blog-post.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting AQI values in Montreal, QC\n",
    "\n",
    "This notebook contains the code referenced in the original blog post: https://tlbvr.com/blog/predicting-montreal-aqi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  2033  100  2033    0     0   2180      0 --:--:-- --:--:-- --:--:--  2178\n",
      "100 20.8M  100 20.8M    0     0  5131k      0  0:00:04  0:00:04 --:--:-- 9338k\n"
     ]
    }
   ],
   "source": [
    "!curl -o aqi-2022-2024.csv -A \"some-user-agent\" -L https://donnees.montreal.ca/dataset/547b8052-1710-4d69-8760-beaa3aa35ec6/resource/0c325562-e742-4e8e-8c36-971f3c9e58cd/download/rsqa-indice-qualite-air-2022-2024.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stationId</th>\n",
       "      <th>pollutant</th>\n",
       "      <th>value</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103</td>\n",
       "      <td>O3</td>\n",
       "      <td>15</td>\n",
       "      <td>2022-01-15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103</td>\n",
       "      <td>NO2</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-01-15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>PM</td>\n",
       "      <td>12</td>\n",
       "      <td>2022-01-15</td>\n",
       "      <td>3</td>\n",
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       "      <th>835003</th>\n",
       "      <td>99</td>\n",
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       "      <td>2024-06-25</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>835004</th>\n",
       "      <td>99</td>\n",
       "      <td>PM</td>\n",
       "      <td>16</td>\n",
       "      <td>2024-06-25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835005</th>\n",
       "      <td>103</td>\n",
       "      <td>O3</td>\n",
       "      <td>6</td>\n",
       "      <td>2024-06-25</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>835006</th>\n",
       "      <td>103</td>\n",
       "      <td>NO2</td>\n",
       "      <td>3</td>\n",
       "      <td>2024-06-25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835007</th>\n",
       "      <td>103</td>\n",
       "      <td>PM</td>\n",
       "      <td>14</td>\n",
       "      <td>2024-06-25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>835008 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        stationId pollutant  value        date  hour\n",
       "0             103        O3     15  2022-01-15     3\n",
       "1             103       NO2      2  2022-01-15     3\n",
       "2             103        PM     12  2022-01-15     3\n",
       "3              17        CO      1  2022-02-04    21\n",
       "4              17        O3     17  2022-02-04    21\n",
       "...           ...       ...    ...         ...   ...\n",
       "835003         99       NO2      1  2024-06-25     3\n",
       "835004         99        PM     16  2024-06-25     3\n",
       "835005        103        O3      6  2024-06-25     3\n",
       "835006        103       NO2      3  2024-06-25     3\n",
       "835007        103        PM     14  2024-06-25     3\n",
       "\n",
       "[835008 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./aqi-2022-2024.csv\")\n",
    "df.rename(columns={\n",
    "    'polluant': 'pollutant',\n",
    "    'valeur': 'value',\n",
    "    'date': 'date',\n",
    "    'heure': 'hour'\n",
    "}, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th>2022-01-01 00:00:00</th>\n",
       "      <td>57</td>\n",
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       "      <th>2022-01-01 01:00:00</th>\n",
       "      <td>58</td>\n",
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       "      <th>2022-01-01 02:00:00</th>\n",
       "      <td>60</td>\n",
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       "      <th>2022-01-01 03:00:00</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">103</th>\n",
       "      <th>2024-06-25 19:00:00</th>\n",
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       "      <th>2024-06-25 20:00:00</th>\n",
       "      <td>26</td>\n",
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       "      <th>2024-06-25 21:00:00</th>\n",
       "      <td>23</td>\n",
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       "      <th>2024-06-25 22:00:00</th>\n",
       "      <td>25</td>\n",
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       "      <th>2024-06-25 23:00:00</th>\n",
       "      <td>26</td>\n",
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       "</table>\n",
       "<p>238490 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "                               value\n",
       "stationId datetime                  \n",
       "3         2022-01-01 00:00:00     57\n",
       "          2022-01-01 01:00:00     58\n",
       "          2022-01-01 02:00:00     60\n",
