{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hourly traffic volume prediction on Interstate 94\n",
    "\n",
    "### Multivariate time series prediction with getML\n",
    "\n",
    "In this tutorial, we demonstrate a time series application of getML. We predict the hourly traffic volume on I-94 westbound from Minneapolis-St Paul.\n",
    "We benchmark our results against [Facebook's Prophet](https://facebook.github.io/prophet/). getML's relational learning algorithms outperform Prophet's classical time series approach by ~15%.\n",
    "\n",
    "Summary:\n",
    "\n",
    "- Prediction type: __Regression model__\n",
    "- Domain: __Transportation__\n",
    "- Prediction target: __Hourly traffic volume__\n",
    "- Source data: __Multivariate time series, 5 components__\n",
    "- Population size: __24096__\n",
    "\n",
    "_Author: Sören Nikolaus_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background\n",
    "\n",
    "The dataset features some particularly interesting characteristics common for time series, which classical models may struggle to deal with appropriately. Such characteristics are:\n",
    "\n",
    "- High frequency (hourly)\n",
    "- Dependence on irregular events (holidays)\n",
    "- Strong and overlapping cycles (daily, weekly)\n",
    "- Anomalies\n",
    "- Multiple seasonalities\n",
    "\n",
    "\n",
    "The analysis is built on top of a dataset provided by the [MN Department of Transportation](https://www.dot.state.mn.us), with some data preparation done by [John Hogue](https://github.com/dreyco676/Anomaly_Detection_A_to_Z/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A web frontend for getML\n",
    "\n",
    "The getML monitor is a frontend built to support your work with getML. The getML monitor displays information such as the imported data frames, trained pipelines and allows easy data and feature exploration. You can launch the getML monitor [here](http://localhost:1709)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Where is this running?\n",
    "\n",
    "Your getML live session is running inside a docker container on [mybinder.org](https://mybinder.org/), a service built by the Jupyter community and funded by Google Cloud, OVH, GESIS Notebooks and the Turing Institute. As it is a free service, this session will shut down after 10 minutes of inactivity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get started with the analysis and set-up your session:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getML API version: 1.2.0\n",
      "\n",
      "Launched the getML engine. The log output will be stored in /home/patrick/.getML/logs/20220324095333.log.\n",
      "\n",
      "\n",
      "Loading pipelines...\n",
      "[========================================] 100%\n",
      "\n",
      "\n",
      "Connected to project 'interstate94'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href='http://localhost:1709/#/listprojects/interstate94/'>http://localhost:1709/#/listprojects/interstate94/</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import Image\n",
    "\n",
    "plt.style.use(\"seaborn\")\n",
    "%matplotlib inline\n",
    "\n",
    "import getml\n",
    "\n",
    "print(f\"getML API version: {getml.__version__}\\n\")\n",
    "\n",
    "getml.engine.launch()\n",
    "getml.engine.set_project(\"interstate94\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Download from source\n",
    "\n",
    "Downloading the raw data and convert it into a prediction ready format takes time. To get to the getML model building as fast as possible, we prepared the data for you and excluded the code from this notebook. It is made available in the example notebook featuring the full analysis. We only include data after 2016 and introduced a fixed train/test split at 80% of the available data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading traffic...\n",
      "[========================================] 100%\n"
     ]
    }
   ],
   "source": [
    "traffic = getml.datasets.load_interstate94()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
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       "          \n",
       "            <td class=\"categorical\">New Years Day</td>\n",
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       "            <td class=\"categorical\">1</td>\n",
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       "            <td class=\"categorical\">1</td>\n",
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       "            <td class=\"time_stamp\">2016-01-01 04:00:00</td>\n",
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       "        <th>24091</th>\n",
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       "            <td class=\"time_stamp\">2018-09-30 20:00:00</td>\n",
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       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">30</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">9</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">6</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">21</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2018</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>24094</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-09-30 22:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">1450</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">30</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">9</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">6</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">22</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2018</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>24095</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-09-30 23:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">954</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">30</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">9</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">6</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">23</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2018</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    24096 rows x 8 columns<br />\n",
       "    memory usage: 0.96 MB<br />\n",
       "    name: traffic<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    url: <a href=\"http://localhost:1709/#/getdataframe/interstate94/traffic/\" target=\"_blank\"> http://localhost:1709/#/getdataframe/interstate94/traffic/</a>\n",
       "  </p>\n"
      ],
      "text/plain": [
       " name                            ds   traffic_volume   holiday         day           month         weekday       hour       \n",
       " role                    time_stamp           target   categorical     categorical   categorical   categorical   categorical\n",
