init

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/parameters.pkl filter=lfs diff=lfs merge=lfs -text
+data/test_data.pkl filter=lfs diff=lfs merge=lfs -text
+model/tft_check.ckpt filter=lfs diff=lfs merge=lfs -text

Untitled.ipynb

@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "40d0c64c-1de1-47ed-aa66-7b28d9e8fd1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle \n",
+    "import pandas as pd \n",
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5043d237-0287-4705-bfd5-73b880b36def",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_pickle('data/test_data.pkl')\n",
+    "df = df.loc[(df[\"Branch\"] == \"15\") & (df[\"Group\"].isin([\"6\",\"7\",\"4\",\"1\"]))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "dce8096f-23d4-4075-8654-6693632c45bc",
+   "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>sales</th>\n",
+       "      <th>DayInYear</th>\n",
+       "      <th>time_idx</th>\n",
+       "      <th>Wahl</th>\n",
+       "      <th>Baustelle</th>\n",
+       "      <th>MontagLangesWE</th>\n",
+       "      <th>FreitagLangesWE</th>\n",
+       "      <th>nosale</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>AufSommerzeit</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Branch</th>\n",
+       "      <th>Weekday</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>MTXWTH_Day_precip</th>\n",
+       "      <th>MTXWTH_Temp_max</th>\n",
+       "      <th>MTXWTH_Temp_min</th>\n",
+       "      <th>Start</th>\n",
+       "      <th>End</th>\n",
+       "      <th>ShiftLength</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>270300</th>\n",
+       "      <td>1600.9030</td>\n",
+       "      <td>177</td>\n",
+       "      <td>2369</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2022-06-26</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>28.52</td>\n",
+       "      <td>17.47</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10.983333</td>\n",
+       "      <td>240.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270301</th>\n",
+       "      <td>1811.1958</td>\n",
+       "      <td>178</td>\n",
+       "      <td>2370</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2022-06-27</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.75</td>\n",
+       "      <td>16.70</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>13.983333</td>\n",
+       "      <td>480.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270302</th>\n",
+       "      <td>1784.2916</td>\n",
+       "      <td>179</td>\n",
+       "      <td>2371</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2022-06-28</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.57</td>\n",
+       "      <td>14.17</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>13.983333</td>\n",
+       "      <td>480.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270303</th>\n",
+       "      <td>1757.3488</td>\n",
+       "      <td>180</td>\n",
+       "      <td>2372</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2022-06-29</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.81</td>\n",
+       "      <td>13.09</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>13.983333</td>\n",
+       "      <td>480.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>270304</th>\n",
+       "      <td>1741.0982</td>\n",
+       "      <td>181</td>\n",
+       "      <td>2373</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2022-06-30</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27.26</td>\n",
+       "      <td>15.00</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>13.983333</td>\n",
+       "      <td>480.0</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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287065</th>\n",
+       "      <td>1643.1700</td>\n",
+       "      <td>173</td>\n",
+       "      <td>2730</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2023-06-22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.93</td>\n",
+       "      <td>13.06</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>16.983333</td>\n",
+       "      <td>660.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287066</th>\n",
+       "      <td>1597.3518</td>\n",
+       "      <td>174</td>\n",
+       "      <td>2731</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-06-23</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>23.99</td>\n",
+       "      <td>15.98</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>16.983333</td>\n",
+       "      <td>660.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287067</th>\n",
+       "      <td>1683.6228</td>\n",
+       "      <td>175</td>\n",
+       "      <td>2732</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-06-24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.99</td>\n",
+       "      <td>12.04</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>15.983333</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287068</th>\n",
+       "      <td>1785.2180</td>\n",
+       "      <td>176</td>\n",
+       "      <td>2733</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-06-25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>28.99</td>\n",
+       "      <td>15.02</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>15.983333</td>\n",
