From c401c5429aa0a2e9bed1cab9e26b773c32535dee Mon Sep 17 00:00:00 2001
From: Tania <>
Date: Fri, 25 Oct 2024 11:20:05 +1300
Subject: [PATCH 1/4] Add best practice notebook using beam analysis_id

---
 event-features/data/restaurant_demand.csv     | 1077 ++++++
 .../data/restaurant_existing_features.csv     | 1077 ++++++
 ...vent-features-for-demand-forecasting.ipynb | 3135 +++++++++++++++++
 event-features/requirements.txt               |    3 +
 4 files changed, 5292 insertions(+)
 create mode 100644 event-features/data/restaurant_demand.csv
 create mode 100644 event-features/data/restaurant_existing_features.csv
 create mode 100644 event-features/get-relevant-event-features-for-demand-forecasting.ipynb
 create mode 100644 event-features/requirements.txt

diff --git a/event-features/data/restaurant_demand.csv b/event-features/data/restaurant_demand.csv
new file mode 100644
index 0000000..4c0fb33
--- /dev/null
+++ b/event-features/data/restaurant_demand.csv
@@ -0,0 +1,1077 @@
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diff --git a/event-features/data/restaurant_existing_features.csv b/event-features/data/restaurant_existing_features.csv
new file mode 100644
index 0000000..3db91e8
--- /dev/null
+++ b/event-features/data/restaurant_existing_features.csv
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+2019-12-24,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.12439450483979998,0.9922328391893008,9913.06916155541
+2019-12-26,1.0,6.123233995736766e-17,-2.4492935982947064e-16,1.0,False,True,-0.09019000427034513,0.9959245770286599,9050.11562071116
+2019-12-27,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,False,True,-0.07304518821104744,0.9973286321364752,9727.69636396227
+2019-12-28,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.05587875690537313,0.9984375616565666,8750.25722135605
+2019-12-29,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,True,-0.03869579017652689,0.9992510374388481,4896.75699680111
+2019-12-30,0.0,1.0,-2.4492935982947064e-16,1.0,False,True,-0.021501372740813793,0.999768818762748,4479.99872572649
+2019-12-31,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.004300592702981974,0.9999907524084426,7646.75189067266
diff --git a/event-features/get-relevant-event-features-for-demand-forecasting.ipynb b/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
new file mode 100644
index 0000000..35eeabf
--- /dev/null
+++ b/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
@@ -0,0 +1,3135 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prerequisites"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Complete the following steps:\n",
+    "\n",
+    "1. Install `requirements.txt`\n",
+    "2. Load necessary libraries and API URLs\n",
+    "3. Replace `ACCESS_TOKEN` with a valid token (for help creating an access token, see [API Quickstart](https://docs.predicthq.com/getting-started/api-quickstart))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install requirements\n",
+    "# %pip install --user -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load libraries and API URLs\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import requests\n",
+    "from io import StringIO\n",
+    "from functools import reduce\n",
+    "\n",
+    "import xgboost as xgb\n",
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "SUGGESTED_RADIUS_API_URL = \"https://api.predicthq.com/v1/suggested-radius\"\n",
+    "BEAM_API_URL = \"https://api.predicthq.com/v1/beam\"\n",
+    "FEATURES_API_URL = \"https://api.predicthq.com/v1/features\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <script type=\"application/javascript\" id=\"jupyter_black\">\n",
+       "                (function() {\n",
+       "                    if (window.IPython === undefined) {\n",
+       "                        return\n",
+       "                    }\n",
+       "                    var msg = \"WARNING: it looks like you might have loaded \" +\n",
+       "                        \"jupyter_black in a non-lab notebook with \" +\n",
+       "                        \"`is_lab=True`. Please double check, and if \" +\n",
+       "                        \"loading with `%load_ext` please review the README!\"\n",
+       "                    console.log(msg)\n",
+       "                    alert(msg)\n",
+       "                })()\n",
+       "                </script>\n",
+       "                "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ACCESS_TOKEN = \"REPLACE_WITH_ACCESS_TOKEN\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gather data"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following are required when creating a new Analysis in Beam:\n",
+    "\n",
+    "1. Demand data: one csv file with columns for `date` and `demand` (see [here](https://docs.predicthq.com/api/beam/upload-demand-data#request-body) for more details)\n",
+    "\n",
+    "2. `lat`/`lon`: the coordinates of the location \n",
+    "\n",
+    "3. `industry`: the industry relevant to the location (see [here](https://docs.predicthq.com/api/beam/create-an-analysis#request-body) for more details)\n",
+    "\n",
+    "4. `analysis_name`: a user-created free-form string to reference the Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>date</th>\n",
+       "      <th>demand</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2017-01-09</td>\n",
+       "      <td>8291.576677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017-01-10</td>\n",
+       "      <td>8629.401781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2017-01-11</td>\n",
+       "      <td>7217.360816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2017-01-12</td>\n",
+       "      <td>8404.744284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2017-01-13</td>\n",
+       "      <td>8450.852853</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date       demand\n",
+       "0  2017-01-09  8291.576677\n",
+       "1  2017-01-10  8629.401781\n",
+       "2  2017-01-11  7217.360816\n",
+       "3  2017-01-12  8404.744284\n",
+       "4  2017-01-13  8450.852853"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# define demand details\n",
+    "DEMAND_FILE_PATH = \"data/restaurant_demand.csv\"\n",
+    "LAT = \"40.74559\"\n",
+    "LON = \"-73.99452\"\n",
+    "INDUSTRY = \"restaurants\"\n",
+    "ANALYSIS_NAME = \"restaurant_daily_demand_example\"\n",
+    "\n",
+    "# inspect demand file\n",
+    "pd.read_csv(DEMAND_FILE_PATH).head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 1. Identify relevant event features with Beam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_suggested_radius(\n",
+    "    lat,\n",
+    "    lon,\n",
+    "    industry,\n",
+    "    access_token=ACCESS_TOKEN,\n",
+    "    suggested_radius_api_url=SUGGESTED_RADIUS_API_URL,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Get a suggested radius for a location.\n",
+    "    \"\"\"\n",
+    "    if industry not in [\"restaurants\", \"retail\", \"accommodation\", \"parking\"]:\n",
+    "        industry = \"other\"\n",
+    "\n",
+    "    url = suggested_radius_api_url\n",
+    "    headers = {\"Authorization\": \"Bearer \" + access_token, \"Accept\": \"application/json\"}\n",
+    "    params = {\n",
+    "        \"location.origin\": f\"{lat},{lon}\",\n",
+    "        \"industry\": industry,\n",
+    "        \"radius_unit\": \"mi\",\n",
+    "    }\n",
+    "\n",
+    "    response = requests.get(url=url, headers=headers, params=params)\n",
+    "\n",
+    "    return response.json()\n",
+    "\n",
+    "\n",
+    "def create_beam_analysis_id(\n",
+    "    lat,\n",
+    "    lon,\n",
+    "    industry,\n",
+    "    analysis_name,\n",
+    "    access_token=ACCESS_TOKEN,\n",
+    "    beam_api_url=BEAM_API_URL,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Create a Beam Analysis ID for a location.\n",
+    "    \"\"\"\n",
+    "    suggested_radius = get_suggested_radius(lat, lon, industry)\n",
+    "\n",
+    "    url = f\"{beam_api_url}/analyses\"\n",
+    "    headers = {\"Authorization\": \"Bearer \" + access_token, \"Accept\": \"application/json\"}\n",
+    "    json = {\n",
+    "        \"name\": analysis_name,\n",
+    "        \"location\": {\n",
+    "            \"geopoint\": {\n",
+    "                \"lat\": lat,\n",
+    "                \"lon\": lon,\n",
+    "            },\n",
+    "            \"radius\": suggested_radius[\"radius\"],\n",
+    "            \"unit\": suggested_radius[\"radius_unit\"],\n",
+    "        },\n",
+    "        \"rank\": {\n",
+    "            \"type\": \"phq\",\n",
+    "        },\n",
+    "        \"demand_type\": {\n",
+    "            \"industry\": industry,\n",
+    "        },\n",
+    "    }\n",
+    "\n",
+    "    response = requests.post(url=url, headers=headers, json=json)\n",
+    "\n",
+    "    analysis_id = response.json()[\"analysis_id\"]\n",
+    "    print(f\"{analysis_id} created\")\n",
+    "\n",
+    "    return analysis_id\n",
+    "\n",
+    "\n",
+    "def upload_demand_to_beam(\n",
+    "    demand_file_path, analysis_id, access_token=ACCESS_TOKEN, beam_api_url=BEAM_API_URL\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Upload demand data for a Beam Analysis.\n",
+    "    \"\"\"\n",
+    "    url = f\"{beam_api_url}/analyses/{analysis_id}/sink\"\n",
+    "    headers = {\"Authorization\": \"Bearer \" + access_token, \"Content-Type\": \"text/csv\"}\n",
+    "\n",
+    "    response = requests.post(url=url, headers=headers, data=open(demand_file_path))\n",
+    "\n",
+    "    if response.status_code == 202:\n",
+    "        print(f\"{analysis_id} demand uploading...\")\n",
+    "    else:\n",
+    "        print(response.content)\n",
+    "\n",
+    "\n",
+    "def get_analysis_status(\n",
+    "    analysis_id, access_token=ACCESS_TOKEN, beam_api_url=BEAM_API_URL\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Check status of a Beam Analysis.\n",
+    "    \"\"\"\n",
+    "    url = f\"{beam_api_url}/analyses/{analysis_id}\"\n",
+    "    headers = {\"Authorization\": \"Bearer \" + access_token, \"Accept\": \"application/json\"}\n",
+    "\n",
+    "    response = requests.get(url=url, headers=headers)\n",
+    "\n",
+    "    print(f\"{analysis_id} status: {response.json()['readiness_status']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Refresh the following block until `status` is  `ready`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bEl8Tv3rPFg status: ready\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create a Beam Analysis ID and upload demand data\n",
+    "if \"analysis_id\" not in globals():\n",
+    "    analysis_id = create_beam_analysis_id(\n",
+    "        lat=LAT,\n",
+    "        lon=LON,\n",
+    "        industry=INDUSTRY,\n",
+    "        analysis_name=ANALYSIS_NAME,\n",
+    "    )\n",
+    "    upload_demand_to_beam(demand_file_path=DEMAND_FILE_PATH, analysis_id=analysis_id)\n",
+    "\n",
+    "get_analysis_status(analysis_id=analysis_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 2. Retrieve event features from Features API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def post_process_rank_features(df):\n",
+    "    \"\"\"\n",
+    "    Post-process rank features.\n",
+    "    \"\"\"\n",
+    "    rank_feature_cols = [col for col in df.columns if \"phq_rank\" in col]\n",
+    "    rank_features = list(\n",
+    "        set(col.rsplit(\"_rank_levels_\", 1)[0] for col in rank_feature_cols)\n",
+    "    )\n",
+    "\n",
+    "    for feature in rank_features:\n",
+    "        related_columns = [col for col in rank_feature_cols if col.startswith(feature)]\n",
+    "        df[f\"{feature}_stats_wsum\"] = sum(\n",
+    "            df[col] * int(col.split(\"_rank_levels_\")[1]) for col in related_columns\n",
+    "        )\n",
+    "        df = df.drop(columns=related_columns)\n",
+    "\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "def get_features(\n",
+    "    analysis_id,\n",
+    "    start,\n",
+    "    end,\n",
+    "    group_id=None,\n",
+    "    access_token=ACCESS_TOKEN,\n",
+    "    features_api_url=FEATURES_API_URL,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Fetch features from Features API for a Beam Analysis.\n",
+    "    \"\"\"\n",
+    "    url = features_api_url\n",
+    "    headers = {\"Authorization\": \"Bearer \" + access_token, \"Accept\": \"text/csv\"}\n",
+    "    beam_info = {\"analysis_id\": analysis_id}\n",
+    "    if group_id is not None:\n",
+    "        beam_info[\"group_id\"] = group_id\n",
+    "    json = {\"beam\": beam_info, \"active\": {\"gte\": start, \"lte\": end}}\n",
