analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocesado de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variables exógenas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Temperatura:** Se ha descargado los valores de temperaturas máximas y mínimas de la AEMET parseando el código de la web \"datosclima.es\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_temps(fname: str = \"exo/temps.html\") -> pd.DataFrame:\n",
    "    \"\"\" Carga las temperaturas diarias del HTML\"\"\"\n",
    "    tabs = pd.read_html(Path(fname))\n",
    "    tabs = [tabs[i] for i in [3,4,5,7,8,9,11,12,13]]  \n",
    "    for t in tabs:\n",
    "        t.columns = [\"date\", \"tmax\", \"tmin\"]\n",
    "        t.drop(index=[0], inplace=True)\n",
    "    temps = pd.concat(tabs, ignore_index=True)\n",
    "    temps[\"date\"] = pd.to_datetime(temps[\"date\"], format=\"%d-%m-%Y\")\n",
    "    temps[[\"tmax\", \"tmin\"]] = temps[[\"tmax\", \"tmin\"]].astype(float)\n",
    "    temps.interpolate(method=\"pad\", inplace=True)\n",
    "    return temps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Festivos:** Se ha descargado los festivos locales y nacionales del dataset [\"Fiestas locales y autonómicas del Principado de Asturias \"](https://datos.gob.es/es/catalogo/a03002951-fiestas-locales-autonomicas-principado-asturias\") del portal de datos público \"datos.gob.es\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_fest() -> pd.DataFrame:\n",
    "    \"\"\" Carga los festivos de los ficheros csv\"\"\"\n",
    "    fest = []\n",
    "    for y in [21, 22, 23]:\n",
    "        dfy = pd.read_csv(f\"exo/festivos_{y}.csv\")\n",
    "        dfy.columns = [\"tipo\", \"lugar\", \"date\", \"fiesta\"]\n",
    "        fest.append(dfy)\n",
    "    fest = pd.concat(fest, ignore_index=True)\n",
    "    fest = fest[(fest[\"tipo\"] == \"AUTONOMICO\") | (fest[\"lugar\"] == \"GIJÓN\") | (fest[\"lugar\"] == \"VILLAVICIOSA\")]\n",
    "    fest[\"date\"] = pd.to_datetime(fest[\"date\"], format=\"%m/%d/%Y\")\n",
    "    fest.sort_values(\"date\", inplace=True)\n",
    "    fest.reset_index(inplace=True, drop=True)\n",
    "    return fest\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Datos de entrenamiento / predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "municipios = {\n",
    "    \"gijon\": \"GIJÓN\",\n",
    "    \"peon\": \"VILLAVICIOSA\"\n",
    "}\n",
    "\n",
    "def load_train_data(place: str = \"gijon\") -> pd.DataFrame:\n",
    "\n",
    "    # load csv files\n",
    "    df = pd.read_csv(f\"datasets/2021_2023_{place}_train.csv\")\n",
    "\n",
    "    df.columns = [\"ts\", \"cons\"]\n",
    "    df[\"ts\"] = pd.to_datetime(df[\"ts\"])\n",
    "    df[\"date\"] = df[\"ts\"].dt.date\n",
    "    df[\"place\"] = place\n",
    "\n",
    "    # add temperatures\n",
    "    temp = load_temps()\n",
    "    temp[\"date\"] = temp[\"date\"].dt.date\n",
    "    df = pd.merge(df, temp, left_on=\"date\", right_on=\"date\", how=\"left\")\n",
    "    df.sort_values([\"place\", \"date\"], inplace=True)\n",
    "    df[[\"tmax\",\"tmin\"]] = df[[\"tmax\",\"tmin\"]].interpolate(method=\"linear\")\n",
    "\n",
    "    # load and assign festives\n",
    "    fest = load_fest()\n",
    "    df[\"festivo\"] = df[\"date\"].isin(fest[(fest[\"tipo\"] == \"AUTONOMICO\") | (fest[\"lugar\"] == municipios[place])][\"date\"].dt.date)\n",
    "\n",
    "    # join both and reorder cols\n",
    "    df = df[[\"cons\", \"tmax\",\"tmin\", \"ts\", \"place\",\"date\",\"festivo\"]]\n",
    "\n",
    "    # add variables for workdays\n",
    "    df[\"finde\"] = df[\"ts\"].dt.weekday.isin([5,6])\n",
    "    df[\"laboral\"] = ~(df[\"finde\"] | df[\"festivo\"])\n",
    "\n",
    "    df[\"hour\"] = df[\"ts\"].dt.hour\n",
    "    df[\"horario_laboral\"] = df[\"hour\"].isin([6,7,8,9,10,11,12,13,14,15,16,17,18]) # de 7 a 19\n",
    "\n",
    "    # create dummy vars for hour weekday and month \n",
    "    df[\"dow\"] = df[\"ts\"].dt.day_of_week   \n",
    "    df[\"doy\"] = df[\"ts\"].dt.day_of_year\n",
    "    \n",
    "    # grab min, max, mean values\n",
    "    df[\"max_daily_cons\"] = df[\"doy\"].replace(df.groupby([\"doy\"])[\"cons\"].max())\n",
    "    df[\"avg_daily_cons\"] = df[\"doy\"].replace(df.groupby([\"doy\"])[\"cons\"].mean())\n",
    "    df[\"min_daily_cons\"] = df[\"doy\"].replace(df.groupby([\"doy\"])[\"cons\"].min())\n",
