1_open_measurement.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Open database\n",
    "\n",
    "We open and merge all the files provided into one database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "from scipy.interpolate import griddata\n",
    "import multiprocess\n",
    "from functools import partial\n",
    "import datetime as dt\n",
    "\n",
    "import os\n",
    "os.chdir(\"F:/Defi_IA/data\")\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from pyproj import Proj, transform\n",
    "inProj = Proj(init='epsg:4326')\n",
    "outProj = Proj(init='epsg:2154')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stations = pd.read_csv(\n",
    "    \"Other/stations_coordinates.csv\",\n",
    "    dtype = {\"number_sta\": int}         \n",
    ").rename(\n",
    "    columns={\"number_sta\": \"station\", \"lat\": \"latitude\", \"lon\": \"longitude\", \"height_sta\": \"height\"}\n",
    ")\n",
    "stations['coord_x'], stations['coord_y'] = transform(inProj, outProj, stations.longitude.to_numpy(), stations.latitude.to_numpy())\n",
    "\n",
    "import geopandas as gpd\n",
    "gdf_stations = gpd.GeoDataFrame(stations, crs=2154, geometry=gpd.points_from_xy(stations.coord_x, stations.coord_y, crs=2154))\n",
    "gdf_stations.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Index Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8525400, 6)\n"
     ]
    },
    {
     "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>station</th>\n",
       "      <th>test</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_index</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station   test  day  hour  day_index  month\n",
       "0  14047002  False    0     0          0      1\n",
       "1  14047002  False    0     1          0      1\n",
       "2  14047002  False    0     2          0      1\n",
       "3  14047002  False    0     3          0      1\n",
       "4  14047002  False    0     4          0      1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index for features and predictions\n",
    "test_features_index = pd.read_csv(\"Test/X_station_test.csv\")[['Id']]\n",
    "test_features_index[\"station\"] = test_features_index[\"Id\"].str.split(\"_\").str[0].astype(int)\n",
    "test_features_index[\"day_index\"] = test_features_index[\"Id\"].str.split(\"_\").str[1].astype(int)\n",
    "\n",
    "df_day_index = pd.concat([\n",
    "    pd.DataFrame({\n",
    "        'day_index':list(range(2*365)),\n",
    "        'test':False,\n",
    "        'day':list(range(2*365))}),\n",
    "    pd.DataFrame({\n",
    "        'day_index':test_features_index[test_features_index.station==56137001].day_index.unique(), # Ne marche pas à creuser...\n",
    "        'test':True,\n",
    "        'day':list(range(363))}),\n",
    "])\n",
    "\n",
    "first_date = np.datetime64(\"2016-01-01T00:00\")\n",
    "df_date_index = pd.DataFrame({'date': [first_date + pd.Timedelta(days=i) for i in range(730)]})\n",
    "df_date_index['day_index'] = range(730)\n",
    "df_date_index['month'] = df_date_index.date.dt.month\n",
    "df_date_index.drop(['date'], axis=1, inplace=True)\n",
    "\n",
    "df_id_month_test = pd.read_csv(\"Test/X_station_test.csv\").assign(day_index=lambda x: x.Id.str.split(\"_\").str[1].astype(int))[['day_index', 'month']].drop_duplicates().reset_index(drop=True)\n",
    "df_date_index = pd.concat([df_date_index.assign(test=False), df_id_month_test.assign(test=True)])\n",
    "\n",
    "df_day_index = pd.merge(df_day_index, df_date_index, on=['test', 'day_index'])\n",
    "df_day_index.to_csv(\"Dataset/Test/index_day.csv\", index=False)\n",
    "\n",
    "df_full_index_day = pd.merge(stations[['station']], df_day_index, how=\"cross\")\n",
    "df_full_index_day.sort_values(['station', 'test', 'day'], inplace=True)\n",
    "df_full_index_day = df_full_index_day[['station', 'test', 'day', 'day_index', 'month']]\n",
    "\n",
    "df_full_index = pd.merge(df_full_index_day, pd.DataFrame({'hour': range(24)}), how='cross')\n",
    "df_full_index.sort_values(['station', 'test', 'day', 'hour'], inplace=True)\n",
    "df_full_index = df_full_index[['station', 'test', 'day', 'hour', 'day_index', 'month']]\n",
    "\n",
    "print(df_full_index.shape)\n",
    "df_full_index.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full_index_day.to_csv(\"Dataset/Full_index_day.csv\", index=False)\n",
    "df_full_index.to_csv(\"Dataset/Full_index.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Y value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train\n",
    "#Ground\n",
    "train_ground = pd.read_csv(\n",
    "    \"Train/Y_train.csv\", \n",
    "    parse_dates=['date'], \n",
    "    infer_datetime_format=True,\n",
    "    dtype = {\"number_sta\": int, \"Ground_truth\": float},\n",
    ").rename(columns = {\"Ground_truth\": \"y\", \"Id\": \"id\"})[['id', 'y']]\n",
    "\n",
    "#Observation\n",
    "train_observation = pd.read_csv(\n",
    "    \"Train/Baselines/Baseline_observation_train.csv\",\n",
    "    dtype = {\"Prediction\": float}\n",
    ").assign(test=True).rename(columns = {\"Prediction\": \"Observation\", \"Id\": \"id\"})[['id', 'Observation']]\n",
    "\n",
    "#Forecast\n",
    "train_forecast = pd.read_csv(\n",
    "    \"Train/Baselines/Baseline_forecast_train.csv\",\n",
    "    dtype = {\"Prediction\": float}\n",
    ").assign(test=True).rename(columns = {\"Prediction\": \"Forecast\", \"Id\": \"id\"})[['id', 'Forecast']]\n",
    "\n",
    "train = pd.merge(train_ground, train_observation, on='id', how='outer')\n",
    "train = pd.merge(train, train_forecast, on='id', how='outer')\n",
    "train[\"station\"] = train[\"id\"].str.split(\"_\").str[0].astype(int)\n",
    "train[\"day_index\"] = train[\"id\"].str.split(\"_\").str[1].astype(int)\n",
    "\n",
    "#Test\n",
    "#Observation\n",
    "test_observation = pd.read_csv(\n",
    "    \"Test/Baselines/Baseline_observation_test.csv\",\n",
    "    dtype = {\"Prediction\": float}\n",
    ").rename(columns = {\"Prediction\": \"Observation\", \"Id\": \"id\"})[['id', 'Observation']]\n",
    "\n",
    "#Forecast\n",
    "test_forecast = pd.read_csv(\n",
    "    \"Test/Baselines/Baseline_forecast_test.csv\",\n",
    "    dtype = {\"Prediction\": float}\n",
    ").rename(columns = {\"Prediction\": \"Forecast\", \"Id\": \"id\"})[['id', 'Forecast']]\n",
    "\n",
    "test = pd.merge(test_observation, test_forecast, on='id', how='outer')\n",
    "test[\"station\"] = test[\"id\"].str.split(\"_\").str[0].astype(int)\n",
    "test[\"day_index\"] = test[\"id\"].str.split(\"_\").str[1].astype(int)\n",
