consumer_expenditures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consumer expenditure - Why relational learning matters\n",
    "\n",
    "This example demonstrates how powerful a real relational learning algorithm can be. Based on a public-domain dataset on consumer behavior, we use a propostionalization algorithm to predict whether purchases were made as a gift. We show that with relational learning, we can get an AUC of over 90%. The generated features would have been impossible to build by hand or by using brute-force approaches.\n",
    "\n",
    "Summary:\n",
    "\n",
    "- Prediction type: __Classification model__\n",
    "- Domain: __Retail__\n",
    "- Prediction target: __If a purchase is a gift__ \n",
    "- Source data: __Relational data set, 4 tables__\n",
    "- Population size: __2.020.634__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "Relational learning is one of the most underappreciated fields of machine learning. Even though relational learning is very relevant to many real world data science projects, many data scientists don't even know what relational learning is. \n",
    "\n",
    "There are many subdomains of relational learning, but the most important one is extracting features from relational data: Most business data is relational, meaning that it is spread out over several relational tables. However, most machine learning algorithms require that the data be presented in the form of a single flat table. So we need to extract features from our relational data. Some people also call this data wrangling.\n",
    "\n",
    "Most data scientists we know extract features from relational data manually or by using crude, brute-force approaches (randomly generate thousands of features and then do a feature selection). This is very time-consuming and does not produce good features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The challenge\n",
    "\n",
    "The Consumer Expenditure Data Set is a public domain data set provided by the American Bureau of Labor Statistics (https://www.bls.gov/cex/pumd.htm). It includes the diary entries, where American consumers are asked to keep diaries of the products they have purchased each month.\n",
    "\n",
    "These consumer goods are categorized using a six-digit classification system the UCC. This system is hierarchical, meaning that every digit represents an increasingly granular category.\n",
    "\n",
    "For instance, all UCC codes beginning with ‘200’ represent beverages. UCC codes beginning with ‘20011’ represents beer and ‘200111’ represents ‘beer and ale’ and ‘200112’ represents ‘nonalcoholic beer’ (https://www.bls.gov/cex/pumd/ce_pumd_interview_diary_dictionary.xlsx).\n",
    "\n",
    "The diaries also contain a flag that indicates whether the product was purchased as a gift. The challenge is to predict that flag using other information in the diary entries.\n",
    "\n",
    "This can be done based on the following considerations:\n",
    "\n",
    "1. Some items are _less likely to be purchased as gifts_ than others (for instance, it is unlikely that toilet paper is ever purchased as a gift).\n",
    "\n",
    "2. Items that diverge from the _usual consumption patterns_ are more likely to be gifts.\n",
    "\n",
    "In total, there are three tables which we find interesting:\n",
    "\n",
    "1. EXPD, which contains information on the _consumer expenditures_, including the target variable GIFT.\n",
    "\n",
    "2. FMLD, which contains socio-demographic information on the _households_.\n",
    "\n",
    "3. MEMD, which contains socio-demographic information on each _member of the households_.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q \"getml==1.5.0\" \"matplotlib==3.9.2\" \"ipywidgets==8.1.5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getML API version: 1.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import getml\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "print(f\"getML API version: {getml.__version__}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching ./getML --allow-push-notifications=true --allow-remote-ips=true --home-directory=/home/user --in-memory=false --install=false --launch-browser=true --log=false --token=token in /home/user/.getML/getml-1.5.0-x64-linux...\n",
      "Launched the getML Engine. The log output will be stored in /home/user/.getML/logs/20240918134630.log.\n",
      "\u001b[2K  Loading pipelines... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Connected to project <span style=\"color: #008000; text-decoration-color: #008000\">'consumer_expenditures'</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Connected to project \u001b[32m'consumer_expenditures'\u001b[0m.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getml.engine.launch(in_memory=False, allow_remote_ips=True, token='token')\n",
    "getml.engine.set_project(\"consumer_expenditures\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 Download from source\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Connection(dbname='ConsumerExpenditures',\n",
       "           dialect='mysql',\n",
       "           host='relational.fel.cvut.cz',\n",
       "           port=3306)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = getml.database.connect_mysql(\n",
    "    host=\"relational.fel.cvut.cz\",\n",
    "    dbname=\"ConsumerExpenditures\",\n",
    "    port=3306,\n",
    "    user=\"guest\",\n",
    "    password=\"ctu-relational\"\n",
    ")\n",
    "\n",
    "conn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_if_needed(name):\n",
    "    \"\"\"\n",
    "    Loads the data from the relational learning\n",
    "    repository, if the data frame has not already\n",
    "    been loaded.\n",
    "    \"\"\"\n",
    "    if getml.data.exists(name):\n",
    "        return getml.data.load_data_frame(name)\n",
    "    data_frame = getml.data.DataFrame.from_db(\n",
    "        name=name,\n",
    "        table_name=name,\n",
    "        conn=conn\n",
    "    )\n",
    "    data_frame.save()\n",
    "    return data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "households = load_if_needed(\"HOUSEHOLDS\")\n",
    "household_members = load_if_needed(\"HOUSEHOLD_MEMBERS\")\n",
    "expenditures = load_if_needed(\"EXPENDITURES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6123</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6207</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">48</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03111051</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2015</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.6926</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6216</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6198</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6224</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6125</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6117</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6201</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">48</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03111052</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2015</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.2817</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.113</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.1128</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.1098</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.1116</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.1092</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.0951</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">37</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03111061</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>56807</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.4828</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.4106</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.3603</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.3958</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.377</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.3984</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.3769</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">67</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">04362582</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>56808</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.6644</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.5975</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6026</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.5949</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.596</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6002</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">52</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">04362661</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>56809</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.6639</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.597</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6021</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
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       "              <span class=\"left\">0</span\n",
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       "                >.5997</span\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.5995</span\n",
       "              >\n",
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       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">52</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">04362662</td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>56810</th>\n",
       "        \n",
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.05217</span\n",
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       "          \n",
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       "              \n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
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       "                >.03955</span\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
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       "            <td class=\"char-align unused_float\">\n",
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       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.02436</span\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
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       "                >.03558</span\n",
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       "              <span class=\"left\">72</span\n",
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       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "            <td class=\"unused_string\">04362671</td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>56811</th>\n",
       "        \n",
       "          \n",
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       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.1616</span\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.03925</span\n",
       "              >\n",
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       "          \n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.05741</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.04595</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.03789</span\n",
       "              >\n",
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       "          \n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.05746</span\n",
       "              >\n",
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       "          \n",
       "            \n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
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       "                >.03931</span\n",
       "              >\n",
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       "          \n",
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       "          \n",
       "            \n",
       "              \n",
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       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">72</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
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       "          \n",
       "            <td class=\"unused_string\">04362672</td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    56812 rows x 10 columns<br />\n",
       "    memory usage: 5.06 MB<br />\n",
       "    name: HOUSEHOLDS<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       " name           YEAR    INCOME_RANK   INCOME_RANK_1   INCOME_RANK_2   ...   INCOME_RANK_4   INCOME_RANK_5   INCOME_RANK_MEAN\n",
       " role   unused_float   unused_float    unused_float    unused_float   ...    unused_float    unused_float       unused_float\n",
       "    0           2015         0.3044         0.1448          0.1427    ...         0.1422          0.1382             0.127  \n",
       "    1           2015         0.3063         0.1462          0.1444    ...         0.1435          0.1395             0.1283 \n",
       "    2           2015         0.6931         0.6222          0.6204    ...         0.6131          0.6123             0.6207 \n",
       "    3           2015         0.6926         0.6216          0.6198    ...         0.6125          0.6117             0.6201 \n",
       "    4           2015         0.2817         0.113           0.1128    ...         0.1116          0.1092             0.0951 \n",
       "                 ...        ...            ...             ...                   ...             ...                ...     \n",
       "56807           2019         0.4828         0.4106          0.3603    ...         0.377           0.3984             0.3769 \n",
       "56808           2019         0.6644         0.5975          0.6026    ...         0.596           0.6002             0.6    \n",
       "56809           2019         0.6639         0.597           0.6021    ...         0.5955          0.5997             0.5995 \n",
       "56810           2019         0.162          0.05217         0.03955   ...         0.04607         0.02436            0.03558\n",
       "56811           2019         0.1616         0.03925         0.05741   ...         0.03789         0.05746            0.03931\n",
       "\n",
       " name        AGE_REF   HOUSEHOLD_ID \n",
       " role   unused_float   unused_string\n",
       "    0             66   03111041     \n",
       "    1             66   03111042     \n",
       "    2             48   03111051     \n",
       "    3             48   03111052     \n",
       "    4             37   03111061     \n",
       "                 ...   ...          \n",
       "56807             67   04362582     \n",
       "56808             52   04362661     \n",
       "56809             52   04362662     \n",
       "56810             72   04362671     \n",
       "56811             72   04362672     \n",
       "\n",
       "\n",
       "56812 rows x 10 columns\n",
       "memory usage: 5.06 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "          <th>  name</th>\n",
