Part-1.ipynb

Part-1.ipynb

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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agency</th>\n",
       "      <th>sku</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>industry_volume</th>\n",
       "      <th>soda_volume</th>\n",
       "      <th>avg_max_temp</th>\n",
       "      <th>price_regular</th>\n",
       "      <th>price_actual</th>\n",
       "      <th>discount</th>\n",
       "      <th>...</th>\n",
       "      <th>football_gold_cup</th>\n",
       "      <th>beer_capital</th>\n",
       "      <th>music_fest</th>\n",
       "      <th>discount_in_percent</th>\n",
       "      <th>timeseries</th>\n",
       "      <th>time_idx</th>\n",
       "      <th>month</th>\n",
       "      <th>log_volume</th>\n",
       "      <th>avg_volume_by_sku</th>\n",
       "      <th>avg_volume_by_agency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>Agency_25</td>\n",
       "      <td>SKU_03</td>\n",
       "      <td>0.5076</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>492612703</td>\n",
       "      <td>718394219</td>\n",
       "      <td>25.845238</td>\n",
       "      <td>1264.162234</td>\n",
       "      <td>1152.473405</td>\n",
       "      <td>111.688829</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>8.835008</td>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.678062</td>\n",
       "      <td>1225.306376</td>\n",
       "      <td>99.650400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>Agency_29</td>\n",
       "      <td>SKU_02</td>\n",
       "      <td>8.7480</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>498567142</td>\n",
       "      <td>762225057</td>\n",
       "      <td>27.584615</td>\n",
       "      <td>1316.098485</td>\n",
       "      <td>1296.804924</td>\n",
       "      <td>19.293561</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.465966</td>\n",
       "      <td>177</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2.168825</td>\n",
       "      <td>1634.434615</td>\n",
       "      <td>11.397086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19532</th>\n",
       "      <td>Agency_47</td>\n",
       "      <td>SKU_01</td>\n",
       "      <td>4.9680</td>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>454252482</td>\n",
       "      <td>789624076</td>\n",
       "      <td>30.665957</td>\n",
       "      <td>1269.250000</td>\n",
       "      <td>1266.490490</td>\n",
       "      <td>2.759510</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.217413</td>\n",
       "      <td>322</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1.603017</td>\n",
       "      <td>2625.472644</td>\n",
       "      <td>48.295650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2089</th>\n",
       "      <td>Agency_53</td>\n",
       "      <td>SKU_07</td>\n",
       "      <td>21.6825</td>\n",
       "      <td>2013-10-01</td>\n",
       "      <td>480693900</td>\n",
       "      <td>791658684</td>\n",
       "      <td>29.197727</td>\n",
       "      <td>1193.842373</td>\n",
       "      <td>1128.124395</td>\n",
       "      <td>65.717978</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>beer_capital</td>\n",
       "      <td>-</td>\n",
       "      <td>5.504745</td>\n",
       "      <td>240</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>3.076505</td>\n",
       "      <td>38.529107</td>\n",
       "      <td>2511.035175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9755</th>\n",
       "      <td>Agency_17</td>\n",
       "      <td>SKU_02</td>\n",
       "      <td>960.5520</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>515468092</td>\n",
       "      <td>871204688</td>\n",
       "      <td>23.608120</td>\n",
       "      <td>1338.334248</td>\n",
       "      <td>1232.128069</td>\n",
       "      <td>106.206179</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>music_fest</td>\n",
       "      <td>7.935699</td>\n",
       "      <td>259</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>6.867508</td>\n",
       "      <td>2143.677462</td>\n",
       "      <td>396.022140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7561</th>\n",
       "      <td>Agency_05</td>\n",
       "      <td>SKU_03</td>\n",
       "      <td>1184.6535</td>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>425528909</td>\n",
       "      <td>734443953</td>\n",
       "      <td>28.668254</td>\n",
       "      <td>1369.556376</td>\n",
       "      <td>1161.135214</td>\n",
       "      <td>208.421162</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>15.218151</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>7.077206</td>\n",
       "      <td>1566.643589</td>\n",