       " unit   time stamp, comparison only                                    day           month         weekday       hour       \n",
       "    0                    2016-01-01             1513   New Years Day   1             1             4             0          \n",
       "    1           2016-01-01 01:00:00             1550   New Years Day   1             1             4             1          \n",
       "    2           2016-01-01 02:00:00              993   New Years Day   1             1             4             2          \n",
       "    3           2016-01-01 03:00:00              719   New Years Day   1             1             4             3          \n",
       "    4           2016-01-01 04:00:00              533   New Years Day   1             1             4             4          \n",
       "                                ...              ...   ...             ...           ...           ...           ...        \n",
       "24091           2018-09-30 19:00:00             3543   No holiday      30            9             6             19         \n",
       "24092           2018-09-30 20:00:00             2781   No holiday      30            9             6             20         \n",
       "24093           2018-09-30 21:00:00             2159   No holiday      30            9             6             21         \n",
       "24094           2018-09-30 22:00:00             1450   No holiday      30            9             6             22         \n",
       "24095           2018-09-30 23:00:00              954   No holiday      30            9             6             23         \n",
       "\n",
       " name   year       \n",
       " role   categorical\n",
       " unit   year       \n",
       "    0   2016       \n",
       "    1   2016       \n",
       "    2   2016       \n",
       "    3   2016       \n",
       "    4   2016       \n",
       "        ...        \n",
       "24091   2018       \n",
       "24092   2018       \n",
       "24093   2018       \n",
       "24094   2018       \n",
       "24095   2018       \n",
       "\n",
       "\n",
       "24096 rows x 8 columns\n",
       "memory usage: 0.96 MB\n",
       "name: traffic\n",
       "type: getml.DataFrame\n",
       "url: http://localhost:1709/#/getdataframe/interstate94/traffic/"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Prepare data for getML\n",
    "\n",
    "The `getml.datasets.load_interstate94` method took care of the entire data preparation:\n",
    "* Downloads csv's from our servers into python\n",
    "* Converts csv's to getML [DataFrames]()\n",
    "* Sets [roles]() & [units]() to columns inside getML DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Data visualization__\n",
    "\n",
    "The first week of the original traffic time series is plotted below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_data = \"black\"\n",
    "col_getml = \"darkviolet\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fba75cb8850>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "# 2016/01/01 was a friday, we'd like to start the visualizations on a monday\n",
    "start = 72\n",
    "end = 72 + 168\n",
    "\n",
    "fig.suptitle(\n",
    "    \"Traffic volume for first full week of the training set\",\n",
    "    fontsize=14,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "ax.plot(\n",
    "    traffic[\"ds\"].to_numpy()[start:end],\n",
    "    traffic[\"traffic_volume\"].to_numpy()[start:end],\n",
    "    color=col_data,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Traffic__: population table\n",
    "\n",
    "To allow the algorithm to capture seasonal information, we include time components (such as the day of the week) as categorical variables. Note that we could have also used getML's Seasonal preprocessor (`getml.prepreprocessors.Seasonal()`), but in this case the information was already included in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Train/test split__\n",
    "\n",
    "We use [getML's split functionality](https://docs.getml.com/latest/api/getml.data.split.html) to retrieve a lazily evaluated split column, that we can supply to the time series api below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = getml.data.split.time(traffic, \"ds\", test=getml.data.time.datetime(2018, 3, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split columns are columns of mere strings that can be used to subset the data by forming bolean conditions over them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th>name</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"time_stamp\">                         ds</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">traffic_volume</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">holiday    </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">day        </th>\n",
       "        \n",
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       "        \n",
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       "        \n",
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       "        \n",
       "          <th class=\"categorical\">hour       </th>\n",
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       "    </tr>\n",
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       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">role</th>\n",
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       "            <th class=\"sub-header time_stamp\">                 time_stamp</th>\n",
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       "            <th class=\"sub-header target\">        target</th>\n",
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       "        \n",
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       "          \n",
       "            <th class=\"sub-header categorical\">categorical</th>\n",
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       "          \n",
       "            <th class=\"sub-header categorical\">categorical</th>\n",
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       "            <th class=\"sub-header categorical\">categorical</th>\n",
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       "            <th class=\"sub-header categorical\">categorical</th>\n",
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       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">unit</th>\n",
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       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">time stamp, comparison only</th>\n",