+       "      <td>540.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287069</th>\n",
+       "      <td>1589.9020</td>\n",
+       "      <td>177</td>\n",
+       "      <td>2734</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>none</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2023-06-26</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27.96</td>\n",
+       "      <td>17.01</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>16.983333</td>\n",
+       "      <td>660.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1464 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            sales  DayInYear  time_idx Wahl Baustelle MontagLangesWE  \\\n",
+       "270300  1600.9030        177      2369  0.0       0.0            0.0   \n",
+       "270301  1811.1958        178      2370  0.0       0.0            0.0   \n",
+       "270302  1784.2916        179      2371  0.0       0.0            0.0   \n",
+       "270303  1757.3488        180      2372  0.0       0.0            0.0   \n",
+       "270304  1741.0982        181      2373  0.0       0.0            0.0   \n",
+       "...           ...        ...       ...  ...       ...            ...   \n",
+       "287065  1643.1700        173      2730  0.0       0.0            0.0   \n",
+       "287066  1597.3518        174      2731  0.0       0.0            0.0   \n",
+       "287067  1683.6228        175      2732  0.0       0.0            0.0   \n",
+       "287068  1785.2180        176      2733  0.0       0.0            0.0   \n",
+       "287069  1589.9020        177      2734  0.0       0.0            0.0   \n",
+       "\n",
+       "       FreitagLangesWE nosale holiday AufSommerzeit  ... Branch Weekday  \\\n",
+       "270300             0.0      0    none           0.0  ...     15       6   \n",
+       "270301             0.0      0    none           0.0  ...     15       0   \n",
+       "270302             0.0      0    none           0.0  ...     15       1   \n",
+       "270303             0.0      0    none           0.0  ...     15       2   \n",
+       "270304             0.0      0    none           0.0  ...     15       3   \n",
+       "...                ...    ...     ...           ...  ...    ...     ...   \n",
+       "287065             0.0      0    none           0.0  ...     15       3   \n",
+       "287066             0.0      0    none           0.0  ...     15       4   \n",
+       "287067             0.0      0    none           0.0  ...     15       5   \n",
+       "287068             0.0      0    none           0.0  ...     15       6   \n",
+       "287069             0.0      0    none           0.0  ...     15       0   \n",
+       "\n",
+       "             Date MTXWTH_Day_precip MTXWTH_Temp_max  MTXWTH_Temp_min  Start  \\\n",
+       "270300 2022-06-26               0.0           28.52            17.47    7.0   \n",
+       "270301 2022-06-27               0.0           25.75            16.70    6.0   \n",
+       "270302 2022-06-28               0.0           23.57            14.17    6.0   \n",
+       "270303 2022-06-29               0.0           26.81            13.09    6.0   \n",
+       "270304 2022-06-30               0.0           27.26            15.00    6.0   \n",
+       "...           ...               ...             ...              ...    ...   \n",
+       "287065 2023-06-22               0.0           26.93            13.06    6.0   \n",
+       "287066 2023-06-23               1.0           23.99            15.98    6.0   \n",
+       "287067 2023-06-24               0.0           25.99            12.04    6.0   \n",
+       "287068 2023-06-25               0.0           28.99            15.02    7.0   \n",
+       "287069 2023-06-26               0.0           27.96            17.01    6.0   \n",
+       "\n",
+       "              End  ShiftLength  weight  \n",
+       "270300  10.983333        240.0       1  \n",
+       "270301  13.983333        480.0       1  \n",
+       "270302  13.983333        480.0       1  \n",
+       "270303  13.983333        480.0       1  \n",
+       "270304  13.983333        480.0       1  \n",
+       "...           ...          ...     ...  \n",
+       "287065  16.983333        660.0       1  \n",
+       "287066  16.983333        660.0       1  \n",
+       "287067  15.983333        600.0       1  \n",
+       "287068  15.983333        540.0       1  \n",
+       "287069  16.983333        660.0       1  \n",
+       "\n",
+       "[1464 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5ea34c2b-fc24-4cfa-ad46-f369bea42364",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp('2023-06-26 00:00:00')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(df[\"Date\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a024d9e3-e018-43fe-9bb0-20b9d9e91b53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.date(2023, 5, 27)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datetime.date(2023, 6, 26) - datetime.timedelta(days = 30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "52e0a2c8-5b53-42f4-91c9-3ca7d6a8d356",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['sales', 'DayInYear', 'time_idx', 'Wahl', 'Baustelle', 'MontagLangesWE',\n",
+       "       'FreitagLangesWE', 'nosale', 'holiday', 'AufSommerzeit',\n",