       "      <td>1881.866367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19204</th>\n",
       "      <td>Agency_11</td>\n",
       "      <td>SKU_05</td>\n",
       "      <td>5.5593</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>623319783</td>\n",
       "      <td>1049868815</td>\n",
       "      <td>31.915385</td>\n",
       "      <td>1922.486644</td>\n",
       "      <td>1651.307674</td>\n",
       "      <td>271.178970</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>14.105636</td>\n",
       "      <td>17</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>1.715472</td>\n",
       "      <td>1385.225478</td>\n",
       "      <td>109.699200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8781</th>\n",
       "      <td>Agency_48</td>\n",
       "      <td>SKU_04</td>\n",
       "      <td>4275.1605</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>509281531</td>\n",
       "      <td>892192092</td>\n",
       "      <td>26.767857</td>\n",
       "      <td>1761.258209</td>\n",
       "      <td>1546.059670</td>\n",
       "      <td>215.198539</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>music_fest</td>\n",
       "      <td>12.218455</td>\n",
       "      <td>151</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8.360577</td>\n",
       "      <td>1757.950603</td>\n",
       "      <td>1925.272108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2540</th>\n",
       "      <td>Agency_07</td>\n",
       "      <td>SKU_21</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>544203593</td>\n",
       "      <td>761469815</td>\n",
       "      <td>28.987755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300</td>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "      <td>-18.420681</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2418.719550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12084</th>\n",
       "      <td>Agency_21</td>\n",
       "      <td>SKU_03</td>\n",
       "      <td>46.3608</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>589969396</td>\n",
       "      <td>940912941</td>\n",
       "      <td>32.478910</td>\n",
       "      <td>1675.922116</td>\n",
       "      <td>1413.571789</td>\n",
       "      <td>262.350327</td>\n",
       "      <td>...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>15.654088</td>\n",
       "      <td>181</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>3.836454</td>\n",
       "      <td>2034.293024</td>\n",
       "      <td>109.381800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          agency     sku     volume       date  industry_volume  soda_volume  \\\n",
       "291    Agency_25  SKU_03     0.5076 2013-01-01        492612703    718394219   \n",
       "871    Agency_29  SKU_02     8.7480 2015-01-01        498567142    762225057   \n",
       "19532  Agency_47  SKU_01     4.9680 2013-09-01        454252482    789624076   \n",
       "2089   Agency_53  SKU_07    21.6825 2013-10-01        480693900    791658684   \n",
       "9755   Agency_17  SKU_02   960.5520 2015-03-01        515468092    871204688   \n",
       "7561   Agency_05  SKU_03  1184.6535 2014-02-01        425528909    734443953   \n",
       "19204  Agency_11  SKU_05     5.5593 2017-08-01        623319783   1049868815   \n",
       "8781   Agency_48  SKU_04  4275.1605 2013-03-01        509281531    892192092   \n",
       "2540   Agency_07  SKU_21     0.0000 2015-10-01        544203593    761469815   \n",
       "12084  Agency_21  SKU_03    46.3608 2017-04-01        589969396    940912941   \n",
       "\n",
       "       avg_max_temp  price_regular  price_actual    discount  ...  \\\n",
       "291       25.845238    1264.162234   1152.473405  111.688829  ...   \n",
       "871       27.584615    1316.098485   1296.804924   19.293561  ...   \n",
       "19532     30.665957    1269.250000   1266.490490    2.759510  ...   \n",
       "2089      29.197727    1193.842373   1128.124395   65.717978  ...   \n",
       "9755      23.608120    1338.334248   1232.128069  106.206179  ...   \n",
       "7561      28.668254    1369.556376   1161.135214  208.421162  ...   \n",
       "19204     31.915385    1922.486644   1651.307674  271.178970  ...   \n",
       "8781      26.767857    1761.258209   1546.059670  215.198539  ...   \n",
       "2540      28.987755       0.000000      0.000000    0.000000  ...   \n",
       "12084     32.478910    1675.922116   1413.571789  262.350327  ...   \n",
       "\n",
       "       football_gold_cup  beer_capital  music_fest discount_in_percent  \\\n",
       "291                    -             -           -            8.835008   \n",
       "871                    -             -           -            1.465966   \n",