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       "            <td class=\"unused_string\">1</td>\n",
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       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2015</span\n",
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       "                >&nbsp;</span\n",
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       "          \n",
       "        \n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2015</span\n",
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       "          \n",
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       "                >&nbsp;</span\n",
       "              >\n",
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       "            <td class=\"unused_string\">03111092</td>\n",
       "          \n",
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       "        \n",
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       "    \n",
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       "        \n",
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       "              \n",
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       "              <span class=\"left\">2015</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
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       "              <span class=\"left\">50</span\n",
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       "                >&nbsp;</span\n",
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       "          \n",
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       "          \n",
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       "              \n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
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       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
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       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
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       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>137350</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">22</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
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       "          \n",
       "            <td class=\"unused_string\">04362422</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">5</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>137351</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">11</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">04362431</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">5</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>137352</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">11</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">04362432</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">5</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>137353</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">72</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">04362671</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">5</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>137354</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">72</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">04362672</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">5</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    137355 rows x 6 columns<br />\n",
       "    memory usage: 8.59 MB<br />\n",
       "    name: HOUSEHOLD_MEMBERS<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "  name           YEAR            AGE   HOUSEHOLD_ID    MARITAL         SEX             WORK_STATUS  \n",
       "  role   unused_float   unused_float   unused_string   unused_string   unused_string   unused_string\n",
       "     0           2015             66   03111041        1               1               NULL         \n",
       "     1           2015             66   03111042        1               1               NULL         \n",
       "     2           2015             56   03111091        1               1               NULL         \n",
       "     3           2015             56   03111092        1               1               NULL         \n",
       "     4           2015             50   03111111        1               1               1            \n",
       "                  ...            ...   ...             ...             ...             ...          \n",
       "137350           2019             22   04362422        5               2               NULL         \n",
       "137351           2019             11   04362431        5               2               NULL         \n",
       "137352           2019             11   04362432        5               2               NULL         \n",
       "137353           2019             72   04362671        5               2               NULL         \n",
       "137354           2019             72   04362672        5               2               NULL         \n",
       "\n",
       "\n",
       "137355 rows x 6 columns\n",
       "memory usage: 8.59 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "household_members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "        \n",
       "          \n",
       "            <td class=\"unused_string\">100</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03111051</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">190211</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2015</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.95</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1000</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03111402</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">040510</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2015</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.12</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">10000</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03114161</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">190321</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020629</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2017</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.99</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999995</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">150110</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020630</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2017</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.619</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999996</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">150110</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020631</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2017</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.2727</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999997</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">150211</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020632</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2017</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.6894</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999998</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">150310</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020633</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2017</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.7177</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999999</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">160310</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    2020634 rows x 8 columns<br />\n",
       "    memory usage: 176.70 MB<br />\n",
       "    name: EXPENDITURES<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "   name           YEAR          MONTH           COST           GIFT    IS_TRAINING   EXPENDITURE_ID   HOUSEHOLD_ID \n",
       "   role   unused_float   unused_float   unused_float   unused_float   unused_float   unused_string    unused_string\n",
       "      0           2015              1         3.89                0              1   1                03111041     \n",
       "      1           2015              1         4.66                0              1   10               03111041     \n",
       "      2           2015              2         9.79                0              1   100              03111051     \n",
       "      3           2015              2         2.95                0              1   1000             03111402     \n",
       "      4           2015              1         2.12                0              1   10000            03114161     \n",
       "                   ...            ...        ...                ...            ...   ...              ...          \n",
       "2020629           2017              6         1.99                0              1   999995           03708582     \n",
       "2020630           2017              6         3.619               0              1   999996           03708582     \n",
       "2020631           2017              6         5.2727              0              1   999997           03708582     \n",
       "2020632           2017              6         4.6894              0              1   999998           03708582     \n",
       "2020633           2017              6         5.7177              0              1   999999           03708582     \n",
       "\n",
       "   name   PRODUCT_CODE \n",
       "   role   unused_string\n",
       "      0   010210       \n",
       "      1   120310       \n",
       "      2   190211       \n",
       "      3   040510       \n",
       "      4   190321       \n",
       "          ...          \n",
       "2020629   150110       \n",
       "2020630   150110       \n",
       "2020631   150211       \n",
       "2020632   150310       \n",
       "2020633   160310       \n",
       "\n",
       "\n",
       "2020634 rows x 8 columns\n",
       "memory usage: 176.70 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expenditures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Prepare data for getML\n",
    "\n",
    "We now have to assign roles to the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "            <td class=\"char-align numerical\">\n",
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       "            <td class=\"char-align numerical\">\n",
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       "            <td class=\"char-align numerical\">\n",
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       "                >.6224</span\n",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
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       "                >.6117</span\n",
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       "            <td class=\"char-align numerical\">\n",
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       "                >.6201</span\n",
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       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">48</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "            <td class=\"join_key\">03111061</td>\n",
       "          \n",
       "        \n",
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       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2015</span\n",
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       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.1098</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.1116</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.1092</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.0951</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">37</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
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       "            <td class=\"join_key\">...</td>\n",
       "          \n",
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       "          \n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
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       "              >\n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
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       "                >&nbsp;</span\n",
       "              >\n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
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       "          \n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
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       "          \n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </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: 5ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </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",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>56807</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.4828</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.4106</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.3603</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.3958</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
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       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.377</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.3984</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.3769</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">67</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>56808</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362661</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.6644</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.5975</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6026</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.5949</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.596</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6002</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">52</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>56809</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362662</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.6639</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.597</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.6021</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.5944</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.5955</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.5997</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.5995</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">52</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>56810</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362671</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.162</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.05217</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.03955</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.04507</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.04607</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.02436</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.03558</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">72</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>56811</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362672</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.1616</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.03925</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
