5x_SdGv"
      },
      "source": [
        "sentineldatasite1[\"Date\"] = [ x[:4] + '-' + x[4:6] + '-' + x[6:8] for x in sentineldatasite1[\"Date\"] ]\n",
        "sentineldatasite1[\"Hour\"] = [ x[:2] + ':' + '00' + ':' + '00' for x in sentineldatasite1[\"Hour\"] ]"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlQBSCDITQZC"
      },
      "source": [
        "sentineldatasite2[\"Date\"] = [ x[:4] + '-' + x[4:6] + '-' + x[6:8] for x in sentineldatasite2[\"Date\"] ]\n",
        "sentineldatasite2[\"Hour\"] = [ x[:2] + ':' + '00' + ':' + '00'  for x in sentineldatasite2[\"Hour\"] ]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajaJSG9CQmwP"
      },
      "source": [
        "sentineldatasite3[\"Date\"] = [ x[:4] + '-' + x[4:6] + '-' + x[6:8] for x in sentineldatasite3[\"Date\"] ]\n",
        "sentineldatasite3[\"Hour\"] = [ x[:2] + ':' + '00' + ':' + '00' for x in sentineldatasite3[\"Hour\"] ]"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RL1ncOh1Rg4X"
      },
      "source": [
        "sentineldatasite1[\"Leaf_Area_index\"] = [2.88 * x + 1.14 * (1 + 0.104 * math.exp(4.1 * x)) for x in sentineldatasite1['NDWI']]\n",
        "sentineldatasite2[\"Leaf_Area_index\"] = [2.88 * x + 1.14 * (1 + 0.104 * math.exp(4.1 * x)) for x in sentineldatasite2['NDWI']]\n",
        "sentineldatasite3[\"Leaf_Area_index\"] = [2.88 * x + 1.14 * (1 + 0.104 * math.exp(4.1 * x)) for x in sentineldatasite3['NDWI']]"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNMV3r1gZ1Rx"
      },
      "source": [
        "sentineldatasite1[\"Date\"] = pd.to_datetime(sentineldatasite1[\"Date\"], format='%Y-%m-%d', errors='ignore')"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imfMttDqiA7b"
      },
      "source": [
        "sentineldatasite2[\"Date\"] = pd.to_datetime(sentineldatasite2[\"Date\"], format='%Y-%m-%d', errors='ignore')"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgHQ2uXviD-M"
      },
      "source": [
        "sentineldatasite3[\"Date\"] = pd.to_datetime(sentineldatasite3[\"Date\"], format='%Y-%m-%d', errors='ignore')"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PKF4HM7fRabq"
      },
      "source": [
        "Datasetintermediairesite1 = pd.merge(sentineldatasite1,weatherdata[weatherdata[\"Hour\"]==\"11:00:00\"][[\"Temperature\", \"Precipitation\", \"Date\"]], on='Date',suffixes=('_dataset', ''))\n",
        "Datasetintermediairesite2 = pd.merge(sentineldatasite2,weatherdata[weatherdata[\"Hour\"]==\"11:00:00\"][[\"Temperature\", \"Precipitation\", \"Date\"]], on='Date',suffixes=('_dataset', ''))\n",
        "Datasetintermediairesite3 = pd.merge(sentineldatasite3,weatherdata[weatherdata[\"Hour\"]==\"11:00:00\"][[\"Temperature\", \"Precipitation\", \"Date\"]], on='Date',suffixes=('_dataset', ''))"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "qVFGcAR1-GR8",
        "outputId": "1b3ad8b5-efd7-4f63-8968-0713dbbcca64"
      },
      "source": [
        "Datasetintermediairesite1.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>system:index</th>\n",
              "      <th>NDVI</th>\n",
              "      <th>NDWI</th>\n",
              "      <th>SAVI</th>\n",
              "      <th>RED</th>\n",
              "      <th>BLUE</th>\n",
              "      <th>GREEN</th>\n",
              "      <th>NIR</th>\n",
              "      <th>SWIR1</th>\n",
              "      <th>SWIR2</th>\n",
              "      <th>Date</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Leaf_Area_index</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20200406T110621_20200406T111909_T29SPR</td>\n",
              "      <td>0.341724</td>\n",
              "      <td>-0.452421</td>\n",
              "      <td>0.512537</td>\n",
              "      <td>1726.0</td>\n",
              "      <td>792.0</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>3514.0</td>\n",
              "      <td>3840.0</td>\n",
              "      <td>3048.0</td>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>-0.144421</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20200501T110619_20200501T111347_T29SPR</td>\n",
              "      <td>0.353303</td>\n",
              "      <td>-0.454786</td>\n",
              "      <td>0.529901</td>\n",
              "      <td>1576.0</td>\n",
              "      <td>753.0</td>\n",
              "      <td>1236.0</td>\n",
              "      <td>3292.0</td>\n",
              "      <td>3565.0</td>\n",
              "      <td>2870.0</td>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>-0.151412</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20200506T110621_20200506T112111_T29SPR</td>\n",
              "      <td>0.353986</td>\n",
              "      <td>-0.456319</td>\n",
              "      <td>0.530928</td>\n",
              "      <td>1688.0</td>\n",
              "      <td>805.0</td>\n",
              "      <td>1324.0</td>\n",
              "      <td>3542.0</td>\n",
              "      <td>3686.0</td>\n",
              "      <td>2996.0</td>\n",
              "      <td>2020-05-06</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>-0.155941</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20200511T110619_20200511T111534_T29SPR</td>\n",
              "      <td>0.229318</td>\n",
              "      <td>-0.394263</td>\n",
              "      <td>0.343941</td>\n",
              "      <td>1844.0</td>\n",
              "      <td>868.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>3008.0</td>\n",
              "      <td>4073.0</td>\n",
              "      <td>3426.0</td>\n",
              "      <td>2020-05-11</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.028067</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20200521T110619_20200521T111355_T29SPR</td>\n",
              "      <td>0.186961</td>\n",
              "      <td>-0.369803</td>\n",
              "      <td>0.280416</td>\n",
              "      <td>2164.0</td>\n",
              "      <td>976.0</td>\n",
              "      <td>1442.0</td>\n",
              "      <td>3168.0</td>\n",
              "      <td>4349.0</td>\n",
              "      <td>3736.0</td>\n",
              "      <td>2020-05-21</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.100997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             system:index      NDVI  ...  Temperature  Precipitation\n",
              "0  20200406T110621_20200406T111909_T29SPR  0.341724  ...         21.0            0.0\n",
              "1  20200501T110619_20200501T111347_T29SPR  0.353303  ...         25.0            0.0\n",
              "2  20200506T110621_20200506T112111_T29SPR  0.353986  ...         27.0            0.0\n",
              "3  20200511T110619_20200511T111534_T29SPR  0.229318  ...         22.0            0.0\n",
              "4  20200521T110619_20200521T111355_T29SPR  0.186961  ...          NaN            0.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "id": "ikkVLbahh1Cq",
        "outputId": "da6980e6-797b-4493-b818-24726c906010"
      },
      "source": [
        "datasetcapteursite1.head(15)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>node_id</th>\n",
              "      <th>short_addr</th>\n",
              "      <th>channel</th>\n",
              "      <th>lqi</th>\n",
              "      <th>rssi</th>\n",
              "      <th>unixTs</th>\n",
              "      <th>battery</th>\n",
              "      <th>onboard_temperature</th>\n",
              "      <th>usu_frame_seq</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>air_humidity</th>\n",
              "      <th>soil_temperature_orgi</th>\n",
              "      <th>watermark_1_orgi</th>\n",
              "      <th>watermark_2_orgi</th>\n",
              "      <th>watermark_3_orgi</th>\n",
              "      <th>soil_temperature</th>\n",
              "      <th>watermark_1</th>\n",
              "      <th>watermark_2</th>\n",
              "      <th>watermark_3</th>\n",
              "      <th>rpi_frame_seq</th>\n",
              "      <th>rpi_timestamp</th>\n",
              "      <th>db_timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:59:55.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34404</td>\n",
              "      <td>12.8</td>\n",
              "      <td>80.9</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.36</td>\n",
              "      <td>14.07</td>\n",
              "      <td>294</td>\n",
              "      <td>2020-04-01T00:01:22.000Z</td>\n",
              "      <td>2020-04-01T00:02:23.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:58:51.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34403</td>\n",
              "      <td>12.8</td>\n",
              "      <td>80.6</td>\n",
              "      <td>108,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.67</td>\n",
              "      <td>31.71</td>\n",
              "      <td>15.44</td>\n",
              "      <td>14.15</td>\n",
              "      <td>291</td>\n",
              "      <td>2020-04-01T00:00:18.000Z</td>\n",
              "      <td>2020-04-01T00:00:39.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:57:47.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34402</td>\n",
              "      <td>12.8</td>\n",
              "      <td>80.5</td>\n",
              "      <td>108,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.67</td>\n",
              "      <td>31.71</td>\n",
              "      <td>15.44</td>\n",
              "      <td>14.15</td>\n",
              "      <td>287</td>\n",
              "      <td>2020-03-31T23:59:14.000Z</td>\n",
              "      <td>2020-04-01T00:00:34.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:56:43.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34401</td>\n",
              "      <td>12.8</td>\n",
              "      <td>80.4</td>\n",
              "      <td>108,2</td>\n",
              "      <td>191,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>255,10</td>\n",
              "      <td>21.67</td>\n",
              "      <td>31.84</td>\n",
              "      <td>15.44</td>\n",
              "      <td>14.17</td>\n",
              "      <td>284</td>\n",