+    "\n",
+    "    limit = 100\n",
+    "    offset = 0\n",
+    "    results = []\n",
+    "\n",
+    "    while True:\n",
+    "        params = {\"limit\": limit, \"offset\": offset}\n",
+    "\n",
+    "        response = requests.post(url=url, headers=headers, json=json, params=params)\n",
+    "        if not response.text.strip():\n",
+    "            break\n",
+    "        results.append(response.text)\n",
+    "        offset += limit\n",
+    "\n",
+    "    df = pd.concat(\n",
+    "        [pd.read_csv(StringIO(result), parse_dates=[\"date\"]) for result in results]\n",
+    "    )\n",
+    "    df = post_process_rank_features(df)\n",
+    "\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the desired date range to get relevant event features from Features API via the Beam `analysis_id`. Alternatively, add the `group_id` if the Analysis is part of a group and the relevant event features at the group level are of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>phq_attendance_school_holidays_stats_sum</th>\n",
+       "      <th>phq_attendance_conferences_hospitality_stats_sum</th>\n",
+       "      <th>phq_attendance_expos_hospitality_stats_sum</th>\n",
+       "      <th>phq_attendance_festivals_hospitality_stats_sum</th>\n",
+       "      <th>phq_attendance_performing_arts_hospitality_stats_sum</th>\n",
+       "      <th>phq_impact_observances_hospitality_stats_max</th>\n",
+       "      <th>phq_impact_public_holidays_hospitality_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_air_quality_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_blizzard_retail_stats_max</th>\n",
+       "      <th>...</th>\n",
+       "      <th>phq_impact_severe_weather_cold_wave_snow_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_cold_wave_storm_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_dust_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_dust_storm_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_flood_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_heat_wave_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_hurricane_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_thunderstorm_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_tornado_retail_stats_max</th>\n",
+       "      <th>phq_impact_severe_weather_tropical_storm_retail_stats_max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2017-01-09</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13383</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017-01-10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19597</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2017-01-11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>142</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22086</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2017-01-12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>567</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20406</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2017-01-13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>2833</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24046</td>\n",
+       "      <td>30</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  phq_attendance_school_holidays_stats_sum  \\\n",
+       "0 2017-01-09                                         0   \n",
+       "1 2017-01-10                                         0   \n",
+       "2 2017-01-11                                         0   \n",
+       "3 2017-01-12                                         0   \n",
+       "4 2017-01-13                                         0   \n",
+       "\n",
+       "   phq_attendance_conferences_hospitality_stats_sum  \\\n",
+       "0                                                 0   \n",
+       "1                                               145   \n",
+       "2                                               100   \n",
+       "3                                               100   \n",
+       "4                                               100   \n",
+       "\n",
+       "   phq_attendance_expos_hospitality_stats_sum  \\\n",
+       "0                                           0   \n",
+       "1                                           0   \n",
+       "2                                         142   \n",
+       "3                                         567   \n",
+       "4                                        2833   \n",
+       "\n",
+       "   phq_attendance_festivals_hospitality_stats_sum  \\\n",
+       "0                                               0   \n",
+       "1                                               0   \n",
+       "2                                               0   \n",
+       "3                                               0   \n",
+       "4                                               0   \n",
+       "\n",
+       "   phq_attendance_performing_arts_hospitality_stats_sum  \\\n",
+       "0                                              13383      \n",
+       "1                                              19597      \n",
+       "2                                              22086      \n",
+       "3                                              20406      \n",
+       "4                                              24046      \n",
+       "\n",
+       "   phq_impact_observances_hospitality_stats_max  \\\n",
+       "0                                             0   \n",
+       "1                                             0   \n",
+       "2                                             0   \n",
+       "3                                             9   \n",
+       "4                                            30   \n",
+       "\n",
+       "   phq_impact_public_holidays_hospitality_stats_max  \\\n",
+       "0                                                 0   \n",
+       "1                                                 0   \n",
+       "2                                                 0   \n",
+       "3                                                 0   \n",
+       "4                                                 7   \n",
+       "\n",
+       "   phq_impact_severe_weather_air_quality_retail_stats_max  \\\n",
+       "0                                                  0        \n",
+       "1                                                  0        \n",
+       "2                                                  0        \n",
+       "3                                                  0        \n",
+       "4                                                  0        \n",
+       "\n",
+       "   phq_impact_severe_weather_blizzard_retail_stats_max  ...  \\\n",
+       "0                                                  0    ...   \n",
+       "1                                                  0    ...   \n",
+       "2                                                  0    ...   \n",
+       "3                                                  0    ...   \n",
+       "4                                                  0    ...   \n",
+       "\n",
+       "   phq_impact_severe_weather_cold_wave_snow_retail_stats_max  \\\n",
+       "0                                                  0           \n",
+       "1                                                  0           \n",
+       "2                                                  0           \n",
+       "3                                                  0           \n",
+       "4                                                  0           \n",
+       "\n",
+       "   phq_impact_severe_weather_cold_wave_storm_retail_stats_max  \\\n",
+       "0                                                  0            \n",
+       "1                                                  0            \n",
+       "2                                                  0            \n",
+       "3                                                  0            \n",
+       "4                                                  0            \n",
+       "\n",
+       "   phq_impact_severe_weather_dust_retail_stats_max  \\\n",
+       "0                                                0   \n",
+       "1                                                0   \n",
+       "2                                                0   \n",
+       "3                                                0   \n",
+       "4                                                0   \n",
+       "\n",
+       "   phq_impact_severe_weather_dust_storm_retail_stats_max  \\\n",
+       "0                                                  0       \n",
+       "1                                                  0       \n",
+       "2                                                  0       \n",
+       "3                                                  0       \n",
+       "4                                                  0       \n",
+       "\n",
+       "   phq_impact_severe_weather_flood_retail_stats_max  \\\n",
+       "0                                                 0   \n",
+       "1                                                 0   \n",
+       "2                                                 0   \n",
+       "3                                                 0   \n",
+       "4                                                 0   \n",
+       "\n",
+       "   phq_impact_severe_weather_heat_wave_retail_stats_max  \\\n",
+       "0                                                  0      \n",
+       "1                                                  0      \n",
+       "2                                                  0      \n",
+       "3                                                  0      \n",
+       "4                                                  0      \n",
+       "\n",
+       "   phq_impact_severe_weather_hurricane_retail_stats_max  \\\n",
+       "0                                                  0      \n",
+       "1                                                  0      \n",
+       "2                                                  0      \n",
+       "3                                                  0      \n",
+       "4                                                  0      \n",
+       "\n",
+       "   phq_impact_severe_weather_thunderstorm_retail_stats_max  \\\n",
+       "0                                                  0         \n",
+       "1                                                  0         \n",
+       "2                                                  0         \n",
+       "3                                                  0         \n",
+       "4                                                  0         \n",
+       "\n",
+       "   phq_impact_severe_weather_tornado_retail_stats_max  \\\n",
+       "0                                                  0    \n",
+       "1                                                  0    \n",
+       "2                                                  0    \n",
+       "3                                                  0    \n",
+       "4                                                  0    \n",
+       "\n",
+       "   phq_impact_severe_weather_tropical_storm_retail_stats_max  \n",
+       "0                                                  0          \n",
+       "1                                                  0          \n",
+       "2                                                  0          \n",
+       "3                                                  0          \n",
+       "4                                                  0          \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "START = \"2017-01-09\"\n",
+    "END = \"2020-01-31\"\n",
+    "\n",
+    "# fetch features for the analysis\n",
+    "event_features_df = get_features(\n",
+    "    analysis_id=analysis_id, start=START, end=END, group_id=None\n",
+    ")\n",
+    "event_features_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 3. Use event features in demand forecasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Merge existing and event features together with demand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "demand_df = pd.read_csv(DEMAND_FILE_PATH, parse_dates=[\"date\"])\n",
+    "existing_features_df = pd.read_csv(\n",
+    "    \"data/restaurant_existing_features.csv\", parse_dates=[\"date\"]\n",
+    ")\n",
+    "\n",
+    "# merge demand and features dataframes\n",
+    "dataframes = [demand_df, existing_features_df, event_features_df]\n",
+    "features_and_demand_df = reduce(\n",
