MLCeres.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First implementation of Machine Learning Algorithm\n",
    "\n",
    "In this file, we are going to import data from a file .csv that we take from chornograf.\n",
    "We have to parse him and put into an array.\n",
    "Finally we will insert some machin Learning algorithm with the librairy sklearn.\n",
    "\n",
    "To do this, we will use some librairy as pandas, matplotlib, numpy, sklearn and some others.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "import csv\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans le fichier CeresTrain.csv\n",
    "\n",
    "Toutes les données de puis 1300h à partir 3 mars au 4 mai\n",
    "\n",
    "SELECT mean(\"moisture-percent\") AS \"mean_moisture-percent\", mean(\"temperature\") AS \"mean_temperature\" \n",
    "\n",
    "FROM \"ceres\".\"autogen\".\"chirp-left\" \n",
    "\n",
    "WHERE time > now() - 1500h AND time < now() -150h \n",
    "\n",
    "GROUP BY time(30m)\n",
    "\n",
    "\n",
    "SELECT mean(\"moisture-percent\") AS \"mean_moisture-percent\", mean(\"temperature\") AS \"mean_temperature\" FROM \"ceres\".\"autogen\".\"chirp-left\" WHERE time > now() - 2554h AND time <now() -220h GROUP BY time(30m) FILL(null)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import des données \n",
    "\n",
    "Leccture d'un fichier csv en un fichier dataframe.\n",
    "\n",
    "Création d'une base dataframe avec comme colonne le temps, l'humidité, la tempérture, l'humidité + 1, la température + 1.\n",
    "\n",
    "Il faut régler le problème tu dernier temps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparation des donnees ...\n",
      "Preparation des donnees ...\n"
     ]
    }
   ],
   "source": [
    "from traitement import Traitement\n",
    "\n",
    "\n",
    "#df = pd.read_csv('DataCeres.csv')\n",
    "#print(df)\n",
    "#print(df.iloc[:,2])\n",
    "name = 'DataCeres.csv'\n",
    "init = 6\n",
    "t= Traitement()\n",
    "\n",
    "#Création des deux autres colonnes\n",
    "dfCeres = t.preparation(name, init)\n",
    "\n",
    "name = 'DataDemeter.csv'\n",
    "dfDemeter = t.preparation(name, init)\n",
    "\n",
    "DF = pd.concat([dfCeres, dfDemeter],ignore_index = True)\n",
    "nligne = DF.shape[0]\n",
    "df = DF.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ajout de l'arrosage\n",
    "\n",
    "L'arrosage est ajouté au temps 10h30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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>date</th>\n",
       "      <th>mean_moisture-percent</th>\n",
       "      <th>mean_temperature</th>\n",
       "      <th>moistureAdd</th>\n",
       "      <th>temperatureAdd</th>\n",
       "      <th>Arrosage</th>\n",
       "      <th>TAfterArrosage</th>\n",
       "      <th>ArrosageHist</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4/26/2019 11:30:00.000000000 AM</td>\n",
       "      <td>25.670000</td>\n",
       "      <td>23.888333</td>\n",
       "      <td>11.695000</td>\n",
       "      <td>0.188333</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/26/2019 12:00:00.000000000 PM</td>\n",
       "      <td>37.365000</td>\n",
       "      <td>24.076667</td>\n",
       "      <td>-4.485000</td>\n",
       "      <td>-0.468333</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/26/2019 12:30:00.000000000 PM</td>\n",
       "      <td>32.880000</td>\n",
       "      <td>23.608333</td>\n",
       "      <td>-1.371667</td>\n",
       "      <td>0.151667</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/26/2019 1:00:00.000000000 PM</td>\n",
       "      <td>31.508333</td>\n",
       "      <td>23.760000</td>\n",
       "      <td>-0.758333</td>\n",
       "      <td>-0.001667</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/26/2019 1:30:00.000000000 PM</td>\n",
       "      <td>30.750000</td>\n",
       "      <td>23.758333</td>\n",
       "      <td>-0.306667</td>\n",
       "      <td>-0.410000</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4/26/2019 2:00:00.000000000 PM</td>\n",
       "      <td>30.443333</td>\n",
       "      <td>23.348333</td>\n",
       "      <td>-0.430000</td>\n",
       "      <td>-0.316667</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4/26/2019 2:30:00.000000000 PM</td>\n",
       "      <td>30.013333</td>\n",
       "      <td>23.031667</td>\n",
       "      <td>-0.486667</td>\n",
       "      <td>0.471667</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4/26/2019 3:00:00.000000000 PM</td>\n",
       "      <td>29.526667</td>\n",
       "      <td>23.503333</td>\n",
       "      <td>-0.353333</td>\n",
       "      <td>0.078333</td>\n",
       "      <td>0</td>\n",
       "      <td>210</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4/26/2019 3:30:00.000000000 PM</td>\n",
       "      <td>29.173333</td>\n",
       "      <td>23.581667</td>\n",
       "      <td>-0.413333</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4/26/2019 4:00:00.000000000 PM</td>\n",
       "      <td>28.760000</td>\n",
       "      <td>23.448333</td>\n",
       "      <td>-0.213333</td>\n",
       "      <td>-0.048333</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4/26/2019 4:30:00.000000000 PM</td>\n",
       "      <td>28.546667</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>-0.103333</td>\n",
       "      <td>-0.091667</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4/26/2019 5:00:00.000000000 PM</td>\n",
       "      <td>28.443333</td>\n",
       "      <td>23.308333</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.110000</td>\n",
       "      <td>0</td>\n",
       "      <td>330</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4/26/2019 5:30:00.000000000 PM</td>\n",
       "      <td>28.193333</td>\n",
       "      <td>23.198333</td>\n",
       "      <td>-0.186667</td>\n",
       "      <td>0.223333</td>\n",
       "      <td>0</td>\n",
       "      <td>360</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4/26/2019 6:00:00.000000000 PM</td>\n",
       "      <td>28.006667</td>\n",
       "      <td>23.421667</td>\n",
       "      <td>-0.270000</td>\n",
       "      <td>0.248333</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4/26/2019 6:30:00.000000000 PM</td>\n",
       "      <td>27.736667</td>\n",
       "      <td>23.670000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.408333</td>\n",
       "      <td>0</td>\n",
       "      <td>420</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4/26/2019 7:00:00.000000000 PM</td>\n",
       "      <td>27.736667</td>\n",
       "      <td>23.261667</td>\n",
       "      <td>-0.191667</td>\n",
       "      <td>-0.026667</td>\n",
       "      <td>0</td>\n",
       "      <td>450</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4/26/2019 7:30:00.000000000 PM</td>\n",
       "      <td>27.545000</td>\n",
       "      <td>23.235000</td>\n",
       "      <td>-0.253333</td>\n",
       "      <td>-0.191667</td>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4/26/2019 8:00:00.000000000 PM</td>\n",
       "      <td>27.291667</td>\n",
       "      <td>23.043333</td>\n",
       "      <td>-0.068333</td>\n",
       "      <td>-0.196667</td>\n",
       "      <td>0</td>\n",
       "      <td>510</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4/26/2019 8:30:00.000000000 PM</td>\n",
       "      <td>27.223333</td>\n",
       "      <td>22.846667</td>\n",
       "      <td>-0.065000</td>\n",
       "      <td>-0.118333</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4/26/2019 9:00:00.000000000 PM</td>\n",
       "      <td>27.158333</td>\n",