              "      <td>2020-03-31T23:58:10.000Z</td>\n",
              "      <td>2020-03-31T23:58:24.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:55:39.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34400</td>\n",
              "      <td>12.8</td>\n",
              "      <td>80.5</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.36</td>\n",
              "      <td>14.07</td>\n",
              "      <td>280</td>\n",
              "      <td>2020-03-31T23:57:06.000Z</td>\n",
              "      <td>2020-03-31T23:57:42.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:54:35.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34399</td>\n",
              "      <td>12.8</td>\n",
              "      <td>80.3</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.36</td>\n",
              "      <td>14.07</td>\n",
              "      <td>277</td>\n",
              "      <td>2020-03-31T23:56:02.000Z</td>\n",
              "      <td>2020-03-31T23:56:04.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:53:31.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34398</td>\n",
              "      <td>12.9</td>\n",
              "      <td>80.2</td>\n",
              "      <td>108,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>255,10</td>\n",
              "      <td>21.67</td>\n",
              "      <td>31.71</td>\n",
              "      <td>15.44</td>\n",
              "      <td>14.17</td>\n",
              "      <td>273</td>\n",
              "      <td>2020-03-31T23:54:58.000Z</td>\n",
              "      <td>2020-03-31T23:55:16.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:52:27.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34397</td>\n",
              "      <td>12.9</td>\n",
              "      <td>80.3</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>0,10</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.34</td>\n",
              "      <td>14.07</td>\n",
              "      <td>270</td>\n",
              "      <td>2020-03-31T23:53:54.000Z</td>\n",
              "      <td>2020-03-31T23:55:15.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:51:23.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34396</td>\n",
              "      <td>12.9</td>\n",
              "      <td>80.1</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.36</td>\n",
              "      <td>14.07</td>\n",
              "      <td>266</td>\n",
              "      <td>2020-03-31T23:52:50.000Z</td>\n",
              "      <td>2020-03-31T23:53:24.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:50:19.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34395</td>\n",
              "      <td>12.9</td>\n",
              "      <td>80.2</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.36</td>\n",
              "      <td>14.07</td>\n",
              "      <td>263</td>\n",
              "      <td>2020-03-31T23:51:46.000Z</td>\n",
              "      <td>2020-03-31T23:51:48.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:49:15.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34394</td>\n",
              "      <td>12.9</td>\n",
              "      <td>79.8</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.36</td>\n",
              "      <td>14.07</td>\n",
              "      <td>259</td>\n",
              "      <td>2020-03-31T23:50:41.000Z</td>\n",
              "      <td>2020-03-31T23:51:10.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:48:11.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34393</td>\n",
              "      <td>12.9</td>\n",
              "      <td>79.8</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.36</td>\n",
              "      <td>14.07</td>\n",
              "      <td>256</td>\n",
              "      <td>2020-03-31T23:49:37.000Z</td>\n",
              "      <td>2020-03-31T23:49:40.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:47:06.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34392</td>\n",
              "      <td>12.9</td>\n",
              "      <td>79.5</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>0,10</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.34</td>\n",
              "      <td>14.07</td>\n",
              "      <td>252</td>\n",
              "      <td>2020-03-31T23:48:33.000Z</td>\n",
              "      <td>2020-03-31T23:49:17.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:46:02.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34391</td>\n",
              "      <td>13.0</td>\n",
              "      <td>79.2</td>\n",
              "      <td>103,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>0,10</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.36</td>\n",
              "      <td>31.53</td>\n",
              "      <td>15.34</td>\n",
              "      <td>14.07</td>\n",
              "      <td>248</td>\n",
              "      <td>2020-03-31T23:47:29.000Z</td>\n",
              "      <td>2020-03-31T23:47:38.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>A-N4-E02-C02</td>\n",
              "      <td>E002</td>\n",
              "      <td>15</td>\n",
              "      <td>255</td>\n",
              "      <td>-69</td>\n",
              "      <td>2020-03-31T23:44:58.000Z</td>\n",
              "      <td>8163</td>\n",
              "      <td>12.0</td>\n",
              "      <td>34390</td>\n",
              "      <td>13.1</td>\n",
              "      <td>79.1</td>\n",
              "      <td>108,2</td>\n",
              "      <td>196,4</td>\n",
              "      <td>251,9</td>\n",
              "      <td>4,11</td>\n",
              "      <td>21.67</td>\n",
              "      <td>31.71</td>\n",
              "      <td>15.44</td>\n",
              "      <td>14.15</td>\n",
              "      <td>245</td>\n",
              "      <td>2020-03-31T23:46:25.000Z</td>\n",
              "      <td>2020-03-31T23:47:36.000Z</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         node_id short_addr  ...             rpi_timestamp              db_timestamp\n",
              "0   A-N4-E02-C02       E002  ...  2020-04-01T00:01:22.000Z  2020-04-01T00:02:23.000Z\n",
              "1   A-N4-E02-C02       E002  ...  2020-04-01T00:00:18.000Z  2020-04-01T00:00:39.000Z\n",
              "2   A-N4-E02-C02       E002  ...  2020-03-31T23:59:14.000Z  2020-04-01T00:00:34.000Z\n",
              "3   A-N4-E02-C02       E002  ...  2020-03-31T23:58:10.000Z  2020-03-31T23:58:24.000Z\n",
              "4   A-N4-E02-C02       E002  ...  2020-03-31T23:57:06.000Z  2020-03-31T23:57:42.000Z\n",
              "5   A-N4-E02-C02       E002  ...  2020-03-31T23:56:02.000Z  2020-03-31T23:56:04.000Z\n",
              "6   A-N4-E02-C02       E002  ...  2020-03-31T23:54:58.000Z  2020-03-31T23:55:16.000Z\n",
              "7   A-N4-E02-C02       E002  ...  2020-03-31T23:53:54.000Z  2020-03-31T23:55:15.000Z\n",
              "8   A-N4-E02-C02       E002  ...  2020-03-31T23:52:50.000Z  2020-03-31T23:53:24.000Z\n",
              "9   A-N4-E02-C02       E002  ...  2020-03-31T23:51:46.000Z  2020-03-31T23:51:48.000Z\n",
              "10  A-N4-E02-C02       E002  ...  2020-03-31T23:50:41.000Z  2020-03-31T23:51:10.000Z\n",
              "11  A-N4-E02-C02       E002  ...  2020-03-31T23:49:37.000Z  2020-03-31T23:49:40.000Z\n",
              "12  A-N4-E02-C02       E002  ...  2020-03-31T23:48:33.000Z  2020-03-31T23:49:17.000Z\n",
              "13  A-N4-E02-C02       E002  ...  2020-03-31T23:47:29.000Z  2020-03-31T23:47:38.000Z\n",
              "14  A-N4-E02-C02       E002  ...  2020-03-31T23:46:25.000Z  2020-03-31T23:47:36.000Z\n",
              "\n",
              "[15 rows x 22 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "qeWboCnIPpEk",
        "outputId": "63e2e049-dce3-4d60-b2d3-ae54ec2e7073"
      },
      "source": [
        "Datasetintermediairesite2.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>system:index</th>\n",
              "      <th>NDVI</th>\n",
              "      <th>NDWI</th>\n",
              "      <th>SAVI</th>\n",
              "      <th>RED</th>\n",
              "      <th>BLUE</th>\n",
              "      <th>GREEN</th>\n",
              "      <th>NIR</th>\n",
              "      <th>SWIR1</th>\n",
              "      <th>SWIR2</th>\n",
              "      <th>Date</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Leaf_Area_index</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20200406T110621_20200406T111909_T29SPR</td>\n",
              "      <td>0.259175</td>\n",
              "      <td>-0.390183</td>\n",
              "      <td>0.388726</td>\n",
              "      <td>1938.0</td>\n",
              "      <td>924.0</td>\n",
              "      <td>1434.0</td>\n",
              "      <td>3316.0</td>\n",
              "      <td>3906.0</td>\n",
              "      <td>2983.0</td>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.040216</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20200501T110619_20200501T111347_T29SPR</td>\n",
              "      <td>0.241242</td>\n",
              "      <td>-0.378426</td>\n",
              "      <td>0.361827</td>\n",
              "      <td>1906.0</td>\n",
              "      <td>952.0</td>\n",
              "      <td>1406.0</td>\n",
              "      <td>3112.0</td>\n",
              "      <td>3757.0</td>\n",
              "      <td>3090.0</td>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.075258</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20200506T110621_20200506T112111_T29SPR</td>\n",
              "      <td>0.223538</td>\n",
              "      <td>-0.361821</td>\n",
              "      <td>0.335277</td>\n",
              "      <td>2142.0</td>\n",
              "      <td>1054.0</td>\n",
              "      <td>1580.0</td>\n",
              "      <td>3388.0</td>\n",
              "      <td>3960.0</td>\n",
              "      <td>3122.0</td>\n",
              "      <td>2020-05-06</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.124851</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20200511T110619_20200511T111534_T29SPR</td>\n",
              "      <td>0.215671</td>\n",
