model_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>temp</th>\n",
       "      <th>dwpt</th>\n",
       "      <th>rhum</th>\n",
       "      <th>prcp</th>\n",
       "      <th>snow</th>\n",
       "      <th>wdir</th>\n",
       "      <th>wspd</th>\n",
       "      <th>wpgt</th>\n",
       "      <th>pres</th>\n",
       "      <th>coco</th>\n",
       "      <th>el_price</th>\n",
       "      <th>consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8587</th>\n",
       "      <td>2022-08-24 19:00:00+03:00</td>\n",
       "      <td>27.1</td>\n",
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       "      <td>54.0</td>\n",
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       "      <td>0.457</td>\n",
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       "    <tr>\n",
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       "      <td>24.9</td>\n",
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       "      <td>66.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1020.9</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.500</td>\n",
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       "      <td>9.3</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>2.321</td>\n",
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       "    <tr>\n",
       "      <th>8591</th>\n",
       "      <td>2022-08-24 23:00:00+03:00</td>\n",
       "      <td>22.1</td>\n",
       "      <td>18.1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.43108</td>\n",
       "      <td>0.678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           time  temp  dwpt  rhum  prcp  snow   wdir  wspd  \\\n",
       "8587  2022-08-24 19:00:00+03:00  27.1  17.0  54.0   0.0   NaN  180.0   4.0   \n",
       "8588  2022-08-24 20:00:00+03:00  25.1  17.1  61.0   0.0   NaN  220.0   6.0   \n",
       "8589  2022-08-24 21:00:00+03:00  24.9  18.1  66.0   0.0   NaN  150.0   4.0   \n",
       "8590  2022-08-24 22:00:00+03:00  22.1  17.0  73.0   0.0   NaN  160.0   6.0   \n",
       "8591  2022-08-24 23:00:00+03:00  22.1  18.1  78.0   0.0   NaN  310.0  15.0   \n",
       "\n",
       "      wpgt    pres  coco  el_price  consumption  \n",
       "8587  11.1  1020.0   2.0   0.53494        0.678  \n",
       "8588  11.1  1021.0   1.0   0.49990        0.457  \n",
       "8589   9.3  1020.9   4.0   0.43149        0.500  \n",
       "8590   9.3  1021.0   2.0   0.55203        2.321  \n",
       "8591   9.3  1021.0   2.0   0.43108        0.678  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/train.csv')\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8424</th>\n",
       "      <td>2022-08-18 00:00:00+03:00</td>\n",
       "      <td>0.245</td>\n",
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       "    <tr>\n",
       "      <th>8425</th>\n",
       "      <td>2022-08-18 01:00:00+03:00</td>\n",
       "      <td>0.178</td>\n",
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       "    <tr>\n",
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       "      <td>2022-08-18 02:00:00+03:00</td>\n",
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       "    <tr>\n",
       "      <th>8427</th>\n",
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       "    <tr>\n",
       "      <th>8428</th>\n",
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       "      <td>1.043</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>8587</th>\n",
       "      <td>2022-08-24 19:00:00+03:00</td>\n",
       "      <td>0.678</td>\n",
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       "    <tr>\n",
       "      <th>8588</th>\n",
       "      <td>2022-08-24 20:00:00+03:00</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>8591</th>\n",
       "      <td>2022-08-24 23:00:00+03:00</td>\n",
       "      <td>0.678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           time  consumption\n",
       "8424  2022-08-18 00:00:00+03:00        0.245\n",
       "8425  2022-08-18 01:00:00+03:00        0.178\n",
       "8426  2022-08-18 02:00:00+03:00        0.203\n",
       "8427  2022-08-18 03:00:00+03:00        0.215\n",
       "8428  2022-08-18 04:00:00+03:00        1.043\n",
       "...                         ...          ...\n",
       "8587  2022-08-24 19:00:00+03:00        0.678\n",
       "8588  2022-08-24 20:00:00+03:00        0.457\n",
       "8589  2022-08-24 21:00:00+03:00        0.500\n",
       "8590  2022-08-24 22:00:00+03:00        2.321\n",
       "8591  2022-08-24 23:00:00+03:00        0.678\n",
       "\n",
       "[168 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true = df.iloc[df.shape[0]-24*7:][['time', 'consumption']]\n",
    "y_true"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime as dt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_cyclical_features(df, col_name, period, start_num=0):\n",
    "    kwargs = {\n",