    "\n",
    "    # set index frequency\n",
    "    df.set_index(df[\"ts\"], inplace=True, drop=True)\n",
    "    df = df.asfreq(\"H\",method=\"pad\")\n",
    "    df.index.name = None\n",
    "    \n",
    "    # drop unused columns and assign correct dtypes\n",
    "    df.drop(columns=[\"ts\", \"date\", \"place\", \"doy\"], inplace=True)\n",
    "    # for c in [\"laboral\", \"finde\", \"festivo\", \"horario_laboral\"]:\n",
    "    #     df[c] = df[c].astype(int)\n",
    "\n",
    "    # fix 0 values (shorcuts?)\n",
    "    df.loc[df[\"cons\"] == 0, \"cons\"] = np.NAN\n",
    "\n",
    "    # return the data\n",
    "    return df\n",
    "\n",
    "\n",
    "def load_pred_data(start: np.datetime64, nhours: int, place: str = \"gijon\") -> pd.DataFrame:\n",
    "\n",
    "    \"\"\" Prepares the input data for the predictions. \"\"\"\n",
    "\n",
    "    df = pd.DataFrame(index=pd.date_range(start=start, periods=nhours, freq=\"H\"))\n",
    "    df.index: pd.DatetimeIndex\n",
    "    df[\"date\"] = df.index.date\n",
    "    df[\"place\"] = place\n",
    "\n",
    "    # add temperatures\n",
    "    temp = load_temps()\n",
    "    temp[\"date\"] = temp[\"date\"].dt.date\n",
    "    df = pd.merge(df, temp, left_on=\"date\", right_on=\"date\", how=\"left\")\n",
    "    df.set_index(pd.date_range(start=start, periods=nhours, freq=\"H\"), inplace=True, drop=True)\n",
    "    \n",
    "    df.sort_values([\"place\", \"date\"], inplace=True)\n",
    "\n",
    "    df[[\"tmax\",\"tmin\"]] = df[[\"tmax\",\"tmin\"]].interpolate()\n",
    "\n",
    "    # load and assign festives\n",
    "    fest = load_fest()\n",
    "    df[\"festivo\"] = df[\"date\"].isin(fest[(fest[\"tipo\"] == \"AUTONOMICO\") | (fest[\"lugar\"] == municipios[place])][\"date\"].dt.date)\n",
    "\n",
    "    # join both and reorder cols\n",
    "    df = df[[\"tmax\",\"tmin\", \"place\",\"date\",\"festivo\"]]\n",
    "\n",
    "    # add variables for workdays\n",
    "    df[\"finde\"] = df.index.weekday.isin([5,6])\n",
    "    df[\"laboral\"] = ~(df[\"finde\"] | df[\"festivo\"])\n",
    "\n",
    "    # add variables for work hours\n",
    "    df[\"hour\"] = df.index.hour\n",
    "    df[\"horario_laboral\"] = df[\"hour\"].isin([6,7,8,9,10,11,12,13,14,15,16,17,18]) # de 7 a 19\n",
    "\n",
    "    # create dummy vars for dayofweek and dayofyear\n",
    "    df[\"dow\"] = df.index.day_of_week\n",
    "    #df[\"month\"] = df[\"ts\"].dt.month\n",
    "    df[\"doy\"] = df.index.day_of_year\n",
    "\n",
    "    # read cons data to get daily averages\n",
    "    dfdat = pd.read_csv(f\"datasets/2021_2023_{place}_train.csv\")\n",
    "    dfdat.columns = [\"ts\", \"cons\"]\n",
    "    dfdat[\"ts\"] = pd.to_datetime(dfdat[\"ts\"])\n",
    "    dfdat[\"doy\"] = dfdat[\"ts\"].dt.day_of_year\n",
    "    \n",
    "    # grab min, max, mean values\n",
    "    df[\"max_daily_cons\"] = df[\"doy\"].replace(dfdat.groupby(\"doy\")[\"cons\"].max())\n",
    "    df[\"avg_daily_cons\"] = df[\"doy\"].replace(dfdat.groupby([\"doy\"])[\"cons\"].mean())\n",
    "    df[\"min_daily_cons\"] = df[\"doy\"].replace(dfdat.groupby(\"doy\")[\"cons\"].min())\n",
    "\n",
    "    # drop unused columns and assign correct dtypes\n",
    "    df.drop(columns=[\"date\", \"place\", \"doy\"], inplace=True)\n",
    "    for c in [\"laboral\", \"finde\", \"festivo\", \"horario_laboral\"]:\n",
    "        df[c] = df[c].astype(int)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimización de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_mem_usage(df):\n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type\n",
    "        to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "        \n",
    "        if col_type != object:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrenamiento de Modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_453116/472041089.py:11: FutureWarning:\n",
      "\n",
      "DataFrame.interpolate with method=pad is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n",
      "/tmp/ipykernel_453116/472041089.py:11: FutureWarning:\n",
      "\n",
      "DataFrame.interpolate with method=pad is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 0.92 MB\n",
      "Memory usage after optimization is: 0.43 MB\n",
      "Decreased by 53.3%\n",
      "Memory usage of dataframe is 0.11 MB\n",
      "Memory usage after optimization is: 0.03 MB\n",