              "      <td>-0.359691</td>\n",
              "      <td>0.323476</td>\n",
              "      <td>2040.0</td>\n",
              "      <td>1050.0</td>\n",
              "      <td>1494.0</td>\n",
              "      <td>3188.0</td>\n",
              "      <td>3887.0</td>\n",
              "      <td>3217.0</td>\n",
              "      <td>2020-05-11</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.131222</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20200521T110619_20200521T111355_T29SPR</td>\n",
              "      <td>0.216667</td>\n",
              "      <td>-0.359038</td>\n",
              "      <td>0.324969</td>\n",
              "      <td>2080.0</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>1524.0</td>\n",
              "      <td>3234.0</td>\n",
              "      <td>3917.0</td>\n",
              "      <td>3243.0</td>\n",
              "      <td>2020-05-21</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.133174</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             system:index      NDVI  ...  Temperature  Precipitation\n",
              "0  20200406T110621_20200406T111909_T29SPR  0.259175  ...         21.0            0.0\n",
              "1  20200501T110619_20200501T111347_T29SPR  0.241242  ...         25.0            0.0\n",
              "2  20200506T110621_20200506T112111_T29SPR  0.223538  ...         27.0            0.0\n",
              "3  20200511T110619_20200511T111534_T29SPR  0.215671  ...         22.0            0.0\n",
              "4  20200521T110619_20200521T111355_T29SPR  0.216667  ...          NaN            0.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVs-4UCY5s3i"
      },
      "source": [
        "def hour(s):\n",
        "    return s.split(\":\")[0]"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJSlwdPlnj63"
      },
      "source": [
        "datasetcapteursite1[\"Date\"] = [x[:10] for x in datasetcapteursite1[\"rpi_timestamp\"]]\n",
        "datasetcapteursite2[\"Date\"] = [x[:10] for x in datasetcapteursite2[\"rpi_timestamp\"]]\n",
        "datasetcapteursite3[\"Date\"] = [x[:10] for x in datasetcapteursite3[\"rpi_timestamp\"]]"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTbhkm4u5Rm3"
      },
      "source": [
        "datasetcapteursite1[\"Hour\"] = [x[11:16] for x in datasetcapteursite1[\"rpi_timestamp\"]]\n",
        "datasetcapteursite2[\"Hour\"] = [x[11:16] for x in datasetcapteursite2[\"rpi_timestamp\"]]\n",
        "datasetcapteursite3[\"Hour\"] = [x[11:16] for x in datasetcapteursite3[\"rpi_timestamp\"]]"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gmz_8mb6oC6Z"
      },
      "source": [
        "datasetcapteursite1[\"Date\"] = pd.to_datetime(datasetcapteursite1[\"Date\"], format='%Y-%m-%d', errors='ignore')\n",
        "datasetcapteursite2[\"Date\"] = pd.to_datetime(datasetcapteursite2[\"Date\"], format='%Y-%m-%d', errors='ignore')\n",
        "datasetcapteursite3[\"Date\"] = pd.to_datetime(datasetcapteursite3[\"Date\"], format='%Y-%m-%d', errors='ignore')"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_F9sj9OZnuTj"
      },
      "source": [
        "Sentineldatasetfin1 = pd.merge(Datasetintermediairesite1,datasetcapteursite1[datasetcapteursite1[\"Hour\"]==\"11:00\"][[\"watermark_1\", \"watermark_2\",\t\"watermark_3\", \"Date\"]], on='Date',suffixes=('_dataset', ''))"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPvDAOz3BH1n"
      },
      "source": [
        "Sentineldatasetfin2 = pd.merge(Datasetintermediairesite2,datasetcapteursite2[datasetcapteursite2[\"Hour\"]==\"11:00\"][[\"watermark_1\", \"watermark_2\",\t\"watermark_3\", \"Date\"]], on='Date',suffixes=('_dataset', ''))"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZ5b52EsBVJf"
      },
      "source": [
        "Sentineldatasetfin3 = pd.merge(Datasetintermediairesite3,datasetcapteursite3[datasetcapteursite3[\"Hour\"]==\"11:00\"][[\"watermark_1\", \"watermark_2\",\t\"watermark_3\", \"Date\"]], on='Date',suffixes=('_dataset', ''))"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "jIK3ybg18mz-",
        "outputId": "239a5a9c-bcdc-476e-b2a8-550b351bd381"
      },
      "source": [
        "Sentineldatasetfin1.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>NDVI</th>\n",
              "      <th>NDWI</th>\n",
              "      <th>SAVI</th>\n",
              "      <th>RED</th>\n",
              "      <th>BLUE</th>\n",
              "      <th>GREEN</th>\n",
              "      <th>NIR</th>\n",
              "      <th>SWIR1</th>\n",
              "      <th>SWIR2</th>\n",
              "      <th>Date</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Leaf_Area_index</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>watermark_1</th>\n",
              "      <th>watermark_2</th>\n",
              "      <th>watermark_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20200501T110619_20200501T111347_T29SPR</td>\n",
              "      <td>0.353303</td>\n",
              "      <td>-0.454786</td>\n",
              "      <td>0.529901</td>\n",
              "      <td>1576.0</td>\n",
              "      <td>753.0</td>\n",
              "      <td>1236.0</td>\n",
              "      <td>3292.0</td>\n",
              "      <td>3565.0</td>\n",
              "      <td>2870.0</td>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>-0.151412</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.42</td>\n",
              "      <td>5.93</td>\n",
              "      <td>8.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20200506T110621_20200506T112111_T29SPR</td>\n",
              "      <td>0.353986</td>\n",
              "      <td>-0.456319</td>\n",
              "      <td>0.530928</td>\n",
              "      <td>1688.0</td>\n",
              "      <td>805.0</td>\n",
              "      <td>1324.0</td>\n",
              "      <td>3542.0</td>\n",
              "      <td>3686.0</td>\n",
              "      <td>2996.0</td>\n",
              "      <td>2020-05-06</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>-0.155941</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.33</td>\n",
              "      <td>5.14</td>\n",
              "      <td>4.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20200521T110619_20200521T111355_T29SPR</td>\n",
              "      <td>0.186961</td>\n",
              "      <td>-0.369803</td>\n",
              "      <td>0.280416</td>\n",
              "      <td>2164.0</td>\n",
              "      <td>976.0</td>\n",
              "      <td>1442.0</td>\n",
              "      <td>3168.0</td>\n",
              "      <td>4349.0</td>\n",
              "      <td>3736.0</td>\n",
              "      <td>2020-05-21</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.100997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.79</td>\n",
              "      <td>15.43</td>\n",
              "      <td>12.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20200526T110631_20200526T112106_T29SPR</td>\n",
              "      <td>0.170197</td>\n",
              "      <td>-0.364982</td>\n",
              "      <td>0.255271</td>\n",
              "      <td>2060.0</td>\n",
              "      <td>915.0</td>\n",
              "      <td>1384.0</td>\n",
              "      <td>2946.0</td>\n",
              "      <td>4108.0</td>\n",
              "      <td>3517.0</td>\n",
              "      <td>2020-05-26</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.115400</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.27</td>\n",
              "      <td>35.88</td>\n",
              "      <td>17.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20200531T110619_20200531T111348_T29SPR</td>\n",
              "      <td>0.177872</td>\n",
              "      <td>-0.367114</td>\n",
              "      <td>0.266780</td>\n",
              "      <td>1948.0</td>\n",
              "      <td>894.0</td>\n",
              "      <td>1318.0</td>\n",
              "      <td>2880.0</td>\n",
              "      <td>4211.0</td>\n",
              "      <td>3728.0</td>\n",
              "      <td>2020-05-31</td>\n",
              "      <td>11:00:00</td>\n",
              "      <td>0.109030</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.52</td>\n",
              "      <td>38.34</td>\n",
              "      <td>15.57</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             system:index      NDVI  ...  watermark_2  watermark_3\n",
              "0  20200501T110619_20200501T111347_T29SPR  0.353303  ...         5.93         8.87\n",
              "1  20200506T110621_20200506T112111_T29SPR  0.353986  ...         5.14         4.57\n",
              "2  20200521T110619_20200521T111355_T29SPR  0.186961  ...        15.43        12.64\n",
              "3  20200526T110631_20200526T112106_T29SPR  0.170197  ...        35.88        17.95\n",
              "4  20200531T110619_20200531T111348_T29SPR  0.177872  ...        38.34        15.57\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IbYiEmTqDWh"
      },
      "source": [
        "Datasetfinalsenti = pd.concat((Sentineldatasetfin1, Sentineldatasetfin2, Sentineldatasetfin3))"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZqYlhhTB8sk"
      },
      "source": [
        "Datasetfinalsenti.drop(['system:index', 'Date', 'Hour'], inplace=True, axis=1)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dOSL7bVJtko"
      },
      "source": [
        "def van_guenut(h, alpha=0.0230, theta_r=0.0946, theta_s=0.4674, n=1.2089):\n",
        "  calcul1 = h/(1*9.81)\n",
        "  calcul2 = (((calcul1*alpha)**n + 1)**(-1 + 1/n))*(theta_s - theta_r) + theta_r\n",
        "  return calcul2"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "296dmFfdOURX",
        "outputId": "1eb42dbc-3051-4140-aa55-2a80201ab104"
      },
      "source": [
        "\"\"\"Datasetfinalsenti[\"watermark_1_orgi\"] = Datasetfinalsenti[\"watermark_1_orgi\"].str.replace(',','.')\n",
        "Datasetfinalsenti[\"watermark_2_orgi\"] = Datasetfinalsenti[\"watermark_2_orgi\"].str.replace(',','.')\n",
        "Datasetfinalsenti[\"watermark_3_orgi\"] = Datasetfinalsenti[\"watermark_3_orgi\"].str.replace(',','.')\n",
