Modelvarification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d844edf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b32c281f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6de7d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>1.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>26</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   day  month  year  Temperature  RH  Ws  Rain  FFMC  DMC    DC  ISI  BUI  \\\n",
       "0    1      6  2012           29  57  18   0.0  65.7  3.4   7.6  1.3  3.4   \n",
       "1    2      6  2012           29  61  13   1.3  64.4  4.1   7.6  1.0  3.9   \n",
       "2    3      6  2012           26  82  22  13.1  47.1  2.5   7.1  0.3  2.7   \n",
       "3    4      6  2012           25  89  13   2.5  28.6  1.3   6.9  0.0  1.7   \n",
       "4    5      6  2012           27  77  16   0.0  64.8  3.0  14.2  1.2  3.9   \n",
       "\n",
       "   FWI  Classes  Region  \n",
       "0  0.5        0       1  \n",
       "1  0.4        0       1  \n",
       "2  0.1        0       1  \n",
       "3  0.0        0       1  \n",
       "4  0.5        0       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Algerian_forest_fire_cleaned_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4ba4e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day', 'month', 'year', 'Temperature', 'RH', 'Ws', 'Rain', 'FFMC',\n",
       "       'DMC', 'DC', 'ISI', 'BUI', 'FWI', 'Classes', 'Region'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6039e441",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:,['Temperature', 'Ws', 'FFMC','DMC','ISI']]\n",
    "\n",
    "y = df['FWI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5093ef0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a640285",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((182, 5), (61, 5), (182,), (61,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb1ae952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random forest\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "RF = RandomForestRegressor(random_state=0)\n",
    "RF.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11a4aa2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Actual</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.2</td>\n",
       "      <td>7.366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>9.7</td>\n",
       "      <td>10.231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     Actual    pred\n",
       "140     0.4   0.389\n",
       "95      0.5   0.613\n",
       "6       7.2   7.366\n",
       "41      0.9   0.851\n",
       "110     9.7  10.231"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = RF.predict(x_test)\n",
    "diff = pd.DataFrame({'Actual': y_test, 'pred': y_pred})\n",
    "diff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e691000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9709252558567671"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "71a8d548",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "417c78f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.873])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF.predict([[33,14,81.1,8.1,2.6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "442cc9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_reg.pickle', 'wb') as f:\n",
    "    pickle.dump(RF,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0c6d379b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_reg.pickle', 'rb') as f:\n",
    "    RF_reg_model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aa06c04f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.873])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_reg_model.predict([[33,14,81.1,8.1,2.6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fdfd86ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import bz2,pickle\n",
    "file = bz2.BZ2File('Regression_trial.pkl','wb')\n",
    "pickle.dump(RF,file)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52a0de67",
   "metadata": {},
   "outputs": [],
   "source": [
    "R_pickle = bz2.BZ2File('Regression_trial.pkl', 'rb')\n",
    "model_R = pickle.load(R_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2666e4cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.873])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_R.predict([[33,14,81.1,8.1,2.6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f93b9a6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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