+    "    lambda left, right: pd.merge(left, right, on=\"date\", how=\"inner\"), dataframes\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_data(df):\n",
+    "    df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
+    "    df = df.sort_values(\"date\")\n",
+    "\n",
+    "    cutoff_date = df[\"date\"].max() - pd.Timedelta(days=7)\n",
+    "    train = df[df[\"date\"] < cutoff_date]\n",
+    "    test = df[df[\"date\"] >= cutoff_date]\n",
+    "    return train, test\n",
+    "\n",
+    "\n",
+    "def train_model(x_train, y_train):\n",
+    "    model = xgb.XGBRegressor(objective=\"reg:squarederror\", random_state=42)\n",
+    "    model.fit(x_train, y_train)\n",
+    "    return model\n",
+    "\n",
+    "\n",
+    "def plot_results(train, test, y_train, y_test, y_pred, feature_set, accuracy):\n",
+    "    plotting_cutoff_date = test[\"date\"].max() - pd.DateOffset(months=3)\n",
+    "    train = train[train[\"date\"] >= plotting_cutoff_date]\n",
+    "    test = test[test[\"date\"] >= plotting_cutoff_date]\n",
+    "    y_train = y_train[train.index]\n",
+    "    y_test = y_test[test.index]\n",
+    "\n",
+    "    actual_x = pd.concat([train[\"date\"], test[\"date\"]])\n",
+    "    actual_y = pd.concat([pd.Series(y_train), pd.Series(y_test)])\n",
+    "\n",
+    "    actual_trace = go.Scatter(\n",
+    "        x=actual_x,\n",
+    "        y=actual_y,\n",
+    "        mode=\"lines+markers\",\n",
+    "        name=\"Actual Demand\",\n",
+    "        line=dict(color=\"lightseagreen\"),\n",
+    "    )\n",
+    "\n",
+    "    predicted_trace = go.Scatter(\n",
+    "        x=test[\"date\"],\n",
+    "        y=y_pred,\n",
+    "        mode=\"lines+markers\",\n",
+    "        name=\"Forecasted Demand\",\n",
+    "        line=dict(color=\"lightcoral\"),\n",
+    "    )\n",
+    "\n",
+    "    fig = go.Figure()\n",
+    "    fig.add_trace(actual_trace)\n",
+    "    fig.add_trace(predicted_trace)\n",
+    "    fig.add_vline(\n",
+    "        x=test[\"date\"].iloc[0], line_width=2, line_dash=\"dot\", line_color=\"lightgray\"\n",
+    "    )\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        title=(\n",
+    "            f\"<b>Actual vs. Forecasted Demand</b><br>\"\n",
+    "            f\"<sub>Features: {feature_set.replace('_', ' ')} | \"\n",
+    "            f\"MAPE: {round(accuracy, 2)}%</sub>\"\n",
+    "        ),\n",
+    "        xaxis_title=\"Date\",\n",
+    "        yaxis_title=\"Demand\",\n",
+    "        legend_title=\"Type\",\n",
+    "    )\n",
+    "\n",
+    "    fig.show()\n",
+    "\n",
+    "\n",
+    "def calculate_forecast_error(y_test, y_pred):\n",
+    "    mape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100\n",
+    "    return mape\n",
+    "\n",
+    "\n",
+    "def calculate_forecast_uplift(base_metrics, new_metrics):\n",
+    "    uplifts = {}\n",
+    "    for metric in base_metrics:\n",
+    "        base_value = base_metrics[metric]\n",
+    "        new_value = new_metrics[metric]\n",
+    "        uplift = (base_value - new_value) / base_value * 100\n",
+    "        uplifts[f\"{metric}_uplift\"] = uplift\n",
+    "\n",
+    "    return uplifts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "line": {
+          "color": "lightseagreen"
+         },
+         "mode": "lines+markers",
+         "name": "Actual Demand",
+         "type": "scatter",
+         "x": [
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+          "2019-10-04T00:00:00",
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+          "2019-10-06T00:00:00",
+          "2019-10-07T00:00:00",
+          "2019-10-08T00:00:00",
+          "2019-10-09T00:00:00",
+          "2019-10-10T00:00:00",
+          "2019-10-11T00:00:00",
+          "2019-10-12T00:00:00",
+          "2019-10-13T00:00:00",
+          "2019-10-14T00:00:00",
+          "2019-10-15T00:00:00",
+          "2019-10-16T00:00:00",
+          "2019-10-17T00:00:00",
+          "2019-10-18T00:00:00",
+          "2019-10-19T00:00:00",
+          "2019-10-20T00:00:00",
+          "2019-10-21T00:00:00",
+          "2019-10-22T00:00:00",
+          "2019-10-23T00:00:00",
+          "2019-10-24T00:00:00",
+          "2019-10-25T00:00:00",
+          "2019-10-26T00:00:00",
+          "2019-10-27T00:00:00",
+          "2019-10-28T00:00:00",
+          "2019-10-29T00:00:00",
+          "2019-10-30T00:00:00",
+          "2019-10-31T00:00:00",
+          "2019-11-01T00:00:00",
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+          "2019-11-09T00:00:00",
+          "2019-11-10T00:00:00",
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+          "2019-11-12T00:00:00",
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+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "<b>Actual vs. Forecasted Demand</b><br><sub>Features: existing features only | MAPE: 49.27%</sub>"
+        },
+        "xaxis": {
+         "title": {
+          "text": "Date"
+         }
+        },
+        "yaxis": {
+         "title": {
+          "text": "Demand"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forecast Error (MAPE):\n",
+      "--- Features: existing and event features: 7.79%\n",
+      "--- Features: existing features only: 49.27%\n",
+      "Forecast Accuracy Uplift (MAPE reduction): 84.20%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train and evaluate models with different feature sets\n",
+    "# and compare forecast accuracy\n",
+    "results = {}\n",
+    "\n",
+    "train, test = split_data(df=features_and_demand_df)\n",
+    "\n",
+    "for include_event_features in [True, False]:\n",
+    "    if include_event_features:\n",
+    "        feature_columns = train.columns.difference([\"date\", \"demand\"])\n",
+    "        feature_set = \"existing_and_event_features\"\n",
+    "    else:\n",
+    "        feature_columns = [\n",
+    "            col\n",
+    "            for col in train.columns\n",
+    "            if col not in [\"date\", \"demand\"] and not col.startswith(\"phq_\")\n",
+    "        ]\n",
+    "        feature_set = \"existing_features_only\"\n",
+    "\n",
+    "    X_train = train[feature_columns]\n",
+    "    y_train = train[\"demand\"]\n",
+    "    X_test = test[feature_columns]\n",
+    "    y_test = test[\"demand\"]\n",
+    "\n",
+    "    model = train_model(x_train=X_train, y_train=y_train)\n",
+    "    y_pred = model.predict(X_test)\n",
+    "\n",
+    "    mape = calculate_forecast_error(y_test=y_test, y_pred=y_pred)\n",
+    "\n",
+    "    plot_results(\n",
+    "        train=train,\n",
+    "        test=test,\n",
+    "        y_train=y_train,\n",
+    "        y_test=y_test,\n",
+    "        y_pred=y_pred,\n",
+    "        feature_set=feature_set,\n",
+    "        accuracy=mape,\n",
+    "    )\n",
+    "\n",
+    "    results[feature_set] = {\"mape\": mape}\n",
+    "\n",
+    "\n",
+    "print(\"Forecast Error (MAPE):\")\n",
+    "for features_set in results:\n",
+    "    print(\n",
+    "        f\"--- Features: {features_set.replace('_',' ')}: {results[features_set]['mape']:.2f}%\"\n",
+    "    )\n",
+    "forecast_uplift = calculate_forecast_uplift(\n",
+    "    base_metrics=results[\"existing_features_only\"],\n",
+    "    new_metrics=results[\"existing_and_event_features\"],\n",
+    ")\n",
+    "print(\n",
+    "    f\"Forecast Accuracy Uplift (MAPE reduction): {forecast_uplift['mape_uplift']:.2f}%\"\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ds-notebooks-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/event-features/requirements.txt b/event-features/requirements.txt
new file mode 100644
index 0000000..725b22b
--- /dev/null
+++ b/event-features/requirements.txt
@@ -0,0 +1,3 @@
+plotly==5.22.0
+scikit-learn==1.4.2
+xgboost==2.0.3
\ No newline at end of file

From c12d0daa4ac19868912bde3698d44ccc4858354a Mon Sep 17 00:00:00 2001
From: Tania <>
Date: Wed, 30 Oct 2024 08:50:39 +1300
Subject: [PATCH 2/4] Update imputation, rank post-processing and model
 training based on feedback

---
 event-features/data/restaurant_demand.csv     |  221 +---
 .../data/restaurant_existing_features.csv     |  335 ++----
 ...vent-features-for-demand-forecasting.ipynb | 1006 +++++++++--------
 3 files changed, 594 insertions(+), 968 deletions(-)

diff --git a/event-features/data/restaurant_demand.csv b/event-features/data/restaurant_demand.csv
index 4c0fb33..57fa73f 100644
--- a/event-features/data/restaurant_demand.csv
+++ b/event-features/data/restaurant_demand.csv
@@ -48,6 +48,8 @@ date,demand
 2017-02-24,9802.28460433018
 2017-02-25,6251.50712115299
 2017-02-26,5363.40773281181
+2017-02-27,8882.555153888954
+2017-02-28,9165.196971170077
 2017-03-01,10224.5412492967
 2017-03-02,11454.4761017059
 2017-03-03,10527.8304315466
@@ -94,6 +96,7 @@ date,demand
 2017-04-13,10637.8350053683
 2017-04-14,9189.50839233891
 2017-04-15,5797.11172509805
+2017-04-16,9163.630074809089
 2017-04-17,11089.7635229944
 2017-04-18,11359.8813102486
 2017-04-19,11493.6553638906
@@ -314,6 +317,7 @@ date,demand
 2017-11-20,8708.29763190802
 2017-11-21,9177.80480201161
 2017-11-22,6554.91339706477
+2017-11-23,7236.831099490939
 2017-11-24,3610.44683301299
 2017-11-25,4625.60840173454
 2017-11-26,4928.07305791736
@@ -345,6 +349,7 @@ date,demand
 2017-12-22,6110.57359367784
 2017-12-23,4291.51589812807
 2017-12-24,1714.86855131384
+2017-12-25,5813.563191065511
 2017-12-26,4842.08463819334
 2017-12-27,6282.28654958781
 2017-12-28,5744.02529838607
@@ -441,6 +446,7 @@ date,demand
 2018-03-29,10234.8784046617
 2018-03-30,8115.07582625966
 2018-03-31,5210.51672027864
+2018-04-01,8335.401044217046
 2018-04-02,9849.39956549746
 2018-04-03,9891.88503880554
 2018-04-04,10027.3690541616
@@ -675,6 +681,7 @@ date,demand
 2018-11-19,11081.4217837028
 2018-11-20,10166.4751114252
 2018-11-21,7530.12551400349
+2018-11-22,8561.13964435585
 2018-11-23,4700.68323546675
 2018-11-24,4897.28391629194
 2018-11-25,5785.35010893042
@@ -707,6 +714,7 @@ date,demand
 2018-12-22,5324.27400855047
 2018-12-23,5014.93684036782
 2018-12-24,2498.83251005468
+2018-12-25,6588.000141671883
 2018-12-26,6606.9210357883
 2018-12-27,7416.2774022699
 2018-12-28,7132.98660610133
@@ -823,6 +831,7 @@ date,demand
 2019-04-18,11283.3667637131
 2019-04-19,8662.28169327976
 2019-04-20,5810.93765961046
+2019-04-21,9533.72905376603
 2019-04-22,12191.3444538979
 2019-04-23,12358.6881467325
 2019-04-24,12727.0827802395
@@ -863,215 +872,3 @@ date,demand
 2019-05-29,9803.32549798157
 2019-05-30,10078.0631201852
 2019-05-31,9467.5003772262
-2019-06-01,6190.70128035891
-2019-06-02,5252.27217506178
-2019-06-03,10335.7669730261
-2019-06-04,10484.6317917231
-2019-06-05,11049.3442217309
-2019-06-06,10150.0310546746
-2019-06-07,9425.4486295691
-2019-06-08,5589.0461677857
-2019-06-09,6108.08836185011
-2019-06-10,9937.65621909709
-2019-06-11,10699.6586958857
-2019-06-12,10234.8570372738
-2019-06-13,10427.2726199883
-2019-06-14,10210.6983435909
-2019-06-15,5617.47051434631
-2019-06-16,4939.92658212437
-2019-06-17,10404.2678811572
-2019-06-18,9484.8144769629
-2019-06-19,10251.9571204871
-2019-06-20,11298.8788646759
-2019-06-21,9193.32146638083
-2019-06-22,5956.79520112564
-2019-06-23,5855.22364369788
-2019-06-24,10883.4653088306
-2019-06-25,10664.6386437162
-2019-06-26,10468.03551989
-2019-06-27,10913.6224751418
-2019-06-28,9679.00059514413
-2019-06-29,6234.97483976136
-2019-06-30,9160.1660094448
-2019-07-01,10718.4641585049
-2019-07-02,11179.5143329018
-2019-07-03,9769.65423737652
-2019-07-04,2064.06816361681
-2019-07-05,6673.7066914611
-2019-07-06,4509.74574230073
-2019-07-07,6076.01663269201
-2019-07-08,10509.8316560772
-2019-07-09,10189.8950486431
-2019-07-10,10495.7859145061
-2019-07-11,10971.0331548626
-2019-07-12,9917.5420001265
-2019-07-13,5856.83998361601
-2019-07-14,5634.78214651234
-2019-07-15,10295.8846018963
-2019-07-16,10786.5011425848
-2019-07-17,10214.457510547
-2019-07-18,10852.6270276159
-2019-07-19,10071.6731381307
-2019-07-20,5070.99728201287
-2019-07-21,5418.10110601929
-2019-07-22,10424.7684491111
-2019-07-23,10549.1142032521
-2019-07-24,11002.743376566
-2019-07-25,9896.67645254846
-2019-07-26,10319.8049082307
-2019-07-27,5544.81319064055
-2019-07-28,5559.37679416818
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-2019-08-20,11139.9574821482
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-2019-08-25,6347.85604323437
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diff --git a/event-features/data/restaurant_existing_features.csv b/event-features/data/restaurant_existing_features.csv
index 3db91e8..3afa509 100644
--- a/event-features/data/restaurant_existing_features.csv
+++ b/event-features/data/restaurant_existing_features.csv