        "Datasetfinalsenti[\"watermark_1_orgi\"] = pd.to_numeric(Datasetfinalsenti[\"watermark_1_orgi\"], downcast=\"float\")\n",
        "Datasetfinalsenti[\"watermark_2_orgi\"] = pd.to_numeric(Datasetfinalsenti[\"watermark_2_orgi\"], downcast=\"float\")\n",
        "Datasetfinalsenti[\"watermark_3_orgi\"] = pd.to_numeric(Datasetfinalsenti[\"watermark_3_orgi\"], downcast=\"float\")\n",
        "Datasetfinalsenti.dtypes\"\"\""
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Datasetfinalsenti[\"watermark_1_orgi\"] = Datasetfinalsenti[\"watermark_1_orgi\"].str.replace(\\',\\',\\'.\\')\\nDatasetfinalsenti[\"watermark_2_orgi\"] = Datasetfinalsenti[\"watermark_2_orgi\"].str.replace(\\',\\',\\'.\\')\\nDatasetfinalsenti[\"watermark_3_orgi\"] = Datasetfinalsenti[\"watermark_3_orgi\"].str.replace(\\',\\',\\'.\\')\\nDatasetfinalsenti[\"watermark_1_orgi\"] = pd.to_numeric(Datasetfinalsenti[\"watermark_1_orgi\"], downcast=\"float\")\\nDatasetfinalsenti[\"watermark_2_orgi\"] = pd.to_numeric(Datasetfinalsenti[\"watermark_2_orgi\"], downcast=\"float\")\\nDatasetfinalsenti[\"watermark_3_orgi\"] = pd.to_numeric(Datasetfinalsenti[\"watermark_3_orgi\"], downcast=\"float\")\\nDatasetfinalsenti.dtypes'"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hH3K2JpFDezL"
      },
      "source": [
        "Datasetfinalsenti[\"Soil_Moisture20\"] = [van_guenut(x) for x in Datasetfinalsenti[\"watermark_1\"]]\n",
        "Datasetfinalsenti[\"Soil_Moisture40\"] = [van_guenut(x) for x in Datasetfinalsenti[\"watermark_2\"]]\n",
        "Datasetfinalsenti[\"Soil_Moisture60\"] = [van_guenut(x) for x in Datasetfinalsenti[\"watermark_3\"]]"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VaPqbSjoVDim",
        "outputId": "c6a16571-dc64-40d1-9827-55f9db02fc12"
      },
      "source": [
        "Datasetfinalsenti.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(121, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UkRSrk2U5W2"
      },
      "source": [
        "Datasetfinalsenti = Datasetfinalsenti.drop([2])"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6BChjvDy3BN"
      },
      "source": [
        "Datasetfinalsenti[\"RED\"] = [x*0.0001 for x in Datasetfinalsenti[\"RED\"]]\n",
        "Datasetfinalsenti[\"BLUE\"] = [x*0.0001 for x in Datasetfinalsenti[\"BLUE\"]]\n",
        "Datasetfinalsenti[\"GREEN\"] = [x*0.0001 for x in Datasetfinalsenti[\"GREEN\"]]\n",
        "Datasetfinalsenti[\"NIR\"] = [x*0.0001 for x in Datasetfinalsenti[\"NIR\"]]\n",
        "Datasetfinalsenti[\"SWIR1\"] = [x*0.0001 for x in Datasetfinalsenti[\"SWIR1\"]]\n",
        "Datasetfinalsenti[\"SWIR2\"] = [x*0.0001 for x in Datasetfinalsenti[\"SWIR2\"]]"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "K5v7WsQ_zVkR",
        "outputId": "fccc7a75-bc76-435b-8da0-474e088c1f48"
      },
      "source": [
        "Datasetfinalsenti.corr()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NDVI</th>\n",
              "      <th>NDWI</th>\n",
              "      <th>SAVI</th>\n",
              "      <th>RED</th>\n",
              "      <th>BLUE</th>\n",
              "      <th>GREEN</th>\n",
              "      <th>NIR</th>\n",
              "      <th>SWIR1</th>\n",
              "      <th>SWIR2</th>\n",
              "      <th>Leaf_Area_index</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>watermark_1</th>\n",
              "      <th>watermark_2</th>\n",
              "      <th>watermark_3</th>\n",
              "      <th>Soil_Moisture20</th>\n",
              "      <th>Soil_Moisture40</th>\n",
              "      <th>Soil_Moisture60</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>NDVI</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.944150</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.906362</td>\n",
              "      <td>-0.782217</td>\n",
              "      <td>-0.785747</td>\n",
              "      <td>-0.082184</td>\n",
              "      <td>-0.828386</td>\n",
              "      <td>-0.819112</td>\n",
              "      <td>-0.943687</td>\n",
              "      <td>-0.492243</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.266536</td>\n",
              "      <td>-0.495431</td>\n",
              "      <td>-0.466413</td>\n",
              "      <td>-0.281038</td>\n",
              "      <td>0.477740</td>\n",
              "      <td>0.458001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NDWI</th>\n",
              "      <td>-0.944150</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.944145</td>\n",
              "      <td>0.953404</td>\n",
              "      <td>0.926190</td>\n",
              "      <td>0.920514</td>\n",
              "      <td>0.305229</td>\n",
              "      <td>0.860419</td>\n",
              "      <td>0.827569</td>\n",
              "      <td>0.999991</td>\n",
              "      <td>0.581301</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.235848</td>\n",
              "      <td>0.541757</td>\n",
              "      <td>0.498396</td>\n",
              "      <td>0.250569</td>\n",
              "      <td>-0.525733</td>\n",
              "      <td>-0.490093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAVI</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.944145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.906352</td>\n",
              "      <td>-0.782204</td>\n",
              "      <td>-0.785733</td>\n",
              "      <td>-0.082159</td>\n",
              "      <td>-0.828373</td>\n",
              "      <td>-0.819101</td>\n",
              "      <td>-0.943682</td>\n",
              "      <td>-0.492228</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.266528</td>\n",
              "      <td>-0.495428</td>\n",
              "      <td>-0.466412</td>\n",
              "      <td>-0.281030</td>\n",
              "      <td>0.477738</td>\n",
              "      <td>0.458001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RED</th>\n",
              "      <td>-0.906362</td>\n",
              "      <td>0.953404</td>\n",
              "      <td>-0.906352</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.943331</td>\n",
              "      <td>0.959444</td>\n",
              "      <td>0.481916</td>\n",
              "      <td>0.940601</td>\n",
              "      <td>0.898866</td>\n",
              "      <td>0.953776</td>\n",
              "      <td>0.678799</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.210163</td>\n",
              "      <td>0.567839</td>\n",
              "      <td>0.492694</td>\n",
              "      <td>0.226063</td>\n",
              "      <td>-0.551744</td>\n",
              "      <td>-0.485191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BLUE</th>\n",
              "      <td>-0.782217</td>\n",
              "      <td>0.926190</td>\n",
              "      <td>-0.782204</td>\n",
              "      <td>0.943331</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.990828</td>\n",
              "      <td>0.610628</td>\n",
              "      <td>0.879908</td>\n",
              "      <td>0.832826</td>\n",
              "      <td>0.926828</td>\n",
              "      <td>0.674247</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.147409</td>\n",
              "      <td>0.553701</td>\n",
              "      <td>0.447638</td>\n",
              "      <td>0.162309</td>\n",
              "      <td>-0.540294</td>\n",
              "      <td>-0.440632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GREEN</th>\n",
              "      <td>-0.785747</td>\n",
              "      <td>0.920514</td>\n",
              "      <td>-0.785733</td>\n",
              "      <td>0.959444</td>\n",
              "      <td>0.990828</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.643374</td>\n",
              "      <td>0.901416</td>\n",
              "      <td>0.843980</td>\n",
              "      <td>0.921192</td>\n",
              "      <td>0.712221</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.185584</td>\n",
              "      <td>0.560888</td>\n",
              "      <td>0.468898</td>\n",
              "      <td>0.200322</td>\n",
              "      <td>-0.547665</td>\n",
              "      <td>-0.461955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NIR</th>\n",
              "      <td>-0.082184</td>\n",
              "      <td>0.305229</td>\n",
              "      <td>-0.082159</td>\n",
              "      <td>0.481916</td>\n",
              "      <td>0.610628</td>\n",
              "      <td>0.643374</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.537630</td>\n",
              "      <td>0.464166</td>\n",
              "      <td>0.306456</td>\n",
              "      <td>0.611476</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.058332</td>\n",
              "      <td>0.264579</td>\n",
              "      <td>0.141542</td>\n",
              "      <td>0.066490</td>\n",
              "      <td>-0.260944</td>\n",
              "      <td>-0.139902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SWIR1</th>\n",
              "      <td>-0.828386</td>\n",
              "      <td>0.860419</td>\n",
              "      <td>-0.828373</td>\n",
              "      <td>0.940601</td>\n",
              "      <td>0.879908</td>\n",
              "      <td>0.901416</td>\n",
              "      <td>0.537630</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.979837</td>\n",
              "      <td>0.860461</td>\n",
              "      <td>0.725716</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.280268</td>\n",
              "      <td>0.475306</td>\n",
              "      <td>0.337737</td>\n",
              "      <td>0.294687</td>\n",
              "      <td>-0.458383</td>\n",
              "      <td>-0.329721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SWIR2</th>\n",
              "      <td>-0.819112</td>\n",
              "      <td>0.827569</td>\n",
              "      <td>-0.819101</td>\n",
              "      <td>0.898866</td>\n",
              "      <td>0.832826</td>\n",
              "      <td>0.843980</td>\n",
              "      <td>0.464166</td>\n",