       "      <td>22.728333</td>\n",
       "      <td>-0.060000</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>0</td>\n",
       "      <td>570</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4/26/2019 9:30:00.000000000 PM</td>\n",
       "      <td>27.098333</td>\n",
       "      <td>22.698333</td>\n",
       "      <td>-0.141667</td>\n",
       "      <td>-0.045000</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4/26/2019 10:00:00.000000000 PM</td>\n",
       "      <td>26.956667</td>\n",
       "      <td>22.653333</td>\n",
       "      <td>-0.093333</td>\n",
       "      <td>-0.053333</td>\n",
       "      <td>0</td>\n",
       "      <td>630</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4/26/2019 10:30:00.000000000 PM</td>\n",
       "      <td>26.863333</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>-0.026667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>660</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4/26/2019 11:00:00.000000000 PM</td>\n",
       "      <td>26.836667</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>-0.143333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4/26/2019 11:30:00.000000000 PM</td>\n",
       "      <td>26.693333</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>-0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>720</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4/27/2019 12:00:00.000000000 AM</td>\n",
       "      <td>26.653333</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>-0.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>750</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4/27/2019 12:30:00.000000000 AM</td>\n",
       "      <td>26.523333</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>-0.076667</td>\n",
       "      <td>-0.073333</td>\n",
       "      <td>0</td>\n",
       "      <td>780</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4/27/2019 1:00:00.000000000 AM</td>\n",
       "      <td>26.446667</td>\n",
       "      <td>22.526667</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>-0.026667</td>\n",
       "      <td>0</td>\n",
       "      <td>810</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4/27/2019 1:30:00.000000000 AM</td>\n",
       "      <td>26.426667</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>-0.070000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>840</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4/27/2019 2:00:00.000000000 AM</td>\n",
       "      <td>26.356667</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>870</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6627</th>\n",
       "      <td>6/20/2019 10:00:00.000000000 AM</td>\n",
       "      <td>14.330000</td>\n",
       "      <td>28.108333</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>-0.283333</td>\n",
       "      <td>0</td>\n",
       "      <td>1350</td>\n",
       "      <td>15</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6628</th>\n",
       "      <td>6/20/2019 10:30:00.000000000 AM</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>27.825000</td>\n",
       "      <td>0.086667</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>1380</td>\n",
       "      <td>15</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6629</th>\n",
       "      <td>6/20/2019 11:00:00.000000000 AM</td>\n",
       "      <td>14.420000</td>\n",
       "      <td>27.425000</td>\n",
       "      <td>-0.116667</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0</td>\n",
       "      <td>1410</td>\n",
       "      <td>15</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6630</th>\n",
       "      <td>6/20/2019 11:30:00.000000000 AM</td>\n",
       "      <td>14.303333</td>\n",
       "      <td>27.440000</td>\n",
       "      <td>5.503333</td>\n",
       "      <td>-0.933333</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6631</th>\n",
       "      <td>6/20/2019 12:00:00.000000000 PM</td>\n",
       "      <td>19.806667</td>\n",
       "      <td>26.506667</td>\n",
       "      <td>-2.783333</td>\n",
       "      <td>0.478333</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6632</th>\n",
       "      <td>6/20/2019 12:30:00.000000000 PM</td>\n",
       "      <td>17.023333</td>\n",
       "      <td>26.985000</td>\n",
       "      <td>-0.566667</td>\n",
       "      <td>0.586667</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>6/20/2019 1:00:00.000000000 PM</td>\n",
       "      <td>16.456667</td>\n",
       "      <td>27.571667</td>\n",
       "      <td>-0.356667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6634</th>\n",
       "      <td>6/20/2019 1:30:00.000000000 PM</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>28.405000</td>\n",
       "      <td>-0.196667</td>\n",
       "      <td>1.613333</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6635</th>\n",
       "      <td>6/20/2019 2:00:00.000000000 PM</td>\n",
       "      <td>15.903333</td>\n",
       "      <td>30.018333</td>\n",
       "      <td>-0.293333</td>\n",
       "      <td>0.746667</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6636</th>\n",
       "      <td>6/20/2019 2:30:00.000000000 PM</td>\n",
       "      <td>15.610000</td>\n",
       "      <td>30.765000</td>\n",
       "      <td>-0.203333</td>\n",
       "      <td>0.228333</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6637</th>\n",
       "      <td>6/20/2019 3:00:00.000000000 PM</td>\n",
       "      <td>15.406667</td>\n",
       "      <td>30.993333</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>-1.195000</td>\n",
       "      <td>0</td>\n",
       "      <td>210</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6638</th>\n",
       "      <td>6/20/2019 3:30:00.000000000 PM</td>\n",
       "      <td>15.420000</td>\n",
       "      <td>29.798333</td>\n",
       "      <td>-0.416667</td>\n",
       "      <td>3.261667</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6639</th>\n",
       "      <td>6/20/2019 4:00:00.000000000 PM</td>\n",
       "      <td>15.003333</td>\n",
       "      <td>33.060000</td>\n",
       "      <td>-0.070000</td>\n",
       "      <td>-1.145000</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6640</th>\n",
       "      <td>6/20/2019 4:30:00.000000000 PM</td>\n",
       "      <td>14.933333</td>\n",
       "      <td>31.915000</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>-0.838333</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6641</th>\n",
       "      <td>6/20/2019 5:00:00.000000000 PM</td>\n",
       "      <td>14.903333</td>\n",
       "      <td>31.076667</td>\n",
       "      <td>-0.043333</td>\n",
       "      <td>-0.216667</td>\n",
       "      <td>0</td>\n",
       "      <td>330</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6642</th>\n",
       "      <td>6/20/2019 5:30:00.000000000 PM</td>\n",
       "      <td>14.860000</td>\n",
       "      <td>30.860000</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>-0.285000</td>\n",
       "      <td>0</td>\n",
       "      <td>360</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6643</th>\n",
       "      <td>6/20/2019 6:00:00.000000000 PM</td>\n",
       "      <td>14.913333</td>\n",
       "      <td>30.575000</td>\n",
       "      <td>-0.056667</td>\n",
       "      <td>-0.156667</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6644</th>\n",
       "      <td>6/20/2019 6:30:00.000000000 PM</td>\n",
       "      <td>14.856667</td>\n",
       "      <td>30.418333</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>-0.280000</td>\n",
       "      <td>0</td>\n",