       "19532                  -             -           -            0.217413   \n",
       "2089                   -  beer_capital           -            5.504745   \n",
       "9755                   -             -  music_fest            7.935699   \n",
       "7561                   -             -           -           15.218151   \n",
       "19204                  -             -           -           14.105636   \n",
       "8781                   -             -  music_fest           12.218455   \n",
       "2540                   -             -           -            0.000000   \n",
       "12084                  -             -           -           15.654088   \n",
       "\n",
       "      timeseries time_idx month log_volume avg_volume_by_sku  \\\n",
       "291          228        0     1  -0.678062       1225.306376   \n",
       "871          177       24     1   2.168825       1634.434615   \n",
       "19532        322        8     9   1.603017       2625.472644   \n",
       "2089         240        9    10   3.076505         38.529107   \n",
       "9755         259       26     3   6.867508       2143.677462   \n",
       "7561          21       13     2   7.077206       1566.643589   \n",
       "19204         17       55     8   1.715472       1385.225478   \n",
       "8781         151        2     3   8.360577       1757.950603   \n",
       "2540         300       33    10 -18.420681          0.000000   \n",
       "12084        181       51     4   3.836454       2034.293024   \n",
       "\n",
       "      avg_volume_by_agency  \n",
       "291              99.650400  \n",
       "871              11.397086  \n",
       "19532            48.295650  \n",
       "2089           2511.035175  \n",
       "9755            396.022140  \n",
       "7561           1881.866367  \n",
       "19204           109.699200  \n",
       "8781           1925.272108  \n",
       "2540           2418.719550  \n",
       "12084           109.381800  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "source": [
    "import os\n",
    "import warnings\n",
    "import copy\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pytorch_lightning as pl\n",
    "from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "import torch\n",
    "\n",
    "from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")  # avoid printing out absolute paths\n",
    "from pytorch_forecasting.data import GroupNormalizer\n",
    "from pytorch_forecasting.metrics import SMAPE, PoissonLoss, QuantileLoss\n",
    "from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters\n",
    "\n",
    "from pytorch_forecasting.data.examples import get_stallion_data\n",
    "\n",
    "data = get_stallion_data()\n",
    "\n",
    "# add time index\n",
    "data[\"time_idx\"] = data[\"date\"].dt.year * 12 + data[\"date\"].dt.month\n",
    "data[\"time_idx\"] -= data[\"time_idx\"].min()\n",
    "\n",
    "# add additional features\n",
    "data[\"month\"] = data.date.dt.month.astype(str).astype(\"category\")  # categories have be strings\n",
    "data[\"log_volume\"] = np.log(data.volume + 1e-8)\n",
    "data[\"avg_volume_by_sku\"] = data.groupby([\"time_idx\", \"sku\"], observed=True).volume.transform(\"mean\")\n",
    "data[\"avg_volume_by_agency\"] = data.groupby([\"time_idx\", \"agency\"], observed=True).volume.transform(\"mean\")\n",
    "\n",
    "# we want to encode special days as one variable and thus need to first reverse one-hot encoding\n",
    "special_days = [\n",
    "    \"easter_day\",\n",
    "    \"good_friday\",\n",
    "    \"new_year\",\n",
    "    \"christmas\",\n",
    "    \"labor_day\",\n",
    "    \"independence_day\",\n",
    "    \"revolution_day_memorial\",\n",
    "    \"regional_games\",\n",
    "    \"fifa_u_17_world_cup\",\n",
    "    \"football_gold_cup\",\n",
    "    \"beer_capital\",\n",
    "    \"music_fest\",\n",
    "]\n",
    "data[special_days] = data[special_days].apply(lambda x: x.map({0: \"-\", 1: x.name})).astype(\"category\")\n",
    "data.sample(10, random_state=521)"
   ]
  }
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Hi,can i ask why i run code by pycharm above and get no outcome like you?even no outcome and just a word 'Process finished with exit code 0'

you need a print(data.sample(10, random_state=521))

Hi, may I know what version of PyTorch you are using?

Hi, may I know what version of PyTorch you are using?

Installation Steps and Version Instructions can be found here - https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-PyTorch

Hi,can i ask why i run code by pycharm above and get no outcome like you?even no outcome and just a word 'Process finished with exit code 0'

Try Running this on google colab