@@ -1,4 +1,4 @@
-date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annual,demand_lag7
+date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annual,lag7
 2017-01-09,0.0,1.0,0.49999999999999994,0.8660254037844387,False,False,0.1542040475626894,0.988039023376751,5552.01918615576
 2017-01-10,0.49999999999999994,0.8660254037844387,0.49999999999999994,0.8660254037844387,False,False,0.17117705961019986,0.985240282501282,8299.94186310385
 2017-01-11,0.8660254037844386,0.5000000000000001,0.49999999999999994,0.8660254037844387,False,False,0.18809941761179758,0.9821499931752291,8556.73007198137
@@ -48,13 +48,15 @@ date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annu
 2017-02-24,0.8660254037844388,-0.4999999999999998,0.8660254037844386,0.5000000000000001,False,False,0.8111602420785621,0.5848239578463322,10032.9486061733
 2017-02-25,0.49999999999999994,-0.8660254037844387,0.8660254037844386,0.5000000000000001,False,False,0.821100117812889,0.5707841943569038,5142.12208665988
 2017-02-26,1.2246467991473532e-16,-1.0,0.8660254037844386,0.5000000000000001,False,False,0.8307970168121247,0.5565755266413304,5112.47722130811
-2017-03-01,0.8660254037844386,0.5000000000000001,1.0,6.123233995736766e-17,False,False,0.8584015232918917,0.5129783862992281,7557.6640007467
-2017-03-02,1.0,6.123233995736766e-17,1.0,6.123233995736766e-17,False,False,0.8670985522812081,0.49813662847840556,10415.5943530264
-2017-03-03,0.8660254037844388,-0.4999999999999998,1.0,6.123233995736766e-17,False,False,0.8755389928586301,0.4831474640149688,10810.9496881223
-2017-03-04,0.49999999999999994,-0.8660254037844387,1.0,6.123233995736766e-17,False,False,0.8837203473619977,0.4680153284438345,10284.3553297787
-2017-03-05,1.2246467991473532e-16,-1.0,1.0,6.123233995736766e-17,False,False,0.8916401947968874,0.45274469960737107,9802.28460433018
-2017-03-06,0.0,1.0,1.0,6.123233995736766e-17,False,False,0.8992961915530253,0.4373400963303323,6251.50712115299
-2017-03-07,0.49999999999999994,0.8660254037844387,1.0,6.123233995736766e-17,False,False,0.9066860720977985,0.4218060770826634,5363.40773281181
+2017-02-27,0.0,1.0,0.8660254037844386,0.5000000000000001,False,False,0.8402480696078469,0.5422021592729845,7557.6640007467
+2017-02-28,0.49999999999999994,0.8660254037844387,0.8660254037844386,0.5000000000000001,False,False,0.8494504794814806,0.527668345562516,10415.5943530264
+2017-03-01,0.8660254037844386,0.5000000000000001,1.0,6.123233995736766e-17,False,False,0.8584015232918917,0.5129783862992281,10810.9496881223
+2017-03-02,1.0,6.123233995736766e-17,1.0,6.123233995736766e-17,False,False,0.8670985522812081,0.49813662847840556,10284.3553297787
+2017-03-03,0.8660254037844388,-0.4999999999999998,1.0,6.123233995736766e-17,False,False,0.8755389928586301,0.4831474640149688,9802.28460433018
+2017-03-04,0.49999999999999994,-0.8660254037844387,1.0,6.123233995736766e-17,False,False,0.8837203473619977,0.4680153284438345,6251.50712115299
+2017-03-05,1.2246467991473532e-16,-1.0,1.0,6.123233995736766e-17,False,False,0.8916401947968874,0.45274469960737107,5363.40773281181
+2017-03-06,0.0,1.0,1.0,6.123233995736766e-17,False,False,0.8992961915530253,0.4373400963303323,8882.555153888954
+2017-03-07,0.49999999999999994,0.8660254037844387,1.0,6.123233995736766e-17,False,False,0.9066860720977985,0.4218060770826634,9165.196971170077
 2017-03-08,0.8660254037844386,0.5000000000000001,1.0,6.123233995736766e-17,False,False,0.9138076496466615,0.40614723863057883,10224.5412492967
 2017-03-09,1.0,6.123233995736766e-17,1.0,6.123233995736766e-17,False,False,0.9206588168102428,0.3903682146763026,11454.4761017059
 2017-03-10,0.8660254037844388,-0.4999999999999998,1.0,6.123233995736766e-17,False,False,0.9272375462179537,0.374473674486883,10527.8304315466
@@ -94,13 +96,14 @@ date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annu
 2017-04-13,1.0,6.123233995736766e-17,0.8660254037844388,-0.4999999999999998,False,False,0.9798567700958665,-0.1997015525660636,10556.7271978649
 2017-04-14,0.8660254037844388,-0.4999999999999998,0.8660254037844388,-0.4999999999999998,False,False,0.9762766110869321,-0.2165270852448144,10436.7719350187
 2017-04-15,0.49999999999999994,-0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,False,0.9724075561219968,-0.2332885440754552,5782.02623294065
-2017-04-17,0.0,1.0,0.8660254037844388,-0.4999999999999998,False,False,0.9638074231367307,-0.26659942067929354,5167.43009449842
-2017-04-18,0.49999999999999994,0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,False,0.9590788900341131,-0.28313898122818354,9105.44323721686
-2017-04-19,0.8660254037844386,0.5000000000000001,0.8660254037844388,-0.4999999999999998,False,False,0.9540665500578387,-0.2995947563972602,9960.97196821151
-2017-04-20,1.0,6.123233995736766e-17,0.8660254037844388,-0.4999999999999998,False,False,0.9487718864399473,-0.31596187665790293,9800.57241672557
-2017-04-21,0.8660254037844388,-0.4999999999999998,0.8660254037844388,-0.4999999999999998,False,False,0.9431964659565866,-0.33223549871590435,10637.8350053683
-2017-04-22,0.49999999999999994,-0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,False,0.9373419384643772,-0.3484108069446808,9189.50839233891
-2017-04-23,1.2246467991473532e-16,-1.0,0.8660254037844388,-0.4999999999999998,False,False,0.9312100364121947,-0.3644830148102912,5797.11172509805
+2017-04-16,1.2246467991473532e-16,-1.0,0.8660254037844388,-0.4999999999999998,False,False,0.9682507501166718,-0.2499809690726521,5167.43009449842
+2017-04-17,0.0,1.0,0.8660254037844388,-0.4999999999999998,False,False,0.9638074231367307,-0.26659942067929354,9105.44323721686
+2017-04-18,0.49999999999999994,0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,False,0.9590788900341131,-0.28313898122818354,9960.97196821151
+2017-04-19,0.8660254037844386,0.5000000000000001,0.8660254037844388,-0.4999999999999998,False,False,0.9540665500578387,-0.2995947563972602,9800.57241672557
+2017-04-20,1.0,6.123233995736766e-17,0.8660254037844388,-0.4999999999999998,False,False,0.9487718864399473,-0.31596187665790293,10637.8350053683
+2017-04-21,0.8660254037844388,-0.4999999999999998,0.8660254037844388,-0.4999999999999998,False,False,0.9431964659565866,-0.33223549871590435,9189.50839233891
+2017-04-22,0.49999999999999994,-0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,False,0.9373419384643772,-0.3484108069446808,5797.11172509805
+2017-04-23,1.2246467991473532e-16,-1.0,0.8660254037844388,-0.4999999999999998,False,False,0.9312100364121947,-0.3644830148102912,9163.630074809089
 2017-04-24,0.0,1.0,0.8660254037844388,-0.4999999999999998,False,False,0.9248025743285089,-0.38044736628785164,11089.7635229944
 2017-04-25,0.49999999999999994,0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,False,0.9181214482844365,-0.39629913726891813,11359.8813102486
 2017-04-26,0.8660254037844386,0.5000000000000001,0.8660254037844388,-0.4999999999999998,False,False,0.9111686353326631,-0.41203363695942635,11493.6553638906
@@ -314,13 +317,14 @@ date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annu
 2017-11-20,0.0,1.0,-0.5000000000000004,0.8660254037844384,False,False,-0.6515303615068021,0.75862255966641,8431.72105718264
 2017-11-21,0.49999999999999994,0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,False,-0.6383844591746642,0.7697176640069211,9007.75811319213
 2017-11-22,0.8660254037844386,0.5000000000000001,-0.5000000000000004,0.8660254037844384,False,False,-0.6250496486104377,0.7805849965070866,8991.0311530717
-2017-11-24,0.8660254037844388,-0.4999999999999998,-0.5000000000000004,0.8660254037844384,False,False,-0.5978291414589552,0.801623551065242,9770.96110601502
-2017-11-25,0.49999999999999994,-0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,False,-0.5839514998576998,0.8117885474764613,8226.7623654536
-2017-11-26,1.2246467991473532e-16,-1.0,-0.5000000000000004,0.8660254037844384,False,False,-0.5699010576127624,0.821713322596059,5154.90919050322
-2017-11-27,0.0,1.0,-0.5000000000000004,0.8660254037844384,False,False,-0.5556819724760489,0.8313949395234058,4890.16815015908
-2017-11-28,0.49999999999999994,0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,False,-0.5412984521036173,0.8408305333122887,8708.29763190802
-2017-11-29,0.8660254037844386,0.5000000000000001,-0.5000000000000004,0.8660254037844384,False,False,-0.5267547528105542,0.8500173118187018,9177.80480201161
-2017-11-30,1.0,6.123233995736766e-17,-0.5000000000000004,0.8660254037844384,False,False,-0.5120551783114646,0.8589525565270845,6554.91339706477
+2017-11-23,1.0,6.123233995736766e-17,-0.5000000000000004,0.8660254037844384,False,False,-0.6115298757991053,0.7912213413483807,9770.96110601502
+2017-11-24,0.8660254037844388,-0.4999999999999998,-0.5000000000000004,0.8660254037844384,False,False,-0.5978291414589552,0.801623551065242,8226.7623654536
+2017-11-25,0.49999999999999994,-0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,False,-0.5839514998576998,0.8117885474764613,5154.90919050322
+2017-11-26,1.2246467991473532e-16,-1.0,-0.5000000000000004,0.8660254037844384,False,False,-0.5699010576127624,0.821713322596059,4890.16815015908
+2017-11-27,0.0,1.0,-0.5000000000000004,0.8660254037844384,False,False,-0.5556819724760489,0.8313949395234058,8708.29763190802
+2017-11-28,0.49999999999999994,0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,False,-0.5412984521036173,0.8408305333122887,9177.80480201161
+2017-11-29,0.8660254037844386,0.5000000000000001,-0.5000000000000004,0.8660254037844384,False,False,-0.5267547528105542,0.8500173118187018,6554.91339706477
+2017-11-30,1.0,6.123233995736766e-17,-0.5000000000000004,0.8660254037844384,False,False,-0.5120551783114646,0.8589525565270845,7236.831099490939
 2017-12-01,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,False,False,-0.4972040784469378,0.8676336233547726,3610.44683301299
 2017-12-02,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,False,False,-0.4822058478963526,0.8760579434344281,4625.60840173454
 2017-12-03,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,False,-0.46706492487743034,0.8842230238742036,4928.07305791736
@@ -345,13 +349,14 @@ date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annu
 2017-12-22,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,False,False,-0.15845177496083634,0.9873667175937016,8122.46225790476
 2017-12-23,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,False,False,-0.14144406768794202,0.9899462489023779,5029.02929040772
 2017-12-24,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,False,-0.12439450483979998,0.9922328391893008,5165.32456260275
-2017-12-26,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,False,False,-0.09019000427034513,0.9959245770286599,8362.3791144942
-2017-12-27,0.8660254037844386,0.5000000000000001,-2.4492935982947064e-16,1.0,False,False,-0.07304518821104744,0.9973286321364752,8684.31762173736
-2017-12-28,1.0,6.123233995736766e-17,-2.4492935982947064e-16,1.0,False,False,-0.05587875690537313,0.9984375616565666,8506.02265921701
-2017-12-29,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,False,False,-0.03869579017652689,0.9992510374388481,8589.44010397679
-2017-12-30,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,False,False,-0.021501372740813793,0.999768818762748,6110.57359367784
-2017-12-31,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,False,-0.004300592702981974,0.9999907524084426,4291.51589812807
-2018-01-01,0.0,1.0,0.49999999999999994,0.8660254037844387,True,False,0.017201575418260506,0.9998520419557735,1714.86855131384
+2017-12-25,0.0,1.0,-2.4492935982947064e-16,1.0,False,False,-0.10730813165636247,0.9942258118156161,8362.3791144942
+2017-12-26,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,False,False,-0.09019000427034513,0.9959245770286599,8684.31762173736
+2017-12-27,0.8660254037844386,0.5000000000000001,-2.4492935982947064e-16,1.0,False,False,-0.07304518821104744,0.9973286321364752,8506.02265921701
+2017-12-28,1.0,6.123233995736766e-17,-2.4492935982947064e-16,1.0,False,False,-0.05587875690537313,0.9984375616565666,8589.44010397679
+2017-12-29,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,False,False,-0.03869579017652689,0.9992510374388481,6110.57359367784
+2017-12-30,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,False,False,-0.021501372740813793,0.999768818762748,4291.51589812807
+2017-12-31,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,False,-0.004300592702981974,0.9999907524084426,1714.86855131384
+2018-01-01,0.0,1.0,0.49999999999999994,0.8660254037844387,True,False,0.017201575418260506,0.9998520419557735,5813.563191065511