      "Decreased by 72.5%\n"
     ]
    }
   ],
   "source": [
    "target = \"cons\" # objetivo de la predicción\n",
    "place = \"gijon\" # \"gijon\" / \"peon\"\n",
    "forecast_horizon = 24*30*2 # 2 meses, en horas\n",
    "\n",
    "# generate the data\n",
    "df_train = load_train_data(place=place)\n",
    "df_pred = load_pred_data(place=place, start=np.datetime64('2023-04-27T01:00'), nhours=forecast_horizon)\n",
    "\n",
    "# optimize the data\n",
    "df_train = reduce_mem_usage(df_train)\n",
    "df_pred = reduce_mem_usage(df_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cons</th>\n",
       "      <th>tmax</th>\n",
       "      <th>tmin</th>\n",
       "      <th>festivo</th>\n",
       "      <th>finde</th>\n",
       "      <th>laboral</th>\n",
       "      <th>hour</th>\n",
       "      <th>horario_laboral</th>\n",
       "      <th>dow</th>\n",
       "      <th>max_daily_cons</th>\n",
       "      <th>avg_daily_cons</th>\n",
       "      <th>min_daily_cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-26 01:00:00</th>\n",
       "      <td>26.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>16.406250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>25.953125</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-26 02:00:00</th>\n",
       "      <td>26.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>16.406250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>25.953125</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-26 03:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>16.406250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>25.953125</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-26 04:00:00</th>\n",
       "      <td>26.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>16.406250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>25.953125</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-26 05:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>16.406250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>25.953125</td>\n",
       "      <td>23</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-26 20:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>22.906250</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>41.562500</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-26 21:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>22.906250</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>41.562500</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-26 22:00:00</th>\n",
       "      <td>26.0</td>\n",
       "      <td>22.906250</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>41.562500</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-26 23:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>22.906250</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>41.562500</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-27 00:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>22.703125</td>\n",
       "      <td>14.296875</td>\n",
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       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>39.906250</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16080 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     cons       tmax       tmin  festivo  finde  laboral  \\\n",
       "2021-06-26 01:00:00  26.0  19.000000  16.406250      0.0    1.0      0.0   \n",
       "2021-06-26 02:00:00  26.0  19.000000  16.406250      0.0    1.0      0.0   \n",
       "2021-06-26 03:00:00  27.0  19.000000  16.406250      0.0    1.0      0.0   \n",
       "2021-06-26 04:00:00  26.0  19.000000  16.406250      0.0    1.0      0.0   \n",
       "2021-06-26 05:00:00  27.0  19.000000  16.406250      0.0    1.0      0.0   \n",
       "...                   ...        ...        ...      ...    ...      ...   \n",
       "2023-04-26 20:00:00  27.0  22.906250  15.500000      0.0    0.0      1.0   \n",
       "2023-04-26 21:00:00  27.0  22.906250  15.500000      0.0    0.0      1.0   \n",
       "2023-04-26 22:00:00  26.0  22.906250  15.500000      0.0    0.0      1.0   \n",
       "2023-04-26 23:00:00  27.0  22.906250  15.500000      0.0    0.0      1.0   \n",
       "2023-04-27 00:00:00  27.0  22.703125  14.296875      0.0    0.0      1.0   \n",
       "\n",
       "                     hour  horario_laboral  dow  max_daily_cons  \\\n",