    "test['y'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>station</th>\n",
       "      <th>test</th>\n",
       "      <th>day</th>\n",
       "      <th>day_index</th>\n",
       "      <th>month</th>\n",
       "      <th>y</th>\n",
       "      <th>Observation</th>\n",
       "      <th>Forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.410274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.642678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.331558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.967951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.360032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station   test  day  day_index  month   y  Observation   Forecast\n",
       "0  14047002  False    0          0      1 NaN          NaN   8.410274\n",
       "1  14047002  False    1          1      1 NaN          NaN  12.642678\n",
       "2  14047002  False    2          2      1 NaN          NaN   5.331558\n",
       "3  14047002  False    3          3      1 NaN          NaN   3.967951\n",
       "4  14047002  False    4          4      1 NaN          NaN   3.360032"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pd.merge(\n",
    "    df_full_index_day,\n",
    "    pd.concat([train.assign(test=False), test.assign(test=True)], axis = \"rows\"),\n",
    "    on = ['station', 'test', 'day_index'],\n",
    "    how = \"left\",\n",
    ")\n",
    "predictions = predictions.drop(\"id\", axis=1).sort_values(['station', 'test', 'day']).reset_index(drop=True)\n",
    "predictions.to_csv(\"Dataset/predictions.csv\", index=False)\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load X Features Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>dd</th>\n",
       "      <th>hu</th>\n",
       "      <th>td</th>\n",
       "      <th>t</th>\n",
       "      <th>ff</th>\n",
       "      <th>precip</th>\n",
       "      <th>test</th>\n",
       "      <th>station</th>\n",
       "      <th>day_index</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>14047002</td>\n",
       "      <td>277</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>14047002</td>\n",
       "      <td>277</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>14047002</td>\n",
       "      <td>277</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>14047002</td>\n",
       "      <td>277</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>14047002</td>\n",
       "      <td>277</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dd  hu  td       t  ff  precip  test   station  day_index  hour\n",
       "0 NaN NaN NaN  278.35 NaN     NaN  True  14047002        277     4\n",
       "1 NaN NaN NaN  278.40 NaN     0.0  True  14047002        277     5\n",
       "2 NaN NaN NaN  279.01 NaN     0.0  True  14047002        277     6\n",
       "3 NaN NaN NaN  279.66 NaN     0.0  True  14047002        277     7\n",
       "4 NaN NaN NaN  279.99 NaN     0.0  True  14047002        277     8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_mesured = ['ff', 't', 'td', 'hu', 'dd', 'precip']\n",
    "\n",
    "#Train\n",
    "train_features = pd.read_csv(\n",
    "    \"Train/X_station_train.csv\", \n",
    "    parse_dates=['date'], \n",
    "    infer_datetime_format=True,\n",
    "    dtype = {\"number_sta\": int},\n",
    ").rename(\n",
    "    columns = {\"number_sta\": \"station\", \"Id\": \"id\"}\n",
    ").assign(test=False)\n",
    "\n",
    "train_features[\"day_index\"] = train_features[\"id\"].str.split(\"_\").str[1].astype(int)\n",
    "train_features[\"hour\"] = train_features[\"id\"].str.split(\"_\").str[2].astype(int)\n",
    "train_features.drop([\"date\", \"id\"], axis=1, inplace=True)\n",
    "\n",
    "#Test\n",
    "test_features = pd.read_csv(\n",
    "    \"Test/X_station_test.csv\",\n",
    ").rename(\n",
    "    columns = {\"Id\": \"id\"}\n",
    ").assign(test=True)\n",
    "\n",
    "test_features[\"station\"] = test_features[\"id\"].str.split(\"_\").str[0].astype(int)\n",
    "test_features[\"day_index\"] = test_features[\"id\"].str.split(\"_\").str[1].astype(int)\n",
    "test_features[\"hour\"] = test_features[\"id\"].str.split(\"_\").str[2].astype(int)\n",
    "\n",
    "test_features.drop([\"id\", \"month\"], axis=1, inplace=True)\n",
    "test_features.head()"
   ]
  },
  {
   "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>station</th>\n",
       "      <th>test</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_index</th>\n",
       "      <th>month</th>\n",
       "      <th>ff</th>\n",
       "      <th>t</th>\n",
       "      <th>td</th>\n",
       "      <th>hu</th>\n",
       "      <th>dd</th>\n",
       "      <th>precip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station   test  day  hour  day_index  month  ff   t  td  hu  dd  precip\n",
       "0  14047002  False    0     0          0      1 NaN NaN NaN NaN NaN     NaN\n",
       "1  14047002  False    0     1          0      1 NaN NaN NaN NaN NaN     NaN\n",
       "2  14047002  False    0     2          0      1 NaN NaN NaN NaN NaN     NaN\n",
       "3  14047002  False    0     3          0      1 NaN NaN NaN NaN NaN     NaN\n",
       "4  14047002  False    0     4          0      1 NaN NaN NaN NaN NaN     NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Final features\n",
    "features = pd.merge(\n",
    "    df_full_index,\n",
    "    pd.concat([train_features, test_features], axis = \"rows\"),\n",
    "    on=['station', 'test', 'day_index', 'hour'],\n",
    "    how='left')\n",
    "\n",
    "features = features.sort_values(['station', 'test', 'day', 'hour']).reset_index(drop=True)\n",
    "features.to_csv(\"Dataset/features.csv\", index=False)\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporal Interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>station</th>\n",
       "      <th>test</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_index</th>\n",
       "      <th>month</th>\n",
       "      <th>ff</th>\n",
       "      <th>t</th>\n",
       "      <th>td</th>\n",
       "      <th>hu</th>\n",
       "      <th>dd</th>\n",
       "      <th>precip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station   test  day  hour  day_index  month  ff   t  td  hu  dd  precip\n",
       "0  14047002  False    0     0          0      1 NaN NaN NaN NaN NaN     NaN\n",
       "1  14047002  False    0     1          0      1 NaN NaN NaN NaN NaN     NaN\n",
       "2  14047002  False    0     2          0      1 NaN NaN NaN NaN NaN     NaN\n",
       "3  14047002  False    0     3          0      1 NaN NaN NaN NaN NaN     NaN\n",