 2018-01-02,0.49999999999999994,0.8660254037844387,0.49999999999999994,0.8660254037844387,True,False,0.03439806061360801,0.9994082116062598,4842.08463819334
 2018-01-03,0.8660254037844386,0.5000000000000001,0.49999999999999994,0.8660254037844387,True,False,0.051584366869408364,0.9986686402879997,6282.28654958781
 2018-01-04,1.0,6.123233995736766e-17,0.49999999999999994,0.8660254037844387,True,False,0.06875540848113941,0.9976335468520451,5744.02529838607
@@ -441,13 +446,14 @@ date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annu
 2018-03-29,1.0,6.123233995736766e-17,1.0,6.123233995736766e-17,True,False,0.998376906467142,0.056952196034039754,10366.833132712
 2018-03-30,0.8660254037844388,-0.4999999999999998,1.0,6.123233995736766e-17,True,False,0.9992088560679753,0.03977011384605573,9743.63003806847
 2018-03-31,0.49999999999999994,-0.8660254037844387,1.0,6.123233995736766e-17,True,False,0.9997451236925734,0.022576263041545485,6170.1831540281
-2018-04-02,0.0,1.0,0.8660254037844388,-0.4999999999999998,True,False,0.9999300657962057,-0.011826390691832589,5389.16632424554
-2018-04-03,0.49999999999999994,0.8660254037844387,0.8660254037844388,-0.4999999999999998,True,False,0.9995786855478958,-0.029025013321975237,8899.61291361492
-2018-04-04,0.8660254037844386,0.5000000000000001,0.8660254037844388,-0.4999999999999998,True,False,0.9989315138848577,-0.046215046983708354,7277.40399057202
-2018-04-05,1.0,6.123233995736766e-17,0.8660254037844388,-0.4999999999999998,True,False,0.9979887423155984,-0.06339140486951039,9179.04150839164
-2018-04-06,0.8660254037844388,-0.4999999999999998,0.8660254037844388,-0.4999999999999998,True,False,0.9967506498213929,-0.08054900421874167,10234.8784046617
-2018-04-07,0.49999999999999994,-0.8660254037844387,0.8660254037844388,-0.4999999999999998,True,False,0.9952176027737294,-0.09768276782171599,8115.07582625966
-2018-04-08,1.2246467991473532e-16,-1.0,0.8660254037844388,-0.4999999999999998,True,False,0.9933900548258936,-0.11478762552212701,5210.51672027864
+2018-04-01,1.2246467991473532e-16,-1.0,0.8660254037844388,-0.4999999999999998,True,False,0.9999855506507186,0.005375731557583884,5389.16632424554
+2018-04-02,0.0,1.0,0.8660254037844388,-0.4999999999999998,True,False,0.9999300657962057,-0.011826390691832589,8899.61291361492
+2018-04-03,0.49999999999999994,0.8660254037844387,0.8660254037844388,-0.4999999999999998,True,False,0.9995786855478958,-0.029025013321975237,7277.40399057202
+2018-04-04,0.8660254037844386,0.5000000000000001,0.8660254037844388,-0.4999999999999998,True,False,0.9989315138848577,-0.046215046983708354,9179.04150839164
+2018-04-05,1.0,6.123233995736766e-17,0.8660254037844388,-0.4999999999999998,True,False,0.9979887423155984,-0.06339140486951039,10234.8784046617
+2018-04-06,0.8660254037844388,-0.4999999999999998,0.8660254037844388,-0.4999999999999998,True,False,0.9967506498213929,-0.08054900421874167,8115.07582625966
+2018-04-07,0.49999999999999994,-0.8660254037844387,0.8660254037844388,-0.4999999999999998,True,False,0.9952176027737294,-0.09768276782171599,5210.51672027864
+2018-04-08,1.2246467991473532e-16,-1.0,0.8660254037844388,-0.4999999999999998,True,False,0.9933900548258936,-0.11478762552212701,8335.401044217046
 2018-04-09,0.0,1.0,0.8660254037844388,-0.4999999999999998,True,False,0.9912685467787256,-0.13185851571739118,9849.39956549746
 2018-04-10,0.49999999999999994,0.8660254037844387,0.8660254037844388,-0.4999999999999998,True,False,0.9888537064205883,-0.1488903868564548,9891.88503880554
 2018-04-11,0.8660254037844386,0.5000000000000001,0.8660254037844388,-0.4999999999999998,True,False,0.986146248341595,-0.16587819893463143,10027.3690541616
@@ -675,13 +681,14 @@ date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annu
 2018-11-19,0.0,1.0,-0.5000000000000004,0.8660254037844384,True,False,-0.6644834655228552,0.7473029667054297,9740.19749955851
 2018-11-20,0.49999999999999994,0.8660254037844387,-0.5000000000000004,0.8660254037844384,True,False,-0.6515303615068021,0.75862255966641,10094.368764844
 2018-11-21,0.8660254037844386,0.5000000000000001,-0.5000000000000004,0.8660254037844384,True,False,-0.6383844591746642,0.7697176640069211,11055.7928204953
-2018-11-23,0.8660254037844388,-0.4999999999999998,-0.5000000000000004,0.8660254037844384,True,False,-0.6115298757991053,0.7912213413483807,10103.4854134585
-2018-11-24,0.49999999999999994,-0.8660254037844387,-0.5000000000000004,0.8660254037844384,True,False,-0.5978291414589552,0.801623551065242,10070.3103728901
-2018-11-25,1.2246467991473532e-16,-1.0,-0.5000000000000004,0.8660254037844384,True,False,-0.5839514998576998,0.8117885474764613,6905.88613567193
-2018-11-26,0.0,1.0,-0.5000000000000004,0.8660254037844384,True,False,-0.5699010576127624,0.821713322596059,5725.42019630836
-2018-11-27,0.49999999999999994,0.8660254037844387,-0.5000000000000004,0.8660254037844384,True,False,-0.5556819724760489,0.8313949395234058,11081.4217837028
-2018-11-28,0.8660254037844386,0.5000000000000001,-0.5000000000000004,0.8660254037844384,True,False,-0.5412984521036173,0.8408305333122887,10166.4751114252
-2018-11-29,1.0,6.123233995736766e-17,-0.5000000000000004,0.8660254037844384,True,False,-0.5267547528105542,0.8500173118187018,7530.12551400349
+2018-11-22,1.0,6.123233995736766e-17,-0.5000000000000004,0.8660254037844384,True,False,-0.6250496486104377,0.7805849965070866,10103.4854134585
+2018-11-23,0.8660254037844388,-0.4999999999999998,-0.5000000000000004,0.8660254037844384,True,False,-0.6115298757991053,0.7912213413483807,10070.3103728901
+2018-11-24,0.49999999999999994,-0.8660254037844387,-0.5000000000000004,0.8660254037844384,True,False,-0.5978291414589552,0.801623551065242,6905.88613567193
+2018-11-25,1.2246467991473532e-16,-1.0,-0.5000000000000004,0.8660254037844384,True,False,-0.5839514998576998,0.8117885474764613,5725.42019630836
+2018-11-26,0.0,1.0,-0.5000000000000004,0.8660254037844384,True,False,-0.5699010576127624,0.821713322596059,11081.4217837028
+2018-11-27,0.49999999999999994,0.8660254037844387,-0.5000000000000004,0.8660254037844384,True,False,-0.5556819724760489,0.8313949395234058,10166.4751114252
+2018-11-28,0.8660254037844386,0.5000000000000001,-0.5000000000000004,0.8660254037844384,True,False,-0.5412984521036173,0.8408305333122887,7530.12551400349
+2018-11-29,1.0,6.123233995736766e-17,-0.5000000000000004,0.8660254037844384,True,False,-0.5267547528105542,0.8500173118187018,8561.13964435585
 2018-11-30,0.8660254037844388,-0.4999999999999998,-0.5000000000000004,0.8660254037844384,True,False,-0.5120551783114646,0.8589525565270845,4700.68323546675
 2018-12-01,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,True,False,-0.4972040784469378,0.8676336233547726,4897.28391629194
 2018-12-02,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,True,False,-0.4822058478963526,0.8760579434344281,5785.35010893042
@@ -707,13 +714,14 @@ date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annu
 2018-12-22,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,True,False,-0.15845177496083634,0.9873667175937016,6620.55214337436
 2018-12-23,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,True,False,-0.14144406768794202,0.9899462489023779,5141.78834141441
 2018-12-24,0.0,1.0,-2.4492935982947064e-16,1.0,True,False,-0.12439450483979998,0.9922328391893008,10865.9599414149
-2018-12-26,0.8660254037844386,0.5000000000000001,-2.4492935982947064e-16,1.0,True,False,-0.09019000427034513,0.9959245770286599,9485.64743677938
-2018-12-27,1.0,6.123233995736766e-17,-2.4492935982947064e-16,1.0,True,False,-0.07304518821104744,0.9973286321364752,9362.75982318266
-2018-12-28,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,True,False,-0.05587875690537313,0.9984375616565666,9574.34661297328
-2018-12-29,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,True,False,-0.03869579017652689,0.9992510374388481,8184.19089079468
-2018-12-30,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,True,False,-0.021501372740813793,0.999768818762748,5324.27400855047
-2018-12-31,0.0,1.0,-2.4492935982947064e-16,1.0,True,False,-0.004300592702981974,0.9999907524084426,5014.93684036782
-2019-01-01,0.49999999999999994,0.8660254037844387,0.49999999999999994,0.8660254037844387,False,True,0.017201575418260506,0.9998520419557735,2498.83251005468
+2018-12-25,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,True,False,-0.10730813165636247,0.9942258118156161,9485.64743677938
+2018-12-26,0.8660254037844386,0.5000000000000001,-2.4492935982947064e-16,1.0,True,False,-0.09019000427034513,0.9959245770286599,9362.75982318266
+2018-12-27,1.0,6.123233995736766e-17,-2.4492935982947064e-16,1.0,True,False,-0.07304518821104744,0.9973286321364752,9574.34661297328
+2018-12-28,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,True,False,-0.05587875690537313,0.9984375616565666,8184.19089079468
+2018-12-29,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,True,False,-0.03869579017652689,0.9992510374388481,5324.27400855047
+2018-12-30,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,True,False,-0.021501372740813793,0.999768818762748,5014.93684036782
+2018-12-31,0.0,1.0,-2.4492935982947064e-16,1.0,True,False,-0.004300592702981974,0.9999907524084426,2498.83251005468
+2019-01-01,0.49999999999999994,0.8660254037844387,0.49999999999999994,0.8660254037844387,False,True,0.017201575418260506,0.9998520419557735,6588.000141671883
 2019-01-02,0.8660254037844386,0.5000000000000001,0.49999999999999994,0.8660254037844387,False,True,0.03439806061360801,0.9994082116062598,6606.9210357883
 2019-01-03,1.0,6.123233995736766e-17,0.49999999999999994,0.8660254037844387,False,True,0.051584366869408364,0.9986686402879997,7416.2774022699
 2019-01-04,0.8660254037844388,-0.4999999999999998,0.49999999999999994,0.8660254037844387,False,True,0.06875540848113941,0.9976335468520451,7132.98660610133
@@ -823,13 +831,14 @@ date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annu
 2019-04-18,1.0,6.123233995736766e-17,0.8660254037844388,-0.4999999999999998,False,True,0.9590788900341131,-0.28313898122818354,10848.8236611701
 2019-04-19,0.8660254037844388,-0.4999999999999998,0.8660254037844388,-0.4999999999999998,False,True,0.9540665500578387,-0.2995947563972602,10548.7483939305
 2019-04-20,0.49999999999999994,-0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,True,0.9487718864399473,-0.31596187665790293,6110.72454464911
-2019-04-22,0.0,1.0,0.8660254037844388,-0.4999999999999998,False,True,0.9373419384643772,-0.3484108069446808,5799.03327214624
-2019-04-23,0.49999999999999994,0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,True,0.9312100364121947,-0.3644830148102912,6482.49526012702
-2019-04-24,0.8660254037844386,0.5000000000000001,0.8660254037844388,-0.4999999999999998,False,True,0.9248025743285089,-0.38044736628785164,11344.1655469815
-2019-04-25,1.0,6.123233995736766e-17,0.8660254037844388,-0.4999999999999998,False,True,0.9181214482844365,-0.39629913726891813,10873.058807869
-2019-04-26,0.8660254037844388,-0.4999999999999998,0.8660254037844388,-0.4999999999999998,False,True,0.9111686353326631,-0.41203363695942635,11283.3667637131
-2019-04-27,0.49999999999999994,-0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,True,0.9039461929224011,-0.42764620926777464,8662.28169327976
-2019-04-28,1.2246467991473532e-16,-1.0,0.8660254037844388,-0.4999999999999998,False,True,0.8964562582905575,-0.44313223418263453,5810.93765961046
+2019-04-21,1.2246467991473532e-16,-1.0,0.8660254037844388,-0.4999999999999998,False,True,0.9431964659565866,-0.33223549871590435,5799.03327214624
+2019-04-22,0.0,1.0,0.8660254037844388,-0.4999999999999998,False,True,0.9373419384643772,-0.3484108069446808,6482.49526012702
+2019-04-23,0.49999999999999994,0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,True,0.9312100364121947,-0.3644830148102912,11344.1655469815
+2019-04-24,0.8660254037844386,0.5000000000000001,0.8660254037844388,-0.4999999999999998,False,True,0.9248025743285089,-0.38044736628785164,10873.058807869
+2019-04-25,1.0,6.123233995736766e-17,0.8660254037844388,-0.4999999999999998,False,True,0.9181214482844365,-0.39629913726891813,11283.3667637131
+2019-04-26,0.8660254037844388,-0.4999999999999998,0.8660254037844388,-0.4999999999999998,False,True,0.9111686353326631,-0.41203363695942635,8662.28169327976
+2019-04-27,0.49999999999999994,-0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,True,0.9039461929224011,-0.42764620926777464,5810.93765961046
+2019-04-28,1.2246467991473532e-16,-1.0,0.8660254037844388,-0.4999999999999998,False,True,0.8964562582905575,-0.44313223418263453,9533.72905376603
 2019-04-29,0.0,1.0,0.8660254037844388,-0.4999999999999998,False,True,0.8887010478292915,-0.45848712914008766,12191.3444538979