       "          2022-01-01 03:00:00     62\n",
       "          2022-01-01 04:00:00     68\n",
       "...                              ...\n",
       "103       2024-06-25 19:00:00     27\n",
       "          2024-06-25 20:00:00     26\n",
       "          2024-06-25 21:00:00     23\n",
       "          2024-06-25 22:00:00     25\n",
       "          2024-06-25 23:00:00     26\n",
       "\n",
       "[238490 rows x 1 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a datetime column for easier manipulation later on\n",
    "df['datetime'] = pd.to_datetime(df['date'] + ' ' + df['hour'].astype(str) + ':00:00',\n",
    "                                format = '%Y-%m-%d %H:%M:%S',\n",
    "                                errors = 'coerce')\n",
    "# Dropping columns we won't need anymore\n",
    "df.drop([\"hour\", \"pollutant\", \"date\"], axis=1, inplace=True)\n",
    "\n",
    "# Indexing by station and datetime and grab the maximum value for each index (related to our assumption earlier)\n",
    "df = df.groupby(['stationId', 'datetime']).max(\"value\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>21763</th>\n",
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       "      <td>27.100000</td>\n",
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       "      <th>21764</th>\n",
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       "      <th>21765</th>\n",
       "      <td>2024-06-25 21:00:00</td>\n",
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       "      <th>21766</th>\n",
       "      <td>2024-06-25 22:00:00</td>\n",
       "      <td>24.500000</td>\n",
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       "      <th>21767</th>\n",
       "      <td>2024-06-25 23:00:00</td>\n",
       "      <td>26.500000</td>\n",
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       "<p>21768 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "                 datetime      value\n",
       "0     2022-01-01 00:00:00  47.818182\n",
       "1     2022-01-01 01:00:00  53.727273\n",
       "2     2022-01-01 02:00:00  60.272727\n",
       "3     2022-01-01 03:00:00  65.454545\n",
       "4     2022-01-01 04:00:00  65.363636\n",
       "...                   ...        ...\n",
       "21763 2024-06-25 19:00:00  27.100000\n",
       "21764 2024-06-25 20:00:00  26.100000\n",
       "21765 2024-06-25 21:00:00  23.900000\n",
       "21766 2024-06-25 22:00:00  24.500000\n",
       "21767 2024-06-25 23:00:00  26.500000\n",
       "\n",
       "[21768 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.groupby(\"datetime\").mean(\"value\").reset_index()\n",
    "# Sorting the dataframe by datetime for better visualization\n",
    "df.sort_values(\"datetime\", inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This station is located close to the airport and is the one I found holds the most interesting data\n",
    "station_id=\"30165\"\n",
    "years = range(2022, 2025)\n",
    "months = range(1, 13)\n",
    "# Hourly data\n",
    "timeframe=\"1\"\n",
    "dates = [(month, year) for year in years for month in months]\n",
    "all_files = []\n",
    "\n",
    "for date in dates:\n",
    "    month, year = date\n",
    "    url = f\"https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID={station_id}&Year={year}&Month={month}&Day=1&timeframe={timeframe}&submit=Download+Data\"\n",
    "    all_files.append(pd.read_csv(url))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>temp</th>\n",
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       "      <th>0</th>\n",
       "      <td>2022-01-01 00:00:00</td>\n",
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       "      <th>739</th>\n",
       "      <td>2024-12-31 19:00:00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>740</th>\n",
       "      <td>2024-12-31 20:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2024-12-31 21:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>2024-12-31 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2024-12-31 23:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26304 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               datetime  temp  rel_humid  precip\n",
       "0   2022-01-01 00:00:00   0.0       93.0     0.0\n",
       "1   2022-01-01 01:00:00   0.1       94.0     0.0\n",
       "2   2022-01-01 02:00:00   0.1       94.0     0.0\n",
       "3   2022-01-01 03:00:00   0.1       97.0     0.0\n",
       "4   2022-01-01 04:00:00  -0.5       96.0     0.0\n",
       "..                  ...   ...        ...     ...\n",
       "739 2024-12-31 19:00:00   NaN        NaN     NaN\n",
       "740 2024-12-31 20:00:00   NaN        NaN     NaN\n",
       "741 2024-12-31 21:00:00   NaN        NaN     NaN\n",
       "742 2024-12-31 22:00:00   NaN        NaN     NaN\n",
       "743 2024-12-31 23:00:00   NaN        NaN     NaN\n",