+       "       'AufWinterzeit', 'Group', 'Branch', 'Weekday', 'Date',\n",
+       "       'MTXWTH_Day_precip', 'MTXWTH_Temp_max', 'MTXWTH_Temp_min', 'Start',\n",
+       "       'End', 'ShiftLength', 'weight'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75415b44-41ec-4893-8452-939766ebaabc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -0,0 +1,116 @@
+## Imports
+import pickle
+import warnings
+import streamlit as st
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import datetime
+
+# import torch
+from torch.distributions import Normal
+from pytorch_forecasting import (
+    TimeSeriesDataSet,
+    TemporalFusionTransformer,
+)
+
+## Functions 
+def raw_preds_to_df(raw,quantiles = None):
+    """
+    raw is output of model.predict with return_index=True
+    quantiles can be provided like [0.1,0.5,0.9] to get interpretable quantiles
+    in the output, time_idx is the first prediction time index (one step after knowledge cutoff)
+    pred_idx the index of the predicted date i.e. time_idx + h - 1
+    """
+    index = raw[2]
+    preds = raw[0].prediction
+    dec_len = preds.shape[1]
+    n_quantiles = preds.shape[-1]
+    preds_df = pd.DataFrame(index.values.repeat(dec_len * n_quantiles, axis=0),columns=index.columns)
+    preds_df = preds_df.assign(h=np.tile(np.repeat(np.arange(1,1+dec_len),n_quantiles),len(preds_df)//(dec_len*n_quantiles)))
+    preds_df = preds_df.assign(q=np.tile(np.arange(n_quantiles),len(preds_df)//n_quantiles))
+    preds_df = preds_df.assign(pred=preds.flatten().cpu().numpy())
+    if quantiles is not None:
+        preds_df['q'] = preds_df['q'].map({i:q for i,q in enumerate(quantiles)})
+
+    preds_df['pred_idx'] = preds_df['time_idx'] + preds_df['h'] - 1
+    return preds_df
+
+def prepare_dataset(parameters, df, rain, temperature, datepicker):
+    if rain != "Default":
+        df["MTXWTH_Day_precip"] = rain_mapping[rain]
+    
+    df["MTXWTH_Temp_min"] = df["MTXWTH_Temp_min"] + temperature
+    df["MTXWTH_Temp_max"] = df["MTXWTH_Temp_max"] + temperature
+
+    lowerbound = datepicker - datetime.timedelta(days = 35) 
+    upperbound = datepicker + datetime.timedelta(days = 30) 
+
+    df = df.loc[(df["Date"]>lowerbound) & (df["Date"]<=upperbound)]
+    
+    df = TimeSeriesDataSet.from_parameters(parameters, df)
+    return df.to_dataloader(train=False, batch_size=256,num_workers = 0)
+
+def predict(model, dataloader): 
+    return model.predict(dataloader, mode="raw", return_x=True, return_index=True)
+    
+## Initiate Data
+with open('data/parameters.pkl', 'rb') as f:
+    parameters = pickle.load(f)
+model = TemporalFusionTransformer.load_from_checkpoint('model/tft_check.ckpt')
+
+df = pd.read_pickle('data/test_data.pkl')
+df = df.loc[(df["Branch"] == 15) & (df["Group"].isin(["6","7","4","1"]))]
+
+rain_mapping = {
+    "Yes" : 1,
+    "No" : , 0
+}
+
+# Start App
+st.title("Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting")
+
+st.markdown(body = """
+    ### Abstract
+    Multi-horizon forecasting often contains a complex mix of inputs – including
+    static (i.e. time-invariant) covariates, known future inputs, and other exogenous
+    time series that are only observed in the past – without any prior information
+    on how they interact with the target. Several deep learning methods have been
+    proposed, but they are typically ‘black-box’ models which do not shed light on
+    how they use the full range of inputs present in practical scenarios. In this pa-
+    per, we introduce the Temporal Fusion Transformer (TFT) – a novel attention-
+    based architecture which combines high-performance multi-horizon forecasting
+    with interpretable insights into temporal dynamics. To learn temporal rela-
+    tionships at different scales, TFT uses recurrent layers for local processing and
+    interpretable self-attention layers for long-term dependencies. TFT utilizes spe-
+    cialized components to select relevant features and a series of gating layers to
+    suppress unnecessary components, enabling high performance in a wide range of
+    scenarios. On a variety of real-world datasets, we demonstrate significant per-
+    formance improvements over existing benchmarks, and showcase three practical
+    interpretability use cases of TFT.
+""")
+
+rain = st.radio("Rain Indicator", ('Default', 'Yes', 'No'))
+
+temperature = st.slider('Change in Temperature', min_value=-10, max_value=+10, value=0, step=0.25)
+
+datepicker = st.date_input("Start of Forecast", datetime.date(2022, 12, 24), min_value=datetime.date(2022, 6, 26) + datetime.timedelta(days = 35), max_value=datetime.date(2023, 6, 26) - datetime.timedelta(days = 30))
+
+arr = np.random.normal(1, 1, size=100)
+fig, ax = plt.subplots()
+ax.hist(arr, bins=20)
+
+st.pyplot(fig)
+
+st.button("Forecast Sales", type="primary") #on_click=None,
+
+# %%
+preds = raw_preds_to_df(out, quantiles = None)
+
+preds = preds.merge(data_selected[['time_idx','Group','Branch','sales','weight','Date','MTXWTH_Day_precip','MTXWTH_Temp_max','MTXWTH_Temp_min']],how='left',left_on=['pred_idx','Group','Branch'],right_on=['time_idx','Group','Branch'])
+preds.rename(columns={'time_idx_x':'time_idx'},inplace=True)
+preds.drop(columns=['time_idx_y'],inplace=True)
+
+