       "4  14047002  False    0     4          0      1 NaN NaN NaN NaN NaN     NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def time_interpolate(x, features):\n",
    "    for feature in features:\n",
    "        if x[feature].isna().mean() < 0.80:\n",
    "            x[feature] = x[feature].interpolate(limit=24) #24\n",
    "    return x\n",
    "\n",
    "features_time_interpolate_train = features[~features.test].groupby(['station']).apply(time_interpolate, features_mesured)\n",
    "\n",
    "# Pas d'interpolation temporelle pour les test car les jours ne sont pas ordonés\n",
    "features_time_interpolate_test = features[features.test] #.groupby(['station']).apply(time_interpolate, features_mesured)\n",
    "\n",
    "features_time_interpolate = pd.concat([features_time_interpolate_train, features_time_interpolate_test])\n",
    "\n",
    "features_time_interpolate[['station', 'test', 'day', 'hour', 'day_index', 'month'] + features_mesured].to_csv('Dataset/features_time_interpolate.csv', index=False)\n",
    "\n",
    "features_time_interpolate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='test,day,hour'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.groupby(['test', 'day', 'hour']).t.agg(lambda x: (~x.isna()).sum()).plot(ylim=(0,340))\n",
    "features_time_interpolate.groupby(['test', 'day', 'hour']).t.agg(lambda x: (~x.isna()).sum()).plot(ylim=(0,340))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "test   day  hour\n",
       "False  135  4       25\n",
       "            5       26\n",
       "            6       56\n",
       "            7       62\n",
       "       139  9       19\n",
       "            10      21\n",
       "            11      21\n",
       "            12      50\n",
       "       144  6       69\n",
       "            10      15\n",
       "            11      17\n",
       "            12      34\n",
       "Name: t, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = features.groupby(['test', 'day', 'hour']).t.agg(lambda x: (~x.isna()).sum())\n",
    "data[data<100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y NA (train) : 31.67% -> 31.1%.\n",
      "Observation NA : 30.47% -> 30.05%.\n",
      "Forecast NA : 5.31% -> 4.03%.\n"
     ]
    },
    {
     "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>station</th>\n",
       "      <th>test</th>\n",
       "      <th>day</th>\n",
       "      <th>day_index</th>\n",
       "      <th>month</th>\n",
       "      <th>y</th>\n",
       "      <th>Observation</th>\n",
       "      <th>Forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.410274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.642678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.331558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.967951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.360032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station   test  day  day_index  month   y  Observation   Forecast\n",
       "0  14047002  False    0          0      1 NaN          NaN   8.410274\n",
       "1  14047002  False    1          1      1 NaN          NaN  12.642678\n",
       "2  14047002  False    2          2      1 NaN          NaN   5.331558\n",
       "3  14047002  False    3          3      1 NaN          NaN   3.967951\n",
       "4  14047002  False    4          4      1 NaN          NaN   3.360032"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_y_var = ['y', 'Observation', 'Forecast']\n",
    "\n",
    "def time_interpolate(x, features):\n",
    "    for feature in features:\n",
    "        if x[feature].isna().mean() < 0.80: #0.80\n",
    "            x[feature] = x[feature].interpolate(limit=5)\n",
    "    return x\n",
    "\n",
    "predictions_time_interpolate_train = predictions[~predictions.test].groupby(['station']).apply(time_interpolate, list_y_var)\n",
    "\n",
    "# Pas d'interpolation temporelle pour les test car les jours ne sont pas ordonés\n",
    "predictions_time_interpolate_test = predictions[predictions.test] #.groupby(['station']).apply(time_interpolate, list_y_var)\n",
    "\n",
    "predictions_time_interpolate = pd.concat([predictions_time_interpolate_train, predictions_time_interpolate_test])\n",
    "\n",
    "predictions_time_interpolate[['station', 'test', 'day', 'day_index', 'month'] + list_y_var].to_csv('Dataset/predictions_time_interpolate.csv', index=False)\n",
    "\n",
    "def y_na(x):\n",
    "    return np.round(x.isna().mean()*100, 2)\n",
    "\n",
    "print(f\"Y NA (train) : {y_na(predictions[~predictions.test].y)}% -> {y_na(predictions_time_interpolate[~predictions_time_interpolate.test].y)}%.\")\n",
    "print(f\"Observation NA : {y_na(predictions.Observation)}% -> {y_na(predictions_time_interpolate.Observation)}%.\")\n",
    "print(f\"Forecast NA : {y_na(predictions.Forecast)}% -> {y_na(predictions_time_interpolate.Forecast)}%.\")\n",
    "predictions_time_interpolate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, (index, df_iter) in enumerate(predictions_time_interpolate[~predictions_time_interpolate.test].groupby('station')):\n",
    "    df_iter[['y']].plot(subplots=True, fontsize=12, figsize=(16, 1))\n",
    "    if i>3:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='test,day'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions.groupby(['test', 'day']).y.agg(lambda x: (~x.isna()).sum()).plot(ylim=(0,340))\n",
    "predictions_time_interpolate.groupby(['test', 'day']).y.agg(lambda x: (~x.isna()).sum()).plot(ylim=(0,340))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial Interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need a minimum of points for each interpolation (4 points). So time interpoolation is necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ff finished.\n",
      "Feature t finished.\n",
      "Feature td finished.\n",
      "Feature hu finished.\n",
      "Feature dd finished.\n",
      "Feature precip finished.\n"
     ]
    },
    {
     "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>station</th>\n",
       "      <th>test</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_index</th>\n",
       "      <th>month</th>\n",
       "      <th>ff</th>\n",
       "      <th>dist_ff</th>\n",
       "      <th>t</th>\n",
       "      <th>dist_t</th>\n",
       "      <th>td</th>\n",
       "      <th>dist_td</th>\n",
       "      <th>hu</th>\n",
       "      <th>dist_hu</th>\n",
       "      <th>dd</th>\n",
       "      <th>dist_dd</th>\n",
       "      <th>precip</th>\n",