              "      <td>0.979837</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.827337</td>\n",
              "      <td>0.687584</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.257040</td>\n",
              "      <td>0.407709</td>\n",
              "      <td>0.247815</td>\n",
              "      <td>0.270824</td>\n",
              "      <td>-0.389800</td>\n",
              "      <td>-0.239200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Leaf_Area_index</th>\n",
              "      <td>-0.943687</td>\n",
              "      <td>0.999991</td>\n",
              "      <td>-0.943682</td>\n",
              "      <td>0.953776</td>\n",
              "      <td>0.926828</td>\n",
              "      <td>0.921192</td>\n",
              "      <td>0.306456</td>\n",
              "      <td>0.860461</td>\n",
              "      <td>0.827337</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.581892</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.234336</td>\n",
              "      <td>0.543179</td>\n",
              "      <td>0.499437</td>\n",
              "      <td>0.249071</td>\n",
              "      <td>-0.527177</td>\n",
              "      <td>-0.491145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperature</th>\n",
              "      <td>-0.492243</td>\n",
              "      <td>0.581301</td>\n",
              "      <td>-0.492228</td>\n",
              "      <td>0.678799</td>\n",
              "      <td>0.674247</td>\n",
              "      <td>0.712221</td>\n",
              "      <td>0.611476</td>\n",
              "      <td>0.725716</td>\n",
              "      <td>0.687584</td>\n",
              "      <td>0.581892</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.174840</td>\n",
              "      <td>0.382735</td>\n",
              "      <td>0.276200</td>\n",
              "      <td>0.182638</td>\n",
              "      <td>-0.374051</td>\n",
              "      <td>-0.270357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precipitation</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>watermark_1</th>\n",
              "      <td>0.266536</td>\n",
              "      <td>-0.235848</td>\n",
              "      <td>0.266528</td>\n",
              "      <td>-0.210163</td>\n",
              "      <td>-0.147409</td>\n",
              "      <td>-0.185584</td>\n",
              "      <td>-0.058332</td>\n",
              "      <td>-0.280268</td>\n",
              "      <td>-0.257040</td>\n",
              "      <td>-0.234336</td>\n",
              "      <td>-0.174840</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.065650</td>\n",
              "      <td>-0.021268</td>\n",
              "      <td>-0.999418</td>\n",
              "      <td>-0.078304</td>\n",
              "      <td>0.016401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>watermark_2</th>\n",
              "      <td>-0.495431</td>\n",
              "      <td>0.541757</td>\n",
              "      <td>-0.495428</td>\n",
              "      <td>0.567839</td>\n",
              "      <td>0.553701</td>\n",
              "      <td>0.560888</td>\n",
              "      <td>0.264579</td>\n",
              "      <td>0.475306</td>\n",
              "      <td>0.407709</td>\n",
              "      <td>0.543179</td>\n",
              "      <td>0.382735</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.065650</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.761680</td>\n",
              "      <td>-0.053359</td>\n",
              "      <td>-0.999115</td>\n",
              "      <td>-0.760316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>watermark_3</th>\n",
              "      <td>-0.466413</td>\n",
              "      <td>0.498396</td>\n",
              "      <td>-0.466412</td>\n",
              "      <td>0.492694</td>\n",
              "      <td>0.447638</td>\n",
              "      <td>0.468898</td>\n",
              "      <td>0.141542</td>\n",
              "      <td>0.337737</td>\n",
              "      <td>0.247815</td>\n",
              "      <td>0.499437</td>\n",
              "      <td>0.276200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.021268</td>\n",
              "      <td>0.761680</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.031363</td>\n",
              "      <td>-0.756441</td>\n",
              "      <td>-0.999786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Soil_Moisture20</th>\n",
              "      <td>-0.281038</td>\n",
              "      <td>0.250569</td>\n",
              "      <td>-0.281030</td>\n",
              "      <td>0.226063</td>\n",
              "      <td>0.162309</td>\n",
              "      <td>0.200322</td>\n",
              "      <td>0.066490</td>\n",
              "      <td>0.294687</td>\n",
              "      <td>0.270824</td>\n",
              "      <td>0.249071</td>\n",
              "      <td>0.182638</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.999418</td>\n",
              "      <td>-0.053359</td>\n",
              "      <td>0.031363</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.066738</td>\n",
              "      <td>-0.026216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Soil_Moisture40</th>\n",
              "      <td>0.477740</td>\n",
              "      <td>-0.525733</td>\n",
              "      <td>0.477738</td>\n",
              "      <td>-0.551744</td>\n",
              "      <td>-0.540294</td>\n",
              "      <td>-0.547665</td>\n",
              "      <td>-0.260944</td>\n",
              "      <td>-0.458383</td>\n",
              "      <td>-0.389800</td>\n",
              "      <td>-0.527177</td>\n",
              "      <td>-0.374051</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.078304</td>\n",
              "      <td>-0.999115</td>\n",
              "      <td>-0.756441</td>\n",
              "      <td>0.066738</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.755552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Soil_Moisture60</th>\n",
              "      <td>0.458001</td>\n",
              "      <td>-0.490093</td>\n",
              "      <td>0.458001</td>\n",
              "      <td>-0.485191</td>\n",
              "      <td>-0.440632</td>\n",
              "      <td>-0.461955</td>\n",
              "      <td>-0.139902</td>\n",
              "      <td>-0.329721</td>\n",
              "      <td>-0.239200</td>\n",
              "      <td>-0.491145</td>\n",
              "      <td>-0.270357</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.016401</td>\n",
              "      <td>-0.760316</td>\n",
              "      <td>-0.999786</td>\n",
              "      <td>-0.026216</td>\n",
              "      <td>0.755552</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     NDVI      NDWI  ...  Soil_Moisture40  Soil_Moisture60\n",
              "NDVI             1.000000 -0.944150  ...         0.477740         0.458001\n",
              "NDWI            -0.944150  1.000000  ...        -0.525733        -0.490093\n",
              "SAVI             1.000000 -0.944145  ...         0.477738         0.458001\n",
              "RED             -0.906362  0.953404  ...        -0.551744        -0.485191\n",
              "BLUE            -0.782217  0.926190  ...        -0.540294        -0.440632\n",
              "GREEN           -0.785747  0.920514  ...        -0.547665        -0.461955\n",
              "NIR             -0.082184  0.305229  ...        -0.260944        -0.139902\n",
              "SWIR1           -0.828386  0.860419  ...        -0.458383        -0.329721\n",
              "SWIR2           -0.819112  0.827569  ...        -0.389800        -0.239200\n",
              "Leaf_Area_index -0.943687  0.999991  ...        -0.527177        -0.491145\n",
              "Temperature     -0.492243  0.581301  ...        -0.374051        -0.270357\n",
              "Precipitation         NaN       NaN  ...              NaN              NaN\n",
              "watermark_1      0.266536 -0.235848  ...        -0.078304         0.016401\n",
              "watermark_2     -0.495431  0.541757  ...        -0.999115        -0.760316\n",
              "watermark_3     -0.466413  0.498396  ...        -0.756441        -0.999786\n",
              "Soil_Moisture20 -0.281038  0.250569  ...         0.066738        -0.026216\n",
              "Soil_Moisture40  0.477740 -0.525733  ...         1.000000         0.755552\n",
              "Soil_Moisture60  0.458001 -0.490093  ...         0.755552         1.000000\n",
              "\n",
              "[18 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zZR3qNU2vzU"
      },
      "source": [
        "Datasetfinalsenti = Datasetfinalsenti.drop([\"watermark_1\", \"watermark_2\", \"watermark_3\"], axis = 1)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DlyVVI5bu0WH",
        "outputId": "d9632e8e-d6ae-4f02-b6da-d82e15830149"
      },
      "source": [
        "pip install lazypredict==0.2.7"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lazypredict==0.2.7\n",
            "  Downloading lazypredict-0.2.7-py2.py3-none-any.whl (11 kB)\n",
            "Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.7/dist-packages (from lazypredict==0.2.7) (7.1.2)\n",
            "Installing collected packages: lazypredict\n",
            "Successfully installed lazypredict-0.2.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgFOuqdUVMf8"
      },
      "source": [
        "from lazypredict.Supervised import LazyClassifier, LazyRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "X20 = Datasetfinalsenti.drop([\"Soil_Moisture20\", \"Soil_Moisture40\", \"Soil_Moisture60\"], axis=1)\n",
        "y20 = Datasetfinalsenti.Soil_Moisture20\n",
        "\n",
        "X40 = Datasetfinalsenti.drop([\"Soil_Moisture20\", \"Soil_Moisture40\", \"Soil_Moisture60\"], axis=1)\n",
        "y40 = Datasetfinalsenti.Soil_Moisture40\n",
        "\n",
        "X60 = Datasetfinalsenti.drop([\"Soil_Moisture20\", \"Soil_Moisture40\", \"Soil_Moisture60\"], axis=1)\n",
        "y60 = Datasetfinalsenti.Soil_Moisture60"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41ButLrwWpMp",