       "2021-06-26 01:00:00     1              0.0    5              31   \n",
       "2021-06-26 02:00:00     2              0.0    5              31   \n",
       "2021-06-26 03:00:00     3              0.0    5              31   \n",
       "2021-06-26 04:00:00     4              0.0    5              31   \n",
       "2021-06-26 05:00:00     5              0.0    5              31   \n",
       "...                   ...              ...  ...             ...   \n",
       "2023-04-26 20:00:00    20              0.0    2              66   \n",
       "2023-04-26 21:00:00    21              0.0    2              66   \n",
       "2023-04-26 22:00:00    22              0.0    2              66   \n",
       "2023-04-26 23:00:00    23              0.0    2              66   \n",
       "2023-04-27 00:00:00     0              0.0    3              61   \n",
       "\n",
       "                     avg_daily_cons  min_daily_cons  \n",
       "2021-06-26 01:00:00       25.953125              23  \n",
       "2021-06-26 02:00:00       25.953125              23  \n",
       "2021-06-26 03:00:00       25.953125              23  \n",
       "2021-06-26 04:00:00       25.953125              23  \n",
       "2021-06-26 05:00:00       25.953125              23  \n",
       "...                             ...             ...  \n",
       "2023-04-26 20:00:00       41.562500              23  \n",
       "2023-04-26 21:00:00       41.562500              23  \n",
       "2023-04-26 22:00:00       41.562500              23  \n",
       "2023-04-26 23:00:00       41.562500              23  \n",
       "2023-04-27 00:00:00       39.906250              27  \n",
       "\n",
       "[16080 rows x 12 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                          tmax       tmin  festivo  finde  laboral  hour  \\\n",
       "2023-04-27 01:00:00  22.703125  14.296875        0      0        1     1   \n",
       "2023-04-27 02:00:00  22.703125  14.296875        0      0        1     2   \n",
       "2023-04-27 03:00:00  22.703125  14.296875        0      0        1     3   \n",
       "2023-04-27 04:00:00  22.703125  14.296875        0      0        1     4   \n",
       "2023-04-27 05:00:00  22.703125  14.296875        0      0        1     5   \n",
       "...                        ...        ...      ...    ...      ...   ...   \n",
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       "2023-06-25 21:00:00  24.000000  19.093750        0      1        0    21   \n",
       "2023-06-25 22:00:00  24.000000  19.093750        0      1        0    22   \n",
       "2023-06-25 23:00:00  24.000000  19.093750        0      1        0    23   \n",
       "2023-06-26 00:00:00  24.000000  19.093750        0      0        1     0   \n",
       "\n",
       "                     horario_laboral  dow  max_daily_cons  avg_daily_cons  \\\n",
       "2023-04-27 01:00:00                0    3              61       39.906250   \n",
       "2023-04-27 02:00:00                0    3              61       39.906250   \n",
       "2023-04-27 03:00:00                0    3              61       39.906250   \n",
       "2023-04-27 04:00:00                0    3              61       39.906250   \n",
       "2023-04-27 05:00:00                0    3              61       39.906250   \n",
       "...                              ...  ...             ...             ...   \n",
       "2023-06-25 20:00:00                0    6              28       25.171875   \n",
       "2023-06-25 21:00:00                0    6              28       25.171875   \n",
       "2023-06-25 22:00:00                0    6              28       25.171875   \n",
       "2023-06-25 23:00:00                0    6              28       25.171875   \n",
       "2023-06-26 00:00:00                0    0              31       25.953125   \n",
       "\n",
       "                     min_daily_cons  \n",
       "2023-04-27 01:00:00              27  \n",
       "2023-04-27 02:00:00              27  \n",
       "2023-04-27 03:00:00              27  \n",
       "2023-04-27 04:00:00              27  \n",
       "2023-04-27 05:00:00              27  \n",
       "...                             ...  \n",
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       "2023-06-25 21:00:00              24  \n",
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       "2023-06-25 23:00:00              24  \n",