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       "            <th class=\"sub-header target\">              </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">           </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">day        </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">month      </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">weekday    </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">hour       </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">year       </th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">577</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">15</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">0</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2018</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15 01:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">354</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">15</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2018</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15 02:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">259</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">15</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2018</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15 03:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">360</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">15</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2018</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15 04:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">910</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">15</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">3</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">4</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2018</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>...</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">...</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    unknown number of rows<br />\n",
       "    \n",
       "    type: getml.data.View<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name                            ds   traffic_volume   holiday       day           month         weekday       hour       \n",
       "role                    time_stamp           target   categorical   categorical   categorical   categorical   categorical\n",
       "unit   time stamp, comparison only                                  day           month         weekday       hour       \n",
       "   0                    2018-03-15              577   No holiday    15            3             3             0          \n",
       "   1           2018-03-15 01:00:00              354   No holiday    15            3             3             1          \n",
       "   2           2018-03-15 02:00:00              259   No holiday    15            3             3             2          \n",
       "   3           2018-03-15 03:00:00              360   No holiday    15            3             3             3          \n",
       "   4           2018-03-15 04:00:00              910   No holiday    15            3             3             4          \n",
       " ...                           ...              ...   ...           ...           ...           ...           ...        \n",
       "\n",
       "name   year       \n",
       "role   categorical\n",
       "unit   year       \n",
       "   0   2018       \n",
       "   1   2018       \n",
       "   2   2018       \n",
       "   3   2018       \n",
       "   4   2018       \n",
       " ...   ...        \n",
       "\n",
       "\n",
       "unknown number of rows x 8 columns\n",
       "type: getml.data.View"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic[split == \"test\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Define relational model\n",
    "\n",
    "To start with relational learning, we need to specify the data model. We manually replicate the appropriate time series structure by setting time series related join conditions (`horizon`, `memory` and `allow_lagged_targets`). We use the [high-level time series api](https://docs.getml.com/latest/api/getml.data.TimeSeries.html) for this.\n",
    "\n",
    "Under the hood, the time series api abstracts away a self cross join of the population table (`traffic`) that allows getML's feature learning algorithms to learn patterns from past observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>data model</h2>\n",
       "            <div style='margin-top: 15px;'><h4>diagram</h4><br>\n",
       "            <div style=\"height:100px;width:660px;position:relative;\"><svg height=\"90\" width=\"650\"><rect y=\"0\" x=\"0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"73.8\" x=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">traffic</text><rect x=\"51\" y=\"10\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"67.0\" y1=\"10\" x2=\"67.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"83.0\" y1=\"10\" x2=\"83.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"26.0\" x2=\"99\" y2=\"26.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"42.0\" x2=\"99\" y2=\"42.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"0\" x=\"500\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"73.8\" x=\"575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">population</text><rect x=\"551\" y=\"10\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"567.0\" y1=\"10\" x2=\"567.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"583.0\" y1=\"10\" x2=\"583.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"26.0\" x2=\"599\" y2=\"26.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"42.0\" x2=\"599\" y2=\"42.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"150\" y1=\"43.0\" x2=\"490\" y2=\"43.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"500, 43.0 490, 37.0 490, 49.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"10.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"30.0\" x=\"324.0\" font-size=\"7pt\" >ds &lt;= ds</tspan><tspan y=\"40.0\" x=\"324.0\" font-size=\"7pt\" >Memory: 7.0 days</tspan><tspan y=\"50.0\" x=\"324.0\" font-size=\"7pt\" >Horizon: 1.0 hours</tspan><tspan y=\"60.0\" x=\"324.0\" font-size=\"7pt\" >Lagged targets allowed</tspan></text></svg></div>\n",
       "            </div>\n",
       "\n",
       "            <div style='margin-top: 15px;'><h4>staging</h4>\n",
       "            <style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
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       "  th:nth-child(1) {\n",
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       "  th.float {\n",
       "    text-align: right !important;\n",
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       "  td.float {\n",
       "    text-align: right !important;\n",
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       "  td.int {\n",
       "    text-align: right !important;\n",