       "      <td>420</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6645</th>\n",
       "      <td>6/20/2019 7:00:00.000000000 PM</td>\n",
       "      <td>14.886667</td>\n",
       "      <td>30.138333</td>\n",
       "      <td>-0.080000</td>\n",
       "      <td>-0.225000</td>\n",
       "      <td>0</td>\n",
       "      <td>450</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6646</th>\n",
       "      <td>6/20/2019 7:30:00.000000000 PM</td>\n",
       "      <td>14.806667</td>\n",
       "      <td>29.913333</td>\n",
       "      <td>-0.126667</td>\n",
       "      <td>-0.211667</td>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6647</th>\n",
       "      <td>6/20/2019 8:00:00.000000000 PM</td>\n",
       "      <td>14.680000</td>\n",
       "      <td>29.701667</td>\n",
       "      <td>0.043333</td>\n",
       "      <td>-0.298333</td>\n",
       "      <td>0</td>\n",
       "      <td>510</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6648</th>\n",
       "      <td>6/20/2019 8:30:00.000000000 PM</td>\n",
       "      <td>14.723333</td>\n",
       "      <td>29.403333</td>\n",
       "      <td>-0.013333</td>\n",
       "      <td>-0.165000</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6649</th>\n",
       "      <td>6/20/2019 9:00:00.000000000 PM</td>\n",
       "      <td>14.710000</td>\n",
       "      <td>29.238333</td>\n",
       "      <td>0.076667</td>\n",
       "      <td>-0.141667</td>\n",
       "      <td>0</td>\n",
       "      <td>570</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6650</th>\n",
       "      <td>6/20/2019 9:30:00.000000000 PM</td>\n",
       "      <td>14.786667</td>\n",
       "      <td>29.096667</td>\n",
       "      <td>-0.056667</td>\n",
       "      <td>-0.118333</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6651</th>\n",
       "      <td>6/20/2019 10:00:00.000000000 PM</td>\n",
       "      <td>14.730000</td>\n",
       "      <td>28.978333</td>\n",
       "      <td>-0.043333</td>\n",
       "      <td>-0.113333</td>\n",
       "      <td>0</td>\n",
       "      <td>630</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6652</th>\n",
       "      <td>6/20/2019 10:30:00.000000000 PM</td>\n",
       "      <td>14.686667</td>\n",
       "      <td>28.865000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>-0.065000</td>\n",
       "      <td>0</td>\n",
       "      <td>660</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6653</th>\n",
       "      <td>6/20/2019 11:00:00.000000000 PM</td>\n",
       "      <td>14.766667</td>\n",
       "      <td>28.800000</td>\n",
       "      <td>-0.123333</td>\n",
       "      <td>-0.081667</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6654</th>\n",
       "      <td>6/20/2019 11:30:00.000000000 PM</td>\n",
       "      <td>14.643333</td>\n",
       "      <td>28.718333</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>-0.065000</td>\n",
       "      <td>0</td>\n",
       "      <td>720</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6655</th>\n",
       "      <td>6/21/2019 12:00:00.000000000 AM</td>\n",
       "      <td>14.653333</td>\n",
       "      <td>28.653333</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>-0.053333</td>\n",
       "      <td>0</td>\n",
       "      <td>750</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6656</th>\n",
       "      <td>6/21/2019 12:30:00.000000000 AM</td>\n",
       "      <td>14.666667</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>780</td>\n",
       "      <td>15</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5697 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 date  mean_moisture-percent  \\\n",
       "0     4/26/2019 11:30:00.000000000 AM              25.670000   \n",
       "1     4/26/2019 12:00:00.000000000 PM              37.365000   \n",
       "2     4/26/2019 12:30:00.000000000 PM              32.880000   \n",
       "3      4/26/2019 1:00:00.000000000 PM              31.508333   \n",
       "4      4/26/2019 1:30:00.000000000 PM              30.750000   \n",
       "5      4/26/2019 2:00:00.000000000 PM              30.443333   \n",
       "6      4/26/2019 2:30:00.000000000 PM              30.013333   \n",
       "7      4/26/2019 3:00:00.000000000 PM              29.526667   \n",
       "8      4/26/2019 3:30:00.000000000 PM              29.173333   \n",
       "9      4/26/2019 4:00:00.000000000 PM              28.760000   \n",
       "10     4/26/2019 4:30:00.000000000 PM              28.546667   \n",
       "11     4/26/2019 5:00:00.000000000 PM              28.443333   \n",
       "12     4/26/2019 5:30:00.000000000 PM              28.193333   \n",
       "13     4/26/2019 6:00:00.000000000 PM              28.006667   \n",
       "14     4/26/2019 6:30:00.000000000 PM              27.736667   \n",
       "15     4/26/2019 7:00:00.000000000 PM              27.736667   \n",
       "16     4/26/2019 7:30:00.000000000 PM              27.545000   \n",
       "17     4/26/2019 8:00:00.000000000 PM              27.291667   \n",
       "18     4/26/2019 8:30:00.000000000 PM              27.223333   \n",
       "19     4/26/2019 9:00:00.000000000 PM              27.158333   \n",
       "20     4/26/2019 9:30:00.000000000 PM              27.098333   \n",
       "21    4/26/2019 10:00:00.000000000 PM              26.956667   \n",
       "22    4/26/2019 10:30:00.000000000 PM              26.863333   \n",
       "23    4/26/2019 11:00:00.000000000 PM              26.836667   \n",
       "24    4/26/2019 11:30:00.000000000 PM              26.693333   \n",
       "25    4/27/2019 12:00:00.000000000 AM              26.653333   \n",
       "26    4/27/2019 12:30:00.000000000 AM              26.523333   \n",
       "27     4/27/2019 1:00:00.000000000 AM              26.446667   \n",
       "28     4/27/2019 1:30:00.000000000 AM              26.426667   \n",
       "29     4/27/2019 2:00:00.000000000 AM              26.356667   \n",
       "...                               ...                    ...   \n",
       "6627  6/20/2019 10:00:00.000000000 AM              14.330000   \n",
       "6628  6/20/2019 10:30:00.000000000 AM              14.333333   \n",
       "6629  6/20/2019 11:00:00.000000000 AM              14.420000   \n",
       "6630  6/20/2019 11:30:00.000000000 AM              14.303333   \n",
       "6631  6/20/2019 12:00:00.000000000 PM              19.806667   \n",
       "6632  6/20/2019 12:30:00.000000000 PM              17.023333   \n",
       "6633   6/20/2019 1:00:00.000000000 PM              16.456667   \n",
       "6634   6/20/2019 1:30:00.000000000 PM              16.100000   \n",
       "6635   6/20/2019 2:00:00.000000000 PM              15.903333   \n",
       "6636   6/20/2019 2:30:00.000000000 PM              15.610000   \n",
       "6637   6/20/2019 3:00:00.000000000 PM              15.406667   \n",
       "6638   6/20/2019 3:30:00.000000000 PM              15.420000   \n",
       "6639   6/20/2019 4:00:00.000000000 PM              15.003333   \n",
       "6640   6/20/2019 4:30:00.000000000 PM              14.933333   \n",
       "6641   6/20/2019 5:00:00.000000000 PM              14.903333   \n",
       "6642   6/20/2019 5:30:00.000000000 PM              14.860000   \n",
       "6643   6/20/2019 6:00:00.000000000 PM              14.913333   \n",
       "6644   6/20/2019 6:30:00.000000000 PM              14.856667   \n",
       "6645   6/20/2019 7:00:00.000000000 PM              14.886667   \n",
       "6646   6/20/2019 7:30:00.000000000 PM              14.806667   \n",
       "6647   6/20/2019 8:00:00.000000000 PM              14.680000   \n",