        "outputId": "f2480f17-cd8a-467e-f050-769ae294a129"
      },
      "source": [
        "X_train20, X_test20, y_train20, y_test20 = train_test_split(X20, y20, test_size=.2, random_state=42)\n",
        "reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)\n",
        "model, predictions = reg.fit(X_train20, X_test20, y_train20, y_test20)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 39/39 [00:02<00:00, 15.32it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "StackingRegressor model failed to execute\n",
            "__init__() missing 1 required positional argument: 'estimators'\n",
            "[11:54:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WGo89helx4r9",
        "outputId": "9158fe11-4770-4598-e388-eba59c774d98"
      },
      "source": [
        "model"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.48</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.47</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.43</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.41</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.38</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.36</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.35</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.33</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.33</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.33</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>0.26</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.23</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.21</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.15</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>0.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>-0.12</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>-0.29</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>-0.60</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>-0.85</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>-18.38</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-46.86</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>-120.75</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>-158.06</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-5053.57</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>-4497906.36</td>\n",
              "      <td>13.80</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                R-Squared  RMSE  Time Taken\n",
              "Model                                                      \n",
              "BaggingRegressor                     0.67  0.00        0.03\n",
              "ExtraTreesRegressor                  0.48  0.00        0.12\n",
              "RandomForestRegressor                0.47  0.00        0.21\n",
              "RidgeCV                              0.43  0.00        0.01\n",
              "TransformedTargetRegressor           0.42  0.00        0.01\n",
              "LinearRegression                     0.42  0.00        0.02\n",
              "OrthogonalMatchingPursuitCV          0.41  0.01        0.04\n",
              "ExtraTreeRegressor                   0.38  0.01        0.01\n",
              "BayesianRidge                        0.36  0.01        0.01\n",
              "GradientBoostingRegressor            0.35  0.01        0.09\n",
              "LassoLarsIC                          0.34  0.01        0.03\n",
              "LassoCV                              0.33  0.01        0.50\n",
              "ElasticNetCV                         0.33  0.01        0.38\n",
              "LassoLarsCV                          0.33  0.01        0.07\n",
              "Ridge                                0.32  0.01        0.01\n",
              "LGBMRegressor                        0.26  0.01        0.03\n",
              "HistGradientBoostingRegressor        0.23  0.01        0.12\n",
              "KNeighborsRegressor                  0.21  0.01        0.01\n",
              "OrthogonalMatchingPursuit            0.15  0.01        0.02\n",
              "AdaBoostRegressor                    0.11  0.01        0.08\n",
              "XGBRegressor                         0.04  0.01        0.04\n",
              "LarsCV                               0.03  0.01        0.07\n",
              "RANSACRegressor                      0.00  0.01        0.11\n",
              "DummyRegressor                      -0.02  0.01        0.01\n",
              "ElasticNet                          -0.02  0.01        0.01\n",
              "Lasso                               -0.02  0.01        0.02\n",
              "LassoLars                           -0.02  0.01        0.02\n",
              "HuberRegressor                      -0.07  0.01        0.04\n",
              "DecisionTreeRegressor               -0.12  0.01        0.01\n",
              "NuSVR                               -0.29  0.01        0.07\n",
              "LinearSVR                           -0.60  0.01        0.04\n",
              "SVR                                 -0.85  0.01        0.01\n",
              "SGDRegressor                       -18.38  0.03        0.01\n",
              "PassiveAggressiveRegressor         -46.86  0.05        0.02\n",
              "GaussianProcessRegressor          -120.75  0.07        0.01\n",
              "MLPRegressor                      -158.06  0.08        0.14\n",
              "KernelRidge                      -5053.57  0.46        0.03\n",
              "Lars                          -4497906.36 13.80        0.05"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z5BzB9WByi5b",
        "outputId": "30767cfa-a1d3-4b7b-87e8-56788be6813a"
      },
      "source": [
        "X_train40, X_test40, y_train40, y_test40 = train_test_split(X40, y40, test_size=.2, random_state=42)\n",
        "reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)\n",
        "model, predictions = reg.fit(X_train40, X_test40, y_train40, y_test40)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 39/39 [00:02<00:00, 18.93it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "StackingRegressor model failed to execute\n",
            "__init__() missing 1 required positional argument: 'estimators'\n",
            "[11:54:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Jq4nrxdg3N3I",
        "outputId": "aa640bf1-7032-4d6a-ef93-fb6b2d66593a"
      },
      "source": [
        "model"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.28</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>-0.24</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>-0.31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>-0.44</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-0.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>-0.64</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>-0.79</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>-1.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>-1.90</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>-7.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>-96.63</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-201.12</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>-398.44</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>-602.05</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>-676.02</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-23092.35</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               R-Squared  RMSE  Time Taken\n",
              "Model                                                     \n",
              "HuberRegressor                      0.28  0.00        0.04\n",
              "NuSVR                               0.21  0.00        0.03\n",
              "KNeighborsRegressor                 0.05  0.00        0.01\n",
              "LassoLarsCV                         0.03  0.00        0.05\n",
              "RidgeCV                             0.02  0.00        0.02\n",
              "Ridge                               0.02  0.00        0.03\n",
              "OrthogonalMatchingPursuitCV        -0.01  0.00        0.02\n",
              "LarsCV                             -0.01  0.00        0.07\n",
              "LassoCV                            -0.03  0.00        0.20\n",
              "ElasticNetCV                       -0.03  0.00        0.22\n",
              "LassoLarsIC                        -0.05  0.00        0.02\n",
              "ExtraTreesRegressor                -0.05  0.00        0.14\n",
              "BayesianRidge                      -0.05  0.00        0.01\n",
              "LinearSVR                          -0.06  0.00        0.03\n",
              "Lasso                              -0.07  0.00        0.01\n",
              "ElasticNet                         -0.07  0.00        0.01\n",
              "DummyRegressor                     -0.07  0.00        0.01\n",
              "LassoLars                          -0.07  0.00        0.03\n",
              "OrthogonalMatchingPursuit          -0.07  0.00        0.02\n",
              "TransformedTargetRegressor         -0.17  0.00        0.02\n",
              "LinearRegression                   -0.17  0.00        0.01\n",
              "RandomForestRegressor              -0.24  0.00        0.20\n",
              "BaggingRegressor                   -0.31  0.00        0.02\n",
              "HistGradientBoostingRegressor      -0.35  0.00        0.11\n",
              "LGBMRegressor                      -0.35  0.00        0.03\n",
              "AdaBoostRegressor                  -0.44  0.00        0.08\n",
              "RANSACRegressor                    -0.45  0.00        0.14\n",
              "GradientBoostingRegressor          -0.64  0.00        0.09\n",
              "XGBRegressor                       -0.79  0.00        0.04\n",
              "DecisionTreeRegressor              -1.14  0.00        0.01\n",
              "ExtraTreeRegressor                 -1.90  0.01        0.02\n",
              "SVR                                -7.03  0.01        0.03\n",
              "SGDRegressor                      -96.63  0.03        0.03\n",
              "PassiveAggressiveRegressor       -201.12  0.04        0.01\n",