       "\n",
       "[26304 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_df = pd.concat(all_files)\n",
    "weather_df.rename(columns={'Date/Time (LST)': 'datetime', \"Temp (°C)\": \"temp\", \"Precip. Amount (mm)\": \"precip\", \"Rel Hum (%)\": \"rel_humid\"}, inplace=True)\n",
    "weather_df['datetime'] = pd.to_datetime(weather_df['datetime'])\n",
    "weather_df = weather_df[['datetime', \"temp\", \"rel_humid\", \"precip\"]]\n",
    "weather_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2022-01-01 01:00:00</td>\n",
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       "      <td>94.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2022-01-01 02:00:00</td>\n",
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       "      <td>2022-01-01 03:00:00</td>\n",
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       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>46.0</td>\n",
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       "      <th>21764</th>\n",
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       "    <tr>\n",
       "      <th>21765</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>24.500000</td>\n",
       "      <td>19.9</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>21767</th>\n",
       "      <td>2024-06-25 23:00:00</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>18.9</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21768 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime      value  temp  rel_humid  precip\n",
       "0     2022-01-01 00:00:00  47.818182   0.0       93.0     0.0\n",
       "1     2022-01-01 01:00:00  53.727273   0.1       94.0     0.0\n",
       "2     2022-01-01 02:00:00  60.272727   0.1       94.0     0.0\n",
       "3     2022-01-01 03:00:00  65.454545   0.1       97.0     0.0\n",
       "4     2022-01-01 04:00:00  65.363636  -0.5       96.0     0.0\n",
       "...                   ...        ...   ...        ...     ...\n",
       "21763 2024-06-25 19:00:00  27.100000  25.2       46.0     0.0\n",
       "21764 2024-06-25 20:00:00  26.100000  23.8       58.0     0.0\n",
       "21765 2024-06-25 21:00:00  23.900000  23.6       59.0     0.0\n",
       "21766 2024-06-25 22:00:00  24.500000  19.9       84.0     0.0\n",
       "21767 2024-06-25 23:00:00  26.500000  18.9       89.0     0.0\n",
       "\n",
       "[21768 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = pd.merge(df, weather_df, on=\"datetime\", how=\"left\")\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>21768 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "                 datetime     value  temp  rel_humid  precip      year  month  \\\n",
       "0     2022-01-01 00:00:00  0.478182   0.0       93.0     0.0  0.999012      1   \n",
       "1     2022-01-01 01:00:00  0.537273   0.1       94.0     0.0  0.999012      1   \n",
       "2     2022-01-01 02:00:00  0.602727   0.1       94.0     0.0  0.999012      1   \n",
       "3     2022-01-01 03:00:00  0.654545   0.1       97.0     0.0  0.999012      1   \n",
       "4     2022-01-01 04:00:00  0.653636  -0.5       96.0     0.0  0.999012      1   \n",
       "...                   ...       ...   ...        ...     ...       ...    ...   \n",
       "21763 2024-06-25 19:00:00  0.271000  25.2       46.0     0.0  1.000000      6   \n",
       "21764 2024-06-25 20:00:00  0.261000  23.8       58.0     0.0  1.000000      6   \n",
       "21765 2024-06-25 21:00:00  0.239000  23.6       59.0     0.0  1.000000      6   \n",
       "21766 2024-06-25 22:00:00  0.245000  19.9       84.0     0.0  1.000000      6   \n",
       "21767 2024-06-25 23:00:00  0.265000  18.9       89.0     0.0  1.000000      6   \n",
       "\n",
       "       day  hour  weekday  \n",
       "0        1     0        5  \n",
       "1        1     1        5  \n",
       "2        1     2        5  \n",
       "3        1     3        5  \n",
       "4        1     4        5  \n",
       "...    ...   ...      ...  \n",
       "21763   25    19        1  \n",
       "21764   25    20        1  \n",
       "21765   25    21        1  \n",
       "21766   25    22        1  \n",
       "21767   25    23        1  \n",
       "\n",
       "[21768 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['year'] = merged_df['datetime'].dt.year\n",
    "# Year has a bigger range than the rest so we divide it up by its maximum to scale it down.\n",
    "merged_df['year'] = merged_df['year'] / merged_df['year'].max()\n",
    "merged_df['month'] = merged_df['datetime'].dt.month\n",
    "merged_df['day'] = merged_df['datetime'].dt.day\n",
    "merged_df['hour'] = merged_df['datetime'].dt.hour\n",
    "merged_df['weekday'] = merged_df['datetime'].dt.weekday\n",
    "\n",
    "# Values above 100 are extreme outliers (and very rare for Montreal). Clamping helps the model not being influenced too much from these rare events.\n",