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       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.05741</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.04595</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.03789</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.05746</span\n",
       "              >\n",
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       "          \n",
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       "          \n",
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       "              \n",
       "              \n",
       "            \n",
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       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.03931</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">72</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    56812 rows x 10 columns<br />\n",
       "    memory usage: 4.32 MB<br />\n",
       "    name: HOUSEHOLDS<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       " name   HOUSEHOLD_ID        YEAR   INCOME_RANK   INCOME_RANK_1   ...   INCOME_RANK_3   INCOME_RANK_4   INCOME_RANK_5\n",
       " role       join_key   numerical     numerical       numerical   ...       numerical       numerical       numerical\n",
       "    0       03111041        2015        0.3044         0.1448    ...         0.1432          0.1422          0.1382 \n",
       "    1       03111042        2015        0.3063         0.1462    ...         0.1446          0.1435          0.1395 \n",
       "    2       03111051        2015        0.6931         0.6222    ...         0.623           0.6131          0.6123 \n",
       "    3       03111052        2015        0.6926         0.6216    ...         0.6224          0.6125          0.6117 \n",
       "    4       03111061        2015        0.2817         0.113     ...         0.1098          0.1116          0.1092 \n",
       "                 ...         ...       ...            ...                   ...             ...             ...     \n",
       "56807       04362582        2019        0.4828         0.4106    ...         0.3958          0.377           0.3984 \n",
       "56808       04362661        2019        0.6644         0.5975    ...         0.5949          0.596           0.6002 \n",
       "56809       04362662        2019        0.6639         0.597     ...         0.5944          0.5955          0.5997 \n",
       "56810       04362671        2019        0.162          0.05217   ...         0.04507         0.04607         0.02436\n",
       "56811       04362672        2019        0.1616         0.03925   ...         0.04595         0.03789         0.05746\n",
       "\n",
       " name   INCOME_RANK_MEAN     AGE_REF\n",
       " role          numerical   numerical\n",
       "    0            0.127            66\n",
       "    1            0.1283           66\n",
       "    2            0.6207           48\n",
       "    3            0.6201           48\n",
       "    4            0.0951           37\n",
       "                ...              ...\n",
       "56807            0.3769           67\n",
       "56808            0.6              52\n",
       "56809            0.5995           52\n",
       "56810            0.03558          72\n",
       "56811            0.03931          72\n",
       "\n",
       "\n",
       "56812 rows x 10 columns\n",
       "memory usage: 4.32 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "households.set_role(\"HOUSEHOLD_ID\", getml.data.roles.join_key)\n",
    "households.set_role(households.roles.unused_float, getml.data.roles.numerical)\n",
    "\n",
    "households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          <th>  name</th>\n",
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       "          <th class=\"join_key\">HOUSEHOLD_ID</th>\n",
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       "          \n",
       "            <td class=\"categorical\">1</td>\n",
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       "            <td class=\"categorical\">1</td>\n",
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       "              <span class=\"left\">56</span\n",
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       "                >&nbsp;</span\n",
       "              >\n",
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       "            <td class=\"join_key\">03111111</td>\n",
       "          \n",
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       "            <td class=\"char-align numerical\">\n",
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       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
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       "      <tr>\n",
       "        <th>137350</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362422</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">5</td>\n",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
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       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">22</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>137351</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362431</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">5</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "              <span class=\"left\">11</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>137352</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362432</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">5</td>\n",
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       "        \n",
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       "            <td class=\"categorical\">2</td>\n",
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       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">11</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>137353</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362671</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">5</td>\n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">72</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>137354</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362672</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">5</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">NULL</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2019</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">72</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    137355 rows x 6 columns<br />\n",
       "    memory usage: 4.40 MB<br />\n",
       "    name: HOUSEHOLD_MEMBERS<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "  name   HOUSEHOLD_ID   MARITAL       SEX           WORK_STATUS        YEAR         AGE\n",
       "  role       join_key   categorical   categorical   categorical   numerical   numerical\n",
       "     0       03111041   1             1             NULL               2015          66\n",
       "     1       03111042   1             1             NULL               2015          66\n",
       "     2       03111091   1             1             NULL               2015          56\n",
       "     3       03111092   1             1             NULL               2015          56\n",
       "     4       03111111   1             1             1                  2015          50\n",
       "                  ...   ...           ...           ...                 ...         ...\n",
       "137350       04362422   5             2             NULL               2019          22\n",
       "137351       04362431   5             2             NULL               2019          11\n",
       "137352       04362432   5             2             NULL               2019          11\n",
       "137353       04362671   5             2             NULL               2019          72\n",
       "137354       04362672   5             2             NULL               2019          72\n",
       "\n",
       "\n",
       "137355 rows x 6 columns\n",
       "memory usage: 4.40 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "household_members.set_role(\"HOUSEHOLD_ID\", getml.data.roles.join_key)\n",
    "household_members.set_role(household_members.roles.unused_float, getml.data.roles.numerical)\n",
    "household_members.set_role(household_members.roles.unused_string, getml.data.roles.categorical)\n",
    "\n",
    "household_members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = expenditures[\"YEAR\"]\n",
    "month = expenditures[\"MONTH\"]\n",
    "\n",
    "ts_strings = year + \"/\" + month\n",
    "\n",
    "expenditures[\"TIME_STAMP\"] = ts_strings.as_ts([\"%Y/%n\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  }\n",
       "  th.sub-header {\n",
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       "  .join_key,\n",
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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\">                 TIME_STAMP</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"join_key\">HOUSEHOLD_ID</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">  GIFT</th>\n",
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       "        \n",
       "          <th class=\"categorical\">MONTH      </th>\n",
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       "          <th class=\"categorical\">YEAR       </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">PRODUCT_CODE</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">     COST</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_float\"> IS_TRAINING</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"unused_string\">EXPENDITURE_ID</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "      <tr>\n",
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       "          \n",
       "            <th class=\"sub-header\">     role</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">                 time_stamp</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">    join_key</th>\n",
       "          \n",
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       "          \n",
       "            <th class=\"sub-header target\">target</th>\n",
       "          \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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       "            <th class=\"sub-header categorical\">categorical </th>\n",
       "          \n",
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       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
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       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
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       "            <th class=\"sub-header unused_string\">unused_string </th>\n",
       "          \n",
       "        \n",
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       "        \n",
       "          \n",
       "            <th class=\"sub-header\">     unit</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">time stamp, comparison only</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">            </th>\n",
       "          \n",
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       "          \n",
       "            <th class=\"sub-header target\">      </th>\n",
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       "          \n",
       "            <th class=\"sub-header categorical\">           </th>\n",
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       "          \n",
       "            <th class=\"sub-header categorical\">           </th>\n",
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       "            <th class=\"sub-header categorical\">            </th>\n",
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       "            <th class=\"sub-header numerical\">         </th>\n",
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       "          \n",
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       "          \n",
       "            <th class=\"sub-header unused_string\">              </th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">subroles:</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">                           </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">            </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <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\">           </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">            </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">         </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">            </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_string\">              </th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">- include</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">                           </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">            </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <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\">           </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">substring   </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">         </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">            </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_string\">              </th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2015-01-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03111041</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2015</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">010210</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.89</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2015-01-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03111041</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2015</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">120310</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.66</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">10</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2015-02-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03111051</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2015</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">190211</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">9</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.79</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">100</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2015-02-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03111402</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2015</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">040510</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.95</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1000</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2015-01-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03114161</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2015</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">190321</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.12</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">10000</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\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",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020629</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2017-06-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">6</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2017</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">150110</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.99</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999995</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020630</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2017-06-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">6</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2017</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">150110</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.619</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999996</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020631</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2017-06-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">6</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2017</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">150211</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.2727</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999997</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020632</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2017-06-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">6</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2017</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">150310</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.6894</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999998</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2020633</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2017-06-01</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03708582</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">6</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">2017</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">160310</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.7177</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">999999</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    2020634 rows x 9 columns<br />\n",
       "    memory usage: 128.21 MB<br />\n",
       "    name: EXPENDITURES<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "     name                    TIME_STAMP   HOUSEHOLD_ID     GIFT   MONTH         YEAR          PRODUCT_CODE        COST\n",
       "     role                    time_stamp       join_key   target   categorical   categorical   categorical    numerical\n",
       "     unit   time stamp, comparison only                                                                               \n",
       "subroles:                                                                                                             \n",
       "- include                                                                                     substring               \n",
       "        0                    2015-01-01       03111041        0   1             2015          010210            3.89  \n",
       "        1                    2015-01-01       03111041        0   1             2015          120310            4.66  \n",
       "        2                    2015-02-01       03111051        0   2             2015          190211            9.79  \n",
       "        3                    2015-02-01       03111402        0   2             2015          040510            2.95  \n",
       "        4                    2015-01-01       03114161        0   1             2015          190321            2.12  \n",
       "                                    ...            ...      ...   ...           ...           ...              ...    \n",
       "  2020629                    2017-06-01       03708582        0   6             2017          150110            1.99  \n",
       "  2020630                    2017-06-01       03708582        0   6             2017          150110            3.619 \n",
       "  2020631                    2017-06-01       03708582        0   6             2017          150211            5.2727\n",
       "  2020632                    2017-06-01       03708582        0   6             2017          150310            4.6894\n",
       "  2020633                    2017-06-01       03708582        0   6             2017          160310            5.7177\n",
       "\n",
       "     name    IS_TRAINING   EXPENDITURE_ID\n",
       "     role   unused_float   unused_string \n",
       "     unit                                \n",
       "subroles:                                \n",
       "- include                                \n",
       "        0              1   1             \n",
       "        1              1   10            \n",
       "        2              1   100           \n",
       "        3              1   1000          \n",
       "        4              1   10000         \n",
       "                     ...   ...           \n",
       "  2020629              1   999995        \n",
       "  2020630              1   999996        \n",
       "  2020631              1   999997        \n",
       "  2020632              1   999998        \n",
       "  2020633              1   999999        \n",
       "\n",
       "\n",
       "2020634 rows x 9 columns\n",
       "memory usage: 128.21 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expenditures.set_role(\"HOUSEHOLD_ID\", getml.data.roles.join_key)\n",
    "expenditures.set_role(\"GIFT\", getml.data.roles.target)\n",
    "expenditures.set_role(\"COST\", getml.data.roles.numerical)\n",
    "expenditures.set_role([\"PRODUCT_CODE\", \"MONTH\", \"YEAR\"], getml.data.roles.categorical)\n",
    "expenditures.set_role(\"TIME_STAMP\", getml.data.roles.time_stamp)\n",
    "\n",
    "expenditures.set_subroles(\"PRODUCT_CODE\", getml.data.subroles.include.substring)\n",
    "\n",
    "expenditures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th.sub-header {\n",
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       "  td {\n",
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       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right !important;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.numerical {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.numerical {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "\n",
       "  td.char-align {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  span.left {\n",
       "    text-align: right;\n",
       "    width: 3em;\n",
       "  }\n",
       "  span.right {\n",
       "    float: right;\n",
       "    text-align: left;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th>       </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th>     </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td>train</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td>train</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td>train</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td>train</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td>train</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td>...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    2020634 rows<br />\n",
       "    \n",
       "    type: StringColumnView<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "               \n",
       "      0   train\n",
       "      1   train\n",
       "      2   train\n",
       "      3   train\n",
       "      4   train\n",
       "          ...  \n",
       "\n",
       "\n",
       "2020634 rows\n",
       "type: StringColumnView"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split = expenditures.rowid.as_str().update(expenditures.IS_TRAINING == 1, \"train\").update(expenditures.IS_TRAINING == 0, \"test\")\n",
    "split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Predictive modeling\n",
    "\n",
    "Enough with the data preparation. Let's get to the fun part: Extracting the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Defining the data model\n",
    "\n",
    "First, we define the data model.\n",
    "\n",
    "What we want to do is the following: \n",
    "\n",
    "1. We want to compare every expenditure made to all *expenditures by the same household* (EXPD).\n",
    "\n",
    "2. We want to check out whether *certain kinds of items have been purchased as a gift in the past* (EXPD).\n",
    "\n",
    "2. We want to aggregate all available information on the *individual members of the household* (MEMD).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span style='font-size: 1.2rem; font-weight: 500;'>data model</span>\n",
       "            <div style='margin-top: 15px; margin-bottom: 5px;'>\n",
       "            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\n",
       "            <div style=\"height:320px;width:660px;position:relative;\"><svg height=\"310\" 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\">EXPENDITURES</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=\"110\" x=\"0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"183.8\" x=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">HOUSEHOLDS</text><rect x=\"51\" y=\"120\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"67.0\" y1=\"120\" x2=\"67.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"83.0\" y1=\"120\" x2=\"83.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"136.0\" x2=\"99\" y2=\"136.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"152.0\" x2=\"99\" y2=\"152.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"220\" x=\"0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"293.8\" x=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">HOUSEHOLD_MEMBERS</text><rect x=\"51\" y=\"230\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"67.0\" y1=\"230\" x2=\"67.0\" y2=\"278\" style=\"stroke:white;stroke-width:3\" /><line x1=\"83.0\" y1=\"230\" x2=\"83.0\" y2=\"278\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"246.0\" x2=\"99\" y2=\"246.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"262.0\" x2=\"99\" y2=\"262.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"220\" x=\"500\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"293.8\" x=\"575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">POPULATION</text><rect x=\"551\" y=\"230\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"567.0\" y1=\"230\" x2=\"567.0\" y2=\"278\" style=\"stroke:white;stroke-width:3\" /><line x1=\"583.0\" y1=\"230\" x2=\"583.0\" y2=\"278\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"246.0\" x2=\"599\" y2=\"246.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"262.0\" x2=\"599\" y2=\"262.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"150\" y1=\"43.0\" x2=\"573.0\" y2=\"43.0\" style=\"stroke:#808080;;stroke-width:4\" /><line x1=\"573.0\" y1=\"41.0\" x2=\"573.0\" y2=\"210\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"573.0, 220 567.0, 210 579.0, 210 \" 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=\"40.0\" x=\"324.0\" font-size=\"7pt\" >HOUSEHOLD_ID = HOUSEHOLD_ID</tspan><tspan y=\"50.0\" x=\"324.0\" font-size=\"7pt\" >TIME_STAMP &lt;= TIME_STAMP</tspan></text><line x1=\"150\" y1=\"153.0\" x2=\"573.0\" y2=\"153.0\" style=\"stroke:#808080;;stroke-width:4\" /><line x1=\"573.0\" y1=\"151.0\" x2=\"573.0\" y2=\"210\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"573.0, 220 567.0, 210 579.0, 210 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"120.0\" 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=\"150.0\" x=\"324.0\" font-size=\"7pt\" >HOUSEHOLD_ID = HOUSEHOLD_ID</tspan><tspan y=\"160.0\" x=\"324.0\" font-size=\"7pt\" >Relationship: many-to-one</tspan></text><line x1=\"150\" y1=\"263.0\" x2=\"490\" y2=\"263.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"500, 263.0 490, 257.0 490, 269.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"230.0\" 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=\"265.0\" x=\"324.0\" font-size=\"7pt\" >HOUSEHOLD_ID = HOUSEHOLD_ID</tspan></text></svg></div>\n",
       "            </div>\n",
       "\n",
       "            <div style='margin-top: 15px;'>\n",
       "            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\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",
       "  }\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=\"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, HOUSEHOLDS</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\">EXPENDITURES</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">EXPENDITURES__STAGING_TABLE_2</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">HOUSEHOLD_MEMBERS</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">HOUSEHOLD_MEMBERS__STAGING_TABLE_3</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "            </div>\n",
       "            \n",
       "<span style='font-size: 1.2rem; font-weight: 500;'>container</span>\n",
       "<div style='margin-top: 15px;'>\n",
       "<div style='float: left; margin-right: 50px;'>\n",
       "<div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\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",
       "  }\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=\"str\">subset</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">name        </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">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\">EXPENDITURES</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">unknown</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\">EXPENDITURES</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">unknown</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",
       "<div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\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",
       "  }\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=\"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\">EXPENDITURES</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">2020634</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">HOUSEHOLDS</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">56812</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">HOUSEHOLD_MEMBERS</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">137355</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "data model\n",
       "\n",
       "  POPULATION:\n",
       "    columns:\n",
       "    - MONTH: categorical\n",
       "    - YEAR: categorical\n",
       "    - PRODUCT_CODE: categorical\n",
       "    - HOUSEHOLD_ID: join_key\n",
       "    - COST: numerical\n",
       "    - ...\n",
       "\n",
       "    joins:\n",
       "    - right: 'EXPENDITURES'\n",