              "Lars                             -398.44  0.06        0.03\n",
              "GaussianProcessRegressor         -602.05  0.08        0.02\n",
              "MLPRegressor                     -676.02  0.08        0.13\n",
              "KernelRidge                    -23092.35  0.47        0.01"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYk7n2Zg0NKR",
        "outputId": "3ba732a8-6862-4c7e-ea1c-1b4cfb6c5d38"
      },
      "source": [
        "X_train60, X_test60, y_train60, y_test60 = train_test_split(X60, y60, test_size=.2, random_state=42)\n",
        "reg = LazyRegressor(verbose=0,ignore_warnings=False, custom_metric=None)\n",
        "model60, predictions = reg.fit(X_train60, X_test60, y_train60, y_test60)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 39/39 [00:02<00:00, 17.67it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "StackingRegressor model failed to execute\n",
            "__init__() missing 1 required positional argument: 'estimators'\n",
            "[11:54:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1qFX4DGO3zMH",
        "outputId": "c1d44bc0-41e1-42e8-a456-0bc88e98a869"
      },
      "source": [
        "model"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.28</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>-0.24</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>-0.31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>-0.44</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-0.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>-0.64</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>-0.79</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>-1.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>-1.90</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>-7.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>-96.63</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-201.12</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>-398.44</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>-602.05</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>-676.02</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-23092.35</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               R-Squared  RMSE  Time Taken\n",
              "Model                                                     \n",
              "HuberRegressor                      0.28  0.00        0.04\n",
              "NuSVR                               0.21  0.00        0.03\n",
              "KNeighborsRegressor                 0.05  0.00        0.01\n",
              "LassoLarsCV                         0.03  0.00        0.05\n",
              "RidgeCV                             0.02  0.00        0.02\n",
              "Ridge                               0.02  0.00        0.03\n",
              "OrthogonalMatchingPursuitCV        -0.01  0.00        0.02\n",
              "LarsCV                             -0.01  0.00        0.07\n",
              "LassoCV                            -0.03  0.00        0.20\n",
              "ElasticNetCV                       -0.03  0.00        0.22\n",
              "LassoLarsIC                        -0.05  0.00        0.02\n",
              "ExtraTreesRegressor                -0.05  0.00        0.14\n",
              "BayesianRidge                      -0.05  0.00        0.01\n",
              "LinearSVR                          -0.06  0.00        0.03\n",
              "Lasso                              -0.07  0.00        0.01\n",
              "ElasticNet                         -0.07  0.00        0.01\n",
              "DummyRegressor                     -0.07  0.00        0.01\n",
              "LassoLars                          -0.07  0.00        0.03\n",
              "OrthogonalMatchingPursuit          -0.07  0.00        0.02\n",
              "TransformedTargetRegressor         -0.17  0.00        0.02\n",
              "LinearRegression                   -0.17  0.00        0.01\n",
              "RandomForestRegressor              -0.24  0.00        0.20\n",
              "BaggingRegressor                   -0.31  0.00        0.02\n",
              "HistGradientBoostingRegressor      -0.35  0.00        0.11\n",
              "LGBMRegressor                      -0.35  0.00        0.03\n",
              "AdaBoostRegressor                  -0.44  0.00        0.08\n",
              "RANSACRegressor                    -0.45  0.00        0.14\n",
              "GradientBoostingRegressor          -0.64  0.00        0.09\n",
              "XGBRegressor                       -0.79  0.00        0.04\n",
              "DecisionTreeRegressor              -1.14  0.00        0.01\n",
              "ExtraTreeRegressor                 -1.90  0.01        0.02\n",
              "SVR                                -7.03  0.01        0.03\n",
              "SGDRegressor                      -96.63  0.03        0.03\n",
              "PassiveAggressiveRegressor       -201.12  0.04        0.01\n",
              "Lars                             -398.44  0.06        0.03\n",
              "GaussianProcessRegressor         -602.05  0.08        0.02\n",
              "MLPRegressor                     -676.02  0.08        0.13\n",
              "KernelRidge                    -23092.35  0.47        0.01"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpr9vjBC0qpj",
        "outputId": "7dbb685a-0f88-4291-f1f7-c18cdb7aa060"
      },
      "source": [
        "from sklearn.linear_model import RidgeCV\n",
        "clf = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X_train60, y_train60)\n",
        "clf.score(X_test60, y_test60)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.214749720736214"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACZc80-vYOHx",
        "outputId": "1f39dacd-adb0-4223-f888-621cf846768c"
      },
      "source": [
        "print(model)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               R-Squared  RMSE  Time Taken\n",
            "Model                                                     \n",
            "HuberRegressor                      0.28  0.00        0.04\n",
            "NuSVR                               0.21  0.00        0.03\n",
            "KNeighborsRegressor                 0.05  0.00        0.01\n",
            "LassoLarsCV                         0.03  0.00        0.05\n",
            "RidgeCV                             0.02  0.00        0.02\n",
            "Ridge                               0.02  0.00        0.03\n",
            "OrthogonalMatchingPursuitCV        -0.01  0.00        0.02\n",
            "LarsCV                             -0.01  0.00        0.07\n",
            "LassoCV                            -0.03  0.00        0.20\n",
            "ElasticNetCV                       -0.03  0.00        0.22\n",
            "LassoLarsIC                        -0.05  0.00        0.02\n",
            "ExtraTreesRegressor                -0.05  0.00        0.14\n",
            "BayesianRidge                      -0.05  0.00        0.01\n",
            "LinearSVR                          -0.06  0.00        0.03\n",
            "Lasso                              -0.07  0.00        0.01\n",
            "ElasticNet                         -0.07  0.00        0.01\n",
            "DummyRegressor                     -0.07  0.00        0.01\n",
            "LassoLars                          -0.07  0.00        0.03\n",
            "OrthogonalMatchingPursuit          -0.07  0.00        0.02\n",
            "TransformedTargetRegressor         -0.17  0.00        0.02\n",
            "LinearRegression                   -0.17  0.00        0.01\n",
            "RandomForestRegressor              -0.24  0.00        0.20\n",
            "BaggingRegressor                   -0.31  0.00        0.02\n",
            "HistGradientBoostingRegressor      -0.35  0.00        0.11\n",
            "LGBMRegressor                      -0.35  0.00        0.03\n",
            "AdaBoostRegressor                  -0.44  0.00        0.08\n",
            "RANSACRegressor                    -0.45  0.00        0.14\n",
            "GradientBoostingRegressor          -0.64  0.00        0.09\n",
            "XGBRegressor                       -0.79  0.00        0.04\n",
            "DecisionTreeRegressor              -1.14  0.00        0.01\n",
            "ExtraTreeRegressor                 -1.90  0.01        0.02\n",
            "SVR                                -7.03  0.01        0.03\n",
            "SGDRegressor                      -96.63  0.03        0.03\n",
            "PassiveAggressiveRegressor       -201.12  0.04        0.01\n",
            "Lars                             -398.44  0.06        0.03\n",
            "GaussianProcessRegressor         -602.05  0.08        0.02\n",
            "MLPRegressor                     -676.02  0.08        0.13\n",
            "KernelRidge                    -23092.35  0.47        0.01\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqBCSVGuXEEn",
        "outputId": "1b7621b6-19eb-4a09-a2f7-0fa551de5472"
      },
      "source": [
        "pip install hyperopt==0.2.3"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: hyperopt==0.2.3 in /usr/local/lib/python3.7/dist-packages (0.2.3)\n",
            "Requirement already satisfied: networkx==2.2 in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3) (2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3) (1.7.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3) (1.15.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3) (0.16.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3) (4.62.3)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3) (2.0.0)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx==2.2->hyperopt==0.2.3) (4.4.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYf1NrbOYP6J"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "from hpsklearn import HyperoptEstimator\n",
        "from hpsklearn import any_regressor\n",
        "from hpsklearn import any_preprocessing\n",
        "from hyperopt import tpe"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "zSzT0VNLYn-o",
        "outputId": "650ab567-2605-42f6-a2b9-416b2b71e9fc"
      },
      "source": [