       "6648   6/20/2019 8:30:00.000000000 PM              14.723333   \n",
       "6649   6/20/2019 9:00:00.000000000 PM              14.710000   \n",
       "6650   6/20/2019 9:30:00.000000000 PM              14.786667   \n",
       "6651  6/20/2019 10:00:00.000000000 PM              14.730000   \n",
       "6652  6/20/2019 10:30:00.000000000 PM              14.686667   \n",
       "6653  6/20/2019 11:00:00.000000000 PM              14.766667   \n",
       "6654  6/20/2019 11:30:00.000000000 PM              14.643333   \n",
       "6655  6/21/2019 12:00:00.000000000 AM              14.653333   \n",
       "6656  6/21/2019 12:30:00.000000000 AM              14.666667   \n",
       "\n",
       "      mean_temperature  moistureAdd  temperatureAdd  Arrosage  TAfterArrosage  \\\n",
       "0            23.888333    11.695000        0.188333        30               0   \n",
       "1            24.076667    -4.485000       -0.468333         0              30   \n",
       "2            23.608333    -1.371667        0.151667         0              60   \n",
       "3            23.760000    -0.758333       -0.001667         0              90   \n",
       "4            23.758333    -0.306667       -0.410000         0             120   \n",
       "5            23.348333    -0.430000       -0.316667         0             150   \n",
       "6            23.031667    -0.486667        0.471667         0             180   \n",
       "7            23.503333    -0.353333        0.078333         0             210   \n",
       "8            23.581667    -0.413333       -0.133333         0             240   \n",
       "9            23.448333    -0.213333       -0.048333         0             270   \n",
       "10           23.400000    -0.103333       -0.091667         0             300   \n",
       "11           23.308333    -0.250000       -0.110000         0             330   \n",
       "12           23.198333    -0.186667        0.223333         0             360   \n",
       "13           23.421667    -0.270000        0.248333         0             390   \n",
       "14           23.670000     0.000000       -0.408333         0             420   \n",
       "15           23.261667    -0.191667       -0.026667         0             450   \n",
       "16           23.235000    -0.253333       -0.191667         0             480   \n",
       "17           23.043333    -0.068333       -0.196667         0             510   \n",
       "18           22.846667    -0.065000       -0.118333         0             540   \n",
       "19           22.728333    -0.060000       -0.030000         0             570   \n",
       "20           22.698333    -0.141667       -0.045000         0             600   \n",
       "21           22.653333    -0.093333       -0.053333         0             630   \n",
       "22           22.600000    -0.026667        0.000000         0             660   \n",
       "23           22.600000    -0.143333        0.000000         0             690   \n",
       "24           22.600000    -0.040000        0.000000         0             720   \n",
       "25           22.600000    -0.130000        0.000000         0             750   \n",
       "26           22.600000    -0.076667       -0.073333         0             780   \n",
       "27           22.526667    -0.020000       -0.026667         0             810   \n",
       "28           22.500000    -0.070000        0.000000         0             840   \n",
       "29           22.500000    -0.006667        0.000000         0             870   \n",
       "...                ...          ...             ...       ...             ...   \n",
       "6627         28.108333     0.003333       -0.283333         0            1350   \n",
       "6628         27.825000     0.086667       -0.400000         0            1380   \n",
       "6629         27.425000    -0.116667        0.015000         0            1410   \n",
       "6630         27.440000     5.503333       -0.933333        15               0   \n",
       "6631         26.506667    -2.783333        0.478333         0              30   \n",
       "6632         26.985000    -0.566667        0.586667         0              60   \n",
       "6633         27.571667    -0.356667        0.833333         0              90   \n",
       "6634         28.405000    -0.196667        1.613333         0             120   \n",
       "6635         30.018333    -0.293333        0.746667         0             150   \n",
       "6636         30.765000    -0.203333        0.228333         0             180   \n",
       "6637         30.993333     0.013333       -1.195000         0             210   \n",
       "6638         29.798333    -0.416667        3.261667         0             240   \n",
       "6639         33.060000    -0.070000       -1.145000         0             270   \n",
       "6640         31.915000    -0.030000       -0.838333         0             300   \n",
       "6641         31.076667    -0.043333       -0.216667         0             330   \n",
       "6642         30.860000     0.053333       -0.285000         0             360   \n",
       "6643         30.575000    -0.056667       -0.156667         0             390   \n",
       "6644         30.418333     0.030000       -0.280000         0             420   \n",
       "6645         30.138333    -0.080000       -0.225000         0             450   \n",
       "6646         29.913333    -0.126667       -0.211667         0             480   \n",
       "6647         29.701667     0.043333       -0.298333         0             510   \n",
       "6648         29.403333    -0.013333       -0.165000         0             540   \n",
       "6649         29.238333     0.076667       -0.141667         0             570   \n",
       "6650         29.096667    -0.056667       -0.118333         0             600   \n",
       "6651         28.978333    -0.043333       -0.113333         0             630   \n",
       "6652         28.865000     0.080000       -0.065000         0             660   \n",
       "6653         28.800000    -0.123333       -0.081667         0             690   \n",
       "6654         28.718333     0.010000       -0.065000         0             720   \n",
       "6655         28.653333     0.013333       -0.053333         0             750   \n",
       "6656         28.600000     0.000000        0.000000         0             780   \n",
       "\n",
       "      ArrosageHist  index  \n",
       "0               30     51  \n",
       "1               30     51  \n",
       "2               30     51  \n",
       "3               30     51  \n",
       "4               30     51  \n",
       "5               30     51  \n",
       "6               30     51  \n",
       "7               30     51  \n",
       "8               30     51  \n",
       "9               30     51  \n",
       "10              30     51  \n",
       "11              30     51  \n",
       "12              30     51  \n",
       "13              30     51  \n",
       "14              30     51  \n",
       "15              30     51  \n",
       "16              30     51  \n",
       "17              30     51  \n",
       "18              30     51  \n",
       "19              30     51  \n",
       "20              30     51  \n",
       "21              30     51  \n",
       "22              30     51  \n",
       "23              30     51  \n",
       "24              30     51  \n",
       "25              30     51  \n",
       "26              30     51  \n",
       "27              30     51  \n",