 2019-04-30,0.49999999999999994,0.8660254037844387,0.8660254037844388,-0.4999999999999998,False,True,0.8806828564301475,-0.47370635037968,12358.6881467325
 2019-05-01,0.8660254037844386,0.5000000000000001,0.49999999999999994,-0.8660254037844387,False,True,0.8724040568049612,-0.48878539428899265,12727.0827802395
@@ -863,215 +872,3 @@ date,sin_day,cos_day,sin_month,cos_month,year_2018,year_2019,sin_annual,cos_annu
 2019-05-29,0.8660254037844386,0.5000000000000001,0.49999999999999994,-0.8660254037844387,False,True,0.5467112728685939,-0.8373211953118121,10397.3090690155
 2019-05-30,1.0,6.123233995736766e-17,0.49999999999999994,-0.8660254037844387,False,True,0.5322271388474397,-0.8466016020976267,10470.6449896596
 2019-05-31,0.8660254037844388,-0.4999999999999998,0.49999999999999994,-0.8660254037844387,False,True,0.5175855102531889,-0.8556314858488706,8643.36653904369
-2019-06-01,0.49999999999999994,-0.8660254037844387,1.2246467991473532e-16,-1.0,False,True,0.5027907197793043,-0.8644081744776648,5419.61983436335
-2019-06-02,1.2246467991473532e-16,-1.0,1.2246467991473532e-16,-1.0,False,True,0.4878471454423119,-0.8729290708206411,4610.88340917781
-2019-06-03,0.0,1.0,1.2246467991473532e-16,-1.0,False,True,0.47275920928627757,-0.881191653407483,5031.43558128854
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-2019-11-09,0.49999999999999994,-0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,True,-0.7825970017677834,0.6225286602431057,6493.1259397372
-2019-11-10,1.2246467991473532e-16,-1.0,-0.5000000000000004,0.8660254037844384,False,True,-0.7717727365467837,0.6358984534680745,6473.05267324596
-2019-11-11,0.0,1.0,-0.5000000000000004,0.8660254037844384,False,True,-0.7607200913564104,0.6490800741100397,11744.2769597104
-2019-11-12,0.49999999999999994,0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,True,-0.7494423368521954,0.6620696215153815,10927.8549515678
-2019-11-13,0.8660254037844386,0.5000000000000001,-0.5000000000000004,0.8660254037844384,False,True,-0.7379428103031386,0.6748632518680403,11055.5876467356
-2019-11-14,1.0,6.123233995736766e-17,-0.5000000000000004,0.8660254037844384,False,True,-0.7262249146041543,0.6874571793269664,11346.207223041
-2019-11-15,0.8660254037844388,-0.4999999999999998,-0.5000000000000004,0.8660254037844384,False,True,-0.7142921172691034,0.699847677146407,10229.2662371576
-2019-11-16,0.49999999999999994,-0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,True,-0.7021479494046978,0.7120310787787131,6261.22425670238
-2019-11-17,1.2246467991473532e-16,-1.0,-0.5000000000000004,0.8660254037844384,False,True,-0.6897960046655887,0.7240037789593305,6842.67122477602
-2019-11-18,0.0,1.0,-0.5000000000000004,0.8660254037844384,False,True,-0.6772399381909491,0.7357622347736527,11122.0974940318
-2019-11-19,0.49999999999999994,0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,True,-0.6644834655228552,0.7473029667054297,10521.6921421032
-2019-11-20,0.8660254037844386,0.5000000000000001,-0.5000000000000004,0.8660254037844384,False,True,-0.6515303615068021,0.75862255966641,10834.4886500883
-2019-11-21,1.0,6.123233995736766e-17,-0.5000000000000004,0.8660254037844384,False,True,-0.6383844591746642,0.7697176640069211,11805.1096038449
-2019-11-22,0.8660254037844388,-0.4999999999999998,-0.5000000000000004,0.8660254037844384,False,True,-0.6250496486104377,0.7805849965070866,10157.4147481868
-2019-11-23,0.49999999999999994,-0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,True,-0.6115298757991053,0.7912213413483807,6529.91581871318
-2019-11-24,1.2246467991473532e-16,-1.0,-0.5000000000000004,0.8660254037844384,False,True,-0.5978291414589552,0.801623551065242,5731.86352206865
-2019-11-25,0.0,1.0,-0.5000000000000004,0.8660254037844384,False,True,-0.5839514998576998,0.8117885474764613,11508.6000926397
-2019-11-26,0.49999999999999994,0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,True,-0.5699010576127624,0.821713322596059,10541.6369296844
-2019-11-27,0.8660254037844386,0.5000000000000001,-0.5000000000000004,0.8660254037844384,False,True,-0.5556819724760489,0.8313949395234058,11483.5861198684
-2019-11-29,0.8660254037844388,-0.4999999999999998,-0.5000000000000004,0.8660254037844384,False,True,-0.5267547528105542,0.8500173118187018,11390.4772844634
-2019-11-30,0.49999999999999994,-0.8660254037844387,-0.5000000000000004,0.8660254037844384,False,True,-0.5120551783114646,0.8589525565270845,10810.0851064581
-2019-12-01,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,True,-0.4972040784469378,0.8676336233547726,6406.61025731396
-2019-12-02,0.0,1.0,-2.4492935982947064e-16,1.0,False,True,-0.4822058478963526,0.8760579434344281,6001.23629315481
-2019-12-03,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.46706492487743034,0.8842230238742036,11425.9513696843
-2019-12-04,0.8660254037844386,0.5000000000000001,-2.4492935982947064e-16,1.0,False,True,-0.451785789832884,0.8921264484954345,10406.98574402
-2019-12-05,1.0,6.123233995736766e-17,-2.4492935982947064e-16,1.0,False,True,-0.4363729641045924,0.8997658785476209,8234.00408194648
-2019-12-06,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,False,True,-0.4208310085956556,0.9071390534005044,4531.71981099334
-2019-12-07,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.4051645224207563,0.9142437912130223,5047.9727268244
-2019-12-08,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,True,-0.3893781415452015,0.9210779895789526,5862.84374636922
-2019-12-09,0.0,1.0,-2.4492935982947064e-16,1.0,False,True,-0.37347653741307074,0.9276396261490467,10985.5552688977
-2019-12-10,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.35746441556486136,0.9339267592294762,11133.1774906625
-2019-12-11,0.8660254037844386,0.5000000000000001,-2.4492935982947064e-16,1.0,False,True,-0.34134651424503615,0.9399375283564133,11279.8518706721
-2019-12-12,1.0,6.123233995736766e-17,-2.4492935982947064e-16,1.0,False,True,-0.3251276029999093,0.9456701548465688,11782.1323456674
-2019-12-13,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,False,True,-0.30881248126625377,0.9511229423235357,10736.4761202962
-2019-12-14,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.29240597695107734,0.9562942772197719,7256.03206913425
-2019-12-15,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,True,-0.27591294500296043,0.9611826292540837,6517.55242474872
-2019-12-16,0.0,1.0,-2.4492935982947064e-16,1.0,False,True,-0.25933826597540555,0.9657865518844574,11377.3960111352
-2019-12-17,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.2426868445825954,0.9701046827361175,11266.504378762
-2019-12-18,0.8660254037844386,0.5000000000000001,-2.4492935982947064e-16,1.0,False,True,-0.22596360824801906,0.974135744004672,10862.0382545371
-2019-12-19,1.0,6.123233995736766e-17,-2.4492935982947064e-16,1.0,False,True,-0.2091735056463564,0.9778785428342386,11997.645582189
-2019-12-20,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,False,True,-0.19232150523909483,0.9813319716704275,10203.0771720997
-2019-12-21,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.17541259380427843,0.9844950085880858,7373.98819820622
-2019-12-22,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,True,-0.15845177496083634,0.9873667175937016,6226.60872428798
-2019-12-23,0.0,1.0,-2.4492935982947064e-16,1.0,False,True,-0.14144406768794202,0.9899462489023779,10620.2088165777
-2019-12-24,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.12439450483979998,0.9922328391893008,9913.06916155541
-2019-12-26,1.0,6.123233995736766e-17,-2.4492935982947064e-16,1.0,False,True,-0.09019000427034513,0.9959245770286599,9050.11562071116
-2019-12-27,0.8660254037844388,-0.4999999999999998,-2.4492935982947064e-16,1.0,False,True,-0.07304518821104744,0.9973286321364752,9727.69636396227
-2019-12-28,0.49999999999999994,-0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.05587875690537313,0.9984375616565666,8750.25722135605
-2019-12-29,1.2246467991473532e-16,-1.0,-2.4492935982947064e-16,1.0,False,True,-0.03869579017652689,0.9992510374388481,4896.75699680111
-2019-12-30,0.0,1.0,-2.4492935982947064e-16,1.0,False,True,-0.021501372740813793,0.999768818762748,4479.99872572649
-2019-12-31,0.49999999999999994,0.8660254037844387,-2.4492935982947064e-16,1.0,False,True,-0.004300592702981974,0.9999907524084426,7646.75189067266
diff --git a/event-features/get-relevant-event-features-for-demand-forecasting.ipynb b/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
index 35eeabf..f962883 100644
--- a/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
+++ b/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
@@ -44,6 +44,9 @@
     "from io import StringIO\n",
     "from functools import reduce\n",
     "\n",
+    "from sklearn.model_selection import TimeSeriesSplit\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import mean_absolute_percentage_error\n",
     "import xgboost as xgb\n",
     "import plotly.graph_objects as go\n",
     "\n",
@@ -326,7 +329,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "bEl8Tv3rPFg status: ready\n"
+      "YDwlR4fSkG4 status: ready\n"
      ]
     }
    ],
@@ -362,14 +365,18 @@
     "    Post-process rank features.\n",
     "    \"\"\"\n",
     "    rank_feature_cols = [col for col in df.columns if \"phq_rank\" in col]\n",
-    "    rank_features = list(\n",
-    "        set(col.rsplit(\"_rank_levels_\", 1)[0] for col in rank_feature_cols)\n",
-    "    )\n",
+    "    rank_features = set(col.rsplit(\"_rank_levels\", 1)[0] for col in rank_feature_cols)\n",
+    "\n",
+    "    def find_max_rank(row):\n",
+    "        for idx, value in enumerate(row[::-1], start=0):\n",
+    "            if value > 0:\n",
+    "                return len(row) - idx\n",
+    "        return 0\n",
     "\n",
     "    for feature in rank_features:\n",
     "        related_columns = [col for col in rank_feature_cols if col.startswith(feature)]\n",
-    "        df[f\"{feature}_stats_wsum\"] = sum(\n",
-    "            df[col] * int(col.split(\"_rank_levels_\")[1]) for col in related_columns\n",
+    "        df[f\"{feature}_stats_max\"] = df[related_columns].apply(\n",
+    "            lambda row: find_max_rank(row) * 20, axis=1\n",
     "        )\n",
     "        df = df.drop(columns=related_columns)\n",
     "\n",
@@ -449,18 +456,16 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>date</th>\n",
-       "      <th>phq_attendance_school_holidays_stats_sum</th>\n",
+       "      <th>phq_attendance_community_hospitality_stats_sum</th>\n",
+       "      <th>phq_attendance_concerts_hospitality_stats_sum</th>\n",
        "      <th>phq_attendance_conferences_hospitality_stats_sum</th>\n",
        "      <th>phq_attendance_expos_hospitality_stats_sum</th>\n",
        "      <th>phq_attendance_festivals_hospitality_stats_sum</th>\n",
        "      <th>phq_attendance_performing_arts_hospitality_stats_sum</th>\n",
+       "      <th>phq_attendance_sports_hospitality_stats_sum</th>\n",
        "      <th>phq_impact_observances_hospitality_stats_max</th>\n",
        "      <th>phq_impact_public_holidays_hospitality_stats_max</th>\n",
-       "      <th>phq_impact_severe_weather_air_quality_retail_stats_max</th>\n",
-       "      <th>phq_impact_severe_weather_blizzard_retail_stats_max</th>\n",
        "      <th>...</th>\n",
-       "      <th>phq_impact_severe_weather_cold_wave_snow_retail_stats_max</th>\n",
-       "      <th>phq_impact_severe_weather_cold_wave_storm_retail_stats_max</th>\n",
        "      <th>phq_impact_severe_weather_dust_retail_stats_max</th>\n",
        "      <th>phq_impact_severe_weather_dust_storm_retail_stats_max</th>\n",
        "      <th>phq_impact_severe_weather_flood_retail_stats_max</th>\n",
@@ -469,19 +474,21 @@
        "      <th>phq_impact_severe_weather_thunderstorm_retail_stats_max</th>\n",
        "      <th>phq_impact_severe_weather_tornado_retail_stats_max</th>\n",
        "      <th>phq_impact_severe_weather_tropical_storm_retail_stats_max</th>\n",
+       "      <th>phq_rank_academic_exam_stats_max</th>\n",
+       "      <th>phq_rank_academic_holiday_stats_max</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2017-01-09</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1936</td>\n",
+       "      <td>3719</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>13383</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>19812</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
@@ -499,13 +506,13 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2017-01-10</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2405</td>\n",
+       "      <td>6135</td>\n",