       "2023-06-26 00:00:00              23  \n",
       "\n",
       "[1440 rows x 11 columns]"
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     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error, r2_score\n",
    "from sklearn.model_selection import KFold\n",
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "import optuna\n",
    "\n",
    "df_train.dropna(inplace=True)\n",
    "X = df_train.drop(\"cons\", axis=1).values\n",
    "y = df_train[[\"cons\"]].values\n",
    "n_splits = 5\n",
    "\n",
    "def objective(trial):\n",
    "    params = {\n",
    "        \"objective\": \"regression\",\n",
    "        \"metric\": \"rmse\",\n",
    "        \"n_estimators\": trial.suggest_int(\"n_estimators\", 200, 1000, log=True),\n",
    "        \"verbosity\": -1,\n",
    "        \"bagging_freq\": 1,\n",
    "        \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-3, 0.1, log=True),\n",
    "        \"num_leaves\": trial.suggest_int(\"num_leaves\", 2, 2**10),\n",
    "        \"subsample\": trial.suggest_float(\"subsample\", 0.05, 1.0),\n",
    "        \"colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.05, 1.0),\n",
    "        \"min_data_in_leaf\": trial.suggest_int(\"min_data_in_leaf\", 1, 100)\n",
    "    }\n",
    "    kf = KFold(n_splits=n_splits)\n",
    "    rmse, mape, r2 = np.zeros(n_splits), np.zeros(n_splits), np.zeros(n_splits)\n",
    "    for i, (train, test) in enumerate(kf.split(np.arange(X.shape[0]))):\n",
    "        X_train, X_val, y_train, y_val = X[train], X[test], y[train], y[test]\n",
    "        model = lgb.LGBMRegressor(**params)\n",
    "        model.fit(X_train, np.squeeze(y_train))\n",
    "        predictions = model.predict(X_val)\n",
    "        rmse[i] = mean_squared_error(np.squeeze(y_val), predictions, squared=False)\n",
    "        mape[i] = mean_absolute_percentage_error(np.squeeze(y_val), predictions)\n",
    "        r2[i] = r2_score(np.squeeze(y_val), predictions)\n",
    " \n",
    "    return rmse.mean(), mape.mean(), r2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2023-11-21 20:12:50,496] Using an existing study with name 'lgbm-gijon' instead of creating a new one.\n"
     ]
    }
   ],
   "source": [
    "study = optuna.create_study(\n",
    "    storage=\"sqlite:///optuna-database.sqlite3\",\n",
    "    study_name=f\"lgbm-{place}\",\n",
    "    load_if_exists=True,\n",
    "    directions=['minimize','minimize', 'maximize'])\n",
    "#study.optimize(objective, n_trials=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
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           "x": 0.05
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          "xaxis": {
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           "ticks": "",
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     "metadata": {},
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    }
   ],
   "source": [
    "optuna.visualization.plot_pareto_front(study, target_names=[\"RMSE\", \"MAPE\", \"R2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters: {'n_estimators': 318, 'learning_rate': 0.012525877577601145, 'num_leaves': 114, 'subsample': 0.3488305512774496, 'colsample_bytree': 0.9959462337762435, 'min_data_in_leaf': 89}\n",
      "Best [RMSE, MAPE, R2]: [5.306669662970827, 0.09615054001575007, 0.8735736532486407]\n"
     ]
    }
   ],
   "source": [
    "tgt_stat = 0 # [RMSE, MAPE, R2]\n",
    "trial = max(study.best_trials, key=lambda t: t.values[tgt_stat])\n",
    "print('Best hyperparameters:', trial.params)\n",
    "print('Best [RMSE, MAPE, R2]:', trial.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "              learning_rate=0.012525877577601145, metric=&#x27;rmse&#x27;,\n",
       "              min_data_in_leaf=89, n_estimators=318, num_leaves=114,\n",
       "              objective=&#x27;regression&#x27;, subsample=0.3488305512774496,\n",
       "              verbosity=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(bagging_freq=1, colsample_bytree=0.9959462337762435,\n",
       "              learning_rate=0.012525877577601145, metric=&#x27;rmse&#x27;,\n",
       "              min_data_in_leaf=89, n_estimators=318, num_leaves=114,\n",
       "              objective=&#x27;regression&#x27;, subsample=0.3488305512774496,\n",