       "28              30     51  \n",
       "29              30     51  \n",
       "...            ...    ...  \n",
       "6627            15    105  \n",
       "6628            15    105  \n",
       "6629            15    105  \n",
       "6630            15    106  \n",
       "6631            15    106  \n",
       "6632            15    106  \n",
       "6633            15    106  \n",
       "6634            15    106  \n",
       "6635            15    106  \n",
       "6636            15    106  \n",
       "6637            15    106  \n",
       "6638            15    106  \n",
       "6639            15    106  \n",
       "6640            15    106  \n",
       "6641            15    106  \n",
       "6642            15    106  \n",
       "6643            15    106  \n",
       "6644            15    106  \n",
       "6645            15    106  \n",
       "6646            15    106  \n",
       "6647            15    106  \n",
       "6648            15    106  \n",
       "6649            15    106  \n",
       "6650            15    106  \n",
       "6651            15    106  \n",
       "6652            15    106  \n",
       "6653            15    106  \n",
       "6654            15    106  \n",
       "6655            15    106  \n",
       "6656            15    106  \n",
       "\n",
       "[5697 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Delete NaN Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'DF' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-f5bcb38f29e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDF\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'index'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m\u001b[0;36m48\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'DF' is not defined"
     ]
    }
   ],
   "source": [
    "DF.loc[DF['index'] >48 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date                     0\n",
      "mean_moisture-percent    0\n",
      "mean_temperature         0\n",
      "moistureAdd              0\n",
      "temperatureAdd           0\n",
      "Arrosage                 0\n",
      "TAfterArrosage           0\n",
      "ArrosageHist             0\n",
      "index                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "nligne = df.shape[0]\n",
    "nColumn = df.shape[1]\n",
    "print(df.isnull().sum())\n",
    "for i in range(nligne):\n",
    "    if df.iloc[i,:].isnull().values.any():\n",
    "        print(df.index[i])\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse de la dataFrame\n",
    "\n",
    "\n",
    "## Linear Regression Multiple Variables sans l'information sur le temps sans arrosage\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(df[['mean_moisture-percent','mean_temperature','Arrosage']],df.moistureAdd)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.0239038 , -0.02332515,  0.24890762])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0407578032109139"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2056872773404401"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[31,25,10]])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Multiple Variables AVEC l'information sur le temps sans arrosage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2 = linear_model.LinearRegression()\n",
    "reg2.fit(df[['mean_moisture-percent','mean_temperature','Arrosage','TAfterArrosage']],df.moistureAdd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.01564893, -0.00474783,  0.25696149,  0.00028094])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17277875051347558"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1385810545774468"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2.predict([[31,25,10,0]])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test de mes résultats\n",
    "\n",
    "SANS DES DONNÉES POUR VERIFICATIONS. PAS DE DATA TEST.\n",
    "\n",
    "Le but est de faire une boucle sur une période en utilisant les valeurs obtenus à l'itérations précédentes\n",
    "\n",
    "Creation de liste de planification de l'arrosage pour le test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Test avec data Test\n",
    "\n",
    "Création d'une data frame pour effectuer les tests.\n",
    "\n",
    "Voici la query effectuer pour obtenir les données.\n",
    "\n",
    "\n",
    "Pour le fichier CeresTest.csv voici la query effectuer : \n",
    "\n",
    "SELECT mean(\"moisture-percent\") AS \"mean_moisture-percent\", mean(\"temperature\") AS \"mean_temperature\" \n",
    "\n",
    "FROM \"ceres\".\"autogen\".\"chirp-left\" \n",
    "\n",
    "WHERE time > now() - 150h \n",
    "\n",
    "GROUP BY time(30m) FILL(null)\n",
    "\n",
    "On ajoute l'information sur la différence de temperature, la différence d'huminidité et l'arrosage.\n",
    "Ensuite on supprime les valeurs nulls et les valeurs qui ne font pas partie d'un cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nb de jour totale :  138\n",
      "jour de test :  [10, 12, 15, 16, 18, 33, 63, 65, 69, 71, 73, 78, 86, 92, 96, 119, 129, 133, 134, 137]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "jour = nligne //48\n",
    "print(\"nb de jour totale : \",jour)\n",
    "incr = 0\n",
    "jourTest = random.sample(range(1,jour),k=20)\n",
    "jourTest.sort()\n",
    "print(\"jour de test : \",jourTest)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "indTest = []\n",
    "for el in jourTest:\n",
    "    indTest = np.append(indTest,range(int(48*(el-1)),int(48*el)))\n",
    "indTest = indTest.astype(int)\n",
    "dataTest = df.iloc[indTest,:].copy()\n",
    "DF = df.copy()\n",
    "df.drop(df.index[indTest],axis = 0,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test avec les donnes SANS le temps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moyenne d'erreur :  -1.97320084246\n",
      "Moyenne d'erreur absolue :  2.30917613647\n"
     ]
    }
   ],
   "source": [
    "init = 0+48*0\n",
    "dataTest['resultSansT'] = dataTest.index\n",
    "position = dataTest.shape[1]-1\n",
    "\n",
    "StartHumidity = dataTest.iloc[init,1]\n",
    "TestSize = dataTest.shape[0]\n",
    "result = np.zeros(TestSize)\n",
    "for i in range(0,TestSize):\n",
    "    dataTest.iloc[int(i+init),position] = StartHumidity\n",
    "\n",
    "    if i%48 == 0:\n",
    "        StartHumidity = dataTest.iloc[init+i,1]\n",
    "    result[i] = StartHumidity + reg.predict([[StartHumidity,dataTest.iloc[init+i,2],dataTest.iloc[init+i,5]]])[0]\n",
    "    StartHumidity = result[i]\n",
    "    \n",
    "\n",
    "#print(dfTest.iloc[range(0+init+1,TestSize+init+1),1])\n",
    "ErrSansTemps = np.mean(dataTest.iloc[:,1].values-dataTest.iloc[:,position].values)\n",
    "ErrSansTempsAbs = np.mean(np.absolute(dataTest.iloc[:,1].values-dataTest.iloc[:,position].values))\n",
    "\n",
    "print(\"Moyenne d'erreur : \",ErrSansTemps)\n",
    "print(\"Moyenne d'erreur absolue : \",ErrSansTempsAbs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test avec les donnes AVEC le temps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moyenne d'erreur :  -1.26031535369\n",
      "Moyenne d'erreur absolue :  2.13661807393\n"
     ]
    }
   ],
   "source": [
    "init = 0+48*0\n",
    "dataTest['resultAvecT'] = dataTest.index\n",
    "position = dataTest.shape[1]-1\n",
    "\n",
    "StartHumidity = dataTest.iloc[init,1]\n",
    "\n",
    "TestSize = dataTest.shape[0]\n",
    "result2 = np.zeros(TestSize)\n",
    "for i in range(0,TestSize):\n",
    "    dataTest.iloc[int(i+init),position] = StartHumidity\n",
    "\n",
    "    if i%48 == 0:\n",
    "        StartHumidity = dataTest.iloc[init+i,1]\n",