        "      <td>145</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>19597</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>6935</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
@@ -523,13 +530,13 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2017-01-11</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1558</td>\n",
+       "      <td>22420</td>\n",
        "      <td>100</td>\n",
        "      <td>142</td>\n",
        "      <td>0</td>\n",
        "      <td>22086</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>6935</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
@@ -547,15 +554,15 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2017-01-12</td>\n",
-       "      <td>0</td>\n",
+       "      <td>2654</td>\n",
+       "      <td>6860</td>\n",
        "      <td>100</td>\n",
        "      <td>567</td>\n",
        "      <td>0</td>\n",
        "      <td>20406</td>\n",
+       "      <td>26115</td>\n",
        "      <td>9</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -571,15 +578,15 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2017-01-13</td>\n",
-       "      <td>0</td>\n",
+       "      <td>5507</td>\n",
+       "      <td>8748</td>\n",
        "      <td>100</td>\n",
        "      <td>2833</td>\n",
        "      <td>0</td>\n",
        "      <td>24046</td>\n",
+       "      <td>31000</td>\n",
        "      <td>30</td>\n",
        "      <td>7</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -594,16 +601,23 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
+       "<p>5 rows × 25 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "        date  phq_attendance_school_holidays_stats_sum  \\\n",
-       "0 2017-01-09                                         0   \n",
-       "1 2017-01-10                                         0   \n",
-       "2 2017-01-11                                         0   \n",
-       "3 2017-01-12                                         0   \n",
-       "4 2017-01-13                                         0   \n",
+       "        date  phq_attendance_community_hospitality_stats_sum  \\\n",
+       "0 2017-01-09                                            1936   \n",
+       "1 2017-01-10                                            2405   \n",
+       "2 2017-01-11                                            1558   \n",
+       "3 2017-01-12                                            2654   \n",
+       "4 2017-01-13                                            5507   \n",
+       "\n",
+       "   phq_attendance_concerts_hospitality_stats_sum  \\\n",
+       "0                                           3719   \n",
+       "1                                           6135   \n",
+       "2                                          22420   \n",
+       "3                                           6860   \n",
+       "4                                           8748   \n",
        "\n",
        "   phq_attendance_conferences_hospitality_stats_sum  \\\n",
        "0                                                 0   \n",
@@ -633,6 +647,13 @@
        "3                                              20406      \n",
        "4                                              24046      \n",
        "\n",
+       "   phq_attendance_sports_hospitality_stats_sum  \\\n",
+       "0                                        19812   \n",
+       "1                                         6935   \n",
+       "2                                         6935   \n",
+       "3                                        26115   \n",
+       "4                                        31000   \n",
+       "\n",
        "   phq_impact_observances_hospitality_stats_max  \\\n",
        "0                                             0   \n",
        "1                                             0   \n",
@@ -640,40 +661,12 @@
        "3                                             9   \n",
        "4                                            30   \n",
        "\n",
-       "   phq_impact_public_holidays_hospitality_stats_max  \\\n",
-       "0                                                 0   \n",
-       "1                                                 0   \n",
-       "2                                                 0   \n",
-       "3                                                 0   \n",
-       "4                                                 7   \n",
-       "\n",
-       "   phq_impact_severe_weather_air_quality_retail_stats_max  \\\n",
-       "0                                                  0        \n",
-       "1                                                  0        \n",
-       "2                                                  0        \n",
-       "3                                                  0        \n",
-       "4                                                  0        \n",
-       "\n",
-       "   phq_impact_severe_weather_blizzard_retail_stats_max  ...  \\\n",
-       "0                                                  0    ...   \n",
-       "1                                                  0    ...   \n",
-       "2                                                  0    ...   \n",
-       "3                                                  0    ...   \n",
-       "4                                                  0    ...   \n",
-       "\n",
-       "   phq_impact_severe_weather_cold_wave_snow_retail_stats_max  \\\n",
-       "0                                                  0           \n",
-       "1                                                  0           \n",
-       "2                                                  0           \n",
-       "3                                                  0           \n",
-       "4                                                  0           \n",
-       "\n",
-       "   phq_impact_severe_weather_cold_wave_storm_retail_stats_max  \\\n",
-       "0                                                  0            \n",
-       "1                                                  0            \n",
-       "2                                                  0            \n",
-       "3                                                  0            \n",
-       "4                                                  0            \n",
+       "   phq_impact_public_holidays_hospitality_stats_max  ...  \\\n",
+       "0                                                 0  ...   \n",
+       "1                                                 0  ...   \n",
+       "2                                                 0  ...   \n",
+       "3                                                 0  ...   \n",
+       "4                                                 7  ...   \n",
        "\n",
        "   phq_impact_severe_weather_dust_retail_stats_max  \\\n",
        "0                                                0   \n",
@@ -724,14 +717,21 @@
        "3                                                  0    \n",
        "4                                                  0    \n",
        "\n",
-       "   phq_impact_severe_weather_tropical_storm_retail_stats_max  \n",
-       "0                                                  0          \n",
-       "1                                                  0          \n",
-       "2                                                  0          \n",
-       "3                                                  0          \n",
-       "4                                                  0          \n",
+       "   phq_impact_severe_weather_tropical_storm_retail_stats_max  \\\n",
+       "0                                                  0           \n",
+       "1                                                  0           \n",
+       "2                                                  0           \n",
+       "3                                                  0           \n",
+       "4                                                  0           \n",
+       "\n",
+       "   phq_rank_academic_exam_stats_max  phq_rank_academic_holiday_stats_max  \n",
+       "0                                 0                                    0  \n",
+       "1                                 0                                    0  \n",
+       "2                                 0                                    0  \n",
+       "3                                 0                                    0  \n",
+       "4                                 0                                    0  \n",
        "\n",
-       "[5 rows x 21 columns]"
+       "[5 rows x 25 columns]"
       ]
      },
      "execution_count": 8,
@@ -741,7 +741,7 @@
    ],
    "source": [
     "START = \"2017-01-09\"\n",
-    "END = \"2020-01-31\"\n",
+    "END = \"2019-06-30\"\n",
     "\n",
     "# fetch features for the analysis\n",
     "event_features_df = get_features(\n",
@@ -789,6 +789,9 @@
    "outputs": [],
    "source": [
     "def split_data(df):\n",
+    "    \"\"\"\n",
+    "    Split data into train and test sets.\n",
+    "    \"\"\"\n",
     "    df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
     "    df = df.sort_values(\"date\")\n",
     "\n",
@@ -799,12 +802,31 @@
     "\n",
     "\n",
     "def train_model(x_train, y_train):\n",
-    "    model = xgb.XGBRegressor(objective=\"reg:squarederror\", random_state=42)\n",
-    "    model.fit(x_train, y_train)\n",
-    "    return model\n",
+    "    \"\"\"\n",
+    "    Train an XGBoost model.\n",
+    "    \"\"\"\n",
+    "    tscv = TimeSeriesSplit(n_splits=5)\n",
+    "    param_grid = {\n",
+    "        \"n_estimators\": [50, 100],\n",
+    "        \"learning_rate\": [0.01, 0.1, 0.3],\n",
+    "        \"max_depth\": [5, 6, 7],\n",
+    "    }\n",
+    "    grid_search = GridSearchCV(\n",
+    "        estimator=xgb.XGBRegressor(),\n",
+    "        param_grid=param_grid,\n",
+    "        cv=tscv,\n",
+    "        scoring=\"neg_mean_squared_error\",\n",
+    "    )\n",
+    "    grid_search.fit(x_train, y_train)\n",
+    "    best_model = grid_search.best_estimator_\n",
+    "\n",
+    "    return best_model\n",
     "\n",
     "\n",
     "def plot_results(train, test, y_train, y_test, y_pred, feature_set, accuracy):\n",
+    "    \"\"\"\n",
+    "    Plot actual vs. forecasted demand.\n",
+    "    \"\"\"\n",
     "    plotting_cutoff_date = test[\"date\"].max() - pd.DateOffset(months=3)\n",
     "    train = train[train[\"date\"] >= plotting_cutoff_date]\n",
     "    test = test[test[\"date\"] >= plotting_cutoff_date]\n",
@@ -841,7 +863,7 @@
     "        title=(\n",
     "            f\"<b>Actual vs. Forecasted Demand</b><br>\"\n",
     "            f\"<sub>Features: {feature_set.replace('_', ' ')} | \"\n",
-    "            f\"MAPE: {round(accuracy, 2)}%</sub>\"\n",
+    "            f\"MAPE: {accuracy*100:.2f}%</sub>\"\n",
     "        ),\n",
     "        xaxis_title=\"Date\",\n",
     "        yaxis_title=\"Demand\",\n",
@@ -851,12 +873,10 @@
     "    fig.show()\n",
     "\n",
     "\n",
-    "def calculate_forecast_error(y_test, y_pred):\n",
-    "    mape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100\n",
-    "    return mape\n",
-    "\n",
-    "\n",
     "def calculate_forecast_uplift(base_metrics, new_metrics):\n",
+    "    \"\"\"\n",
+    "    Calculate uplift in forecast metrics.\n",
+    "    \"\"\"\n",
     "    uplifts = {}\n",
     "    for metric in base_metrics:\n",
     "        base_value = base_metrics[metric]\n",
@@ -887,190 +907,194 @@
          "name": "Actual Demand",
          "type": "scatter",
          "x": [
-          "2019-09-30T00:00:00",
-          "2019-10-01T00:00:00",
-          "2019-10-02T00:00:00",
-          "2019-10-03T00:00:00",
-          "2019-10-04T00:00:00",
-          "2019-10-05T00:00:00",
-          "2019-10-06T00:00:00",
-          "2019-10-07T00:00:00",
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-          "2019-10-09T00:00:00",
-          "2019-10-10T00:00:00",
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-          "2019-10-14T00:00:00",
-          "2019-10-15T00:00:00",
-          "2019-10-16T00:00:00",
-          "2019-10-17T00:00:00",
-          "2019-10-18T00:00:00",
-          "2019-10-19T00:00:00",
-          "2019-10-20T00:00:00",
-          "2019-10-21T00:00:00",
-          "2019-10-22T00:00:00",
-          "2019-10-23T00:00:00",
-          "2019-10-24T00:00:00",
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-          "2019-10-26T00:00:00",
-          "2019-10-27T00:00:00",
-          "2019-10-28T00:00:00",
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-          "2019-10-30T00:00:00",