    "merged_df['value'] = merged_df['value'].clip(upper=100)\n",
    "max_value = merged_df['value'].max()\n",
    "merged_df['value'] = merged_df['value'] / max_value\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime      0\n",
       "value         0\n",
       "temp         14\n",
       "rel_humid    14\n",
       "precip       22\n",
       "year          0\n",
       "month         0\n",
       "day           0\n",
       "hour          0\n",
       "weekday       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/ctbm7cmd6n124vc1h8766rlw0000gn/T/ipykernel_24506/1283295723.py:2: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  \"temp\": merged_df['temp'].fillna(method='bfill'),\n"
     ]
    }
   ],
   "source": [
    "merged_df.fillna({\"precip\": 0,\n",
    "                   \"temp\": merged_df['temp'].fillna(method='bfill'),\n",
    "                   \"rel_humid\": merged_df['rel_humid'].mode()[0]}, inplace=True)\n",
    "\n",
    "assert merged_df[merged_df.isna().any(axis=1)].empty == True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "date_valid = pd.Timestamp('2023-01-01')\n",
    "date_test = pd.Timestamp('2024-01-01')\n",
    "now = pd.Timestamp('2024-06-01')\n",
    "\n",
    "# From Jan 01, 2022 -> Dec 31, 2022\n",
    "train_idx = merged_df['datetime'] < date_valid\n",
    "\n",
    "# From Jan 01, 2023 -> Dec 31, 2023\n",
    "valid_idx = (merged_df['datetime'] >= date_valid) & (merged_df['datetime'] < date_test)\n",
    "\n",
    "# From Jan 01, 2024 -> June 01, 2024\n",
    "test_idx = (merged_df['datetime'] >= date_test) & (merged_df['datetime'] < now)\n",
    "\n",
    "train_idxs = np.where(train_idx)[0].tolist()\n",
    "valid_idxs = np.where(valid_idx)[0].tolist()\n",
    "test_idxs = np.where(test_idx)[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert merged_df.iloc[train_idxs[-1]]['datetime'] == pd.Timestamp('2022-12-31 23:00:00')\n",
    "assert merged_df.iloc[valid_idxs[-1]]['datetime'] == pd.Timestamp('2023-12-31 23:00:00')\n",
    "assert merged_df.iloc[test_idxs[-1]]['datetime'] == pd.Timestamp('2024-05-31 23:00:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <td>0.144545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-09-30 06:00:00</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>93.0</td>\n",
       "      <td>4.669100e-09</td>\n",
       "      <td>0.999012</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.116364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-12-03 23:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.669100e-09</td>\n",
       "      <td>0.999012</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.133636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-04-22 06:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4.669100e-09</td>\n",
       "      <td>0.999012</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.143636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-07-21 17:00:00</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>28.600001</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.669100e-09</td>\n",
       "      <td>0.999012</td>\n",
       "      <td>21.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.250909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-12-15 15:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>4.669100e-09</td>\n",
       "      <td>0.999012</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.125455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-11-27 13:00:00</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.669100e-09</td>\n",
       "      <td>0.999012</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.176364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2022-05-28 23:00:00</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>4.669100e-09</td>\n",
       "      <td>0.999012</td>\n",
       "      <td>28.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.147273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fastai.tabular.all import *\n",
    "\n",
    "cont,cat = cont_cat_split(merged_df, max_card=20, dep_var='value')\n",
    "dls = TabularPandas(\n",
    "  merged_df,\n",
    "  procs=[Categorify, Normalize],\n",
    "  cat_names=cat,\n",
    "  cont_names=cont,\n",
    "  y_names=['value'],\n",
    "  splits=(train_idxs, valid_idxs),\n",
    "  y_block=RegressionBlock()\n",
    ").dataloaders(bs=2048)\n",
    "\n",
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.124582</td>\n",
       "      <td>0.044517</td>\n",
       "      <td>0.189672</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.116288</td>\n",
       "      <td>0.665172</td>\n",
       "      <td>0.810327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.109973</td>\n",
       "      <td>0.665172</td>\n",
       "      <td>0.810327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.103871</td>\n",