       "      <th>dist_precip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.056472</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>279.406394</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>279.452594</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>101.051266</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>208.985256</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>-0.000035</td>\n",
       "      <td>10785.923995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.171511</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>276.540162</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>276.594094</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>100.875933</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>219.652121</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>10785.923995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.813644</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>274.045721</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>274.260788</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>101.680669</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>210.558382</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>0.008242</td>\n",
       "      <td>10785.923995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.631034</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>272.483210</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>272.618118</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>100.645315</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>199.090037</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>0.029730</td>\n",
       "      <td>10785.923995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.231741</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>272.934190</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>272.829550</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>99.381360</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>179.928121</td>\n",
       "      <td>9351.532478</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>10785.923995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station   test  day  hour  day_index  month        ff      dist_ff  \\\n",
       "0  14047002  False    0     0          0      1  4.056472  9351.532478   \n",
       "1  14047002  False    0     1          0      1  4.171511  9351.532478   \n",
       "2  14047002  False    0     2          0      1  3.813644  9351.532478   \n",
       "3  14047002  False    0     3          0      1  3.631034  9351.532478   \n",
       "4  14047002  False    0     4          0      1  2.231741  9351.532478   \n",
       "\n",
       "            t        dist_t          td       dist_td          hu  \\\n",
       "0  279.406394  10785.923995  279.452594  10785.923995  101.051266   \n",
       "1  276.540162  10785.923995  276.594094  10785.923995  100.875933   \n",
       "2  274.045721  10785.923995  274.260788  10785.923995  101.680669   \n",
       "3  272.483210  10785.923995  272.618118  10785.923995  100.645315   \n",
       "4  272.934190  10785.923995  272.829550  10785.923995   99.381360   \n",
       "\n",
       "        dist_hu          dd      dist_dd    precip   dist_precip  \n",
       "0  10785.923995  208.985256  9351.532478 -0.000035  10785.923995  \n",
       "1  10785.923995  219.652121  9351.532478  0.000010  10785.923995  \n",
       "2  10785.923995  210.558382  9351.532478  0.008242  10785.923995  \n",
       "3  10785.923995  199.090037  9351.532478  0.029730  10785.923995  \n",
       "4  10785.923995  179.928121  9351.532478  0.000002  10785.923995  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features_coord = pd.merge(features_time_interpolate, stations, on='station')\n",
    "df_features_coord.drop(['longitude', 'latitude', 'height'], axis=1, inplace=True)\n",
    "\n",
    "df_features_interpolate = df_full_index.copy()\n",
    "for feature in features_mesured:\n",
    "    reference_values = df_features_coord[~df_features_coord[feature].isna()]\n",
    "\n",
    "    def agg_interpolate(key, df_points, feature):\n",
    "        from scipy.interpolate import griddata\n",
    "        from scipy.spatial.distance import cdist\n",
    "        # A function that realize the interpolation of the model and with the staion coordinate\n",
    "        index, group = key\n",
    "        df_out = df_points[['station', 'coord_x', 'coord_y']].copy()\n",
    "        df_out[feature] = griddata((group.coord_x, group.coord_y), group[feature], (df_out.coord_x, df_out.coord_y), method='cubic')\n",
    "\n",
    "        # Nearest Neighboor in case of null value\n",
    "        df_out.loc[df_out[feature].isna(), feature] = griddata((group.coord_x, group.coord_y), group[feature], (df_out[df_out[feature].isna()].coord_x, df_out[df_out[feature].isna()].coord_y), method='nearest')\n",
    "\n",
    "        # Minimum distance\n",
    "        df_out[f'dist_{feature}'] = cdist(df_out[['coord_x', 'coord_y']], group[~group[feature].isna()][['coord_x', 'coord_y']]).min(axis=1)\n",
    "        return df_out.assign(test=index[0], day_index=index[1], hour=index[2]).drop(['coord_x', 'coord_y'], axis=1)\n",
    "\n",
    "    with multiprocess.Pool(processes=12) as p:\n",
    "        df_buffer=pd.concat(p.map(partial(agg_interpolate, df_points=stations, feature=feature), reference_values.groupby(['test', 'day_index', 'hour'])))\n",
    "\n",
    "    #df_buffer=pd.concat([agg_interpolate(key, stations, feature) for key in reference_values.groupby(['test', 'day_index', 'hour'])])\n",
    "    df_features_interpolate = pd.merge(df_features_interpolate, df_buffer, on=['station', 'test', 'day_index', 'hour'], how='outer')\n",
    "\n",
    "    print(f'Feature {feature} finished.')\n",
    "\n",
    "df_features_interpolate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9039.86466722811"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features_interpolate.dist_ff.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9796.010378943078"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features_interpolate.dist_ff.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y NA (train) : 53.22% -> 44.81%.\n",
      "Y NA (train) : 26.51% -> 17.11%.\n",
      "Y NA (train) : 47.56% -> 39.04%.\n",
      "Y NA (train) : 47.51% -> 38.99%.\n",
      "Y NA (train) : 53.25% -> 44.84%.\n",
      "Y NA (train) : 27.74% -> 17.78%.\n"
     ]
    },
    {
     "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>station</th>\n",
       "      <th>test</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_index</th>\n",
       "      <th>month</th>\n",
       "      <th>ff</th>\n",
       "      <th>t</th>\n",