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       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">data frames</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">staging table              </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">population</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">POPULATION__STAGING_TABLE_1</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">traffic</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">TRAFFIC__STAGING_TABLE_2</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "            </div>\n",
       "            \n",
       "<h2>container</h2>\n",
       "<div style='float: left; margin-right: 50px;'>\n",
       "    <h4>population</h4>\n",
       "    <style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.float {\n",
       "    text-align: right !important;\n",
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       "  th.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">subset</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">name   </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> rows</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">type</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">traffic</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">4800</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">View</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">traffic</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">19296</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">View</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div style='float: left;'>\n",
       "    <h4>peripheral</h4>\n",
       "    <style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
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       "  td {\n",
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       "  td.float {\n",
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       "  th.int {\n",
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       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">name   </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> rows</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">type     </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">traffic</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">24096</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "data model\n",
       "\n",
       "  population:\n",
       "    columns:\n",
       "    - holiday: categorical\n",
       "    - day: categorical\n",
       "    - month: categorical\n",
       "    - weekday: categorical\n",
       "    - hour: categorical\n",
       "    - ...\n",
       "\n",
       "    joins:\n",
       "    - right: 'traffic'\n",
       "      time_stamps: (population.ds, traffic.ds)\n",
       "      relationship: 'many-to-many'\n",
       "      memory: 604800.0\n",
       "      horizon: 3600.0\n",
       "      lagged_targets: True\n",
       "\n",
       "  traffic:\n",
       "    columns:\n",
       "    - holiday: categorical\n",
       "    - day: categorical\n",
       "    - month: categorical\n",
       "    - weekday: categorical\n",
       "    - hour: categorical\n",
       "    - ...\n",
       "\n",
       "\n",
       "container\n",
       "\n",
       "  population\n",
       "      subset   name       rows   type\n",
       "  0   test     traffic    4800   View\n",
       "  1   train    traffic   19296   View\n",
       "\n",
       "  peripheral\n",
       "      name       rows   type     \n",
       "  0   traffic   24096   DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series = getml.data.TimeSeries(\n",
    "    population=traffic,\n",
    "    split=split,\n",
    "    time_stamps=\"ds\",\n",
    "    horizon=getml.data.time.hours(1),\n",
    "    memory=getml.data.time.days(7),\n",
    "    lagged_targets=True,\n",
    ")\n",
    "\n",
    "time_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.Predictive modeling\n",
    "\n",
    "We loaded the data, defined the roles, units and the abstract data model. Next, we create a getML pipeline for relational learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 getML Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- #### 2.1.1  -->\n",
    "__Set-up of feature learners, selectors & predictor__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "relmt = getml.feature_learning.RelMT(\n",
    "    num_features=20,\n",
    "    loss_function=getml.feature_learning.loss_functions.SquareLoss,\n",
    "    seed=4367,\n",
    "    num_threads=1,\n",
    ")\n",
    "\n",
    "predictor = getml.predictors.XGBoostRegressor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Build the pipeline__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = getml.pipeline.Pipeline(\n",
    "    tags=[\"memory: 7d\", \"horizon: 1h\", \"relmt\"],\n",
    "    data_model=time_series.data_model,\n",
    "    feature_learners=[relmt],\n",
    "    predictors=[predictor],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data model...\n",
      "\n",
      "\n",
      "Staging...\n",
      "[========================================] 100%\n",
      "\n",
      "Checking...\n",
      "[========================================] 100%\n",
      "\n",
      "\n",
      "OK.\n",
      "\n",
      "\n",
      "Staging...\n",
      "[========================================] 100%\n",
      "\n",
      "RelMT: Training features...\n",
      "[========================================] 100%\n",
      "\n",
      "RelMT: Building features...\n",
      "[========================================] 100%\n",
      "\n",
      "XGBoost: Training as predictor...\n",
      "[========================================] 100%\n",
      "\n",
      "\n",
      "Trained pipeline.\n",
      "Time taken: 0h:5m:2.947328\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='population',\n",
       "         feature_learners=['RelMT'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function=None,\n",
       "         peripheral=['traffic'],\n",
       "         predictors=['XGBoostRegressor'],\n",
       "         preprocessors=[],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['memory: 7d', 'horizon: 1h', 'relmt', 'container-3HBAyM'])</pre><br><pre>url: <a href='http://localhost:1709/#/getpipeline/interstate94/FG5uXx/0/' target=\"_blank\">http://localhost:1709/#/getpipeline/interstate94/FG5uXx/0/</a></pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='population',\n",
       "         feature_learners=['RelMT'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function=None,\n",
       "         peripheral=['traffic'],\n",
       "         predictors=['XGBoostRegressor'],\n",
       "         preprocessors=[],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['memory: 7d', 'horizon: 1h', 'relmt', 'container-3HBAyM'])\n",
       "\n",