       "      <td>0.665172</td>\n",
       "      <td>0.810327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.098426</td>\n",
       "      <td>0.665172</td>\n",
       "      <td>0.810327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.093807</td>\n",
       "      <td>0.665172</td>\n",
       "      <td>0.810327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.089975</td>\n",
       "      <td>0.665172</td>\n",
       "      <td>0.810327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.086668</td>\n",
       "      <td>0.665172</td>\n",
       "      <td>0.810327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.083967</td>\n",
       "      <td>0.665172</td>\n",
       "      <td>0.810327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.081800</td>\n",
       "      <td>0.665172</td>\n",
       "      <td>0.810327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In fastai, models are called learner\n",
    "learn = tabular_learner(dls, metrics=mae, layers=[250, 100], y_range=(0, 1))\n",
    "learn.fit_one_cycle(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This isn't training on purpose**. The reason can be found in the blog post."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TensorBase(0.8181)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = merged_df.loc[test_idxs]\n",
    "test_dl = dls.test_dl(df_test)\n",
    "\n",
    "preds, targets = learn.get_preds(dl=test_dl)\n",
    "\n",
    "mae(preds, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  2033  100  2033    0     0   1699      0  0:00:01  0:00:01 --:--:--  1701\n",
      "100 25.5M  100 25.5M    0     0  5353k      0  0:00:04  0:00:04 --:--:-- 8909k\n"
     ]
    }
   ],
   "source": [
    "!curl -o aqi-2019-2021.csv -A \"some-user-agent\" -L https://donnees.montreal.ca/dataset/547b8052-1710-4d69-8760-beaa3aa35ec6/resource/e43dc1d6-fbdd-49c3-a79f-83f63404c281/download/rsqa-indice-qualite-air-2019-2021.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"./aqi-2022-2024.csv\")\n",
    "df2 = pd.read_csv(\"./aqi-2019-2021.csv\")\n",
    "df = pd.concat([df1, df2])\n",
    "df.rename(columns={\n",
    "    'polluant': 'pollutant',\n",
    "    'valeur': 'value',\n",
    "    'date': 'date',\n",
    "    'heure': 'hour'\n",
    "}, inplace=True)\n",
    "# Creating a datetime column for easier manipulation later on\n",
    "df['datetime'] = pd.to_datetime(df['date'] + ' ' + df['hour'].astype(str) + ':00:00',\n",
    "                                format = '%Y-%m-%d %H:%M:%S',\n",
    "                                errors = 'coerce')\n",
    "# Dropping columns we won't need anymore\n",
    "df.drop([\"hour\", \"pollutant\", \"date\"], axis=1, inplace=True)\n",
    "\n",
    "# Indexing by station and datetime and grab the maximum value for each index (related to our assumption earlier)\n",
    "df = df.groupby(['stationId', 'datetime']).max(\"value\")\n",
    "df = df.groupby(\"datetime\").mean(\"value\").reset_index()\n",
    "# Sorting the dataframe by datetime for better visualization\n",
    "df.sort_values(\"datetime\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>datetime</th>\n",
       "      <th>temp</th>\n",
       "      <th>rel_humid</th>\n",
       "      <th>precip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 01:00:00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 02:00:00</td>\n",
       "      <td>2.1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 03:00:00</td>\n",
       "      <td>2.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 04:00:00</td>\n",
       "      <td>2.3</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>2024-12-31 19:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2024-12-31 20:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2024-12-31 21:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>2024-12-31 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2024-12-31 23:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52608 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               datetime  temp  rel_humid  precip\n",
       "0   2019-01-01 00:00:00   1.9       95.0     1.7\n",
       "1   2019-01-01 01:00:00   1.8       93.0     0.7\n",
       "2   2019-01-01 02:00:00   2.1       95.0     3.0\n",
       "3   2019-01-01 03:00:00   2.2       95.0     4.5\n",
       "4   2019-01-01 04:00:00   2.3       96.0     3.8\n",
       "..                  ...   ...        ...     ...\n",
       "739 2024-12-31 19:00:00   NaN        NaN     NaN\n",
       "740 2024-12-31 20:00:00   NaN        NaN     NaN\n",
       "741 2024-12-31 21:00:00   NaN        NaN     NaN\n",
       "742 2024-12-31 22:00:00   NaN        NaN     NaN\n",
       "743 2024-12-31 23:00:00   NaN        NaN     NaN\n",
       "\n",
       "[52608 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import concurrent.futures\n",
    "\n",