       "      <th>td</th>\n",
       "      <th>hu</th>\n",
       "      <th>dd</th>\n",
       "      <th>precip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.056472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>208.985256</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.171511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219.652121</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.813644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.558382</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.631034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.090037</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.231741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.928121</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station   test  day  hour  day_index  month        ff   t  td  hu  \\\n",
       "0  14047002  False    0     0          0      1  4.056472 NaN NaN NaN   \n",
       "1  14047002  False    0     1          0      1  4.171511 NaN NaN NaN   \n",
       "2  14047002  False    0     2          0      1  3.813644 NaN NaN NaN   \n",
       "3  14047002  False    0     3          0      1  3.631034 NaN NaN NaN   \n",
       "4  14047002  False    0     4          0      1  2.231741 NaN NaN NaN   \n",
       "\n",
       "           dd  precip  \n",
       "0  208.985256     NaN  \n",
       "1  219.652121     NaN  \n",
       "2  210.558382     NaN  \n",
       "3  199.090037     NaN  \n",
       "4  179.928121     NaN  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_features = pd.merge(df_full_index[['station', 'test', 'day_index', 'hour']], df_features_interpolate, on=['station', 'test', 'day_index', 'hour'], how='left')\n",
    "new_features = new_features.sort_values(['station', 'test', 'day_index', 'hour']).reset_index(drop=True)\n",
    "\n",
    "features_time_interpolate.sort_values(['station', 'test', 'day_index', 'hour'], inplace=True)\n",
    "\n",
    "dist_max_interpolate = 10000\n",
    "for feature in features_mesured:\n",
    "    condition = (new_features[f'dist_{feature}'] < dist_max_interpolate) | new_features.test\n",
    "    new_features[feature] = np.where(condition, new_features[feature], features_time_interpolate[feature])\n",
    "    new_features[feature] = np.where(new_features[feature] < 0, 0, new_features[feature]) #For precip feature\n",
    "\n",
    "dist_columns = [f'dist_{feature}' for feature in features_mesured]\n",
    "new_features['nearest_station'] = new_features[dist_columns].min(axis=1)\n",
    "new_features.drop(dist_columns, axis=1, inplace=True)\n",
    "new_features = new_features[['station', 'test', 'day', 'hour', 'day_index', 'month'] + features_mesured]\n",
    "\n",
    "for feature in features_mesured:\n",
    "    print(f\"Y NA (train) : {y_na(features_time_interpolate[~features_time_interpolate.test][feature])}% -> {y_na(new_features[~new_features.test][feature])}%.\")\n",
    "\n",
    "new_features.to_csv('Dataset/features_interpolate_full.csv', index=False)\n",
    "new_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature y finished.\n",
      "Feature Observation finished.\n",
      "Feature Forecast finished.\n"
     ]
    },
    {
     "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>station</th>\n",
       "      <th>test</th>\n",
       "      <th>day</th>\n",
       "      <th>day_index</th>\n",
       "      <th>month</th>\n",
       "      <th>y</th>\n",
       "      <th>dist_y</th>\n",
       "      <th>Observation</th>\n",
       "      <th>dist_Observation</th>\n",
       "      <th>Forecast</th>\n",
       "      <th>dist_Forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.939015</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>0.059930</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>8.410274</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.510043</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>11.939015</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>12.642678</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.215059</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>13.510043</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>5.331558</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.427259</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>3.215059</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>3.967951</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.322350</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>2.427259</td>\n",
       "      <td>10785.923995</td>\n",
       "      <td>3.360032</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station   test  day  day_index  month          y        dist_y  \\\n",
       "0  14047002  False    0          0      1  11.939015  10785.923995   \n",
       "1  14047002  False    1          1      1  13.510043  10785.923995   \n",
       "2  14047002  False    2          2      1   3.215059  10785.923995   \n",
       "3  14047002  False    3          3      1   2.427259  10785.923995   \n",
       "4  14047002  False    4          4      1   4.322350  10785.923995   \n",
       "\n",
       "   Observation  dist_Observation   Forecast  dist_Forecast  \n",
       "0     0.059930      10785.923995   8.410274            0.0  \n",
       "1    11.939015      10785.923995  12.642678            0.0  \n",
       "2    13.510043      10785.923995   5.331558            0.0  \n",
       "3     3.215059      10785.923995   3.967951            0.0  \n",
       "4     2.427259      10785.923995   3.360032            0.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions_coord = pd.merge(predictions_time_interpolate, stations, on='station')\n",
    "df_predictions_coord.drop(['longitude', 'latitude', 'height'], axis=1, inplace=True)\n",
    "\n",
    "df_predictions_interpolate = df_full_index_day.copy()\n",
    "\n",
    "list_y_var = ['y', 'Observation', 'Forecast']\n",
    "\n",
    "for feature in list_y_var:\n",
    "    reference_values = df_predictions_coord[~df_predictions_coord[feature].isna()]\n",
    "\n",
    "    def agg_interpolate(key, df_points, feature):\n",
    "        from scipy.interpolate import griddata\n",
    "        from scipy.spatial.distance import cdist\n",
    "        # A function that realize the interpolation of the model and with the staion coordinate\n",
    "        index, group = key\n",
    "        df_out = df_points[['station', 'coord_x', 'coord_y']].copy()\n",
    "        df_out[feature] = griddata((group.coord_x, group.coord_y), group[feature], (df_out.coord_x, df_out.coord_y), method='cubic')\n",
    "\n",