    "    result2[i] = StartHumidity + reg2.predict([[StartHumidity,dataTest.iloc[init+i,2],dataTest.iloc[init+i,5],dataTest.iloc[init+i,6]]])[0]\n",
    "    StartHumidity = result2[i]\n",
    "\n",
    "#print(result2)\n",
    "#print(dfTest.iloc[range(0+init+1,TestSize+init+1),1])\n",
    "ErrTemps = np.mean(dataTest.iloc[:,1].values-dataTest.iloc[:,position].values)\n",
    "ErrTempsAbs = np.mean(np.absolute(dataTest.iloc[:,1].values-dataTest.iloc[:,position].values))\n",
    "\n",
    "print(\"Moyenne d'erreur : \",ErrTemps)\n",
    "print(\"Moyenne d'erreur absolue : \",ErrTempsAbs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                     5/7/2019 11:30:00.000000000 AM\n",
       "mean_moisture-percent                             25.38\n",
       "mean_temperature                                 20.655\n",
       "moistureAdd                                         4.1\n",
       "temperatureAdd                                 0.638333\n",
       "Arrosage                                             20\n",
       "TAfterArrosage                                        0\n",
       "ArrosageHist                                         20\n",
       "index                                                62\n",
       "resultSansT                                     25.6547\n",
       "resultAvecT                                     24.2073\n",
       "Name: 528, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataTest.iloc[48]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partage de la Data frame\n",
    "\n",
    "Partage de la data frame en deux. \n",
    "\n",
    "Une partie qui contient que les moments où on arrose. On peut selectionner ceci grace à loc et à df['TAfterArrosage'] == 0. Quand on arrose, le temps après arrosage est de 0.\n",
    "\n",
    "L'autre partie contient les moments où on n'arrose pas. C'est-à-dire les momments où l'eau s'évapore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "limite = 240\n",
    "dfArrosage = df.loc[df['TAfterArrosage'] == 0]\n",
    "tmp = df.loc[df['TAfterArrosage'] > 0]\n",
    "dfStabilisation = df.loc[df['TAfterArrosage'] > limite].copy()\n",
    "dfEvaporation = tmp.loc[df['TAfterArrosage'] <= limite].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.09180105,  0.03944178])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regLinearAro = linear_model.LinearRegression()\n",
    "regLinearAro.fit(dfArrosage[['mean_moisture-percent','Arrosage']],dfArrosage.moistureAdd)\n",
    "regLinearAro.intercept_\n",
    "regLinearAro.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.034085  , -0.04987219,  0.0079909 , -0.00571571,  0.00298161])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour faire le régression linear sur le moment d'arrosage\n",
    "regLinearEva = linear_model.LinearRegression()\n",
    "regLinearEva.fit(dfEvaporation[['mean_moisture-percent','mean_temperature','TAfterArrosage','ArrosageHist','index']],dfEvaporation.moistureAdd)\n",
    "regLinearEva.intercept_\n",
    "regLinearEva.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -1.71199435e-03,   4.95886046e-03,   3.27579905e-05,\n",
       "        -1.80211140e-04,   4.01478802e-04])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour faire le régression linear sur le moment d'arrosage\n",
    "regLinearSta = linear_model.LinearRegression()\n",
    "regLinearSta.fit(dfStabilisation[['mean_moisture-percent','mean_temperature','TAfterArrosage','ArrosageHist','index']],dfStabilisation.moistureAdd)\n",
    "regLinearSta.intercept_\n",
    "regLinearSta.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pour tester sur la base de données de training\n",
    "init = 0\n",
    "base = dataTest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moyenne Erreur d'Arrosage :  1.98627105822\n",
      "Moyenne d'erreur evaporation :  0.227131079074\n",
      "Moyenne d'erreur stabilisation :  0.134579047699\n",
      "Moyenne d'erreur :  -1.39715374551\n",
      "Moyenne d'erreur absolue :  1.72195320263\n"
     ]
    }
   ],
   "source": [
    "dataTest['resultSepare'] = dataTest.index\n",
    "StartHumidity = base.iloc[init,1]\n",
    "position = dataTest.shape[1]-1\n",
    "\n",
    "\n",
    "TestSize = dataTest.shape[0]\n",
    "ErrAro = 0\n",
    "result = np.zeros(TestSize)\n",
    "ErrEva = 0\n",
    "nAro = 0\n",
    "\n",
    "for i in range(0,TestSize-1):\n",
    "    dataTest.iloc[int(i),position] = StartHumidity\n",
    "\n",
    "    if dataTest.iloc[i,6] == 0:\n",
    "        nAro =+ 1\n",
    "        StartHumidity = dataTest.iloc[i,1]\n",
    "        result[i] = StartHumidity + regLinearAro.predict([[StartHumidity,base.iloc[init+i,5]]])[0]\n",
    "        ErrAro =+ abs(result[i] - base.iloc[i+1,1])\n",
    "    elif dataTest.iloc[i,6] <= limite:\n",
    "        result[i] = StartHumidity + regLinearEva.predict([[StartHumidity,base.iloc[init+i,2],base.iloc[init+i,6],base.iloc[init+i,7],base.iloc[init+i,8]]])[0]\n",
    "        ErrEva =+ abs(result[i] - base.iloc[i+1,1])\n",
    "    else:\n",
    "        result[i] = StartHumidity + regLinearSta.predict([[StartHumidity,base.iloc[init+i,2],base.iloc[init+i,6],base.iloc[init+i,7],base.iloc[init+i,8]]])[0]\n",
    "        ErrSta =+ abs(result[i] - base.iloc[i+1,1])\n",
    "\n",
    "    StartHumidity = result[i]\n",
    "dataTest.iloc[TestSize-1,position] = StartHumidity\n",
    "\n",
    "ErrAro = ErrAro/nAro\n",
    "ErrEva = ErrEva/(15*nAro)\n",
    "ErrSta = ErrSta/((48-16*nAro))\n",
    "\n",
    "print(\"Moyenne Erreur d'Arrosage : \", ErrAro)\n",
    "print(\"Moyenne d'erreur evaporation : \", ErrEva)\n",
    "print(\"Moyenne d'erreur stabilisation : \", ErrSta)\n",
    "\n",
    "ErrSepare = np.mean(dataTest.iloc[:,1].values-dataTest.iloc[:,position].values)\n",
    "ErrSepareAbs = np.mean(np.absolute(dataTest.iloc[:,1].values-dataTest.iloc[:,position].values))\n",
    "print(\"Moyenne d'erreur : \",ErrSepare)\n",
    "print(\"Moyenne d'erreur absolue : \",ErrSepareAbs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affichage des résultats\n",
    "\n",
    "On Affiche les resultats obtenus avec les différentes méthodes.\n",
    "\n",
    "Méthode : Sans le temps après arrosage, avec le temps le temps après arrosage et la méthode ou on sépare en plusieurs partie le cycle d'arrosage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sans le temps :  -1.97320084246\n",
      "Sans le temps absolue :  2.30917613647\n",
      "\n",
      "Avec le temps :  -1.26031535369\n",
      "Avec le temps absolu :  2.13661807393\n",
      "\n",
      "Avec Temps Séparé :  -1.39715374551\n",
      "Avec Temps Séparé absolu :  1.72195320263\n"
     ]
    }
   ],
   "source": [
    "print(\"Sans le temps : \", ErrSansTemps)\n",
    "print(\"Sans le temps absolue : \", ErrSansTempsAbs)\n",
    "\n",
    "print(\"\\nAvec le temps : \", ErrTemps)\n",
    "print(\"Avec le temps absolu : \", ErrTempsAbs)\n",
    "\n",
    "\n",
    "#print(\"\\nDurant Arrosage : \",ErrAro)\n",
    "#print(\"Evaporation : \" , ErrEva)\n",
    "print(\"\\nAvec Temps Séparé : \", ErrSepare)\n",
    "print(\"Avec Temps Séparé absolu : \", ErrSepareAbs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x-coordinates of left sides of bars  \n",
    "left = [1, 2, 3, 4, 5, 6] \n",
    "  \n",
    "# heights of bars \n",
    "height = [ErrSansTemps,ErrSansTempsAbs, ErrTemps, ErrTempsAbs, ErrSepare,ErrSepareAbs]\n",
    "height = [abs(x) for x in height]\n",
    "\n",
    "  \n",
    "# labels for bars \n",
    "tick_label = ['Sans temps', 'Sans temps Abs', 'Avec Temps','Avec Temps Abs', 'Temps Separe','Temps Separe Abs'] \n",