        "model = HyperoptEstimator(regressor=any_regressor('reg'), preprocessing=any_preprocessing('pre'), loss_fn='mean_absolute_error', algo=tpe.suggest, max_evals=50, trial_timeout=30)\n",
        "# perform the search\n",
        "model.fit(X_train20, y_train20)\n",
        "# summarize performance\n",
        "mae = model.score(X_test20, y_test20)\n",
        "print(\"MAE: %.3f\" % mae)\n",
        "# summarize the best model\n",
        "print(model.best_model())"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  0%|          | 0/1 [00:00<?, ?trial/s, best loss=?]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "job exception: \"None of [Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\\n            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\\n            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\\n            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\\n            68, 69, 70, 71, 72, 73, 74],\\n           dtype='int64')] are in the [columns]\"\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  0%|          | 0/1 [00:00<?, ?trial/s, best loss=?]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-107-6caa2ce7c388>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHyperoptEstimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregressor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0many_regressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'reg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0many_preprocessing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pre'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'mean_absolute_error'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malgo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtpe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_evals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial_timeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# perform the search\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m# summarize performance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hpsklearn/estimator.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, EX_list, valid_size, n_folds, cv_shuffle, warm_start, random_state, weights)\u001b[0m\n\u001b[1;32m    744\u001b[0m             increment = min(self.fit_increment,\n\u001b[1;32m    745\u001b[0m                             adjusted_max_evals - len(self.trials.trials))\n\u001b[0;32m--> 746\u001b[0;31m             \u001b[0mfit_iter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mincrement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    747\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdump_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hpsklearn/estimator.py\u001b[0m in \u001b[0;36mfit_iter\u001b[0;34m(self, X, y, EX_list, valid_size, n_folds, cv_shuffle, warm_start, random_state, weights, increment)\u001b[0m\n\u001b[1;32m    655\u001b[0m                               \u001b[0;31m#    so we notice them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m                               \u001b[0mcatch_eval_exceptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 657\u001b[0;31m                               \u001b[0mreturn_argmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# -- in case no success so far\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    658\u001b[0m                              )\n\u001b[1;32m    659\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mfmin\u001b[0;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar)\u001b[0m\n\u001b[1;32m    480\u001b[0m             \u001b[0mcatch_eval_exceptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcatch_eval_exceptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m             \u001b[0mreturn_argmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_argmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m             \u001b[0mshow_progressbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshow_progressbar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m         )\n\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hyperopt/base.py\u001b[0m in \u001b[0;36mfmin\u001b[0;34m(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar)\u001b[0m\n\u001b[1;32m    684\u001b[0m             \u001b[0mcatch_eval_exceptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcatch_eval_exceptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    685\u001b[0m             \u001b[0mreturn_argmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_argmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m             \u001b[0mshow_progressbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshow_progressbar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m         )\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mfmin\u001b[0;34m(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar)\u001b[0m\n\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m     \u001b[0;31m# next line is where the fmin is actually executed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m     \u001b[0mrval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexhaust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreturn_argmin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mexhaust\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mexhaust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0mn_done\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_evals\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mn_done\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock_until_done\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masynchronous\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    331\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, N, block_until_done)\u001b[0m\n\u001b[1;32m    284\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m                     \u001b[0;31m# -- loop over trials and do the jobs directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserial_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hyperopt/fmin.py\u001b[0m in \u001b[0;36mserial_evaluate\u001b[0;34m(self, N)\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0mctrl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCtrl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_trial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctrl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m                     \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"job exception: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hyperopt/base.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, config, ctrl, attach_attachments)\u001b[0m\n\u001b[1;32m    892\u001b[0m                 \u001b[0mprint_node_on_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrec_eval_print_node_on_error\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m             )\n\u001b[0;32m--> 894\u001b[0;31m             \u001b[0mrval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpyll_rval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/hpsklearn/estimator.py\u001b[0m in \u001b[0;36mfn_with_timeout\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    618\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mfn_rval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'return'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfn_rval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'raise'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mfn_rval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m             \u001b[0;31m# -- remove potentially large objects from the rval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"None of [Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\\n            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\\n            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\\n            51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\\n            68, 69, 70, 71, 72, 73, 74],\\n           dtype='int64')] are in the [columns]\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DJ2Y-24maMJV",
        "outputId": "0db1b1bb-a483-4fb5-df1e-88193875d4d2"
      },
      "source": [
        "X_train20.head()"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NDVI</th>\n",
              "      <th>NDWI</th>\n",
              "      <th>SAVI</th>\n",
              "      <th>RED</th>\n",
              "      <th>BLUE</th>\n",
              "      <th>GREEN</th>\n",
              "      <th>NIR</th>\n",
              "      <th>SWIR1</th>\n",
              "      <th>SWIR2</th>\n",
              "      <th>Leaf_Area_index</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.19</td>\n",
              "      <td>-0.34</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.20</td>\n",
              "      <td>25.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.40</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.30</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>31.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.22</td>\n",
              "      <td>-0.34</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.19</td>\n",
              "      <td>30.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.21</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.13</td>\n",
              "      <td>14.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.36</td>\n",
              "      <td>-0.51</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>19.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    NDVI  NDWI  SAVI  RED  ...  SWIR2  Leaf_Area_index  Temperature  Precipitation\n",
              "6   0.19 -0.34  0.28 0.22  ...   0.34             0.20        25.00           0.00\n",
              "13  0.27 -0.40  0.40 0.18  ...   0.30            -0.00        31.00           0.00\n",
              "16  0.22 -0.34  0.32 0.20  ...   0.31             0.19        30.00           0.00\n",
              "32  0.21 -0.36  0.31 0.17  ...   0.26             0.13        14.00           0.00\n",
              "29  0.36 -0.51  0.54 0.14  ...   0.23            -0.32        19.00           0.00\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    }
  ]
}