data/parameters.pkl

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+size 17761

data/test_data.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6d1cf5ab9ad31de916030c795598d13ba388f95bbde2a3b295088666fb65ac7
+size 31347323

model/tft_check.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6694f37ecd5da5795eb1b0320fa96dda374fe331b05d9d5e2d0a49001fc2f9ed
+size 5176944

requirements.txt

@@ -0,0 +1,109 @@
+absl-py==1.4.0
+aiohttp==3.8.3
+aiosignal==1.3.1
+alembic==1.9.2
+asttokens==2.2.1
+async-timeout==4.0.2
+attrs==22.2.0
+autopage==0.5.1
+backcall==0.2.0
+cachetools==5.2.1
+certifi==2022.12.7
+charset-normalizer==2.1.1
+cliff==4.1.0
+cmaes==0.9.1
+cmd2==2.4.2
+colorlog==6.7.0
+comm==0.1.2
+contourpy==1.0.7
+cycler==0.11.0
+debugpy==1.6.6
+decorator==5.1.1
+executing==1.2.0
+fonttools==4.38.0
+frozenlist==1.3.3
+fsspec==2022.11.0
+future==0.18.3
+google-auth==2.16.0
+google-auth-oauthlib==0.4.6
+greenlet==2.0.1
+#grpcio==1.51.1
+idna==3.4
+importlib-metadata==6.0.0
+importlib-resources==5.10.2
+ipykernel==6.21.2
+ipython==8.10.0
+jedi==0.18.2
+joblib==1.2.0
+jupyter_client==8.0.3
+jupyter_core==5.2.0
+kiwisolver==1.4.4
+lightning-utilities==0.5.0
+lxml==4.9.2
+Mako==1.2.4
+Markdown==3.4.1
+MarkupSafe==2.1.1
+matplotlib==3.6.3
+matplotlib-inline==0.1.6
+multidict==6.0.4
+nest-asyncio==1.5.6
+numpy==1.23.5
+oauthlib==3.2.2
+optuna==2.10.1
+packaging==23.0
+pandas==1.5.2
+parso==0.8.3
+patsy==0.5.3
+pbr==5.11.1
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==9.4.0
+platformdirs==3.0.0
+prettytable==3.6.0
+prompt-toolkit==3.0.37
+protobuf==3.20.1
+psutil==5.9.4
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pyDeprecate==0.3.1
+Pygments==2.14.0
+pyparsing==3.0.9
+pyperclip==1.8.2
+python-dateutil==2.8.2
+pytorch-forecasting==0.10.3
+pytorch-lightning==1.9.0
+pytz==2022.7.1
+PyYAML==6.0
+pyzmq==25.0.0
+requests==2.28.2
+requests-futures==1.0.0
+requests-oauthlib==1.3.1
+rsa==4.9
+scikit-learn==1.1.3
+scipy==1.10.0
+six==1.16.0
+SQLAlchemy==1.4.46
+stack-data==0.6.2
+statsmodels==0.13.5
+stevedore==4.1.1
+tensorboard==2.11.2
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorboardX==2.5.1
+threadpoolctl==3.1.0
+torch==1.10.2
+torchaudio==0.10.2
+torchmetrics==0.11.0
+torchvision==0.11.3
+tornado==6.2
+tqdm==4.64.1
+traitlets==5.9.0
+typing_extensions==4.4.0
+urllib3==1.26.14
+wcwidth==0.2.6
+Werkzeug==2.2.2
+yahooquery==2.3.1
+yarl==1.8.2
+zipp==3.11.0