    "f, ax = plt.subplots(figsize=(10,5)) # set the size that you'd like (width, height)\n",
    "\n",
    "# plotting a bar chart \n",
    "plt.bar(left, height, tick_label = tick_label,\n",
    "        width = 0.8, color = ['blue', 'green']) \n",
    "\n",
    "# naming the x-axis \n",
    "plt.xlabel('Méthode de régression') \n",
    "# naming the y-axis \n",
    "plt.ylabel('Erreur') \n",
    "# plot title \n",
    "plt.title('Erreur suivant la methode') \n",
    "  \n",
    "# function to show the plot \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphic\n",
    "\n",
    "Creation des graphics de la courbe d'évolution de la température\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Watering quantity')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = dataTest.plot(x='date', y=['mean_moisture-percent','resultSepare',],figsize=(11, 6),rot=90,grid=True, title=\"Forecast moisture porcent with several methods\")\n",
    "ax.set_ylabel(\"moisture-percent\")\n",
    "\n",
    "ax = dataTest.plot(x='date', y=['mean_temperature','mean_moisture-percent','ArrosageHist'],secondary_y=['ArrosageHist'],figsize=(11, 6),rot=30,grid=True)\n",
    "\n",
    "ax.set_ylabel(\"Temperature\")\n",
    "ax.right_ax.set_ylabel('Watering quantity')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test de mes données avec une autre regression lineaire\n",
    "\n",
    "Dans mon cas, j'ai choisi d'utiliser la méthode de Lasso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -9.17335180e-02,   1.95775710e-02,   3.22585602e-01,\n",
       "         1.66933647e-04])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg3 = linear_model.Lasso(alpha=0.1)\n",
    "reg3.fit(df[['mean_moisture-percent','mean_temperature','Arrosage','TAfterArrosage']],df.moistureAdd)\n",
    "reg3.intercept_\n",
    "reg3.coef_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test de mes données obtenu avec la régression de Lasso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moyenne d'erreur :  -1.42451191434\n"
     ]
    }
   ],
   "source": [
    "init = 0+48*0\n",
    "dfTest['resultAutre'] = dfTest.index\n",
    "\n",
    "StartHumidity = dfTest.iloc[init,1]\n",
    "\n",
    "TestSize = nligneTest\n",
    "result2 = np.zeros(TestSize)\n",
    "for i in range(0,TestSize):\n",
    "    dfTest.iloc[int(i+init),10] = StartHumidity\n",
    "\n",
    "    if i%48 == 0:\n",
    "        StartHumidity = dfTest.iloc[init+i,1]\n",
    "    result2[i] = StartHumidity + reg3.predict([[StartHumidity,dfTest.iloc[init+i,2],dfTest.iloc[init+i,5],dfTest.iloc[init+i,6]]])[0]\n",
    "    StartHumidity = result2[i]\n",
    "\n",
    "ErrAutre = np.mean(dfTest.iloc[:,1].values-dfTest.iloc[:,10].values)\n",
    "print(\"Moyenne d'erreur : \",ErrAutre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79e36c2b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x-coordinates of left sides of bars  \n",
    "left = [1, 2, 3,4] \n",
    "  \n",
    "# heights of bars \n",
    "height = [ErrSansTemps, ErrTemps, ErrSepare,ErrAutre]\n",
    "height = [abs(x) for x in height]\n",
    "  \n",
    "# labels for bars \n",
    "tick_label = ['Sans le temps', 'Avec Temps', 'Avec T Separe','Lasso a=1.5'] \n",
    "  \n",
    "# plotting a bar chart \n",
    "plt.bar(left, height, tick_label = tick_label, \n",
    "        width = 0.8, color = ['red', 'green','green','blue']) \n",
    "  \n",
    "# naming the x-axis \n",
    "plt.xlabel('Méthode de régression') \n",
    "# naming the y-axis \n",
    "plt.ylabel('Erreur en valeur absolu') \n",
    "# plot title \n",
    "plt.title('Erreur suivant la methode') \n",
    "  \n",
    "# function to show the plot \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sans le temps :  0.520951303641\n",
      "Avec le temps :  0.0815202686961\n",
      "Avec Temps Séparé :  -0.151172161535\n",
      "Avec Lasso a = 1.5 :  -1.42451191434\n"
     ]
    }
   ],
   "source": [
    "print(\"Sans le temps : \", ErrSansTemps)\n",
    "print(\"Avec le temps : \", ErrTemps)\n",
    "\n",
    "#print(\"\\nDurant Arrosage : \",ErrAro)\n",
    "#print(\"Evaporation : \" , ErrEva)\n",
    "print(\"Avec Temps Séparé : \", ErrSepare)\n",
    "print(\"Avec Lasso a = 1.5 : \", ErrAutre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'moisture-percent')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79e3390080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import *\n",
    "ax = dfTest.plot(x='date', y=['mean_moisture-percent','resultSepare','resultAutre'],figsize=(10, 6),rot=30,grid=True, title=\"Forecast moisture porcent with several methods\")\n",
    "ax.set_ylabel(\"moisture-percent\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphique d'un cycle journalier\n",
    "\n",
    "Ce graphique a été utilisé pour la présentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'moisture-percent')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79e35f73c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfGraph = dfTest.iloc[range(44,96),:]\n",
    "ax = dfGraph.plot(x='date', y=['mean_moisture-percent'],figsize=(10, 6),rot=30,grid=True, title=\"Moisture-percent evolution in a day\")\n",
    "ax.set_ylabel(\"moisture-percent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "num must be 1 <= num <= 4, not 5",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-a9cf9d4b5855>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0;31m#print(\"BEST LIMITE pour un arrosage de \", ARRO ,\" : \",bestLimite)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\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     55\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m900\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rx'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1069\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\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-> 1070\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1071\u001b[0m     \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1072\u001b[0m     \u001b[0mbyebye\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~/.local/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1412\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1414\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1416\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_axes_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/matplotlib/axes/_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[1;32m     57\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m                     raise ValueError(\n\u001b[0;32m---> 59\u001b[0;31m                         f\"num must be 1 <= num <= {rows*cols}, not {num}\")\n\u001b[0m\u001b[1;32m     60\u001b[0m                 self._subplotspec = GridSpec(\n\u001b[1;32m     61\u001b[0m                         rows, cols, figure=self.figure)[int(num) - 1]\n",
      "\u001b[0;31mValueError\u001b[0m: num must be 1 <= num <= 4, not 5"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x792 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "j = 1\n",
    "f, ax = plt.subplots(figsize=(16,11))\n",
    "for ARRO in [10,30,20]:\n",
    "    dfarro = DF.loc[DF['ArrosageHist'] == ARRO]\n",
    "    dfArrosage = dfarro.loc[dfarro['TAfterArrosage'] == 0]\n",
    "    tmp = dfarro.loc[dfarro['TAfterArrosage'] > 0]\n",
    "\n",
    "    bestLimite = 0\n",
    "    errmin = 1000\n",
    "    res = np.zeros(900//30-1)\n",
    "    for limite in range(30,900,30):\n",
    "\n",
    "        dfStabilisation = dfarro.loc[dfarro['TAfterArrosage'] > limite].copy()\n",
    "        dfEvaporation = tmp.loc[dfarro['TAfterArrosage'] <= limite].copy()\n",
    "\n",