    "        # Nearest Neighboor in case of null value\n",
    "        df_out.loc[df_out[feature].isna(), feature] = griddata((group.coord_x, group.coord_y), group[feature], (df_out[df_out[feature].isna()].coord_x, df_out[df_out[feature].isna()].coord_y), method='nearest')\n",
    "        \n",
    "        df_out[f'dist_{feature}'] = cdist(df_out[['coord_x', 'coord_y']], group[~group[feature].isna()][['coord_x', 'coord_y']]).min(axis=1)\n",
    "        return df_out.assign(test=index[0], day_index=index[1]).drop(['coord_x', 'coord_y'], axis=1)\n",
    "\n",
    "    df_buffer = pd.concat([agg_interpolate(key, stations, feature) for key in reference_values.groupby(['test', 'day_index'])])\n",
    "    df_predictions_interpolate = pd.merge(df_predictions_interpolate, df_buffer, on=['station', 'test', 'day_index'], how='outer')\n",
    "\n",
    "    print(f'Feature {feature} finished.')\n",
    "\n",
    "df_predictions_interpolate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4142.385196833318"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions_interpolate.dist_y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y NA (train) : 31.1% -> 21.35%.\n",
      "Observation NA : 30.05% -> 14.31%.\n",
      "Forecast NA : 4.03% -> 4.03%.\n"
     ]
    },
    {
     "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>station</th>\n",
       "      <th>test</th>\n",
       "      <th>day</th>\n",
       "      <th>day_index</th>\n",
       "      <th>month</th>\n",
       "      <th>y</th>\n",
       "      <th>Observation</th>\n",
       "      <th>Forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.410274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.642678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.331558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.967951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14047002</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.360032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    station   test  day  day_index  month   y  Observation   Forecast\n",
       "0  14047002  False    0          0      1 NaN          NaN   8.410274\n",
       "1  14047002  False    1          1      1 NaN          NaN  12.642678\n",
       "2  14047002  False    2          2      1 NaN          NaN   5.331558\n",
       "3  14047002  False    3          3      1 NaN          NaN   3.967951\n",
       "4  14047002  False    4          4      1 NaN          NaN   3.360032"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_predictions = pd.merge(df_full_index_day[['station', 'test', 'day_index']], df_predictions_interpolate, on=['station', 'test', 'day_index'], how='left')\n",
    "new_predictions = new_predictions.sort_values(['station', 'test', 'day_index']).reset_index(drop=True)\n",
    "\n",
    "predictions_time_interpolate.sort_values(['station', 'test', 'day_index'], inplace=True)\n",
    "#new_predictions = predictions_time_interpolate.sort_values(['station', 'test', 'day']).copy()\n",
    "\n",
    "dist_max_interpolate = 10000\n",
    "for feature in list_y_var:\n",
    "    condition = (new_predictions[f'dist_{feature}'] < dist_max_interpolate) | new_predictions.test\n",
    "    new_predictions[feature] = np.where(condition, new_predictions[feature], predictions_time_interpolate[feature])\n",
    "    new_predictions[feature] = np.where(new_predictions[feature] < 0, 0, new_predictions[feature]) #For precip feature\n",
    "\n",
    "dist_columns = [f'dist_{feature}' for feature in list_y_var]\n",
    "new_predictions['nearest_station'] = new_predictions[dist_columns].min(axis=1)\n",
    "new_predictions.drop(dist_columns, axis=1, inplace=True)\n",
    "new_predictions = new_predictions[['station', 'test', 'day', 'day_index', 'month'] + list_y_var]\n",
    "new_predictions.to_csv('Dataset/predictions_interpolate_full.csv', index=False)\n",
    "\n",
    "def y_na(x):\n",
    "    return np.round(x.isna().mean()*100, 2)\n",
    "\n",
    "print(f\"Y NA (train) : {y_na(predictions_time_interpolate[~predictions_time_interpolate.test].y)}% -> {y_na(new_predictions[~new_predictions.test].y)}%.\")\n",
    "print(f\"Observation NA : {y_na(predictions_time_interpolate.Observation)}% -> {y_na(new_predictions.Observation)}%.\")\n",
    "print(f\"Forecast NA : {y_na(predictions_time_interpolate.Forecast)}% -> {y_na(new_predictions.Forecast)}%.\")\n",
    "\n",
    "new_predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='test,day,hour'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAnoUlEQVR4nO3deZxUxbn/8c8zK/s+7CCIIIIL6ARx34i7UZN4gzGGRBNifnqzmlwxi/GqURNjlqtZTDSSaDSYTWKMigQ1RgUHRAFBBEVEkH3fhpl5fn9UDfQMPXs3M+P5vl+vfnV3dZ06VX1OP12nzulqc3dERCQ5cpq7AiIicmAp8IuIJIwCv4hIwijwi4gkjAK/iEjC5DV3BQB69OjhgwYNau5qiIi0KrNnz17n7kUNXa5FBP5BgwZRUlLS3NUQEWlVzOydxiynoR4RkYRR4BcRSRgFfhGRhFHgFxFJGAV+EZGEUeAXEUkYBX4RkYRpEdfxN9qahfDXq5q7FiIijTfkdBh3wwFdZesO/Dn50KFXc9dCRKTx2nQ64Kts3YG/xyFw2ZTmroWISKuiMX4RkYRR4BcRSRgFfhGRhFHgFxFJGAV+EZGEUeAXEUkYBX4RkYRR4BcRSRgFfhGRhFHgFxFJGAV+EZGEqTPwm1kbM5tlZq+a2QIzuzGmdzOzaWb2ZrzvmrLMJDNbYmZvmNlZ2WyAiIg0TH16/LuB0939KGAUcLaZjQWuA6a7+1BgenyOmY0AxgMjgbOBn5tZbhbqLiIijVBn4PdgW3yaH28OXAhMjumTgYvi4wuBh919t7u/DSwBxmSy0iIi0nj1GuM3s1wzmwusAaa5+0ygl7uvAoj3PWP2fsC7KYuviGnVy5xoZiVmVrJ27domNEFERBqiXoHf3cvdfRTQHxhjZofXkt3SFZGmzHvcvdjdi4uKiupVWRERaboGXdXj7puAZwhj96vNrA9AvF8Ts60ABqQs1h9Y2dSKiohIZtTnqp4iM+sSH7cFxgGLgKnAhJhtAvBofDwVGG9mhWY2GBgKzMpwvUVEpJHq89eLfYDJ8cqcHGCKuz9mZi8CU8zsSmA5cAmAuy8wsynA60AZcLW7l2en+iIi0lDmvt/w+wFXXFzsJSUlzV0NEZFWxcxmu3txQ5fTL3dFRBJGgV9EJGEU+EVEEkaBX0QkYRT4RUQSRoFfRCRhFPhFRBJGgV9EJGEU+EVEEkaBX0QkYRT4RUQSRoFfRCRhFPhFRBJGgV9EJGEU+EVEEkaBX0QkYRT4RUQSRoFfRCRhFPhFRBJGgV9EJGEU+EVEEkaBX0QkYRT4RUQSps7Ab2YDzGyGmS00swVm9uWY/j0ze8/M5sbbuSnLTDKzJWb2hpmdlc0GiIhIw+TVI08Z8HV3n2NmHYHZZjYtvvZjd78jNbOZjQDGAyOBvsDTZjbM3cszWXEREWmcOnv87r7K3efEx1uBhUC/Wha5EHjY3Xe7+9vAEmBMJiorIiJN16AxfjMbBIwGZsaka8zsNTO7z8y6xrR+wLspi60gzReFmU00sxIzK1m7dm3Day4iIo1S78BvZh2APwNfcfctwC+AIcAoYBXwo8qsaRb3/RLc73H3YncvLioqami9RUSkkeoV+M0snxD0H3T3vwC4+2p3L3f3CuDX7BvOWQEMSFm8P7Ayc1UWEZGmqM9VPQbcCyx09ztT0vukZLsYmB8fTwXGm1mhmQ0GhgKzMldlERFpivpc1XMCcDkwz8zmxrTrgUvNbBRhGGcZ8AUAd19gZlOA1wlXBF2tK3pERFqOOgO/uz9P+nH7x2tZ5hbglibUS0REskS/3BURSRgFfhGRhFHgFxFJGAV+EZGEUeAXEUkYBX4RkYRR4BcRSRgFfhGRhFHgFxFJGAV+EZGEUeAXEUkYBX4RkYRR4BcRSRgFfhGRhFHgFxFJGAV+EZGEUeAXEUkYBX4RkYRR4BcRSRgFfhGRhFHgFxFJGAV+EZGEUeAXEUmYOgO/mQ0wsxlmttDMFpjZl2N6NzObZmZvxvuuKctMMrMlZvaGmZ2VzQaIiEjD1KfHXwZ83d0PA8YCV5vZCOA6YLq7DwWmx+fE18YDI4GzgZ+bWW42Ki8iIg1XZ+B391XuPic+3gosBPoBFwKTY7bJwEXx8YXAw+6+293fBpYAYzJcbxERaaQGjfGb2SBgNDAT6OXuqyB8OQA9Y7Z+wLspi62IadXLmmhmJWZWsnbt2kZUXUREGqPegd/MOgB/Br7i7ltqy5omzfdLcL/H3YvdvbioqKi+1RARkSaqV+A