    "station_id = \"30165\"\n",
    "years = range(2019, 2025)\n",
    "months = range(1, 13)\n",
    "timeframe = \"1\"\n",
    "dates = [(month, year) for year in years for month in months]\n",
    "\n",
    "def download_data(date):\n",
    "    month, year = date\n",
    "    url = f\"https://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID={station_id}&Year={year}&Month={month}&Day=1&timeframe={timeframe}&submit=Download+Data\"\n",
    "    return pd.read_csv(url)\n",
    "\n",
    "with concurrent.futures.ThreadPoolExecutor() as executor:\n",
    "    all_files = executor.map(download_data, dates)\n",
    "   \n",
    "weather_df = pd.concat(all_files)\n",
    "weather_df.rename(columns={'Date/Time (LST)': 'datetime', \"Temp (°C)\": \"temp\", \"Precip. Amount (mm)\": \"precip\", \"Rel Hum (%)\": \"rel_humid\"}, inplace=True)\n",
    "weather_df['datetime'] = pd.to_datetime(weather_df['datetime'])\n",
    "weather_df = weather_df[['datetime', \"temp\", \"rel_humid\", \"precip\"]]\n",
    "\n",
    "weather_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gd/ctbm7cmd6n124vc1h8766rlw0000gn/T/ipykernel_24506/1605622287.py:12: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  \"temp\": merged_df['temp'].fillna(method='bfill'),\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>datetime</th>\n",
       "      <th>value</th>\n",
       "      <th>temp</th>\n",
       "      <th>rel_humid</th>\n",
       "      <th>precip</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:00</td>\n",
       "      <td>0.334545</td>\n",
       "      <td>1.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.99753</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 01:00:00</td>\n",
       "      <td>0.281818</td>\n",
       "      <td>1.8</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.99753</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 02:00:00</td>\n",
       "      <td>0.193636</td>\n",
       "      <td>2.1</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.99753</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 03:00:00</td>\n",
       "      <td>0.134545</td>\n",
       "      <td>2.2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.99753</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 04:00:00</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>2.3</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.99753</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48067</th>\n",
       "      <td>2024-06-25 19:00:00</td>\n",
       "      <td>0.271000</td>\n",
       "      <td>25.2</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48068</th>\n",
       "      <td>2024-06-25 20:00:00</td>\n",
       "      <td>0.261000</td>\n",
       "      <td>23.8</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48069</th>\n",
       "      <td>2024-06-25 21:00:00</td>\n",
       "      <td>0.239000</td>\n",
       "      <td>23.6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48070</th>\n",
       "      <td>2024-06-25 22:00:00</td>\n",
       "      <td>0.245000</td>\n",
       "      <td>19.9</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48071</th>\n",
       "      <td>2024-06-25 23:00:00</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>18.9</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48072 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 datetime     value  temp  rel_humid  precip     year  month  \\\n",
       "0     2019-01-01 00:00:00  0.334545   1.9       95.0     1.7  0.99753      1   \n",
       "1     2019-01-01 01:00:00  0.281818   1.8       93.0     0.7  0.99753      1   \n",
       "2     2019-01-01 02:00:00  0.193636   2.1       95.0     3.0  0.99753      1   \n",
       "3     2019-01-01 03:00:00  0.134545   2.2       95.0     4.5  0.99753      1   \n",
       "4     2019-01-01 04:00:00  0.109091   2.3       96.0     3.8  0.99753      1   \n",
       "...                   ...       ...   ...        ...     ...      ...    ...   \n",
       "48067 2024-06-25 19:00:00  0.271000  25.2       46.0     0.0  1.00000      6   \n",
       "48068 2024-06-25 20:00:00  0.261000  23.8       58.0     0.0  1.00000      6   \n",
       "48069 2024-06-25 21:00:00  0.239000  23.6       59.0     0.0  1.00000      6   \n",
       "48070 2024-06-25 22:00:00  0.245000  19.9       84.0     0.0  1.00000      6   \n",
       "48071 2024-06-25 23:00:00  0.265000  18.9       89.0     0.0  1.00000      6   \n",
       "\n",
       "       day  hour  weekday  \n",
       "0        1     0        1  \n",
       "1        1     1        1  \n",
       "2        1     2        1  \n",
       "3        1     3        1  \n",
       "4        1     4        1  \n",
       "...    ...   ...      ...  \n",
       "48067   25    19        1  \n",
       "48068   25    20        1  \n",
       "48069   25    21        1  \n",