       "      on: (POPULATION.HOUSEHOLD_ID, EXPENDITURES.HOUSEHOLD_ID)\n",
       "      time_stamps: (POPULATION.TIME_STAMP, EXPENDITURES.TIME_STAMP)\n",
       "      relationship: 'many-to-many'\n",
       "      lagged_targets: False\n",
       "    - right: 'HOUSEHOLDS'\n",
       "      on: (POPULATION.HOUSEHOLD_ID, HOUSEHOLDS.HOUSEHOLD_ID)\n",
       "      relationship: 'many-to-one'\n",
       "      lagged_targets: False\n",
       "    - right: 'HOUSEHOLD_MEMBERS'\n",
       "      on: (POPULATION.HOUSEHOLD_ID, HOUSEHOLD_MEMBERS.HOUSEHOLD_ID)\n",
       "      relationship: 'many-to-many'\n",
       "      lagged_targets: False\n",
       "\n",
       "  EXPENDITURES:\n",
       "    columns:\n",
       "    - MONTH: categorical\n",
       "    - YEAR: categorical\n",
       "    - PRODUCT_CODE: categorical\n",
       "    - HOUSEHOLD_ID: join_key\n",
       "    - COST: numerical\n",
       "    - ...\n",
       "\n",
       "  HOUSEHOLDS:\n",
       "    columns:\n",
       "    - HOUSEHOLD_ID: join_key\n",
       "    - YEAR: numerical\n",
       "    - INCOME_RANK: numerical\n",
       "    - INCOME_RANK_1: numerical\n",
       "    - INCOME_RANK_2: numerical\n",
       "    - ...\n",
       "\n",
       "  HOUSEHOLD_MEMBERS:\n",
       "    columns:\n",
       "    - MARITAL: categorical\n",
       "    - SEX: categorical\n",
       "    - WORK_STATUS: categorical\n",
       "    - HOUSEHOLD_ID: join_key\n",
       "    - YEAR: numerical\n",
       "    - ...\n",
       "\n",
       "\n",
       "container\n",
       "\n",
       "  population\n",
       "      subset   name           rows      type\n",
       "  0   test     EXPENDITURES   unknown   View\n",
       "  1   train    EXPENDITURES   unknown   View\n",
       "\n",
       "  peripheral\n",
       "      name                   rows   type     \n",
       "  0   EXPENDITURES        2020634   DataFrame\n",
       "  1   HOUSEHOLDS            56812   DataFrame\n",
       "  2   HOUSEHOLD_MEMBERS    137355   DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "star_schema = getml.data.StarSchema(alias=\"POPULATION\", population=expenditures, split=split)\n",
    "\n",
    "star_schema.join(\n",
    "    expenditures,\n",
    "    on=\"HOUSEHOLD_ID\",\n",
    "    time_stamps=\"TIME_STAMP\"\n",
    ")\n",
    "\n",
    "star_schema.join(\n",
    "    households,\n",
    "    on=\"HOUSEHOLD_ID\",\n",
    "    relationship=getml.data.relationship.many_to_one,\n",
    ")\n",
    "\n",
    "star_schema.join(\n",
    "    household_members,\n",
    "    on=\"HOUSEHOLD_ID\",\n",
    ")\n",
    "\n",
    "star_schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Setting the hyperparameters\n",
    "\n",
    "We use `XGBoost` as our predictor and `FastProp` (short for fast propsitionalization) to generate our features. You are free to play with the hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ucc1 = getml.preprocessors.Substring(0, 1)\n",
    "ucc2 = getml.preprocessors.Substring(0, 2)\n",
    "ucc3 = getml.preprocessors.Substring(0, 3)\n",
    "ucc4 = getml.preprocessors.Substring(0, 4)\n",
    "ucc5 = getml.preprocessors.Substring(0, 5)\n",
    "\n",
    "mapping = getml.preprocessors.Mapping(multithreading=False)\n",
    "\n",
    "fast_prop = getml.feature_learning.FastProp(\n",
    "    aggregation=getml.feature_learning.FastProp.agg_sets.All,\n",
    "    loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n",
    "    num_threads=1,\n",
    "    sampling_factor=0.1,\n",
    "    num_features=100,\n",
    ")\n",
    "\n",
    "relboost = getml.feature_learning.Relboost(\n",
    "    loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n",
    "    num_threads=1,\n",
    "    num_features=20,\n",
    ")\n",
    "\n",
    "feature_selector = getml.predictors.XGBoostClassifier()\n",
    "\n",
    "predictor = getml.predictors.XGBoostClassifier(\n",
    "    booster=\"gbtree\",\n",
    "    n_estimators=100,\n",
    "    max_depth=7,\n",
    "    reg_lambda=0.0,\n",
    "    n_jobs=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.4,\n",
       "         tags=['FastProp'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.4,\n",
       "         tags=['FastProp'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1 = getml.pipeline.Pipeline(\n",
    "    tags=[\"FastProp\"],\n",
    "    data_model=star_schema.data_model,\n",
    "    share_selected_features=0.4,\n",
    "    preprocessors=[mapping],\n",
    "    feature_learners=fast_prop,\n",
    "    feature_selectors=feature_selector,\n",
    "    predictors=predictor\n",
    ")\n",
    "\n",
    "pipe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Substring', 'Substring', 'Substring', 'Substring', 'Substring',\n",
       "                        'Mapping'],\n",
       "         share_selected_features=0.9,\n",
       "         tags=['Relboost'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Substring', 'Substring', 'Substring', 'Substring', 'Substring',\n",
       "                        'Mapping'],\n",
       "         share_selected_features=0.9,\n",
       "         tags=['Relboost'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2 = getml.pipeline.Pipeline(\n",
    "    tags=[\"Relboost\"],\n",
    "    data_model=star_schema.data_model,\n",
    "    share_selected_features=0.9,\n",
    "    preprocessors=[ucc1, ucc2, ucc3, ucc4, ucc5, mapping],\n",
    "    feature_learners=relboost,\n",
    "    feature_selectors=feature_selector,\n",
    "    predictors=predictor\n",
    ")\n",
    "\n",
    "pipe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['FastProp', 'Relboost'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.2,\n",
       "         tags=['FastProp', 'Relboost'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['FastProp', 'Relboost'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.2,\n",
       "         tags=['FastProp', 'Relboost'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe3 = getml.pipeline.Pipeline(\n",
    "    tags=[\"FastProp\", \"Relboost\"],\n",
    "    data_model=star_schema.data_model,\n",
    "    share_selected_features=0.2,\n",
    "    preprocessors=[mapping],\n",
    "    feature_learners=[fast_prop, relboost],\n",
    "    feature_selectors=feature_selector,\n",
    "    predictors=predictor\n",
    ")\n",
    "\n",
    "pipe3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.fit(...)` will automatically call `.check(...)`, but it is always a good idea to call `.check(...)` separately, so we still have time for some last-minute fixes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Training the pipeline\n",
    "\n",
    "OK, let's go:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:23\n",
      "\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">OK.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "OK.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipe1.check(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">OK.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "OK.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Trying 418 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:33\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 01:09\n",
      "\u001b[2K  XGBoost: Training as feature selector... ━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 05:27\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 06:20\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Trained pipeline.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Trained pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 0:13:34.027354.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.4,\n",
       "         tags=['FastProp', 'container-xVgT7b'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.4,\n",
       "         tags=['FastProp', 'container-xVgT7b'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.fit(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 02:16\n",
      "\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">OK.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "OK.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipe2.check(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:10\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">OK.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "OK.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:10\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 21:26\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 18:45\n",
      "\u001b[2K  XGBoost: Training as feature selector... ━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 04:11\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 06:29\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Trained pipeline.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Trained pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 0:51:09.055720.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Substring', 'Substring', 'Substring', 'Substring', 'Substring',\n",
       "                        'Mapping'],\n",
       "         share_selected_features=0.9,\n",
       "         tags=['Relboost', 'container-xVgT7b'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Substring', 'Substring', 'Substring', 'Substring', 'Substring',\n",
       "                        'Mapping'],\n",
       "         share_selected_features=0.9,\n",
       "         tags=['Relboost', 'container-xVgT7b'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.fit(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">OK.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "OK.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipe3.check(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">OK.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "OK.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Retrieving features from cache... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 05:54\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 01:08\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 04:50\n",
      "\u001b[2K  XGBoost: Training as feature selector... ━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 06:31\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 04:03\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Trained pipeline.\n",
       "</pre>\n"
      ],
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       "Trained pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 0:22:31.520442.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['FastProp', 'Relboost'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.2,\n",
       "         tags=['FastProp', 'Relboost', 'container-xVgT7b'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='POPULATION',\n",
       "         feature_learners=['FastProp', 'Relboost'],\n",
       "         feature_selectors=['XGBoostClassifier'],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['EXPENDITURES', 'HOUSEHOLDS', 'HOUSEHOLD_MEMBERS'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.2,\n",
       "         tags=['FastProp', 'Relboost', 'container-xVgT7b'])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe3.fit(star_schema.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Evaluating the pipeline\n",
    "\n",
    "We want to know how well we did. We will to an in-sample and an out-of-sample evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
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    {
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     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:08\n",
      "\u001b[?25h"
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       "          <th class=\"float\">accuracy</th>\n",
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       "        \n",
       "          <th class=\"float\">    auc</th>\n",
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       "          <th class=\"float\">cross entropy</th>\n",
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       "            \n",
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       "            \n",
       "              <td class=\"str\">GIFT</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"float\">0.9826</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9368</td>\n",
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       "            \n",
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       "          \n",
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       "              <td class=\"datetime\">2024-09-18 13:43:03</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">GIFT</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9804</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.8649</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.07713</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
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       "    date time             set used   target   accuracy       auc   cross entropy\n",
       "0   2024-09-18 12:08:28   train      GIFT       0.9826    0.9368         0.05986\n",
       "1   2024-09-18 13:43:03   test       GIFT       0.9804    0.8649         0.07713"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.score(star_schema.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:03\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 02:13\n",
      "\u001b[?25h"
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       "            \n",
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       "            \n",
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       "          \n",
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       "            \n",
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       "            \n",
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       "            \n",
       "              <td class=\"float\">0.9805</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.863</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.07709</td>\n",
       "            \n",
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       "    date time             set used   target   accuracy       auc   cross entropy\n",
       "0   2024-09-18 13:02:15   train      GIFT       0.9822    0.9231         0.06321\n",
       "1   2024-09-18 13:45:21   test       GIFT       0.9805    0.863          0.07709"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.score(star_schema.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:03\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:08\n",
      "\u001b[?25h"
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       "            \n",
       "              <td class=\"float\">0.9331</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.06092</td>\n",
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       "          \n",
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       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">GIFT</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9805</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.8676</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.07667</td>\n",
       "            \n",