       "url: http://localhost:1709/#/getpipeline/interstate94/FG5uXx/0/"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(time_series.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Staging...\n",
      "[========================================] 100%\n",
      "\n",
      "RelMT: Building features...\n",
      "[========================================] 100%\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
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       "    text-align: right !important;\n",
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       "  td.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  th.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"datetime\">date time          </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">set used</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">target        </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">      mae</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">     rmse</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">rsquared</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2022-03-24 09:58:47</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">traffic_volume</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">201.298</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">295.6177</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9774</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2022-03-24 09:59:00</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">traffic_volume</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">183.2422</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">271.2663</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9814</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    date time             set used   target                 mae        rmse   rsquared\n",
       "0   2022-03-24 09:58:47   train      traffic_volume    201.298     295.6177     0.9774\n",
       "1   2022-03-24 09:59:00   test       traffic_volume    183.2422    271.2663     0.9814"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.score(time_series.test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Studying features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Feature correlations__\n",
    "\n",
    "Correlations of the calculated features with the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, correlations = pipe.features.correlations()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, correlations, color=col_getml)\n",
    "plt.title(\"Feature Correlations\")\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Correlations\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Feature importances__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, importances = pipe.features.importances()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, importances, color=col_getml)\n",
    "plt.title(\"Feature Importances\")\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Importances\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Visualizing the learned features__\n",
    "\n",
    "We can also transpile the features as SQL code. Here, we show the most important feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_11\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_11\" AS\n",
       "SELECT SUM( \n",
       "    CASE\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" > 6965.710287 ) AND ( t2.\"hour\" IN ( '0', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 3.056994616117964e-05 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.0004592958687265713 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.47446253771948e-05 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * -1.474462407374121e-05 + 5.7136772527002925e+01\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" > 6965.710287 ) AND ( t2.\"hour\" NOT IN ( '0', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23' ) OR t2.\"hour\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -0.0002728527078393066 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * -0.01052789409335724 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * 0.0001346563960090505 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * 0.0001346563960090505 + -1.6323514509683892e+02\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" <= 6965.710287 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t2.\"hour\" IN ( '0', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -6.450603601298901e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.4011052890935949 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -6.365908267767004e-06 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * -6.368337472002008e-06 + 3.5377949203029338e+01\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" <= 6965.710287 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t2.\"hour\" NOT IN ( '0', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23' ) OR t2.\"hour\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 3.830812354148993e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.8224585907500582 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.352120390568317e-05 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * -1.36062674844675e-05 + 1.0670459471423501e+03\n",
       "        ELSE NULL\n",
       "    END\n",
       ") AS \"feature_1_11\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"POPULATION__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\n",
       "ON 1 = 1\n",
       "WHERE t2.\"ds, '+1.000000 hours'\" <= t1.\"ds\"\n",
       "AND ( t2.\"ds, '+7.041667 days'\" > t1.\"ds\" OR t2.\"ds, '+7.041667 days'\" IS NULL )\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_11\";\\n\\nCREATE TABLE \"FEATURE_1_11\" AS\\nSELECT SUM( \\n    CASE\\n        WHEN ( t1.\"ds\" - t2.\"ds\" > 6965.710287 ) AND ( t2.\"hour\" IN ( \\'0\\', \\'6\\', \\'7\\', \\'8\\', \\'9\\', \\'10\\', \\'11\\', \\'12\\', \\'13\\', \\'14\\', \\'15\\', \\'16\\', \\'17\\', \\'18\\', \\'19\\', \\'20\\', \\'21\\', \\'22\\', \\'23\\' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 3.056994616117964e-05 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.0004592958687265713 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.47446253771948e-05 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * -1.474462407374121e-05 + 5.7136772527002925e+01\\n        WHEN ( t1.\"ds\" - t2.\"ds\" > 6965.710287 ) AND ( t2.\"hour\" NOT IN ( \\'0\\', \\'6\\', \\'7\\', \\'8\\', \\'9\\', \\'10\\', \\'11\\', \\'12\\', \\'13\\', \\'14\\', \\'15\\', \\'16\\', \\'17\\', \\'18\\', \\'19\\', \\'20\\', \\'21\\', \\'22\\', \\'23\\' ) OR t2.\"hour\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -0.0002728527078393066 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * -0.01052789409335724 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * 0.0001346563960090505 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * 0.0001346563960090505 + -1.6323514509683892e+02\\n        WHEN ( t1.