       "48070   25    22        1  \n",
       "48071   25    23        1  \n",
       "\n",
       "[48072 rows x 10 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = pd.merge(df, weather_df, on=\"datetime\", how=\"left\")\n",
    "merged_df['year'] = merged_df['datetime'].dt.year\n",
    "merged_df['year'] = merged_df['year'] / merged_df['year'].max()\n",
    "merged_df['month'] = merged_df['datetime'].dt.month\n",
    "merged_df['day'] = merged_df['datetime'].dt.day\n",
    "merged_df['hour'] = merged_df['datetime'].dt.hour\n",
    "merged_df['weekday'] = merged_df['datetime'].dt.weekday\n",
    "merged_df['value'] = merged_df['value'].clip(upper=100)\n",
    "max_value = merged_df['value'].max()\n",
    "merged_df['value'] = merged_df['value'] / max_value\n",
    "merged_df.fillna({\"precip\": 0,\n",
    "                   \"temp\": merged_df['temp'].fillna(method='bfill'),\n",
    "                   \"rel_humid\": merged_df['rel_humid'].mode()[0]}, inplace=True)\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from fastai.tabular.all import *\n",
    "\n",
    "date_valid = pd.Timestamp('2023-01-01')\n",
    "date_test = pd.Timestamp('2024-01-01')\n",
    "now = pd.Timestamp('2024-06-01')\n",
    "\n",
    "# From Jan 01, 2019 -> Dec 31, 2022\n",
    "train_idx = merged_df['datetime'] < date_valid\n",
    "\n",
    "# From Jan 01, 2023 -> Dec 31, 2023\n",
    "valid_idx = (merged_df['datetime'] >= date_valid) & (merged_df['datetime'] < date_test)\n",
    "\n",
    "# From Jan 01, 2024 -> June 01, 2024\n",
    "test_idx = (merged_df['datetime'] >= date_test) & (merged_df['datetime'] < now)\n",
    "\n",
    "train_idxs = np.where(train_idx)[0].tolist()\n",
    "valid_idxs = np.where(valid_idx)[0].tolist()\n",
    "test_idxs = np.where(test_idx)[0].tolist()\n",
    "\n",
    "cont,cat = cont_cat_split(merged_df, max_card=20, dep_var='value')\n",
    "dls = TabularPandas(\n",
    "  merged_df,\n",
    "  procs=[Categorify, Normalize],\n",
    "  cat_names=cat,\n",
    "  cont_names=cont,\n",
    "  y_names=['value'],\n",
    "  splits=(train_idxs, valid_idxs),\n",
    "  y_block=RegressionBlock()\n",
    ").dataloaders(bs=2048)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
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       "    progress {\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.138118</td>\n",
       "      <td>0.071449</td>\n",
       "      <td>0.259237</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.126728</td>\n",
       "      <td>0.071438</td>\n",
       "      <td>0.254919</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.110009</td>\n",
       "      <td>0.093728</td>\n",
       "      <td>0.286704</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.091794</td>\n",
       "      <td>0.076902</td>\n",
       "      <td>0.249465</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.075047</td>\n",
       "      <td>0.057618</td>\n",
       "      <td>0.200717</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.060928</td>\n",
       "      <td>0.046950</td>\n",
       "      <td>0.167216</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.049792</td>\n",
       "      <td>0.042691</td>\n",
       "      <td>0.153482</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.041468</td>\n",
       "      <td>0.041640</td>\n",
       "      <td>0.145898</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.035485</td>\n",
       "      <td>0.041033</td>\n",
       "      <td>0.145078</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.031253</td>\n",
       "      <td>0.041656</td>\n",
       "      <td>0.146126</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = tabular_learner(dls, metrics=mae, layers=[250, 100], y_range=(0, 1))\n",
    "learn.fit_one_cycle(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TensorBase(0.2495)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = merged_df.loc[test_idxs]\n",
    "test_dl = dls.test_dl(df_test)\n",
    "\n",
    "preds, targets = learn.get_preds(dl=test_dl)\n",
    "\n",
    "mae(preds, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mk_analysis_df(df, preds):\n",
    "  # Create DataFrame\n",
    "  _df = pd.DataFrame({\n",
    "      'datetime': df['datetime'],\n",
    "      'Actual': df['value'] * max_value,\n",
    "      'Predicted': preds.flatten() * max_value,\n",
    "  })\n",
    "  \n",
    "  \n",
    "  _df['Error'] = abs((_df['Actual'] - _df['Predicted']) / _df['Actual']) * 100\n",
    "\n",
    "\n",