    "        regLinearAro = linear_model.LinearRegression()\n",
    "        regLinearAro.fit(dfArrosage[['mean_moisture-percent','Arrosage','index']],dfArrosage.moistureAdd)\n",
    "        regLinearEva = linear_model.LinearRegression()\n",
    "        regLinearEva.fit(dfEvaporation[['mean_moisture-percent','mean_temperature','TAfterArrosage','ArrosageHist','index']],dfEvaporation.moistureAdd)\n",
    "        regLinearSta = linear_model.LinearRegression()\n",
    "        regLinearSta.fit(dfStabilisation[['mean_moisture-percent','mean_temperature','TAfterArrosage','ArrosageHist','index']],dfStabilisation.moistureAdd)\n",
    "\n",
    "\n",
    "        databasetest = dfarro.copy()\n",
    "        databasetest['resultSepare'] = databasetest.index\n",
    "        StartHumidity = databasetest.iloc[0,1]\n",
    "        position = databasetest.shape[1]-1\n",
    "        TestSize = databasetest.shape[0]\n",
    "        result = np.zeros(TestSize)\n",
    "\n",
    "        for i in range(0,TestSize-1):\n",
    "            databasetest.iloc[int(i),position] = StartHumidity\n",
    "\n",
    "            if databasetest.iloc[i,6] == 0:\n",
    "                StartHumidity = databasetest.iloc[i,1]\n",
    "                result[i] = StartHumidity + regLinearAro.predict([[StartHumidity,databasetest.iloc[i,5],databasetest.iloc[i,8]]])[0]\n",
    "            elif databasetest.iloc[i,6] <= limite:\n",
    "                result[i] = StartHumidity + regLinearEva.predict([[StartHumidity,databasetest.iloc[i,2],databasetest.iloc[i,6],databasetest.iloc[i,7],databasetest.iloc[i,8]]])[0]\n",
    "            else:\n",
    "                result[i] = StartHumidity + regLinearSta.predict([[StartHumidity,databasetest.iloc[i,2],databasetest.iloc[i,6],databasetest.iloc[i,7],databasetest.iloc[i,8]]])[0]\n",
    "\n",
    "            StartHumidity = result[i]\n",
    "        databasetest.iloc[TestSize-1,position] = StartHumidity\n",
    "\n",
    "\n",
    "        ErrSepare = np.mean(databasetest.iloc[:,1].values-databasetest.iloc[:,position].values)\n",
    "        ErrSepareAbs = np.mean(np.absolute(databasetest.iloc[:,1].values-databasetest.iloc[:,position].values))\n",
    "        res[limite//30-1]=ErrSepareAbs\n",
    "        if (ErrSepareAbs< errmin):\n",
    "            bestLimite = limite\n",
    "            errmin = ErrSepareAbs\n",
    "    \n",
    "    #print(\"BEST LIMITE pour un arrosage de \", ARRO ,\" : \",bestLimite)\n",
    "    plt.subplot(2,2,j)\n",
    "    plt.grid(b = True)\n",
    "    plt.plot(range(30,900,30), res, 'rx')\n",
    "    plt.xlabel('limite en minutes')\n",
    "    plt.ylabel('Erreur en valeur absolu')\n",
    "    plt.title('Erreur en fonction de la limite, Arrosage = '+str(ARRO))\n",
    "    j = j + 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Moyenne d'erreur :  2.3606986351\n"
     ]
    }
   ],
   "source": [
    "ErrTempsAbs = np.zeros(20)\n",
    "\n",
    "for k in range(20):\n",
    "    df = DF.copy()\n",
    "    jour = nligne //48\n",
    "    incr = 0\n",
    "    jourTest = random.sample(range(1,jour),k=20)\n",
    "    jourTest.sort()\n",
    "    indTest = []\n",
    "    for el in jourTest:\n",
    "        indTest = np.append(indTest,range(int(48*(el-1)),int(48*el)))\n",
    "    indTest = indTest.astype(int)\n",
    "    dataTest = df.iloc[indTest,:].copy()\n",
    "    df.drop(df.index[indTest],axis = 0,inplace = True)\n",
    "    \n",
    "    reg2 = linear_model.LinearRegression()\n",
    "    reg2.fit(df[['mean_moisture-percent','mean_temperature','Arrosage','TAfterArrosage']],df.moistureAdd)\n",
    "    \n",
    "    init = 0+48*0\n",
    "    dataTest['resultAvecT'] = dataTest.index\n",
    "    position = dataTest.shape[1]-1\n",
    "\n",
    "    StartHumidity = dataTest.iloc[init,1]\n",
    "\n",
    "    TestSize = dataTest.shape[0]\n",
    "    result2 = np.zeros(TestSize)\n",
    "    for i in range(0,TestSize):\n",
    "        dataTest.iloc[int(i+init),position] = StartHumidity\n",
    "\n",
    "        if i%48 == 0:\n",
    "            StartHumidity = dataTest.iloc[init+i,1]\n",
    "        result2[i] = StartHumidity + reg2.predict([[StartHumidity,dataTest.iloc[init+i,2],dataTest.iloc[init+i,5],dataTest.iloc[init+i,6]]])[0]\n",
    "        StartHumidity = result2[i]\n",
    "\n",
    "    ErrTempsAbs[k] = np.mean(np.absolute(dataTest.iloc[:,1].values-dataTest.iloc[:,position].values))\n",
    "    \n",
    "print(\"\\n Moyenne d'erreur : \", np.mean(ErrTempsAbs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Moyenne d'erreur :  1.88141508463\n"
     ]
    }
   ],
   "source": [
    "ErrTempsAbsDemeter = np.zeros(20)\n",
    "nligne = dfDemeter.shape[0]\n",
    "for k in range(20):\n",
    "    df = dfDemeter.copy()\n",
    "    jour = nligne //48\n",
    "    incr = 0\n",
    "    jourTest = random.sample(range(1,jour),k=14)\n",
    "    jourTest.sort()\n",
    "    indTest = []\n",
    "    for el in jourTest:\n",
    "        indTest = np.append(indTest,range(int(48*(el-1)),int(48*el)))\n",
    "    indTest = indTest.astype(int)\n",
    "    dataTest = df.iloc[indTest,:].copy()\n",
    "    df.drop(df.index[indTest],axis = 0,inplace = True)\n",
    "    \n",
    "    reg2 = linear_model.LinearRegression()\n",
    "    reg2.fit(df[['mean_moisture-percent','mean_temperature','Arrosage','TAfterArrosage']],df.moistureAdd)\n",
    "    \n",
    "    init = 0+48*0\n",
    "    dataTest['resultAvecT'] = dataTest.index\n",
    "    position = dataTest.shape[1]-1\n",
    "\n",
    "    StartHumidity = dataTest.iloc[init,1]\n",
    "\n",
    "    TestSize = dataTest.shape[0]\n",
    "    result2 = np.zeros(TestSize)\n",
    "    for i in range(0,TestSize):\n",
    "        dataTest.iloc[int(i+init),position] = StartHumidity\n",
    "\n",
    "        if i%48 == 0:\n",
    "            StartHumidity = dataTest.iloc[init+i,1]\n",
    "        result2[i] = StartHumidity + reg2.predict([[StartHumidity,dataTest.iloc[init+i,2],dataTest.iloc[init+i,5],dataTest.iloc[init+i,6]]])[0]\n",
    "        StartHumidity = result2[i]\n",
    "\n",
    "    ErrTempsAbsDemeter[k] = np.mean(np.absolute(dataTest.iloc[:,1].values-dataTest.iloc[:,position].values))\n",
    "    \n",
    "print(\"\\n Moyenne d'erreur : \", np.mean(ErrTempsAbsDemeter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x-coordinates of left sides of bars  \n",
    "ErrTempsAbs_std = np.std(ErrTempsAbs)\n",
    "ErrTempsAbsDemeter_std = np.std(ErrTempsAbsDemeter)\n",
    "\n",
    "error = [ErrTempsAbs_std, ErrTempsAbsDemeter_std]\n",
    "\n",
    "left = [1, 2] \n",
    "  \n",
    "# heights of bars \n",
    "height = [np.mean(ErrTempsAbs), np.mean(ErrTempsAbsDemeter)]\n",
    "  \n",
    "# labels for bars \n",
    "tick_label = ['Demeter et Ceres', 'Demeter'] \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# plotting a bar chart \n",
    "ax.bar(left, height,\n",
    "       yerr=error,\n",
    "       align='center',\n",
    "       alpha=0.5,\n",
    "       ecolor='black',\n",
    "       capsize=10)\n",
    "#plt.bar(left, height, tick_label = tick_label, \n",
    "#        width = 0.8, color = ['red', 'blue']) \n",
    "\n",
    "\n",
    "ax.set_xticks(left)\n",
    "ax.set_xticklabels(tick_label)\n",
    "ax.set_title('Erreur suivant la base de donnée utilisée')\n",
    "ax.yaxis.grid(True)\n",
    "# naming the x-axis \n",
    "plt.xlabel('Choix de la base de donnée') \n",
    "# naming the y-axis \n",
    "plt.ylabel('Erreur en valeur absolu') \n",
    "# plot title \n",
    "  \n",
    "# function to show the plot \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}