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       "    date time             set used   target   accuracy       auc   cross entropy\n",
       "0   2024-09-18 13:42:53   train      GIFT       0.9824    0.9331         0.06092\n",
       "1   2024-09-18 13:45:35   test       GIFT       0.9805    0.8676         0.07667"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pipe3.score(star_schema.test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5 Studying the features\n",
    "\n",
    "It is very important that we get an idea about the features that the propositionalization algorithm has produced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LENGTH=50\n",
    "\n",
    "names, correlations = pipe1.features.correlations()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names[:LENGTH], correlations[:LENGTH])\n",
    "\n",
    "plt.title(\"feature correlations\")\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"features\")\n",
    "plt.ylabel(\"correlations\")\n",
    "plt.xticks(rotation='vertical')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LENGTH=50\n",
    "\n",
    "names, correlations = pipe2.features.correlations()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names[:LENGTH], correlations[:LENGTH])\n",
    "\n",
    "plt.title(\"feature correlations\")\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"features\")\n",
    "plt.ylabel(\"correlations\")\n",
    "plt.xticks(rotation='vertical')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can express the features in SQLite3:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because getML uses a feature learning approach, the concept of feature importances can also be carried over to the individual columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, importances = pipe1.columns.importances()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, importances)\n",
    "\n",
    "plt.title(\"column importances\")\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"columns\")\n",
    "plt.ylabel(\"importance\")\n",
    "plt.xticks(rotation='vertical')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, importances = pipe2.columns.importances()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, importances)\n",
    "\n",
    "plt.title(\"column importances\")\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"columns\")\n",
    "plt.ylabel(\"importance\")\n",
    "plt.xticks(rotation='vertical')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important feature of pipe1 looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\n",
      "\n",
      "CREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\n",
      "\n",
      "INSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\n",
      "VALUES('410901', 0.5265553869499241),\n",
      "      ('410140', 0.5248618784530387),\n",
      "      ('004190', 0.5073846153846154),\n",
      "      ('410120', 0.5013123359580053),\n",
      "      ('410110', 0.4444444444444444),\n",
      "      ('004100', 0.3336306868867083),\n",
      "      ('390110', 0.3132530120481928),\n",
      "      ('390120', 0.3067484662576687),\n",
      "      ('410130', 0.2967448902346707),\n",
      "      ('370110', 0.2948717948717949),\n",
      "      ('370212', 0.2944444444444445),\n",
      "      ('370220', 0.2920353982300885),\n",
      "      ('680140', 0.288135593220339),\n",
      "      ('390322', 0.2795918367346939),\n",
      "      ('390321', 0.2764227642276423),\n",
      "      ('370901', 0.271948608137045),\n",
      "      ('390210', 0.2579837194740138),\n",
      "      ('370125', 0.2519157088122606),\n",
      "      ('390310', 0.2443181818181818),\n",
      "      ('390223', 0.2344706911636046),\n",
      "      ('390230', 0.2238442822384428),\n",
      "      ('370211', 0.2185714285714286),\n",
      "      ('370314', 0.2182952182952183),\n",
      "      ('400220', 0.2164179104477612),\n",
      "      ('610110', 0.2162868883078072),\n",
      "      ('360320', 0.2151898734177215),\n",
      "      ('590220', 0.2075471698113208),\n",
      "      ('370213', 0.2015968063872255),\n",
      "      ('400210', 0.1944764096662831),\n",
      "      ('430120', 0.194006309148265),\n",
      "      ('320130', 0.1899441340782123),\n",
      "      ('390901', 0.1797752808988764),\n",
      "      ('330410', 0.1751831107281344),\n",
      "      ('380410', 0.1386392811296534),\n",
      "      ('590230', 0.13469068128426),\n",
      "      ('360350', 0.1321279554937413),\n",
      "      ('360210', 0.1305555555555556),\n",
      "      ('290420', 0.1282051282051282),\n",
      "      ('280220', 0.1231884057971015),\n",
      "      ('320903', 0.1229724632214259),\n",
      "      ('360420', 0.1222091656874266),\n",
      "      ('005000', 0.1219512195121951),\n",
      "      ('660900', 0.1205479452054795),\n",
      "      ('320345', 0.1176205497972059),\n",
      "      ('610902', 0.1162790697674419),\n",
      "      ('660110', 0.111731843575419),\n",
      "      ('600900', 0.1111111111111111),\n",
      "      ('670110', 0.1111111111111111),\n",
      "      ('320233', 0.1108969866853539),\n",
      "      ('610230', 0.11),\n",
      "      ('660210', 0.1097922848664688),\n",
      "      ('610901', 0.1097560975609756),\n",
      "      ('380510', 0.1081081081081081),\n",
      "      ('290310', 0.1044776119402985),\n",
      "      ('280120', 0.1030640668523677),\n",
      "      ('380901', 0.1010141987829615),\n",
      "      ('320521', 0.1009174311926606),\n",
      "      ('360330', 0.1004366812227074),\n",
      "      ('360311', 0.09981167608286252),\n",
      "      ('430110', 0.09863945578231292),\n",
      "      ('300320', 0.0975609756097561),\n",
      "      ('360312', 0.09716599190283401),\n",
      "      ('660000', 0.09413886384129846),\n",
      "      ('600430', 0.09302325581395349),\n",
      "      ('380110', 0.09302325581395349),\n",
      "      ('310231', 0.09090909090909091),\n",
      "      ('004000', 0.08723998758149643),\n",
      "      ('600410', 0.08408408408408409),\n",
      "      ('310210', 0.08333333333333333),\n",
      "      ('340120', 0.08333333333333333),\n",
      "      ('430130', 0.08226221079691516),\n",
      "      ('600210', 0.08190476190476191),\n",
      "      ('380315', 0.08014981273408239),\n",
      "      ('610120', 0.07865168539325842),\n",
      "      ('620610', 0.07755102040816327),\n",
      "      ('360513', 0.07722969606377678),\n",
      "      ('280140', 0.07646356033452807),\n",
      "      ('320380', 0.07645788336933046),\n",
      "      ('620213', 0.07375643224699828),\n",
      "      ('620510', 0.07370393504059962),\n",
      "      ('380430', 0.07358390682901006),\n",
      "      ('310316', 0.07329842931937172),\n",
      "      ('310232', 0.0726950354609929),\n",
      "      ('200210', 0.07258064516129033),\n",
      "      ('530110', 0.07235621521335807),\n",
      "      ('380320', 0.07142857142857142),\n",
      "      ('640420', 0.06923076923076923),\n",
      "      ('620214', 0.0689900426742532),\n",
      "      ('610130', 0.06882591093117409),\n",
      "      ('290410', 0.06748466257668712),\n",
      "      ('380420', 0.06734816596512327),\n",
      "      ('400310', 0.0672059738643435),\n",
      "      ('690117', 0.06666666666666667),\n",
      "      ('610903', 0.06578947368421052),\n",
      "      ('310220', 0.06555863342566944),\n",
      "      ('320330', 0.06554307116104868),\n",
      "      ('400110', 0.06538692261547691),\n",
      "      ('640120', 0.06442953020134229),\n",
      "      ('690230', 0.0641025641025641),\n",
      "      ('620330', 0.06329113924050633),\n",
      "      ('420115', 0.06281407035175879),\n",
      "      ('380311', 0.0625),\n",
      "      ('310340', 0.06231454005934718),\n",
      "      ('320370', 0.06196746707978312),\n",
      "      ('380340', 0.06157635467980296),\n",
      "      ('380210', 0.06014492753623189),\n",
      "      ('620112', 0.05970149253731343),\n",
      "      ('340110', 0.05929824561403509),\n",
      "      ('320901', 0.05747126436781609),\n",
      "      ('280110', 0.05726600985221675),\n",
      "      ('290120', 0.05673758865248227),\n",
      "      ('320150', 0.05652173913043478),\n",
      "      ('240220', 0.05647840531561462),\n",
      "      ('340907', 0.05555555555555555),\n",
      "      ('600310', 0.05521472392638037),\n",
      "      ('320221', 0.05381727158948686),\n",
      "      ('320522', 0.05371900826446281),\n",
      "      ('620913', 0.05333333333333334),\n",
      "      ('340510', 0.052734375),\n",
      "      ('640130', 0.05263157894736842),\n",
      "      ('310332', 0.05128205128205128),\n",
      "      ('320232', 0.05029013539651837),\n",
      "      ('380333', 0.0501577287066246),\n",
      "      ('690118', 0.05),\n",
      "      ('670903', 0.04895104895104895),\n",
      "      ('320905', 0.04766031195840555),\n",
      "      ('320627', 0.04761904761904762),\n",
      "      ('320902', 0.04666666666666667),\n",
      "      ('690110', 0.04666666666666667),\n",
      "      ('150110', 0.04635643740546312),\n",
      "      ('620221', 0.04615384615384616),\n",
      "      ('670901', 0.04597701149425287),\n",
      "      ('001000', 0.04587155963302753),\n",
      "      ('670310', 0.04553734061930783),\n",
      "      ('340610', 0.04444444444444445),\n",
      "      ('200410', 0.04397394136807817),\n",
      "      ('300900', 0.04375),\n",
      "      ('610320', 0.04300578034682081),\n",
      "      ('300110', 0.0425531914893617),\n",
      "      ('002000', 0.0418848167539267),\n",
      "      ('680220', 0.04184704184704185),\n",
      "      ('570901', 0.04081632653061224),\n",
      "      ('280210', 0.04081632653061224),\n",
      "      ('600420', 0.04044489383215369),\n",
      "      ('320420', 0.0400890868596882),\n",
      "      ('290440', 0.038860103626943),\n",
      "      ('200310', 0.03872966692486444),\n",
      "      ('310900', 0.0380952380952381),\n",
      "      ('520550', 0.03773584905660377),\n",
      "      ('690116', 0.03773584905660377),\n",
      "      ('020410', 0.03773262762506403),\n",
      "      ('440130', 0.03759398496240601),\n",
      "      ('380902', 0.03571428571428571),\n",
      "      ('550320', 0.03547297297297297),\n",
      "      ('290110', 0.03539823008849557),\n",
      "      ('590210', 0.03476151980598222),\n",
      "      ('320904', 0.03454231433506045),\n",
      "      ('490311', 0.03448275862068965),\n",
      "      ('620310', 0.03422053231939164),\n",
      "      ('220000', 0.03418803418803419),\n",
      "      ('320120', 0.03355704697986577),\n",
      "      ('240310', 0.03343949044585987),\n",
      "      ('310351', 0.03333333333333333),\n",
      "      ('640310', 0.03329679364209372),\n",
      "      ('670902', 0.03174603174603174),\n",
      "      ('680903', 0.03137789904502047),\n",
      "      ('310140', 0.0308641975308642),\n",
      "      ('620420', 0.03061224489795918),\n",
      "      ('630220', 0.03052325581395349),\n",
      "      ('330610', 0.03022860381636123),\n",
      "      ('330510', 0.02971188475390156),\n",
      "      ('180620', 0.02942668696093353),\n",
      "      ('240900', 0.02941176470588235),\n",
      "      ('550330', 0.02935420743639922),\n",
      "      ('320610', 0.02929427430093209),\n",
      "      ('620710', 0.02877697841726619),\n",
      "      ('290320', 0.02877697841726619),\n",
      "      ('200111', 0.02867072111207646),\n",
      "      ('240320', 0.02842928216062544),\n",
      "      ('310352', 0.02838427947598253),\n",
      "      ('320410', 0.02791625124626122),\n",
      "      ('300218', 0.02777777777777778),\n",
      "      ('320110', 0.02768166089965398),\n",
      "      ('620121', 0.02765208647561589),\n",
      "      ('340210', 0.02722323049001815),\n",
      "      ('240210', 0.02707581227436823),\n",
      "      ('440150', 0.02702702702702703),\n",
      "      ('320140', 0.02697022767075306),\n",
      "      ('640220', 0.02683461117196057),\n",
      "      ('640410', 0.026232741617357),\n",
      "      ('310335', 0.02593659942363112),\n",
      "      ('490315', 0.02564102564102564),\n",
      "      ('340901', 0.02542372881355932),\n",
      "      ('610310', 0.02461584365209608),\n",
      "      ('680110', 0.02362204724409449),\n",
      "      ('340903', 0.0234375),\n",
      "      ('480213', 0.0231811697574893),\n",
      "      ('320430', 0.02272727272727273),\n",
      "      ('230000', 0.02272727272727273),\n",
      "      ('640210', 0.02267002518891688),\n",
      "      ('550310', 0.02246796559592768),\n",
      "      ('490110', 0.02173913043478261),\n",
      "      ('620410', 0.02165087956698241),\n",
      "      ('340913', 0.02127659574468085),\n",
      "      ('340906', 0.02127659574468085),\n",
      "      ('590110', 0.0209366391184573),\n",
      "      ('620810', 0.02090592334494774),\n",
      "      ('020710', 0.02085600290170475),\n",
      "      ('620926', 0.02076875387476751),\n",
      "      ('480212', 0.02055622732769045),\n",
      "      ('020510', 0.0202097074243193),\n",
      "      ('650210', 0.02016868353502017),\n",
      "      ('530510', 0.02005730659025788),\n",
      "      ('520310', 0.02),\n",
      "      ('480110', 0.01970443349753695),\n",
      "      ('550110', 0.0194300518134715),\n",
      "      ('650110', 0.0190424374319913),\n",
      "      ('320511', 0.01829268292682927),\n",
      "      ('240120', 0.01818181818181818),\n",
      "      ('040610', 0.01785714285714286),\n",
      "      ('170531', 0.0177293934681182),\n",
      "      ('550210', 0.01761658031088083),\n",
      "      ('290430', 0.01748251748251748),\n",
      "      ('002100', 0.01715481171548117),\n",
      "      ('150310', 0.01708217913204063),\n",
      "      ('560310', 0.01682692307692308),\n",
      "      ('640110', 0.01674500587544066),\n",
      "      ('640430', 0.01648351648351648),\n",
      "      ('570000', 0.01633393829401089),\n",
      "      ('240110', 0.0162052667116813),\n",
      "      ('690119', 0.01618122977346278),\n",
      "      ('630110', 0.0158344666796192),\n",
      "      ('330310', 0.01570146818923328),\n",
      "      ('020820', 0.01567783584383646),\n",
      "      ('130320', 0.0156165858912224),\n",
      "      ('630210', 0.0155902004454343),\n",
      "      ('020610', 0.01553829078801332),\n",
      "      ('010120', 0.01547231270358306),\n",
      "      ('180310', 0.01535880227155395),\n",
      "      ('550410', 0.01529571719918423),\n",
      "      ('360110', 0.01515151515151515),\n",
      "      ('620114', 0.01492537313432836),\n",
      "      ('440210', 0.01488095238095238),\n",
      "      ('470220', 0.01478743068391867),\n",
      "      ('620111', 0.01471389645776567),\n",
      "      ('330210', 0.01441871961769795),\n",
      "      ('140320', 0.01423487544483986),\n",
      "      ('340520', 0.01411100658513641),\n",
      "      ('560210', 0.01355661881977671),\n",
      "      ('530311', 0.01341184167484462),\n",
      "      ('330110', 0.01330895052321447),\n",
      "      ('050900', 0.0131578947368421),\n",
      "      ('250900', 0.01309707241910632),\n",