\"ds\" - t2.\"ds\" <= 6965.710287 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t2.\"hour\" IN ( \\'0\\', \\'6\\', \\'7\\', \\'8\\', \\'9\\', \\'10\\', \\'11\\', \\'12\\', \\'13\\', \\'14\\', \\'15\\', \\'16\\', \\'17\\', \\'18\\', \\'19\\', \\'20\\', \\'21\\', \\'22\\', \\'23\\' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -6.450603601298901e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.4011052890935949 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -6.365908267767004e-06 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * -6.368337472002008e-06 + 3.5377949203029338e+01\\n        WHEN ( t1.\"ds\" - t2.\"ds\" <= 6965.710287 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t2.\"hour\" NOT IN ( \\'0\\', \\'6\\', \\'7\\', \\'8\\', \\'9\\', \\'10\\', \\'11\\', \\'12\\', \\'13\\', \\'14\\', \\'15\\', \\'16\\', \\'17\\', \\'18\\', \\'19\\', \\'20\\', \\'21\\', \\'22\\', \\'23\\' ) OR t2.\"hour\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 3.830812354148993e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.8224585907500582 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.352120390568317e-05 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * -1.36062674844675e-05 + 1.0670459471423501e+03\\n        ELSE NULL\\n    END\\n) AS \"feature_1_11\",\\n       t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\\nON 1 = 1\\nWHERE t2.\"ds, \\'+1.000000 hours\\'\" <= t1.\"ds\"\\nAND ( t2.\"ds, \\'+7.041667 days\\'\" > t1.\"ds\" OR t2.\"ds, \\'+7.041667 days\\'\" IS NULL )\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_importance = pipe.features.sort(by=\"importance\")\n",
    "by_importance[0].sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To showcase getML's ability to handle categorical data, we now look for features that contain information from the holiday column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            \n",
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       "            \n",
       "              <td class=\"float\">0.0066</td>\n",
       "            \n",
       "          \n",
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       "        <th>4</th>\n",
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       "            \n",
       "              <td class=\"str\">traffic_volume</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">feature_1_14</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9664</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.0038</td>\n",
       "            \n",
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       "              <td class=\"str\">traffic_volume</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">feature_1_1</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"float\">0.9653</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"float\">0.0029</td>\n",
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       "              <td class=\"str\">traffic_volume</td>\n",
       "            \n",
       "          \n",
       "            \n",
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       "            \n",
       "          \n",
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       "            \n",
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       "            \n",
       "              <td class=\"float\">0.0028</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"str\">feature_1_10</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9602</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.0025</td>\n",
       "            \n",
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       "        <th>9</th>\n",
       "          \n",
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       "              <td class=\"str\">traffic_volume</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"str\">feature_1_6</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9629</td>\n",
       "            \n",
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       "            \n",
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       "</table>"
      ],
      "text/plain": [
       "     target           name           correlation   importance\n",
       " 0   traffic_volume   feature_1_12        0.9597       0.0142\n",
       " 1   traffic_volume   feature_1_5         0.9669       0.0117\n",
       " 2   traffic_volume   feature_1_18        0.9627       0.0087\n",
       " 3   traffic_volume   feature_1_4         0.9634       0.0066\n",
       " 4   traffic_volume   feature_1_14        0.9664       0.0038\n",
       "     ...              ...                    ...          ...\n",
       " 6   traffic_volume   feature_1_1         0.9653       0.0029\n",
       " 7   traffic_volume   feature_1_3         0.9661       0.0028\n",
       " 8   traffic_volume   feature_1_10        0.9602       0.0025\n",
       " 9   traffic_volume   feature_1_8         0.9648       0.0002\n",
       "10   traffic_volume   feature_1_6         0.9629       0.0   "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_holiday = by_importance.filter(lambda feature: \"holiday\" in feature.sql)\n",
    "w_holiday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, getML features which incorporate information about holidays have a rather low importance. This is not that surprising given the fact that most information about holidays is fully reproducible from the extracted calendarial information that is already present. In other words: for the algorithm, it doesn't matter if the traffic is lower on every 4th of July of a given year or if there is a corresponding holiday named 'Independence Day'. Here is the SQL transpilation of the most important feature relying on information about holdidays anyway:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_12\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_12\" AS\n",
       "SELECT AVG( \n",