    "        f'sin_{col_name}': lambda x: np.sin(2 * np.pi * (df[col_name] - start_num) / period),\n",
    "        f'cos_{col_name}': lambda x: np.cos(2 * np.pi * (df[col_name] - start_num) / period)\n",
    "    }\n",
    "    return df.assign(**kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_XGB = df[['time', 'temp', 'el_price', 'coco', 'consumption']].dropna()\n",
    "\n",
    "df_XGB['hour'] = df_XGB['time'].apply(lambda x: dt.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\").hour)\n",
    "df_XGB['month'] = df_XGB['time'].apply(lambda x: dt.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\").month)\n",
    "\n",
    "df_XGB = generate_cyclical_features(df_XGB, 'hour', 24, 0)\n",
    "df_XGB = generate_cyclical_features(df_XGB, 'month', 12, 1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kaggle_XGB = pd.read_csv('data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kaggle_XGB = df_kaggle_XGB[['time', 'temp', 'el_price', 'coco']].dropna()\n",
    "\n",
    "df_kaggle_XGB['hour'] = df_kaggle_XGB['time'].apply(lambda x: dt.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\").hour)\n",
    "df_kaggle_XGB['month'] = df_kaggle_XGB['time'].apply(lambda x: dt.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\").month)\n",
    "\n",
    "df_kaggle_XGB = generate_cyclical_features(df_kaggle_XGB, 'hour', 24, 0)\n",
    "df_kaggle_XGB = generate_cyclical_features(df_kaggle_XGB, 'month', 12, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['temp', 'el_price', 'coco', 'cos_hour', 'cos_month']\n",
    "df_X_kaggle_XGB = df_kaggle_XGB[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8394, 11)\n",
      "(0, 11)\n",
      "(336, 11)\n"
     ]
    }
   ],
   "source": [
    "print(df_XGB.shape)\n",
    "test_days = 0\n",
    "train_days = 14 * 24\n",
    "\n",
    "test_df_XGB = df_XGB.iloc[df_XGB.shape[0]-test_days:] \n",
    "train_df_XGB = df_XGB.iloc[df_XGB.shape[0]-(test_days+train_days):df_XGB.shape[0]-test_days]\n",
    "print(test_df_XGB.shape)\n",
    "print(train_df_XGB.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['temp', 'el_price', 'coco', 'cos_hour', 'cos_month']\n",
    "\n",
    "train_X_XGB = train_df_XGB[features]\n",
    "train_y_XGB = train_df_XGB['consumption']\n",
    "\n",
    "test_X_XGB = test_df_XGB[features]\n",
    "test_y_XGB = test_df_XGB['consumption'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.02, n_estimators=200)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    \"n_estimators\": 200,\n",
    "    \"max_depth\": 3,\n",
    "    \"min_samples_split\": 2,\n",
    "    \"learning_rate\": 0.02,\n",
    "    \"loss\": \"squared_error\",\n",
    "}\n",
    "model = GradientBoostingRegressor(**params)\n",
    "model.fit(train_X_XGB, train_y_XGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(df_X_kaggle_XGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "submissions = pd.DataFrame({'time': df_kaggle_XGB['time'], 'consumption': pred})\n",
    "submissions.to_csv('data/submissions_xgb.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                          time  consumption\n",
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       "4    2022-08-18 04:00:00+03:00     0.406936\n",
       "..                         ...          ...\n",
       "163  2022-08-24 19:00:00+03:00     0.926008\n",
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       "167  2022-08-24 23:00:00+03:00     0.819170\n",
       "\n",
       "[168 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = pd.read_csv('data/best_LR_w_2_3_v2.csv')\n",
    "LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     level_0  index                       time  consumption\n",
       "0          0   8424  2022-08-18 00:00:00+03:00        0.245\n",
       "1          1   8425  2022-08-18 01:00:00+03:00        0.178\n",
       "2          2   8426  2022-08-18 02:00:00+03:00        0.203\n",
       "3          3   8427  2022-08-18 03:00:00+03:00        0.215\n",
       "4          4   8428  2022-08-18 04:00:00+03:00        1.043\n",
       "..       ...    ...                        ...          ...\n",
       "163      163   8587  2022-08-24 19:00:00+03:00        0.678\n",
       "164      164   8588  2022-08-24 20:00:00+03:00        0.457\n",
       "165      165   8589  2022-08-24 21:00:00+03:00        0.500\n",
       "166      166   8590  2022-08-24 22:00:00+03:00        2.321\n",
       "167      167   8591  2022-08-24 23:00:00+03:00        0.678\n",
       "\n",
       "[168 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_true.reset_index(inplace=True)\n",
    "y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36508137344936176"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_true[['consumption']], LR[['consumption']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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