      "      ('690120', 0.01305483028720627),\n",
      "      ('490300', 0.01298701298701299),\n",
      "      ('180320', 0.01298701298701299),\n",
      "      ('170533', 0.01296982530439386),\n",
      "      ('540000', 0.01271259233808624),\n",
      "      ('170510', 0.01269971323228185),\n",
      "      ('620930', 0.01252609603340292),\n",
      "      ('340410', 0.01241642788920726),\n",
      "      ('270000', 0.01241039905852145),\n",
      "      ('520110', 0.01237964236588721),\n",
      "      ('560400', 0.01210898082744702),\n",
      "      ('180612', 0.01201452919810003),\n",
      "      ('620320', 0.01185770750988142),\n",
      "      ('470211', 0.01179941002949852),\n",
      "      ('180520', 0.01179574732267577),\n",
      "      ('100410', 0.01164329187615771),\n",
      "      ('310331', 0.01162790697674419),\n",
      "      ('530412', 0.01158504476040021),\n",
      "      ('020810', 0.01154575219713941),\n",
      "      ('530210', 0.01152737752161383),\n",
      "      ('220110', 0.01149425287356322),\n",
      "      ('320630', 0.01142857142857143),\n",
      "      ('520531', 0.01112484548825711),\n",
      "      ('180710', 0.01103708190322364),\n",
      "      ('030810', 0.01092896174863388),\n",
      "      ('130310', 0.01086556169429098),\n",
      "      ('170210', 0.01082262080178853),\n",
      "      ('340620', 0.01075268817204301),\n",
      "      ('999900', 0.01062416998671979),\n",
      "      ('030210', 0.01055662188099808),\n",
      "      ('030510', 0.01044277360066834),\n",
      "      ('170110', 0.01034780109226789),\n",
      "      ('220210', 0.01027397260273973),\n",
      "      ('680902', 0.01025641025641026),\n",
      "      ('020310', 0.01021667580910587),\n",
      "      ('130212', 0.009969657563935847),\n",
      "      ('030710', 0.009891435464414958),\n",
      "      ('140420', 0.009844993715961458),\n",
      "      ('560330', 0.009771986970684038),\n",
      "      ('270210', 0.009420631182289214),\n",
      "      ('140220', 0.009351432880844645),\n",
      "      ('160320', 0.00933609958506224),\n",
      "      ('560110', 0.009322560596643879),\n",
      "      ('170520', 0.009291360421578144),\n",
      "      ('230110', 0.009202453987730062),\n",
      "      ('170310', 0.009154113557358054),\n",
      "      ('180110', 0.009134615384615385),\n",
      "      ('140210', 0.009130282102305981),\n",
      "      ('160212', 0.009098914000587027),\n",
      "      ('050410', 0.008833922261484099),\n",
      "      ('100210', 0.008741319144525446),\n",
      "      ('170532', 0.008554705087798289),\n",
      "      ('620912', 0.008553654743390357),\n",
      "      ('090210', 0.008506616257088847),\n",
      "      ('490000', 0.008489564909798374),\n",
      "      ('170410', 0.008431932544539644),\n",
      "      ('210210', 0.00823045267489712),\n",
      "      ('020620', 0.008152173913043478),\n",
      "      ('340310', 0.008032128514056224),\n",
      "      ('110410', 0.007990834884720034),\n",
      "      ('490312', 0.007977207977207978),\n",
      "      ('210110', 0.007972665148063782),\n",
      "      ('180420', 0.007866728366496992),\n",
      "      ('180220', 0.007703887363853715),\n",
      "      ('010210', 0.007637017070979336),\n",
      "      ('180510', 0.007588713125267208),\n",
      "      ('470111', 0.007556238768484639),\n",
      "      ('060310', 0.007518796992481203),\n",
      "      ('050310', 0.007514761137949544),\n",
      "      ('030610', 0.007317073170731708),\n",
      "      ('180611', 0.007287611061195967),\n",
      "      ('010320', 0.007257694074414332),\n",
      "      ('500110', 0.007106598984771574),\n",
      "      ('040510', 0.006984459577440196),\n",
      "      ('110310', 0.006973269134982567),\n",
      "      ('250220', 0.006944444444444444),\n",
      "      ('580000', 0.006857142857142857),\n",
      "      ('020210', 0.006824146981627296),\n",
      "      ('180210', 0.006806282722513089),\n",
      "      ('040410', 0.006790744466800805),\n",
      "      ('050110', 0.00675990675990676),\n",
      "      ('010110', 0.006644518272425249),\n",
      "      ('180410', 0.006634078212290503),\n",
      "      ('140230', 0.00663265306122449),\n",
      "      ('050210', 0.00662133142448103),\n",
      "      ('160310', 0.006574892130675981),\n",
      "      ('020110', 0.006501360749924402),\n",
      "      ('070110', 0.006377551020408163),\n",
      "      ('030310', 0.00625),\n",
      "      ('120310', 0.006177540831006178),\n",
      "      ('100510', 0.006119326874043855),\n",
      "      ('030410', 0.006116207951070336),\n",
      "      ('690114', 0.006105834464043419),\n",
      "      ('110510', 0.005989518342899925),\n",
      "      ('160211', 0.005981308411214953),\n",
      "      ('150211', 0.005960568546538285),\n",
      "      ('130211', 0.005947955390334572),\n",
      "      ('520541', 0.005911778080945885),\n",
      "      ('120210', 0.005798018131983976),\n",
      "      ('040110', 0.005780346820809248),\n",
      "      ('260110', 0.005772763054316453),\n",
      "      ('070240', 0.005749668288367979),\n",
      "      ('090110', 0.005704227647576519),\n",
      "      ('110210', 0.005692403229145104),\n",
      "      ('030110', 0.005622410731899783),\n",
      "      ('260210', 0.0055542698449433),\n",
      "      ('080110', 0.005548549810844893),\n",
      "      ('120110', 0.005436931593515224),\n",
      "      ('040310', 0.005404077622205846),\n",
      "      ('250210', 0.005342831700801425),\n",
      "      ('010310', 0.005331627212625293),\n",
      "      ('440120', 0.005319148936170213),\n",
      "      ('100110', 0.005308219178082192),\n",
      "      ('470112', 0.005277044854881266),\n",
      "      ('110110', 0.005152378864284149),\n",
      "      ('160110', 0.005109489051094891),\n",
      "      ('270410', 0.00496031746031746),\n",
      "      ('060110', 0.004922542348342262),\n",
      "      ('520516', 0.004901960784313725),\n",
      "      ('270310', 0.004885574697865775),\n",
      "      ('120410', 0.004865350089766607),\n",
      "      ('220120', 0.004815409309791332),\n",
      "      ('040210', 0.004786324786324786),\n",
      "      ('070230', 0.004725554343874954),\n",
      "      ('130110', 0.004694835680751174),\n",
      "      ('140110', 0.004555336991406978),\n",
      "      ('340530', 0.004530011325028313),\n",
      "      ('060210', 0.00400114318376679),\n",
      "      ('230900', 0.003992015968063872),\n",
      "      ('520410', 0.003937007874015748),\n",
      "      ('140340', 0.003897369275738876),\n",
      "      ('490313', 0.003875968992248062),\n",
      "      ('009000', 0.002952029520295203),\n",
      "      ('350110', 0.002881844380403458),\n",
      "      ('140330', 0.002380952380952381),\n",
      "      ('130122', 0.002169197396963124),\n",
      "      ('150212', 0.001451378809869376),\n",
      "      ('130121', 0.001373626373626374),\n",
      "      ('190323', 0.0009389671361502347),\n",
      "      ('190311', 0.0008796003096193089),\n",
      "      ('200532', 0.0005934718100890207),\n",
      "      ('190312', 0.0005761198329252485),\n",
      "      ('190314', 0.0004549590536851683),\n",
      "      ('190324', 0.0004541326067211626),\n",
      "      ('200522', 0.0004464285714285714),\n",
      "      ('190212', 0.0004089793692629283),\n",
      "      ('190114', 0.0003787878787878788),\n",
      "      ('190112', 0.0003610760064993681),\n",
      "      ('190322', 0.0002765869174388052),\n",
      "      ('190211', 0.0002144925463840132),\n",
      "      ('190111', 0.0002058036633052068),\n",
      "      ('200512', 0.0001853911753800519),\n",
      "      ('190321', 7.427213309566251e-05),\n",
      "      ('440140', 0),\n",
      "      ('200112', 0),\n",
      "      ('620925', 0),\n",
      "      ('250110', 0),\n",
      "      ('200531', 0),\n",
      "      ('310242', 0),\n",
      "      ('600130', 0),\n",
      "      ('580901', 0),\n",
      "      ('200521', 0),\n",
      "      ('490316', 0),\n",
      "      ('200523', 0),\n",
      "      ('190113', 0),\n",
      "      ('310241', 0),\n",
      "      ('550340', 0),\n",
      "      ('450350', 0),\n",
      "      ('190214', 0),\n",
      "      ('300410', 0),\n",
      "      ('530903', 0),\n",
      "      ('200513', 0),\n",
      "      ('140410', 0),\n",
      "      ('002200', 0),\n",
      "      ('630900', 0),\n",
      "      ('680210', 0),\n",
      "      ('290210', 0),\n",
      "      ('140310', 0),\n",
      "      ('200533', 0),\n",
      "      ('440110', 0),\n",
      "      ('190313', 0),\n",
      "      ('190213', 0),\n",
      "      ('270311', 0),\n",
      "      ('270900', 0),\n",
      "      ('200511', 0);\n",
      "\n",
      "ALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\" REAL;\n",
      "\n",
      "UPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\n",
      "\n",
      "UPDATE \"POPULATION__STAGING_TABLE_1\"\n",
      "SET \"product_code__mapping_target_1_avg\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"value\"\n",
      "FROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"\n",
      "WHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"key\";\n",
      "\n",
      "DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\n"
     ]
    }
   ],
   "source": [
    "print(pipe1.features.to_sql()[pipe1.features.sort(by=\"importances\")[0].name])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly their SQL statements can be produced for pipe2 and pipe3 with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\n",
      "\n",
      "CREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\n",
      "\n",
      "INSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\n",
      "VALUES('410901', 0.5265553869499241),\n",
      "      ('410140', 0.5248618784530387),\n",
      "      ('004190', 0.5073846153846154),\n",
      "      ('410120', 0.5013123359580053),\n",
      "      ('410110', 0.4444444444444444),\n",
      "      ('004100', 0.3336306868867083),\n",
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      "      ('230900', 0.003992015968063872),\n",
      "      ('520410', 0.003937007874015748),\n",
      "      ('140340', 0.003897369275738876),\n",
      "      ('490313', 0.003875968992248062),\n",
      "      ('009000', 0.002952029520295203),\n",
      "      ('350110', 0.002881844380403458),\n",
      "      ('140330', 0.002380952380952381),\n",
      "      ('130122', 0.002169197396963124),\n",
      "      ('150212', 0.001451378809869376),\n",
      "      ('130121', 0.001373626373626374),\n",
      "      ('190323', 0.0009389671361502347),\n",
      "      ('190311', 0.0008796003096193089),\n",
      "      ('200532', 0.0005934718100890207),\n",
      "      ('190312', 0.0005761198329252485),\n",
      "      ('190314', 0.0004549590536851683),\n",
      "      ('190324', 0.0004541326067211626),\n",
      "      ('200522', 0.0004464285714285714),\n",
      "      ('190212', 0.0004089793692629283),\n",
      "      ('190114', 0.0003787878787878788),\n",
      "      ('190112', 0.0003610760064993681),\n",
      "      ('190322', 0.0002765869174388052),\n",
      "      ('190211', 0.0002144925463840132),\n",
      "      ('190111', 0.0002058036633052068),\n",
      "      ('200512', 0.0001853911753800519),\n",
      "      ('190321', 7.427213309566251e-05),\n",
      "      ('440140', 0),\n",
      "      ('200112', 0),\n",
      "      ('620925', 0),\n",
      "      ('250110', 0),\n",
      "      ('200531', 0),\n",
      "      ('310242', 0),\n",
      "      ('600130', 0),\n",
      "      ('580901', 0),\n",
      "      ('200521', 0),\n",
      "      ('490316', 0),\n",
      "      ('200523', 0),\n",
      "      ('190113', 0),\n",
      "      ('310241', 0),\n",
      "      ('550340', 0),\n",
      "      ('450350', 0),\n",
      "      ('190214', 0),\n",
      "      ('300410', 0),\n",
      "      ('530903', 0),\n",
      "      ('200513', 0),\n",
      "      ('140410', 0),\n",
      "      ('002200', 0),\n",
      "      ('630900', 0),\n",
      "      ('680210', 0),\n",
      "      ('290210', 0),\n",
      "      ('140310', 0),\n",
      "      ('200533', 0),\n",
      "      ('440110', 0),\n",
      "      ('190313', 0),\n",
      "      ('190213', 0),\n",
      "      ('270311', 0),\n",
      "      ('270900', 0),\n",
      "      ('200511', 0);\n",
      "\n",
      "ALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\" REAL;\n",
      "\n",
      "UPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\n",
      "\n",
      "UPDATE \"POPULATION__STAGING_TABLE_1\"\n",
      "SET \"product_code__mapping_target_1_avg\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"value\"\n",
      "FROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"\n",
      "WHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"key\";\n",
      "\n",
      "DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\n"
     ]
    }
   ],
   "source": [
    "print(pipe2.features.to_sql()[pipe2.features.sort(by=\"importances\")[0].name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\n",
      "\n",
      "CREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\n",
      "\n",
      "INSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\n",
      "VALUES('410901', 0.5265553869499241),\n",
      "      ('410140', 0.5248618784530387),\n",
      "      ('004190', 0.5073846153846154),\n",
      "      ('410120', 0.5013123359580053),\n",
      "      ('410110', 0.4444444444444444),\n",
      "      ('004100', 0.3336306868867083),\n",
      "      ('390110', 0.3132530120481928),\n",
      "      ('390120', 0.3067484662576687),\n",
      "      ('410130', 0.2967448902346707),\n",
      "      ('370110', 0.2948717948717949),\n",
      "      ('370212', 0.2944444444444445),\n",
      "      ('370220', 0.2920353982300885),\n",
      "      ('680140', 0.288135593220339),\n",
      "      ('390322', 0.2795918367346939),\n",
      "      ('390321', 0.2764227642276423),\n",
      "      ('370901', 0.271948608137045),\n",
      "      ('390210', 0.2579837194740138),\n",
      "      ('370125', 0.2519157088122606),\n",
      "      ('390310', 0.2443181818181818),\n",
      "      ('390223', 0.2344706911636046),\n",
      "      ('390230', 0.2238442822384428),\n",
      "      ('370211', 0.2185714285714286),\n",
      "      ('370314', 0.2182952182952183),\n",
      "      ('400220', 0.2164179104477612),\n",
      "      ('610110', 0.2162868883078072),\n",
      "      ('360320', 0.2151898734177215),\n",
      "      ('590220', 0.2075471698113208),\n",
      "      ('370213', 0.2015968063872255),\n",
      "      ('400210', 0.1944764096662831),\n",
      "      ('430120', 0.194006309148265),\n",
      "      ('320130', 0.1899441340782123),\n",
      "      ('390901', 0.1797752808988764),\n",
      "      ('330410', 0.1751831107281344),\n",
      "      ('380410', 0.1386392811296534),\n",
      "      ('590230', 0.13469068128426),\n",
      "      ('360350', 0.1321279554937413),\n",
      "      ('360210', 0.1305555555555556),\n",
      "      ('290420', 0.1282051282051282),\n",