       "    CASE\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" > 604379.286214 ) AND ( t2.\"holiday\" IN ( 'No holiday' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 0.0003655426559629976 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 71.04907704233962 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * 0.0003659565812331032 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * 0.0003659565812330983 + -1.2102245006452505e+02\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" > 604379.286214 ) AND ( t2.\"holiday\" NOT IN ( 'No holiday' ) OR t2.\"holiday\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 7.966581896562819e-05 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 68.9956068955138 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * 7.975106805403055e-05 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * 7.975106805403055e-05 + 5.2980510009154398e+02\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" <= 604379.286214 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t1.\"ds\" - t2.\"ds\" > 6957.303371 ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -1.314712363235606e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.04717003289907078 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.317428773219315e-06 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * -1.317428773219332e-06 + 7.8257840351760217e+00\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" <= 604379.286214 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t1.\"ds\" - t2.\"ds\" <= 6957.303371 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -0.0001622247708287244 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 9.219195611541375 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -0.0001624142455173989 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * -0.0001624142455173987 + -9.1615088286977944e+00\n",
       "        ELSE NULL\n",
       "    END\n",
       ") AS \"feature_1_12\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"POPULATION__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\n",
       "ON 1 = 1\n",
       "WHERE t2.\"ds, '+1.000000 hours'\" <= t1.\"ds\"\n",
       "AND ( t2.\"ds, '+7.041667 days'\" > t1.\"ds\" OR t2.\"ds, '+7.041667 days'\" IS NULL )\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_12\";\\n\\nCREATE TABLE \"FEATURE_1_12\" AS\\nSELECT AVG( \\n    CASE\\n        WHEN ( t1.\"ds\" - t2.\"ds\" > 604379.286214 ) AND ( t2.\"holiday\" IN ( \\'No holiday\\' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 0.0003655426559629976 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 71.04907704233962 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * 0.0003659565812331032 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * 0.0003659565812330983 + -1.2102245006452505e+02\\n        WHEN ( t1.\"ds\" - t2.\"ds\" > 604379.286214 ) AND ( t2.\"holiday\" NOT IN ( \\'No holiday\\' ) OR t2.\"holiday\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 7.966581896562819e-05 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 68.9956068955138 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * 7.975106805403055e-05 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * 7.975106805403055e-05 + 5.2980510009154398e+02\\n        WHEN ( t1.\"ds\" - t2.\"ds\" <= 604379.286214 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t1.\"ds\" - t2.\"ds\" > 6957.303371 ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -1.314712363235606e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.04717003289907078 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.317428773219315e-06 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * -1.317428773219332e-06 + 7.8257840351760217e+00\\n        WHEN ( t1.\"ds\" - t2.\"ds\" <= 604379.286214 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t1.\"ds\" - t2.\"ds\" <= 6957.303371 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -0.0001622247708287244 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 9.219195611541375 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -0.0001624142455173989 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * -0.0001624142455173987 + -9.1615088286977944e+00\\n        ELSE NULL\\n    END\\n) AS \"feature_1_12\",\\n       t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\\nON 1 = 1\\nWHERE t2.\"ds, \\'+1.000000 hours\\'\" <= t1.\"ds\"\\nAND ( t2.\"ds, \\'+7.041667 days\\'\" > t1.\"ds\" OR t2.\"ds, \\'+7.041667 days\\'\" IS NULL )\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_holiday[0].sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Plot predictions & traffic volume vs. time__\n",
    "\n",
    "We now plot the predictions against the observed values of the target for the first 7 days of the testing set. You can see that the predictions closely follows the original series. RelMT was able to identify certain patterns in the series, including:\n",
    "- Day and night separation\n",
    "- The daily commuting peeks (on weekdays)\n",
    "- The decline on weekends\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Staging...\n",
      "[========================================] 100%\n",
      "\n",
      "RelMT: Building features...\n",
      "[========================================] 100%\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = pipe.predict(time_series.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fba74a8edf0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "# the test set starts at 2018/03/15 – a thursday; we introduce an offset to, once again, start on a monday\n",
    "start = 96\n",
    "end = 96 + 168\n",
    "\n",
    "actual = time_series.test.population[start:end].to_pandas()\n",
    "predicted = predictions[start:end]\n",
    "\n",
    "ax.plot(actual[\"ds\"], actual[\"traffic_volume\"], color=col_data, label=\"Actual\")\n",
    "ax.plot(actual[\"ds\"], predicted, color=col_getml, label=\"Predicted\")\n",
    "fig.suptitle(\n",
    "    \"Predicted vs. actual traffic volume for first full week of testing set\",\n",
    "    fontsize=14,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "fig.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Features\n",
    "\n",
    "The most important feature looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_11\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_11\" AS\n",
       "SELECT SUM( \n",
       "    CASE\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" > 6965.710287 ) AND ( t2.\"hour\" IN ( '0', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 3.056994616117964e-05 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.0004592958687265713 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.47446253771948e-05 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * -1.474462407374121e-05 + 5.7136772527002925e+01\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" > 6965.710287 ) AND ( t2.\"hour\" NOT IN ( '0', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23' ) OR t2.\"hour\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -0.0002728527078393066 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * -0.01052789409335724 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * 0.0001346563960090505 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * 0.0001346563960090505 + -1.6323514509683892e+02\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" <= 6965.710287 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t2.\"hour\" IN ( '0', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -6.450603601298901e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.4011052890935949 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -6.365908267767004e-06 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * -6.368337472002008e-06 + 3.5377949203029338e+01\n",