       "              verbosity=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMRegressor(bagging_freq=1, colsample_bytree=0.9959462337762435,\n",
       "              learning_rate=0.012525877577601145, metric='rmse',\n",
       "              min_data_in_leaf=89, n_estimators=318, num_leaves=114,\n",
       "              objective='regression', subsample=0.3488305512774496,\n",
       "              verbosity=-1)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "params = {\"objective\": \"regression\", \"metric\": \"rmse\",\n",
    "        \"n_estimators\": trial.params['n_estimators'],\n",
    "        \"verbosity\": -1, \"bagging_freq\": 1,\n",
    "        \"learning_rate\": trial.params['learning_rate'],\n",
    "        \"num_leaves\": trial.params['num_leaves'],\n",
    "        \"subsample\": trial.params['subsample'],\n",
    "        \"colsample_bytree\": trial.params['colsample_bytree'],\n",
    "        \"min_data_in_leaf\": trial.params['min_data_in_leaf']}\n",
    "\n",
    "model = lgb.LGBMRegressor(**params)\n",
    "model.fit(X, np.squeeze(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.booster_.save_model(f\"model_weights-{place}.txt\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-04-27 01:00:00</th>\n",
       "      <td>27.535782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-27 02:00:00</th>\n",
       "      <td>27.507529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-27 03:00:00</th>\n",
       "      <td>27.490708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-27 04:00:00</th>\n",
       "      <td>27.488944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-27 05:00:00</th>\n",
       "      <td>27.553155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-25 20:00:00</th>\n",
       "      <td>25.513335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-25 21:00:00</th>\n",
       "      <td>25.527072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-25 22:00:00</th>\n",
       "      <td>25.276188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-25 23:00:00</th>\n",
       "      <td>25.248207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26 00:00:00</th>\n",
       "      <td>23.887635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1440 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          cons\n",
       "2023-04-27 01:00:00  27.535782\n",
       "2023-04-27 02:00:00  27.507529\n",
       "2023-04-27 03:00:00  27.490708\n",
       "2023-04-27 04:00:00  27.488944\n",
       "2023-04-27 05:00:00  27.553155\n",
       "...                        ...\n",
       "2023-06-25 20:00:00  25.513335\n",
       "2023-06-25 21:00:00  25.527072\n",
       "2023-06-25 22:00:00  25.276188\n",
       "2023-06-25 23:00:00  25.248207\n",
       "2023-06-26 00:00:00  23.887635\n",
       "\n",
       "[1440 rows x 1 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = pd.DataFrame(index=df_pred.index, data=model.predict(df_pred.values), columns=[\"cons\"])\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "preds.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline RMSE: 5.934\n",
      "Baseline MAPE: 0.0782\n",
      "Baseline R2: 0.8523986064667833\n",
      "RMSE: 3.795291292562366\n",
      "MAPE: 0.06834151302142687\n",
      "MAPE: 0.9396100418466414\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b900fbdc6ce6448a893dda3b4a02a035",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "inpreds = pd.DataFrame(index=df_train.index, data=model.predict(X), columns=[\"pred\"])\n",
    "inpreds[\"truth\"] = df_train[\"cons\"]\n",
    "inpreds[[\"pred\", \"truth\"]].plot()\n",
    "print(\"Baseline RMSE:\", mean_squared_error(inpreds[\"truth\"].iloc[:-1], inpreds[\"truth\"].shift(-1).iloc[:-1], squared=False))\n",
    "print(\"Baseline MAPE:\", mean_absolute_percentage_error(inpreds[\"truth\"].iloc[:-1], inpreds[\"truth\"].shift(-1).iloc[:-1]))\n",
    "print(\"Baseline R2:\",r2_score(inpreds[\"truth\"].iloc[:-1], inpreds[\"truth\"].shift(-1).iloc[:-1]))\n",
    "print(\"RMSE:\", mean_squared_error(inpreds[\"truth\"], inpreds[\"pred\"], squared=False))\n",
    "print(\"MAPE:\", mean_absolute_percentage_error(inpreds[\"truth\"], inpreds[\"pred\"]))\n",
    "print(\"R2:\", r2_score(inpreds[\"truth\"], inpreds[\"pred\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "inpreds[\"dif\"] = inpreds[\"pred\"] - inpreds[\"truth\"]\n",
    "inpreds[\"dif\"].plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}