-          "2019-10-31T00:00:00",
-          "2019-11-01T00:00:00",
-          "2019-11-02T00:00:00",
-          "2019-11-03T00:00:00",
-          "2019-11-04T00:00:00",
-          "2019-11-05T00:00:00",
-          "2019-11-06T00:00:00",
-          "2019-11-07T00:00:00",
-          "2019-11-08T00:00:00",
-          "2019-11-09T00:00:00",
-          "2019-11-10T00:00:00",
-          "2019-11-11T00:00:00",
-          "2019-11-12T00:00:00",
-          "2019-11-13T00:00:00",
-          "2019-11-14T00:00:00",
-          "2019-11-15T00:00:00",
-          "2019-11-16T00:00:00",
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-          "2019-11-18T00:00:00",
-          "2019-11-19T00:00:00",
-          "2019-11-20T00:00:00",
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-          "2019-11-23T00:00:00",
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-          "2019-11-30T00:00:00",
-          "2019-12-01T00:00:00",
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+          "2019-02-28T00:00:00",
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         {
@@ -1081,22 +1105,24 @@
          "name": "Forecasted Demand",
          "type": "scatter",
          "x": [
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-          "2019-12-26T00:00:00",
-          "2019-12-27T00:00:00",
-          "2019-12-28T00:00:00",
-          "2019-12-29T00:00:00",
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@@ -1114,8 +1140,8 @@
            "width": 2
           },
           "type": "line",
-          "x0": "2019-12-24T00:00:00",
-          "x1": "2019-12-24T00:00:00",
+          "x0": "2019-05-24T00:00:00",
+          "x1": "2019-05-24T00:00:00",
           "xref": "x",
           "y0": 0,
           "y1": 1,
@@ -1939,7 +1965,7 @@
          }
         },
         "title": {
-         "text": "<b>Actual vs. Forecasted Demand</b><br><sub>Features: existing and event features | MAPE: 7.79%</sub>"
+         "text": "<b>Actual vs. Forecasted Demand</b><br><sub>Features: existing and event features | MAPE: 7.80%</sub>"
         },
         "xaxis": {
          "title": {
@@ -1972,190 +1998,194 @@
          "name": "Actual Demand",
          "type": "scatter",
          "x": [
-          "2019-09-30T00:00:00",
-          "2019-10-01T00:00:00",
-          "2019-10-02T00:00:00",
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-          "2019-10-04T00:00:00",
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-          "2019-10-11T00:00:00",
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         },
         {
@@ -2166,22 +2196,24 @@
          "name": "Forecasted Demand",
          "type": "scatter",
          "x": [
-          "2019-12-24T00:00:00",
-          "2019-12-26T00:00:00",
-          "2019-12-27T00:00:00",
-          "2019-12-28T00:00:00",
-          "2019-12-29T00:00:00",
-          "2019-12-30T00:00:00",
-          "2019-12-31T00:00:00"
+          "2019-05-24T00:00:00",
+          "2019-05-25T00:00:00",
+          "2019-05-26T00:00:00",
+          "2019-05-27T00:00:00",
+          "2019-05-28T00:00:00",
+          "2019-05-29T00:00:00",
+          "2019-05-30T00:00:00",
+          "2019-05-31T00:00:00"
          ],
          "y": [
-          8237.9892578125,
-          8389.2958984375,
-          8405.71484375,
-          5482.3310546875,
-          4860.89453125,
-          5135.40869140625,
-          5236.544921875
+          10070.8544921875,
+          5346.232421875,
+          4932.212890625,
+          9309.798828125,
+          8638.5146484375,
+          10070.720703125,
+          9931.93359375,
+          10606.232421875
          ]
         }
        ],
@@ -2199,8 +2231,8 @@
            "width": 2
           },
           "type": "line",
-          "x0": "2019-12-24T00:00:00",
-          "x1": "2019-12-24T00:00:00",
+          "x0": "2019-05-24T00:00:00",
+          "x1": "2019-05-24T00:00:00",
           "xref": "x",
           "y0": 0,
           "y1": 1,
@@ -3024,7 +3056,7 @@
          }
         },
         "title": {
-         "text": "<b>Actual vs. Forecasted Demand</b><br><sub>Features: existing features only | MAPE: 49.27%</sub>"
+         "text": "<b>Actual vs. Forecasted Demand</b><br><sub>Features: existing features only | MAPE: 17.21%</sub>"
         },
         "xaxis": {
          "title": {
@@ -3047,9 +3079,9 @@
      "output_type": "stream",
      "text": [
       "Forecast Error (MAPE):\n",
-      "--- Features: existing and event features: 7.79%\n",
-      "--- Features: existing features only: 49.27%\n",
-      "Forecast Accuracy Uplift (MAPE reduction): 84.20%\n"
+      "--- Features: existing and event features: 7.80%\n",
+      "--- Features: existing features only: 17.21%\n",
+      "Forecast Accuracy Uplift (MAPE reduction): 54.70%\n"
      ]
     }
    ],
@@ -3080,7 +3112,7 @@
     "    model = train_model(x_train=X_train, y_train=y_train)\n",
     "    y_pred = model.predict(X_test)\n",
     "\n",
-    "    mape = calculate_forecast_error(y_test=y_test, y_pred=y_pred)\n",
+    "    mape = mean_absolute_percentage_error(y_test, y_pred)\n",
     "\n",
     "    plot_results(\n",
     "        train=train,\n",
@@ -3098,7 +3130,7 @@
     "print(\"Forecast Error (MAPE):\")\n",
     "for features_set in results:\n",
     "    print(\n",
-    "        f\"--- Features: {features_set.replace('_',' ')}: {results[features_set]['mape']:.2f}%\"\n",
+    "        f\"--- Features: {features_set.replace('_',' ')}: {results[features_set]['mape']*100:.2f}%\"\n",
     "    )\n",
     "forecast_uplift = calculate_forecast_uplift(\n",
     "    base_metrics=results[\"existing_features_only\"],\n",

From 5e841aee7116a198fd1a4083ef7358a1039c7c87 Mon Sep 17 00:00:00 2001
From: Tania <>
Date: Wed, 30 Oct 2024 10:39:54 +1300
Subject: [PATCH 3/4] Fix test train split

---
 ...vent-features-for-demand-forecasting.ipynb | 54 +++++++++----------
 1 file changed, 25 insertions(+), 29 deletions(-)

diff --git a/event-features/get-relevant-event-features-for-demand-forecasting.ipynb b/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
index f962883..80bc868 100644
--- a/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
+++ b/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
@@ -796,8 +796,8 @@
     "    df = df.sort_values(\"date\")\n",
     "\n",
     "    cutoff_date = df[\"date\"].max() - pd.Timedelta(days=7)\n",
-    "    train = df[df[\"date\"] < cutoff_date]\n",
-    "    test = df[df[\"date\"] >= cutoff_date]\n",
+    "    train = df[df[\"date\"] <= cutoff_date]\n",
+    "    test = df[df[\"date\"] > cutoff_date]\n",
     "    return train, test\n",
     "\n",
     "\n",
@@ -1105,7 +1105,6 @@
          "name": "Forecasted Demand",
          "type": "scatter",
          "x": [
-          "2019-05-24T00:00:00",
           "2019-05-25T00:00:00",
           "2019-05-26T00:00:00",
           "2019-05-27T00:00:00",
@@ -1115,14 +1114,13 @@
           "2019-05-31T00:00:00"
          ],
          "y": [
-          10002.8115234375,
-          5234.97021484375,
-          4992.7265625,
-          6103.82080078125,
-          9988.640625,
-          10357.40234375,
-          10486.4150390625,
-          9629.0439453125
+          5288.1728515625,
+          4910.8935546875,
+          6160.04052734375,
+          9480.18359375,
+          9924.16015625,
+          10086.5908203125,
+          9116.5537109375
          ]
         }
        ],
@@ -1140,8 +1138,8 @@
            "width": 2
           },
           "type": "line",
-          "x0": "2019-05-24T00:00:00",
-          "x1": "2019-05-24T00:00:00",
+          "x0": "2019-05-25T00:00:00",
+          "x1": "2019-05-25T00:00:00",
           "xref": "x",
           "y0": 0,
           "y1": 1,
@@ -1965,7 +1963,7 @@
          }
         },
         "title": {
-         "text": "<b>Actual vs. Forecasted Demand</b><br><sub>Features: existing and event features | MAPE: 7.80%</sub>"
+         "text": "<b>Actual vs. Forecasted Demand</b><br><sub>Features: existing and event features | MAPE: 5.62%</sub>"
         },
         "xaxis": {
          "title": {
@@ -2196,7 +2194,6 @@
          "name": "Forecasted Demand",
          "type": "scatter",
          "x": [
-          "2019-05-24T00:00:00",
           "2019-05-25T00:00:00",
           "2019-05-26T00:00:00",
           "2019-05-27T00:00:00",
@@ -2206,14 +2203,13 @@
           "2019-05-31T00:00:00"
          ],
          "y": [
-          10070.8544921875,
-          5346.232421875,
-          4932.212890625,
-          9309.798828125,
-          8638.5146484375,
-          10070.720703125,
-          9931.93359375,
-          10606.232421875
+          5536.6044921875,
+          5148.919921875,
+          8783.162109375,
+          8190.251953125,
+          8821.2021484375,
+          9070.0244140625,
+          9048.9375
          ]
         }
        ],
@@ -2231,8 +2227,8 @@
            "width": 2
           },
           "type": "line",
-          "x0": "2019-05-24T00:00:00",
-          "x1": "2019-05-24T00:00:00",
+          "x0": "2019-05-25T00:00:00",
+          "x1": "2019-05-25T00:00:00",
           "xref": "x",
           "y0": 0,
           "y1": 1,
@@ -3056,7 +3052,7 @@
          }
         },
         "title": {
-         "text": "<b>Actual vs. Forecasted Demand</b><br><sub>Features: existing features only | MAPE: 17.21%</sub>"
+         "text": "<b>Actual vs. Forecasted Demand</b><br><sub>Features: existing features only | MAPE: 18.43%</sub>"
         },
         "xaxis": {
          "title": {
@@ -3079,9 +3075,9 @@
      "output_type": "stream",
      "text": [
       "Forecast Error (MAPE):\n",
-      "--- Features: existing and event features: 7.80%\n",
-      "--- Features: existing features only: 17.21%\n",
-      "Forecast Accuracy Uplift (MAPE reduction): 54.70%\n"
+      "--- Features: existing and event features: 5.62%\n",
+      "--- Features: existing features only: 18.43%\n",
+      "Forecast Accuracy Uplift (MAPE reduction): 69.49%\n"
      ]
     }
    ],

From de3db76c53393f7e1ffc138df9b3ac32a804b032 Mon Sep 17 00:00:00 2001
From: Tania <>
Date: Thu, 19 Dec 2024 11:09:50 +1300
Subject: [PATCH 4/4] Remove fields from Beam API request body to align with
 new best practices

---
 ...vent-features-for-demand-forecasting.ipynb | 33 -------------------
 1 file changed, 33 deletions(-)

diff --git a/event-features/get-relevant-event-features-for-demand-forecasting.ipynb b/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
index 80bc868..2d1a90e 100644
--- a/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
+++ b/event-features/get-relevant-event-features-for-demand-forecasting.ipynb
@@ -215,32 +215,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_suggested_radius(\n",
-    "    lat,\n",
-    "    lon,\n",
-    "    industry,\n",
-    "    access_token=ACCESS_TOKEN,\n",
-    "    suggested_radius_api_url=SUGGESTED_RADIUS_API_URL,\n",
-    "):\n",
-    "    \"\"\"\n",
-    "    Get a suggested radius for a location.\n",
-    "    \"\"\"\n",
-    "    if industry not in [\"restaurants\", \"retail\", \"accommodation\", \"parking\"]:\n",
-    "        industry = \"other\"\n",
-    "\n",
-    "    url = suggested_radius_api_url\n",
-    "    headers = {\"Authorization\": \"Bearer \" + access_token, \"Accept\": \"application/json\"}\n",
-    "    params = {\n",
-    "        \"location.origin\": f\"{lat},{lon}\",\n",
-    "        \"industry\": industry,\n",
-    "        \"radius_unit\": \"mi\",\n",
-    "    }\n",
-    "\n",
-    "    response = requests.get(url=url, headers=headers, params=params)\n",
-    "\n",
-    "    return response.json()\n",
-    "\n",
-    "\n",
     "def create_beam_analysis_id(\n",
     "    lat,\n",
     "    lon,\n",
@@ -252,8 +226,6 @@
     "    \"\"\"\n",
     "    Create a Beam Analysis ID for a location.\n",
     "    \"\"\"\n",
-    "    suggested_radius = get_suggested_radius(lat, lon, industry)\n",
-    "\n",
     "    url = f\"{beam_api_url}/analyses\"\n",
     "    headers = {\"Authorization\": \"Bearer \" + access_token, \"Accept\": \"application/json\"}\n",
     "    json = {\n",
@@ -263,11 +235,6 @@
     "                \"lat\": lat,\n",
     "                \"lon\": lon,\n",
     "            },\n",
-    "            \"radius\": suggested_radius[\"radius\"],\n",
-    "            \"unit\": suggested_radius[\"radius_unit\"],\n",
-    "        },\n",
-    "        \"rank\": {\n",
-    "            \"type\": \"phq\",\n",
     "        },\n",
     "        \"demand_type\": {\n",
     "            \"industry\": industry,\n",