3s3xC0H/Q3f8Sk1ebWZ/4eh9gTUxfAQxIWbw/sDIz1RURkaaqz1U9BtwLLHT3O1NemgpMiI8nAI+mpI83s0IzGwwMBWZlrsoiItIUefXIcwJwOTDPzObGtOuB24ApZnYlsBy4BMDdF5jZFOB1whVBV7t7eaYrLiIijVNn4Hf350k/bg9wRg3L3ALc0oR6iYhIluiXuyIiCaPALyKSMAr8IiIJo8AvIpIwCvwiIgmjwC8ikjAK/CIiCaPALyKSMAr8IiIJo8AvIpIwCvwiIgmjwC8ikjAK/CIiCaPALyKSMAr8IiIJo8AvIpIwCvwiIgmjwC8ikjAK/CIiCaPALyKSMAr8IiIJk9fcFZCWZf223XTvUAjA3TOW8MMn3+C4g7vz0MSxe/Ps2lPOrj3lAOwoLSc/N4f8XKMgL4d2BXlV8uXlGNt3l9OpbR5mVmVdm3aUUlpWgZnRvjCXbbvLaJOfiwG5Oca23WW0L8ijfWHV3XT77jLMQp6tu8ooyMuhrNzp3Daf3BzD3Vm/vZT8nBz2VFQAUF7htCvIpUPh/vVoqh2lZezaU0GFO+0L8thRWoYDFe7k5eTQsU0e+bn797HcPeN12bWnfO97kp8b3sPCvFw6t83nJ08vpjAvl+27y7hrxhJe/tY4ijoWVll+++4yILy3u/dU0KFNHjkGW3aVUVZeQW6OUV7hFOTlUF4R6t+pTXhP3Z3tpeUU5uWQn5vDxu2l5OXa3ve8tKyCHaVhG7fJz81ouzNlZ2k5m3aW0iYvl7YFuezaU06XdgVxX8xl6+4ycs1oX5iHu1NW4ZRX3twpL3cq3HEgx4wN20sZd+ezAJx/ZB8ee23Vfuv80umH8LUzDz2g7TR3P6ArTKe4uNhLSkqauxqJN+i6f9T42kdH9+PGC0dyxPeeqrOcs0b2ok/nttz/wrKM1e3MEb146vXVDO/dkUXvb02bp2NhHucf1ZeHZi2vtaz/Ku7Pl84YSv+u7WrN99SC99m4o5SDizpwyS9fBODV755J53b5AFRUOJffN5P/LFlfZ/2vPXMYl48dxC+fW0qOwd0zlgLQr0tb3tu0E4CRfTtx1IAu/O2V9zBge2k5Hzu6PyP6duKOJ9+gZ6dCLh97EEvXbuczxw/i0N4d95b/ucklPL1wdY3r/875I7jpsdf3Sz/u4O5s3FHKLRcfwcd+8UKd7ajJh0f0YtrrNa+/d6c2vL9l197ng3u0p0u7fN7buJM1W3dTfFBXVmzcSc9Ohdz4kZHcMHUBm3bs4aGJY/n9i+/wy2fD+zWgW1se+vxY2ubn8ql7Z7F6yy7uunQ0M9/ewFc/PGy/9a7avJNHSlZw57TFVdKH9+7IacN7cuIhPZj51np+9q8lDWrv2SN788SC9xu0TE2O7N+Zqdec2KhlzWy2uxc3eLm6Ar+Z3QecD6xx98Nj2veAzwNrY7br3f3x+Nok4EqgHPiSuz9ZVyUU+Gvn7ixctZVDenZg8eqtbNtdRpd2+ZSWVbB+eyn9u7SlS7sClq3fjgGFebnk5hi9O7dh3nub6doun85t89m2u4zhvTuxbXcZy9fvoGv7fDbv3MO7G3Zw1QNzmruZB9ykc4Zz7hF9GNCt6hfAhu2lHH3TtFqXbV+Qy/bS8mxWr17+fs2JbNpZyuX3zmruqrQYbfNz2bmn+bdNqoLcHErLK9K+9vat5zb6yC+bgf9kYBvwu2qBf5u731Et7wjgIWAM0Bd4Ghjm7rVuhSQH/tKyCn7z/FvsLC1n7MHdeX/zLj52TH8gDLt86JanqWjGg7KvjBvKT55+M+1rnzx2IGeP7M3Jw4qY8cYaPvvblxtU9mdPGMQNF4yk+OanWbdtN//6+ik89toqfvXs0hqD6qVjBnDDBSOpcOeOJxfziQ8N4KFZy7ngqD4c0rMjf3x5Od9/fBG3f+wI1m0r5erTDtm77LTXV/P53+2/n7046XT6dG4L7BveaqgJxx1El3YFjBncjRMO6bE3fd6KzVxw1/P1LuebZx/KD56ouv7CvBx2l6UPGuksuulshn/nCQB+cdnRfPHBzH6p/+Kyo7n7mSXMf28LYwZ1Y9ayDfVazgx+On40C1ZuZsnqbWzeuYeSdzZmtG6ZcO4RvVm4ais7S8urHKXU5fMnDeZ/zh5OXm4OZeUV5KUM763btpvjb/0XV506hCtPHMzX/jiX6YvWALDstvMaXdesBf5Y+CDgsXoE/kkA7n5rfP4k8D13f7G28psa+F9bsYnXV27h/S27GHtwdwz40+wVHDWgC6cMK2Llpp28v2UXA7q148Wl6zlzRC/yc3PYXlrGlp1l/O2V9/hjybs1lj/5ijHMW7GJnh3bUFpewdZdZQzu0Y6la7czekAXLr9vFuVpovNXxw1j1eadPLt4Las2138Hak5TvnAcLy/bQO9ObTjl0CJ6dCisMgT0nfNH8NHR/fYOgVRauzV8SaXz+JdOYt223fTt0oZxdz4HwDfOOpSJJx9Mfm4OO0rLWL5hB8N7d6qy3IbtpTy7eA3ff3wRbfNz+dvVJ9CtfUGT2rdg5WYuvvuFGntfNTljeE9GD+zCHU/tGzI49dAiRvTpxJUnDt57XqS6Re9v4eyf/LtK2tNfO4Vtu8s4sl9nrn3kVQZ2b8dJQ4vo3r6AQT3aA7B6yy72xH3toO7taFeQxz/nreKLD87hn18+ifuef5tHZq+oUu5B3dtx74RiDunZkb+/upL/fugVZn97HMfcvG+7nHN4b64/9zBO+sGMvWmfPHYgf5gZhsd+On4UHQrzuHJyCS9cdzrtC/IYfdNTfOGUIXzjzEN5dcUmRg/syq495Sxdu42RfTvz/Jvr+NS9M/eW1bEwj1899xYQgtrXpszl76+uZPHN56Tt2c5ZvpFde8o5dnB3cnOMOcs3snVXGS8sWcevnnuLqdecwHcfXcDcdzdx3pF9+EeacfLazPrWGazbWsrStds49uBu9OzYhlsfX7i3jhC27/RFaxg1oAt/u/qEvekX//w/vLJ8097nz1x7Kqfe8QwAxw7uxsvLNrD45nOqBPn6enbxWvp1acMhPTvWnbkGzRH4PwNsAUqAr7v7RjO7C3jJ3R+I+e4F/unuf0pT5kRgIsDAgQOPeeeddxpad97dsIMb/76ApxeuafCyLc2nxg7kgZdqH5uuVL2HsH7b7iof7kp9O7ehf9d2fPr4g3hqwWqmvrqyxrHedOVWWrlpJ8ff9i9+f+UYThpalDbPmq27GHPLdHp0KGDdttK96SXfHkePGoJic5qzfCMf/XntY9pN6YlVqqhwfjr9TS4p7k/Jso0M6tGeUQO6NLlcd2f4d57guCHdGdC1Hb9/6R1mXHsqg+MXR6oHZ77Dt/46HwhtcnfunrGE847suzf//Pc285nfzmLm9ePIzWnaCWd3557n3uIjo/ruPZLKpNufWMQvnllK2/xcOrbJY83W3WnzPXDlsZw4tEfa1yo9UvIuh/XpxJ9mr+D+F5bxnfNHcOWJg/e+Xl7h/OTpcGRZ1zmh5nCgA38vYB3gwE1AH3e/wszuBl6sFvgfd/c/11Z+Y3v8NR26N9afv3gcW3aW0bNTId9/fGG9Tto1RP+ubfnm2cNZ8N5mHnjpHX78iVFM/P1sAJ79xqlMX7iG/41B+caPjOTHTy/m0F4d+cq4YRzZvzP//dArHN63U41XALy7YQel5RV0j73iLu1q7h27O9MXrqF35zac/3/P8/DEsYw9uHuj21Y5Ln5Ev87Me28zEE6i/uDjRzW6zAOh+gntYwd345aLD6dXpzZ0bJPfTLXKvJfeWs+oAV1a7NU0DVHZ0WlXkMsL153O5p17OOWHzwDhi23O8o38vwfm8NTXTqZTPbfh96Yu4P4XlnHDBSP47AmD616ghWhs4G/U5Zzuvvf0vZn9GngsPl0BDEjJ2h9Y2Zh11MchPfcNNXz+pMG8snxTlTHD684Zzt0zlrB1V1mt5Rzcoz0/u3Q0h/frvDftwc+NTTtu/b0LRjDh+EE8OHM5Jw8t4uQfhkPmZbedV6VnVV1q7/EjR/Vl0rmHAXDpmIE8NGs5/bq05YoTB/PE/PcZ2qsDE44fxITjB1Up477PfKjWdlQ/SVkbM2PciF771a2xurUv4KfjR3H8kB57h3wG9+hQx1LNb/rXT2HZuu3s2lPBkf07N+g9bE2a8qXe0uTlhGGVHDO6tCugc9uqwf3ogV156fozGlRmRewA52T48tqWqlGB38z6uHvlQNvFQGW0mwr8wczuJJzcHQpk7XKD3JSN9K3zRgDhkPXH0xbzi08dQ0FeDledMoTNO/dw1I3hMsSeHQv3Hhr++5un0a9LW3JqOLQ97dCenDWyF0f068yjc1fy5pptXH7cIMyMT409CIDFN59DWbxW/LJjD+K8I/ow6n9rvyIk1c0XHc715w7fO0Y45arjGvgutBwXjupX5XlBXsv/feCQog4MKWr5X1CyT35e+LwO7RW2W+V5g6MHdml0mZUDHwmJ+3UHfjN7CDgV6GFmK4AbgFPNbBRhqGcZ8AUAd19gZlOA14Ey4Oq6ruhpivjFX2UM+fB+nbm3Ws+4c9t8lt12Hs8uXstR/Tsz4401FOTm1qt396vLw1HU+DEDeeP9rfuNfxbk5VCQ8gPo2oZX0snNsQ/UkEKq1hD4pfVpV5DH764YwxEpR+hNPWoNP7mChMT9ugO/u1+aJvneWvLfAtzSlErVV0NPQp0yLJyYvHh0/wavq0eHQnoc0vJOUrZEXdvls3HHHgobcaWDSH2cPCz9RQaNVbG3x5+M0N+qP5m5CdlIrc1xQ8J4snr80lpUXuSSlJDSuj+ZCdlIrc3j88JP2VvLbxdEKsf4k3Jyt3UH/r2af74h2d+fZtf8oziRlmTvyd3mrcYB06oDvyVmM7Uu3z4vXKp63TmHNXNNROpn78ndhISUVj0tc35u2EoDP6DXXrdWnzvpYD5yVF96dmrT3FURqZekndxt1YG/S7sCfnX5MXxoULfmropUo6AvrUnlD7iSEfZbeeAHOGtk7+augoi0djq5KyKSLBeODr86P+agrs1ckwOj1ff4RUSa6pRhRRmZs6q1UI9fRCRhFPhFRBJGgV9EJGEU+EVEEkaBX0QkYRT4RUQSRoFfRCRhFPhFRBJGgV9EJGEU+EVEEkaBX0QkYRT4RUQSRoFfRCRhFPhFRBKmzsBvZveZ2Rozm5+S1s3MppnZm/G+a8prk8xsiZm9YWZnZaviIiLSOPXp8d8PnF0t7TpgursPBabH55jZCGA8MDIu83Mzy81YbUVEpMnqDPzu/hywoVryhcDk+HgycFFK+sPuvtvd3waWAGMyU1UREcmExo7x93L3VQDxvmdM7we8m5JvRUzbj5lNNLMSMytZu3ZtI6shIiINlemTu+n+qdjTZXT3e9y92N2Li4qKMlwNERGpSWMD/2oz6wMQ79fE9BXAgJR8/YGVja+eiIhkWmMD/1RgQnw8AXg0JX28mRWa2WBgKDCraVUUEZFMyqsrg5k9BJwK9DCzFcANwG3AFDO7ElgOXALg7gvMbArwOlAGXO3u5Vmqu4iINEKdgd/dL63hpTNqyH8LcEtTKiUiItmjX+6KiCSMAr+ISMIo8IuIJIwCv4hIwijwi4gkjAK/iEjCKPCLiCSMAr+ISMIo8IuIJIwCv4hIwijwi4gkjAK/iEjCKPCLiCSMAr+ISMIo8IuIJIwCv4hIwijwi4gkjAK/iEjCKPCLiCSMAr+ISMIo8IuIJIwCv4hIwijwi4gkTF5TFjazZcBWoBwoc/diM+sG/BEYBCwD/svdNzatmiIikimZ6PGf5u6j3L04Pr8OmO7uQ4Hp8bmIiLQQ2RjquRCYHB9PBi7KwjpERKSRmhr4HXjKzGab2cSY1svdVwHE+57pFjSziWZWYmYla9eubWI1RESkvpo0xg+c4O4rzawnMM3MFtV3QXe/B7gHoLi42JtYDxERqacm9fjdfWW8XwP8FRgDrDazPgDxfk1TKykiIpnT6MBvZu3NrGPlY+BMYD4wFZgQs00AHm1qJUVEJHOaMtTTC/irmVWW8wd3f8LMXgammNmVwHLgkqZXU0REMqXRgd/d3wKOSpO+HjijKZUSEZHs0S93RUQSRoFfRCRhFPhFRBJGgV9EJGEU+EVEEkaBX0QkYRT4RUQSRoFfRCRhFPhFRBJGgV9EJGEU+EVEEkaBX0QkYRT4RUQSRoFfRCRhFPhFRBJGgV9EJGEU+EVEEkaBX0QkYRT4RUQSRoFfRCRhFPizoHPb/OaugohIjRT4s2DaV09u7iqIiNRIgT8LenZqw2XHDmzuaoiIpJW1wG9mZ5vZG2a2xMyuy9Z6WqobLhjJ1GtOaO5qiIjsJyuB38xygbuBc4ARwKVmNiIb62qpCvJyOLJ/l+auhojIfrLV4x8DLHH3t9y9FHgYuDBL62rxenQoaO4qiIjslZelcvsB76Y8XwEcm5rBzCYCE+PTbWb2RhPW1wNY14Tls+odwL6TkaJadDszSO38YFE7s+egxiyUrcBvadK8yhP3e4B7MrIysxJ3L85EWS2Z2vnBonZ+sLSmdmZrqGcFMCDleX9gZZbWJSIiDZCtwP8yMNTMBptZATAemJqldYmISANkZajH3cvM7BrgSSAXuM/dF2RjXVFGhoxaAbXzg0Xt/GBpNe00d687l4iIfGDol7siIgmjwC8ikjAZD/xm1tbMnjWzXDMbZGY7zWxuyi3tr5li3vkZrks3M5tmZm/G+671WKbKVBMp7fmRmV3azO35oZktMrPXzOyvZtYl5bVJsc5vmNlZKemXmtm8uMwTZtYj5bWattWG+HihmU1Kyf+0mXXNRttS1nGtmXllPc2su5nNMLNtZnZXtbwFZnaPmS2O78vH6ii7e2xvuZndXa3Nr5rZuuplmdk1ZvbZbLXZzP47brMFZvaDmPZhM5sdt9tsMzs9Jf8zMX/l/tezjvLbmtlbZrbczHaZ2dK4fd82s61m9kym25Sy7mviPrl3e8Z0M7OfxddeM7OjY3obM5sVt8UCM7uxHm17Pm7TnfG2LbbtVTPbaGZZOY9pZvfGdbxmZn8ysw4xfbiZvWhmu83s2mrL3Gdma+q7H9VRVpe43kXxc3pcTL8jdX+pkbtn9AZcDXw5Ph4EzK/ncvXO24C6/AC4Lj6+Dri9jvy5wFLgYKAAeBW4Cfgy4YcSzzVze84E8uLj2yvbQ5gW41WgEBgc25BLOHm/BuiR8n58r7ZtBXwSeDimtQOWAYPi8wnAt7LRtlj+AMIFAe+k1Lk9cCJwFXBXtfw3AjfHxzmVy9RSfnvgR8AfgbtS21FTWfE9eCVL2/M04GmgMD7vGe9HA33j48OB91KWeQYobsA6bgLeq7ZvTAY+Xrk9U/LmZbh9o+P7tix12wDnAv8k/N5nLDAzphvQIT7OB2YCY2sp/2rgsZTPQRGwGxgfn98AXJaltnVKeXwn++JMT+BDwC3AtdWWORk4ur77UR1lTQY+Fx8XAF3i44OAp+oqOxtDPZcBj9b0Yuw5/dvM5sTb8WnyjIzf/HPjN+rQmP6plPRfWZgTqDYXEt4g4v1FdeRPN9XEp4FH3f0doCvVroQ6kO1x96fcvSw+fYnw+4jKdj7s7rvd/W1gSWyLxVt7MzOgE1V/T5FuW3nMnwcMI+x8j5rZHOB94NJstC36MfBNUn7s5+7b3f15YFea/FcAt8Z8Fe5e668m3X07cBzhcuO0ZZnZIOBZ4KnY5lGEwHVUFtr8ReA2d98d67cm3r/i7pXbaQHQxswK6yirJp8GHqi2b1T2vqcC15vZ983sWeDLZna/mX08pa3bUh5/w8xejm2utTee0o5laV66EPidBy8BXcysT3xeub78eKvt6pPLCO9Px7h/dyAE/vL4+vnAjVlq25a4nAFtK+vp7mvc/WVgT5plngM21FV2Sv60ZZlZJ8KXyL0xX6m7b4qP3wG6m1nv2srOaOC3MOxxcLWNPcT2HZbeTeiBftjdjwY+AfwsTVFXAT9191FAMbDCzA6L+U+I6eWEDV+bXu6+CiDe13pYzP5TTawi9FQq2zMfGNyM7Ul1BaHXlK7eK4B+7r6HEFzmEQL+COLOUtO2Av6H0MPeCvyH0NM4Ktb1FkLPsUum22ZmHyH0bF+tT+Nt3zDXTfEL9xEz61XHMgWEo7nUL4ghZjYP6EVo718J2/Qc9m3PEkLPq1Kmtucw4CQzm2lhuOJDafJ8DHil8ssh+m3c/74TA09t7e1B2P6VVhCOYnD3jYSOQS93P8Xdf1RLWWcCQwkdilHAMWbW2D+eSLu/xvXkmtlcwjaY5u4za6hP5ba8GTiMsH/PA2ax78tiO+FzkJW2mdlvCZ2h4cD/1ZU/gw4G1hL2g1fM7Ddm1j7l9TlArVMDZ7rH3wPYVC1tqbuPirerCd/iv44ftkcIwai6Fwk9kf8BDnL3ncAZwDHAy3HHOIPwBmRS9Q9RR0IPotJ6YH1zt8fMvgWUAQ/WUG8AN7N8QuAfDfQFXgMqx+zTbivC4fPjhKODIwm94EUpbVtDCJIZa5uZtSMMIX23jqanyiMc8fwnfum+CNxRxzI1tfk0wna8CTiVcLic1TantKErYbjjG8CU1EBuZiMJQ3pfSFnmMnc/Ajgp3i6vo72lddRhD/DvOvJAGGY8kzDsNYcQ7IbWY7l0apzSxd3L4xdnf2CMmR1eQxmV2/IsYC5h/x5FmBOsbUq+bWbWsY76NKpt7v7ZuN6FhC/9AyWPMGT0C3cfTfiCS536fk2sV60FZNJOoE0deb4KrCYcOueQ5hDe3f9gZjOB84AnzexzhJ1lsrtPqp6/FqvjIeQqM+tDeENqU32qiR5U3UkL2f/Q80C2BzObQDiEPcPjoF6aeldOkTEqrn9pXHYK+3aQmrbVJ4En3H2PmV0OLCf0qv4c29YmtY0ZatsQwvjzqzHu9QfmmNkYd3+/hmXWAzsIPXQIQfrKOtZTU5tTy/ouMJsQPIrJXpshbLe/xO04y8wqCPvcWjPrH+vz6crtF9f9XrzfamZ/IPRSf1dLe2H/fWNHyvMcqn4ZlsW0ymGMyosXDLjV3X/VgPbVpM4pXdx9k4UTz2cTjrSrq9yWnyUMlzmwJA7f9EvJl8++bZfxtrl7uZn9kfDF/duGLt9IK4AVKUdDf6Jq4G/Dvm2fVkZ7/PHQMdfMagv+nYFV7l5B6K3sNw5qZgcDb7n7zwjjkEcC04GPW7yKwcIVOwfFx78zszFp1jWVcAKLeP9ozN/PzKanyV99qomLgF0p7RnM/oH9gLXHzM4mDMV8xN1TP7xTgfFmVmhmgwm9lVmEk3ojzKwo5vswoXcCoWfbLc22Wg6cHj8Y3QknzBamtK03YcfLWNvcfZ6793T3Qe4+KJZ/dC1Bn/hB/3tsB4Qe9utxHReb2a1pltkY25BfLT21rM6Eo53XU9o8DFicyTZHfwNOj3mGEQLRujiM9Q9gkrv/J2W9ebbvaqd8Qgdgfk1tju3dRfg/jNR9Y11cpjL4rUpZbBnhyAXCWHzle/UkcIXtu3qlX0pbp5tZarCty1Tg0xaMBTbHzllRbDtm1hYYRzjyqrxC6Jpqbcsl7ONnxDy9CNtudcyWB2yMQ54Za1us9yGVj4ELKuvZGNXbVpf4uXjXzA6NSXv3/WgY6b8sqxSS0RthDHlcfDyIamewCTvea4STk7cC26rnJQxHLCAcwj0BdIvpn4hprxF6ZWNj+lxgQJq6dCd8IN+M95XlFANP1lD/cwkf8qWE4Yd7CTtgPuHEWHO2ZwlhbHRuvP0y5bVvxTq/AZyTkn4VIXC/Rghu3WP6tYQvuirbinCC7JFY3yWEoFDZth2Enn/G21atncuoehXIMsJJsW2EL4URvu8KhufiOqYDA1PaNqmGsrcCW2JZq4A3q5W1KOaZU7k94+PRWdieBcAD8X2fA5we079NOHyfm3LrSbgqaXYsfwHwUyC3tjYT9t/7SNk3gPsJV/UUE8aKi1Py94rbexYp+3N87cuEcfR5hOGuIYTO4ztA2zTr/lLcXmWEHv1vYroR/qhpaSyrOKYfSRhueS2+J99NKesu4NI0bfsE8FQsZ37chh+Pry8gnNjOaNti+n9S1vkg8Sof9nWMthCOpFakvPYQYZ/bE9OvrKlt9ShrFOHc02uEDkTXmJ5P+LzXehVTNgL/aOD3mS63lvV1Ah5p4DLXEHrN9W4PcDFwU0tsTyPX8wBhbLve24oQaM5oJW0rqm17NqCsJu3Pzd3m2urf1O0ZyzgcuPMAtO8xoKAh2wb4C3Boa2xbE8qqV5zKylw9ZnYFYbyzvM7MrUBsz3bCUcKmZq5ORjVkW5nZ59391wegWlnVwDZ/mHBUsCzrFcuSmtr7QdietbStgHA9f03nPz6QzOwSwtVQm2rNl43ALyIiLZfm6hERSRgFfhGRhFHgFxFJGAV+adEszEL4/xq57Fcs/Cq4rnynmtljjVlHtXI+Y9VmEBVpiRT4paXrAjQq8ANfIc5L05pZlqYWluRS4JeW7jb2TfT3Q0szi6KZtTezf1iYH32+mX3CzL5EmK9khpnNqF6ohf9dWGRmzwMfTUkfY2YvWJj86oXKX0damIF1VEq+/5jZkWnq29fC/x68aXF+/Zi/8n8R5pvZ7SnpqTNEftzM7o+P7zezO2Pdb0ckg9STkJbuOuBwdx9lYRbFj7NvyumpFmZRLAJWuvt5AGbW2d03m9nXgNO82nTNcZqKXxOmS1hCmJ+/0iLgZHcvM7NxwPcJM2T+BvgM8JU4vUKhu7+Wpr6jCD8s2g28YWb/R5ip83bCdAEbCVM+X+Tuf6uj7cMIv6z+QPweRloO9filNalpFsV5wDgzu93MTnL3zXWUMxx4293f9PBDlgdSXusMPGLhX5J+DIyM6Y8A58c5cq4gTHuQznR33+zuuwjzpxxEmNL5GXdf6+H/FB4kzKdel0cU9CUb1OOX1qTGWRTN7BjCPEu3mtlT7v6/dZRV0y8XbwJmuPvFFv6U5RkAd99hZtMIk3v9F2Gem3RSp/EuJ3zGapwzv1o9qk+Yt72W5UQaTT1+aem2Ev4XAWqYRdHM+gI73P0Bwpz8R6dZNnWWzEWEP9QZEl9K/VexzoQZHyEM7aT6DeGPWV529w2xzDFmVte0ADOBU8ysh4V/5bqU8C9fEKYOP8zMcgjzrIhknXr80qK5+/p4InU+4R/H/gC8GGbDZRvwKeAQ4IcW5rOv/NcxgHuAf5rZKnc/jTD74yp332VmE4F/mNk64HnChFwQ/pd4cjw/8K9qdZltZluoOu/6QOqY+9zDlMOTgBmE3v/j7l75l5fXESbpepd9s6OKZJXm6pFEsPA/pfe6+yVNKKMvYehnuIf/X8DMfkiYITLdiV6RFkmBX6QezOzThP8c/pq7P9Lc9RFpCgV+EZGE0cldEZGEUeAXEUkYBX4RkYRR4BcRSRgFfhGRhPn/HSN2s/Sw4LAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.groupby(['test', 'day', 'hour']).ff.agg(lambda x: (~x.isna()).sum()).plot(ylim=(0,340))\n",
    "new_features.groupby(['test', 'day', 'hour']).ff.agg(lambda x: (~x.isna()).sum()).plot(ylim=(0,340))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(new_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(new_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_agg(x, on, by, how):\n",
    "    \"\"\"Function to make aggregates easilly.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "        x (pd.DataFrame): Features dataframe.\n",
    "        on (list): List of targets aggregates.\n",
    "        by (list): List of columns we will use to group data.\n",
    "        how (list): List of statistics.\n",
    "    \n",
    "    \"\"\"\n",
    "    columns = []\n",
    "    for col in on:\n",
    "        for stat in how:\n",
    "            columns.append(f\"{stat}_{col}\") #_by_{'_'.join(by)}\")\n",
    "    x = x.groupby(by)[on].agg(how)  \n",
    "    x.columns = columns\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_agg = make_agg(\n",
    "    x = new_features,\n",
    "    on = features_mesured, \n",
    "    by = [\"test\", \"station\", \"day\"],\n",
    "    how = [\"mean\"]\n",
    ").reset_index()\n",
    "\n",
    "df = pd.merge(\n",
    "    left = new_predictions,\n",
    "    right = features_agg,\n",
    "    on = [\"test\", \"station\", \"day\"],\n",
    "    how = \"left\",\n",
    ")\n",
    "\n",
    "df.sort_values(['station', 'test', 'day'], inplace=True)\n",
    "df.to_csv(\"Dataset/weather_full.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a0844becf624f5b51054fd414835e9fe059074f2359c4ebe10f54f1c18d3b468"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('DataScience': conda)",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}