       "        WHEN ( t1.\"ds\" - t2.\"ds\" <= 6965.710287 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t2.\"hour\" NOT IN ( '0', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23' ) OR t2.\"hour\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 3.830812354148993e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.8224585907500582 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.352120390568317e-05 + COALESCE( t2.\"ds, '+1.000000 hours'\" - 1486339200, 0.0 ) * -1.36062674844675e-05 + 1.0670459471423501e+03\n",
       "        ELSE NULL\n",
       "    END\n",
       ") AS \"feature_1_11\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"POPULATION__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\n",
       "ON 1 = 1\n",
       "WHERE t2.\"ds, '+1.000000 hours'\" <= t1.\"ds\"\n",
       "AND ( t2.\"ds, '+7.041667 days'\" > t1.\"ds\" OR t2.\"ds, '+7.041667 days'\" IS NULL )\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_11\";\\n\\nCREATE TABLE \"FEATURE_1_11\" AS\\nSELECT SUM( \\n    CASE\\n        WHEN ( t1.\"ds\" - t2.\"ds\" > 6965.710287 ) AND ( t2.\"hour\" IN ( \\'0\\', \\'6\\', \\'7\\', \\'8\\', \\'9\\', \\'10\\', \\'11\\', \\'12\\', \\'13\\', \\'14\\', \\'15\\', \\'16\\', \\'17\\', \\'18\\', \\'19\\', \\'20\\', \\'21\\', \\'22\\', \\'23\\' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 3.056994616117964e-05 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.0004592958687265713 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.47446253771948e-05 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * -1.474462407374121e-05 + 5.7136772527002925e+01\\n        WHEN ( t1.\"ds\" - t2.\"ds\" > 6965.710287 ) AND ( t2.\"hour\" NOT IN ( \\'0\\', \\'6\\', \\'7\\', \\'8\\', \\'9\\', \\'10\\', \\'11\\', \\'12\\', \\'13\\', \\'14\\', \\'15\\', \\'16\\', \\'17\\', \\'18\\', \\'19\\', \\'20\\', \\'21\\', \\'22\\', \\'23\\' ) OR t2.\"hour\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -0.0002728527078393066 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * -0.01052789409335724 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * 0.0001346563960090505 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * 0.0001346563960090505 + -1.6323514509683892e+02\\n        WHEN ( t1.\"ds\" - t2.\"ds\" <= 6965.710287 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t2.\"hour\" IN ( \\'0\\', \\'6\\', \\'7\\', \\'8\\', \\'9\\', \\'10\\', \\'11\\', \\'12\\', \\'13\\', \\'14\\', \\'15\\', \\'16\\', \\'17\\', \\'18\\', \\'19\\', \\'20\\', \\'21\\', \\'22\\', \\'23\\' ) ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * -6.450603601298901e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.4011052890935949 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -6.365908267767004e-06 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * -6.368337472002008e-06 + 3.5377949203029338e+01\\n        WHEN ( t1.\"ds\" - t2.\"ds\" <= 6965.710287 OR t1.\"ds\" IS NULL OR t2.\"ds\" IS NULL ) AND ( t2.\"hour\" NOT IN ( \\'0\\', \\'6\\', \\'7\\', \\'8\\', \\'9\\', \\'10\\', \\'11\\', \\'12\\', \\'13\\', \\'14\\', \\'15\\', \\'16\\', \\'17\\', \\'18\\', \\'19\\', \\'20\\', \\'21\\', \\'22\\', \\'23\\' ) OR t2.\"hour\" IS NULL ) THEN COALESCE( t1.\"ds\" - 1486337853.612977, 0.0 ) * 3.830812354148993e-06 + COALESCE( t2.\"traffic_volume\" - 3302.204612593936, 0.0 ) * 0.8224585907500582 + COALESCE( t2.\"ds\" - 1486335600, 0.0 ) * -1.352120390568317e-05 + COALESCE( t2.\"ds, \\'+1.000000 hours\\'\" - 1486339200, 0.0 ) * -1.36062674844675e-05 + 1.0670459471423501e+03\\n        ELSE NULL\\n    END\\n) AS \"feature_1_11\",\\n       t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\\nON 1 = 1\\nWHERE t2.\"ds, \\'+1.000000 hours\\'\" <= t1.\"ds\"\\nAND ( t2.\"ds, \\'+7.041667 days\\'\" > t1.\"ds\" OR t2.\"ds, \\'+7.041667 days\\'\" IS NULL )\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.features.to_sql()[pipe.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 Productionization\n",
    "\n",
    "It is possible to productionize the pipeline by transpiling the features into production-ready SQL code. Please also refer to getML's `sqlite3` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a folder named interstate94_pipeline containing\n",
    "# the SQL code.\n",
    "pipe.features.to_sql().save(\"interstate94_pipeline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Conclusion\n",
    "\n",
    "__Benchmarks against Prophet__\n",
    "\n",
    "By design, Prophet isn't capable of delivering the 1-step ahead predictions we did with getML. To retrieve a benchmark in the 1-step case nonetheless, we mimic 1-step ahead predictions through cross-validating the model on a rolling origin. This gives Prophet an advantage as all information up to the origin is incorporated when *fitting* the model and a new fit is calculated for every 1-step ahead forecast. If you are interested in the full analysis please refer to the extended version of this [notebook](getml_examples/interstate94/interstate94.ipynb).\n",
    "\n",
    "\n",
    "__Results__\n",
    "\n",
    "We have benchmarked getML against Facebook’s Prophet library on a univariate time series with strong seasonal components.\n",
    "Prophet is made for exactly these sort of data sets, so you would expect this to be a home run for Prophet. The opposite is true - getML’s relational learning algorithms outperform Prophet's 1-step ahead predictions by ~15 percentage points:\n",
    "\n",
    "* R-squared Prophet: 83.3%\n",
    "* R-squared getML: 98.1%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Steps\n",
    "\n",
    "This tutorial went through the basics of applying getML to time series.\n",
    "\n",
    "If you are interested in further real-world applications of getML, head back to the [notebook overview](welcome.md) and choose one of the remaining examples.\n",
    "\n",
    "Here is some additional material from our [documentation](https://docs.getml.com/latest/) if you want to learn more about getML:\n",
    "* [Feature learning with Multirel](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html#multirel)\n",
    "* [Feature learning with Relboost](https://docs.getml.com/latest/user_guide/feature_engineering/feature_engineering.html#relboost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get in contact\n",
    "\n",
    "If you have any question schedule a [call with Alex](https://go.getml.com/meetings/alexander-uhlig/getml-demo), the co-founder of getML, or write us an [email](team@getml.com). Prefer a private demo of getML? Just contact us to make an appointment."
   ]
  }
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