    "  return _df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"mean_error\": 153.57933550541063, \"results\": [{\"date\": 1704067200000, \"Actual\": 15.7272727273, \"Predicted\": 33.9958229065, \"Error\": 116.1584115442}, {\"date\": 1704070800000, \"Actual\": 16.2727272727, \"Predicted\": 33.2849845886, \"Error\": 104.5445980306}, {\"date\": 1704074400000, \"Actual\": 17.0909090909, \"Predicted\": 32.5719261169, \"Error\": 90.5804187693}, {\"date\": 1704078000000, \"Actual\": 17.6363636364, \"Predicted\": 32.1839447021, \"Error\": 82.4862843936}, {\"date\": 1704081600000, \"Actual\": 18.2727272727, \"Predicted\": 31.9055175781, \"Error\": 74.607310129}, {\"date\": 1704085200000, \"Actual\": 18.2727272727, \"Predicted\": 31.7841377258, \"Error\": 73.9430422807}, {\"date\": 1704088800000, \"Actual\": 18.3636363636, \"Predicted\": 32.0669670105, \"Error\": 74.6220975819}, {\"date\": 1704092400000, \"Actual\": 16.8181818182, \"Predicted\": 35.6623191833, \"Error\": 112.0462221713}, {\"date\": 1704096000000, \"Actual\": 15.5454545455, \"Predicted\": 37.3688278198, \"Error\": 140.3842725252}, {\"date\": 1704099600000, \"Actual\": 16.0, \"Predicted\": 73.4874954224, \"Error\": 359.2968463898}, {\"date\": 1704103200000, \"Actual\": 19.3636363636, \"Predicted\": 51.539730072, \"Error\": 166.1676200903}, {\"date\": 1704106800000, \"Actual\": 20.0909090909, \"Predicted\": 37.8739395142, \"Error\": 88.5128211112}, {\"date\": 1704110400000, \"Actual\": 20.4545454545, \"Predicted\": 77.8539276123, \"Error\": 280.6192016602}, {\"date\": 1704114000000, \"Actual\": 20.3636363636, \"Predicted\": 35.5644035339, \"Error\": 74.646624497}, {\"date\": 1704117600000, \"Actual\": 20.2727272727, \"Predicted\": 35.723526001, \"Error\": 76.214702247}, {\"date\": 1704121200000, \"Actual\": 19.9090909091, \"Predicted\": 36.7724914551, \"Error\": 84.7020118748}, {\"date\": 1704124800000, \"Actual\": 19.7272727273, \"Predicted\": 37.1513633728, \"Error\": 88.3248834566}, {\"date\": 1704128400000, \"Actual\": 18.9090909091, \"Predicted\": 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\"Actual\": 16.0, \"Predicted\": 40.6265182495, \"Error\": 153.9157390594}, {\"date\": 1717120800000, \"Actual\": 16.0909090909, \"Predicted\": 39.7085647583, \"Error\": 146.7763911533}, {\"date\": 1717124400000, \"Actual\": 15.8181818182, \"Predicted\": 38.852684021, \"Error\": 145.6204162247}, {\"date\": 1717128000000, \"Actual\": 14.0909090909, \"Predicted\": 37.6342048645, \"Error\": 167.0814538771}, {\"date\": 1717131600000, \"Actual\": 12.4545454545, \"Predicted\": 33.077457428, \"Error\": 165.5854246042}, {\"date\": 1717135200000, \"Actual\": 12.8181818182, \"Predicted\": 34.2638092041, \"Error\": 167.3063129398}, {\"date\": 1717138800000, \"Actual\": 13.3636363636, \"Predicted\": 46.1092910767, \"Error\": 245.035511458}, {\"date\": 1717142400000, \"Actual\": 14.0909090909, \"Predicted\": 37.3475036621, \"Error\": 165.0468001827}, {\"date\": 1717146000000, \"Actual\": 16.3636363636, \"Predicted\": 51.4365615845, \"Error\": 214.3345430162}, {\"date\": 1717149600000, \"Actual\": 20.2727272727, \"Predicted\": 52.9074249268, \"Error\": 160.9783292351}, {\"date\": 1717153200000, \"Actual\": 22.9090909091, \"Predicted\": 53.1333618164, \"Error\": 131.9313412621}, {\"date\": 1717156800000, \"Actual\": 23.1818181818, \"Predicted\": 34.7489051819, \"Error\": 49.8972380395}, {\"date\": 1717160400000, \"Actual\": 23.3636363636, \"Predicted\": 33.897315979, \"Error\": 45.085788237}, {\"date\": 1717164000000, \"Actual\": 23.5454545455, \"Predicted\": 33.6169052124, \"Error\": 42.7745009021}, {\"date\": 1717167600000, \"Actual\": 24.0909090909, \"Predicted\": 33.417388916, \"Error\": 38.7136898401}, {\"date\": 1717171200000, \"Actual\": 25.0, \"Predicted\": 32.6272659302, \"Error\": 30.5090637207}, {\"date\": 1717174800000, \"Actual\": 24.6363636364, \"Predicted\": 32.3565063477, \"Error\": 31.3363726289}, {\"date\": 1717178400000, \"Actual\": 24.3636363636, \"Predicted\": 32.2299842834, \"Error\": 32.2872489246}, {\"date\": 1717182000000, \"Actual\": 22.8181818182, \"Predicted\": 31.823551178, \"Error\": 39.4657621346}, {\"date\": 1717185600000, \"Actual\": 19.4545454545, \"Predicted\": 31.1527824402, \"Error\": 60.1311246925}, {\"date\": 1717189200000, \"Actual\": 15.2727272727, \"Predicted\": 30.6925029755, \"Error\": 100.9628171013}, {\"date\": 1717192800000, \"Actual\": 15.0909090909, \"Predicted\": 30.9026489258, \"Error\": 104.7765892672}, {\"date\": 1717196400000, \"Actual\": 16.1818181818, \"Predicted\": 28.5628547668, \"Error\": 76.5120238401}]}'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_df = mk_analysis_df(df_test, preds.flatten())\n",
    "export_df.rename(columns={'datetime': 'date'}, inplace=True)\n",
    "\n",
    "mean_error = export_df['Error'].mean()\n",
    "\n",
    "json_output = export_df.to_json(orient='records', indent=2)\n",
    "json_dict = json.loads(json_output)\n",
    "\n",
    "# Create a new dictionary with the \"results\" property\n",
    "result_dict = {\"mean_error\": mean_error, \"results\": json_dict}\n",
    "\n",
    "# Convert dictionary to JSON string with indentation\n",
    "json.dumps(result_dict)\n"
   ]
  }
 ],
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