solar-radiation-Copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "portable-access",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "nonprofit-listening",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('solar-radiation.csv') #load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "looking-generator",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                            datetime64[ns]\n",
       "total_precipitation                    float64\n",
       "mean_relative_humidity                 float64\n",
       "mean_air_temperature                   float64\n",
       "mean_wind_speed                        float64\n",
       "total_sunshine_duration                float64\n",
       "percentage_possible_sunshine           float64\n",
       "solar_radiation                        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to explicitly convert the date column to type DATETIME\n",
    "data['date'] = pd.to_datetime(data['date'], dayfirst=True)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "improved-examination",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.set_index('date') #set the index of the dataset as the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "greenhouse-relationship",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexing.py:1597: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = value\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>solar_radiation</th>\n",
       "      <th>last_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>9.2</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>14.2</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>13.4</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>14.8</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>17.6</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>17.9</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>18.6</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01</th>\n",
       "      <td>15.2</td>\n",
       "      <td>18.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            solar_radiation  last_month\n",
       "date                                   \n",
       "2010-02-01              9.2         9.1\n",
       "2010-03-01             14.2         9.2\n",
       "2010-04-01             13.4        14.2\n",
       "2010-05-01             14.8        13.4\n",
       "2010-06-01             17.6        14.8\n",
       "...                     ...         ...\n",
       "2022-07-01             17.5        17.5\n",
       "2022-08-01             17.5        17.5\n",
       "2022-09-01             17.9        17.5\n",
       "2022-10-01             18.6        17.9\n",
       "2022-11-01             15.2        18.6\n",
       "\n",
       "[142 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_solar_radiation = data[['solar_radiation']] # creating new dataframe from solar_radiation column\n",
    "data_solar_radiation.loc[:,'last_month'] = data_solar_radiation.loc[:,'solar_radiation'].shift() # inserting new column with last month's solar_radiation values\n",
    "data_solar_radiation = data_solar_radiation.dropna() # dropping NAs\n",
    "data_solar_radiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dental-commitment",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(actual, predict):\n",
    "    predict = np.array(predict)\n",
    "    actual = np.array(actual)\n",
    "    distance = predict - actual\n",
    "    square_distance = distance ** 2\n",
    "    mean_square_distance = square_distance.mean()\n",
    "    score = np.sqrt(mean_square_distance)\n",
    "    return score\n",
    "rmse_score = make_scorer(rmse, greater_is_better = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "weird-causing",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data_solar_radiation.drop(['solar_radiation'], axis = 1)\n",
    "y_train = data_solar_radiation.loc[:'2022', 'solar_radiation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "finite-fetish",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01</th>\n",
       "      <td>18.6</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            last_month\n",
       "date                  \n",
       "2010-02-01         9.1\n",
       "2010-03-01         9.2\n",
       "2010-04-01        14.2\n",
       "2010-05-01        13.4\n",
       "2010-06-01        14.8\n",
       "...                ...\n",
       "2022-07-01        17.5\n",
       "2022-08-01        17.5\n",
       "2022-09-01        17.5\n",
       "2022-10-01        17.9\n",
       "2022-11-01        18.6\n",
       "\n",
       "[142 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "olive-brush",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2010-02-01     9.2\n",
       "2010-03-01    14.2\n",
       "2010-04-01    13.4\n",
       "2010-05-01    14.8\n",
       "2010-06-01    17.6\n",
       "              ... \n",
       "2022-07-01    17.5\n",
       "2022-08-01    17.5\n",
       "2022-09-01    17.9\n",
       "2022-10-01    18.6\n",
       "2022-11-01    15.2\n",
       "Name: solar_radiation, Length: 142, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "equipped-accounting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17.69769183]\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv('predicted-solar-radiation.csv')\n",
    "test_data = test_data.set_index('date')\n",
    "X_test = test_data.drop(['solar_radiation'], axis = 1)\n",
    "model = RandomForestRegressor()\n",
    "param_search = { \n",
    "    'n_estimators': [20, 50, 100],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [i for i in range(5,15)]\n",
    "}\n",
    "tscv = TimeSeriesSplit(n_splits=10)\n",
    "gsearch = GridSearchCV(estimator=model, cv=tscv, param_grid=param_search, scoring = rmse_score)\n",
    "gsearch.fit(X_train, y_train)\n",
    "best_model = gsearch.best_estimator_\n",
    "y_pred = best_model.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "plastic-confusion",
   "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>date</th>\n",
       "      <th>last_month</th>\n",
       "      <th>solar_radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date  last_month  solar_radiation\n",
       "0  Jan-22         NaN             18.5\n",
       "1  Feb-22         NaN             15.0\n",
       "2  Mar-22         NaN             17.8\n",
       "3  Apr-22         NaN             18.8\n",
       "4  May-22         NaN             16.2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_data = pd.read_csv('predicted-solar-radiation.csv')\n",
    "predicted_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "universal-spread",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create an empty figure\n",
    "plt.bar(predicted_data['date'], predicted_data['solar_radiation']) #set the dates as the x-axis and the predicted-solar_radiation as the y-axis\n",
    "plt.ylabel(\"Solar radiation\") # set the label for the y-axis\n",
    "plt.xlabel(\"Dates\") # set the label for the x-axis\n",
    "fig.set_size_inches(20, 5) #set the size of the figure\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}