      "      ('280220', 0.1231884057971015),\n",
      "      ('320903', 0.1229724632214259),\n",
      "      ('360420', 0.1222091656874266),\n",
      "      ('005000', 0.1219512195121951),\n",
      "      ('660900', 0.1205479452054795),\n",
      "      ('320345', 0.1176205497972059),\n",
      "      ('610902', 0.1162790697674419),\n",
      "      ('660110', 0.111731843575419),\n",
      "      ('600900', 0.1111111111111111),\n",
      "      ('670110', 0.1111111111111111),\n",
      "      ('320233', 0.1108969866853539),\n",
      "      ('610230', 0.11),\n",
      "      ('660210', 0.1097922848664688),\n",
      "      ('610901', 0.1097560975609756),\n",
      "      ('380510', 0.1081081081081081),\n",
      "      ('290310', 0.1044776119402985),\n",
      "      ('280120', 0.1030640668523677),\n",
      "      ('380901', 0.1010141987829615),\n",
      "      ('320521', 0.1009174311926606),\n",
      "      ('360330', 0.1004366812227074),\n",
      "      ('360311', 0.09981167608286252),\n",
      "      ('430110', 0.09863945578231292),\n",
      "      ('300320', 0.0975609756097561),\n",
      "      ('360312', 0.09716599190283401),\n",
      "      ('660000', 0.09413886384129846),\n",
      "      ('600430', 0.09302325581395349),\n",
      "      ('380110', 0.09302325581395349),\n",
      "      ('310231', 0.09090909090909091),\n",
      "      ('004000', 0.08723998758149643),\n",
      "      ('600410', 0.08408408408408409),\n",
      "      ('310210', 0.08333333333333333),\n",
      "      ('340120', 0.08333333333333333),\n",
      "      ('430130', 0.08226221079691516),\n",
      "      ('600210', 0.08190476190476191),\n",
      "      ('380315', 0.08014981273408239),\n",
      "      ('610120', 0.07865168539325842),\n",
      "      ('620610', 0.07755102040816327),\n",
      "      ('360513', 0.07722969606377678),\n",
      "      ('280140', 0.07646356033452807),\n",
      "      ('320380', 0.07645788336933046),\n",
      "      ('620213', 0.07375643224699828),\n",
      "      ('620510', 0.07370393504059962),\n",
      "      ('380430', 0.07358390682901006),\n",
      "      ('310316', 0.07329842931937172),\n",
      "      ('310232', 0.0726950354609929),\n",
      "      ('200210', 0.07258064516129033),\n",
      "      ('530110', 0.07235621521335807),\n",
      "      ('380320', 0.07142857142857142),\n",
      "      ('640420', 0.06923076923076923),\n",
      "      ('620214', 0.0689900426742532),\n",
      "      ('610130', 0.06882591093117409),\n",
      "      ('290410', 0.06748466257668712),\n",
      "      ('380420', 0.06734816596512327),\n",
      "      ('400310', 0.0672059738643435),\n",
      "      ('690117', 0.06666666666666667),\n",
      "      ('610903', 0.06578947368421052),\n",
      "      ('310220', 0.06555863342566944),\n",
      "      ('320330', 0.06554307116104868),\n",
      "      ('400110', 0.06538692261547691),\n",
      "      ('640120', 0.06442953020134229),\n",
      "      ('690230', 0.0641025641025641),\n",
      "      ('620330', 0.06329113924050633),\n",
      "      ('420115', 0.06281407035175879),\n",
      "      ('380311', 0.0625),\n",
      "      ('310340', 0.06231454005934718),\n",
      "      ('320370', 0.06196746707978312),\n",
      "      ('380340', 0.06157635467980296),\n",
      "      ('380210', 0.06014492753623189),\n",
      "      ('620112', 0.05970149253731343),\n",
      "      ('340110', 0.05929824561403509),\n",
      "      ('320901', 0.05747126436781609),\n",
      "      ('280110', 0.05726600985221675),\n",
      "      ('290120', 0.05673758865248227),\n",
      "      ('320150', 0.05652173913043478),\n",
      "      ('240220', 0.05647840531561462),\n",
      "      ('340907', 0.05555555555555555),\n",
      "      ('600310', 0.05521472392638037),\n",
      "      ('320221', 0.05381727158948686),\n",
      "      ('320522', 0.05371900826446281),\n",
      "      ('620913', 0.05333333333333334),\n",
      "      ('340510', 0.052734375),\n",
      "      ('640130', 0.05263157894736842),\n",
      "      ('310332', 0.05128205128205128),\n",
      "      ('320232', 0.05029013539651837),\n",
      "      ('380333', 0.0501577287066246),\n",
      "      ('690118', 0.05),\n",
      "      ('670903', 0.04895104895104895),\n",
      "      ('320905', 0.04766031195840555),\n",
      "      ('320627', 0.04761904761904762),\n",
      "      ('320902', 0.04666666666666667),\n",
      "      ('690110', 0.04666666666666667),\n",
      "      ('150110', 0.04635643740546312),\n",
      "      ('620221', 0.04615384615384616),\n",
      "      ('670901', 0.04597701149425287),\n",
      "      ('001000', 0.04587155963302753),\n",
      "      ('670310', 0.04553734061930783),\n",
      "      ('340610', 0.04444444444444445),\n",
      "      ('200410', 0.04397394136807817),\n",
      "      ('300900', 0.04375),\n",
      "      ('610320', 0.04300578034682081),\n",
      "      ('300110', 0.0425531914893617),\n",
      "      ('002000', 0.0418848167539267),\n",
      "      ('680220', 0.04184704184704185),\n",
      "      ('570901', 0.04081632653061224),\n",
      "      ('280210', 0.04081632653061224),\n",
      "      ('600420', 0.04044489383215369),\n",
      "      ('320420', 0.0400890868596882),\n",
      "      ('290440', 0.038860103626943),\n",
      "      ('200310', 0.03872966692486444),\n",
      "      ('310900', 0.0380952380952381),\n",
      "      ('520550', 0.03773584905660377),\n",
      "      ('690116', 0.03773584905660377),\n",
      "      ('020410', 0.03773262762506403),\n",
      "      ('440130', 0.03759398496240601),\n",
      "      ('380902', 0.03571428571428571),\n",
      "      ('550320', 0.03547297297297297),\n",
      "      ('290110', 0.03539823008849557),\n",
      "      ('590210', 0.03476151980598222),\n",
      "      ('320904', 0.03454231433506045),\n",
      "      ('490311', 0.03448275862068965),\n",
      "      ('620310', 0.03422053231939164),\n",
      "      ('220000', 0.03418803418803419),\n",
      "      ('320120', 0.03355704697986577),\n",
      "      ('240310', 0.03343949044585987),\n",
      "      ('310351', 0.03333333333333333),\n",
      "      ('640310', 0.03329679364209372),\n",
      "      ('670902', 0.03174603174603174),\n",
      "      ('680903', 0.03137789904502047),\n",
      "      ('310140', 0.0308641975308642),\n",
      "      ('620420', 0.03061224489795918),\n",
      "      ('630220', 0.03052325581395349),\n",
      "      ('330610', 0.03022860381636123),\n",
      "      ('330510', 0.02971188475390156),\n",
      "      ('180620', 0.02942668696093353),\n",
      "      ('240900', 0.02941176470588235),\n",
      "      ('550330', 0.02935420743639922),\n",
      "      ('320610', 0.02929427430093209),\n",
      "      ('620710', 0.02877697841726619),\n",
      "      ('290320', 0.02877697841726619),\n",
      "      ('200111', 0.02867072111207646),\n",
      "      ('240320', 0.02842928216062544),\n",
      "      ('310352', 0.02838427947598253),\n",
      "      ('320410', 0.02791625124626122),\n",
      "      ('300218', 0.02777777777777778),\n",
      "      ('320110', 0.02768166089965398),\n",
      "      ('620121', 0.02765208647561589),\n",
      "      ('340210', 0.02722323049001815),\n",
      "      ('240210', 0.02707581227436823),\n",
      "      ('440150', 0.02702702702702703),\n",
      "      ('320140', 0.02697022767075306),\n",
      "      ('640220', 0.02683461117196057),\n",
      "      ('640410', 0.026232741617357),\n",
      "      ('310335', 0.02593659942363112),\n",
      "      ('490315', 0.02564102564102564),\n",
      "      ('340901', 0.02542372881355932),\n",
      "      ('610310', 0.02461584365209608),\n",
      "      ('680110', 0.02362204724409449),\n",
      "      ('340903', 0.0234375),\n",
      "      ('480213', 0.0231811697574893),\n",
      "      ('320430', 0.02272727272727273),\n",
      "      ('230000', 0.02272727272727273),\n",
      "      ('640210', 0.02267002518891688),\n",
      "      ('550310', 0.02246796559592768),\n",
      "      ('490110', 0.02173913043478261),\n",
      "      ('620410', 0.02165087956698241),\n",
      "      ('340913', 0.02127659574468085),\n",
      "      ('340906', 0.02127659574468085),\n",
      "      ('590110', 0.0209366391184573),\n",
      "      ('620810', 0.02090592334494774),\n",
      "      ('020710', 0.02085600290170475),\n",
      "      ('620926', 0.02076875387476751),\n",
      "      ('480212', 0.02055622732769045),\n",
      "      ('020510', 0.0202097074243193),\n",
      "      ('650210', 0.02016868353502017),\n",
      "      ('530510', 0.02005730659025788),\n",
      "      ('520310', 0.02),\n",
      "      ('480110', 0.01970443349753695),\n",
      "      ('550110', 0.0194300518134715),\n",
      "      ('650110', 0.0190424374319913),\n",
      "      ('320511', 0.01829268292682927),\n",
      "      ('240120', 0.01818181818181818),\n",
      "      ('040610', 0.01785714285714286),\n",
      "      ('170531', 0.0177293934681182),\n",
      "      ('550210', 0.01761658031088083),\n",
      "      ('290430', 0.01748251748251748),\n",
      "      ('002100', 0.01715481171548117),\n",
      "      ('150310', 0.01708217913204063),\n",
      "      ('560310', 0.01682692307692308),\n",
      "      ('640110', 0.01674500587544066),\n",
      "      ('640430', 0.01648351648351648),\n",
      "      ('570000', 0.01633393829401089),\n",
      "      ('240110', 0.0162052667116813),\n",
      "      ('690119', 0.01618122977346278),\n",
      "      ('630110', 0.0158344666796192),\n",
      "      ('330310', 0.01570146818923328),\n",
      "      ('020820', 0.01567783584383646),\n",
      "      ('130320', 0.0156165858912224),\n",
      "      ('630210', 0.0155902004454343),\n",
      "      ('020610', 0.01553829078801332),\n",
      "      ('010120', 0.01547231270358306),\n",
      "      ('180310', 0.01535880227155395),\n",
      "      ('550410', 0.01529571719918423),\n",
      "      ('360110', 0.01515151515151515),\n",
      "      ('620114', 0.01492537313432836),\n",
      "      ('440210', 0.01488095238095238),\n",
      "      ('470220', 0.01478743068391867),\n",
      "      ('620111', 0.01471389645776567),\n",
      "      ('330210', 0.01441871961769795),\n",
      "      ('140320', 0.01423487544483986),\n",
      "      ('340520', 0.01411100658513641),\n",
      "      ('560210', 0.01355661881977671),\n",
      "      ('530311', 0.01341184167484462),\n",
      "      ('330110', 0.01330895052321447),\n",
      "      ('050900', 0.0131578947368421),\n",
      "      ('250900', 0.01309707241910632),\n",
      "      ('690120', 0.01305483028720627),\n",
      "      ('490300', 0.01298701298701299),\n",
      "      ('180320', 0.01298701298701299),\n",
      "      ('170533', 0.01296982530439386),\n",
      "      ('540000', 0.01271259233808624),\n",
      "      ('170510', 0.01269971323228185),\n",
      "      ('620930', 0.01252609603340292),\n",
      "      ('340410', 0.01241642788920726),\n",
      "      ('270000', 0.01241039905852145),\n",
      "      ('520110', 0.01237964236588721),\n",
      "      ('560400', 0.01210898082744702),\n",
      "      ('180612', 0.01201452919810003),\n",
      "      ('620320', 0.01185770750988142),\n",
      "      ('470211', 0.01179941002949852),\n",
      "      ('180520', 0.01179574732267577),\n",
      "      ('100410', 0.01164329187615771),\n",
      "      ('310331', 0.01162790697674419),\n",
      "      ('530412', 0.01158504476040021),\n",
      "      ('020810', 0.01154575219713941),\n",
      "      ('530210', 0.01152737752161383),\n",
      "      ('220110', 0.01149425287356322),\n",
      "      ('320630', 0.01142857142857143),\n",
      "      ('520531', 0.01112484548825711),\n",
      "      ('180710', 0.01103708190322364),\n",
      "      ('030810', 0.01092896174863388),\n",
      "      ('130310', 0.01086556169429098),\n",
      "      ('170210', 0.01082262080178853),\n",
      "      ('340620', 0.01075268817204301),\n",
      "      ('999900', 0.01062416998671979),\n",
      "      ('030210', 0.01055662188099808),\n",
      "      ('030510', 0.01044277360066834),\n",
      "      ('170110', 0.01034780109226789),\n",
      "      ('220210', 0.01027397260273973),\n",
      "      ('680902', 0.01025641025641026),\n",
      "      ('020310', 0.01021667580910587),\n",
      "      ('130212', 0.009969657563935847),\n",
      "      ('030710', 0.009891435464414958),\n",
      "      ('140420', 0.009844993715961458),\n",
      "      ('560330', 0.009771986970684038),\n",
      "      ('270210', 0.009420631182289214),\n",
      "      ('140220', 0.009351432880844645),\n",
      "      ('160320', 0.00933609958506224),\n",
      "      ('560110', 0.009322560596643879),\n",
      "      ('170520', 0.009291360421578144),\n",
      "      ('230110', 0.009202453987730062),\n",
      "      ('170310', 0.009154113557358054),\n",
      "      ('180110', 0.009134615384615385),\n",
      "      ('140210', 0.009130282102305981),\n",
      "      ('160212', 0.009098914000587027),\n",
      "      ('050410', 0.008833922261484099),\n",
      "      ('100210', 0.008741319144525446),\n",
      "      ('170532', 0.008554705087798289),\n",
      "      ('620912', 0.008553654743390357),\n",
      "      ('090210', 0.008506616257088847),\n",
      "      ('490000', 0.008489564909798374),\n",
      "      ('170410', 0.008431932544539644),\n",
      "      ('210210', 0.00823045267489712),\n",
      "      ('020620', 0.008152173913043478),\n",
      "      ('340310', 0.008032128514056224),\n",
      "      ('110410', 0.007990834884720034),\n",
      "      ('490312', 0.007977207977207978),\n",
      "      ('210110', 0.007972665148063782),\n",
      "      ('180420', 0.007866728366496992),\n",
      "      ('180220', 0.007703887363853715),\n",
      "      ('010210', 0.007637017070979336),\n",
      "      ('180510', 0.007588713125267208),\n",
      "      ('470111', 0.007556238768484639),\n",
      "      ('060310', 0.007518796992481203),\n",
      "      ('050310', 0.007514761137949544),\n",
      "      ('030610', 0.007317073170731708),\n",
      "      ('180611', 0.007287611061195967),\n",
      "      ('010320', 0.007257694074414332),\n",
      "      ('500110', 0.007106598984771574),\n",
      "      ('040510', 0.006984459577440196),\n",
      "      ('110310', 0.006973269134982567),\n",
      "      ('250220', 0.006944444444444444),\n",
      "      ('580000', 0.006857142857142857),\n",
      "      ('020210', 0.006824146981627296),\n",
      "      ('180210', 0.006806282722513089),\n",
      "      ('040410', 0.006790744466800805),\n",
      "      ('050110', 0.00675990675990676),\n",
      "      ('010110', 0.006644518272425249),\n",
      "      ('180410', 0.006634078212290503),\n",
      "      ('140230', 0.00663265306122449),\n",
      "      ('050210', 0.00662133142448103),\n",
      "      ('160310', 0.006574892130675981),\n",
      "      ('020110', 0.006501360749924402),\n",
      "      ('070110', 0.006377551020408163),\n",
      "      ('030310', 0.00625),\n",
      "      ('120310', 0.006177540831006178),\n",
      "      ('100510', 0.006119326874043855),\n",
      "      ('030410', 0.006116207951070336),\n",
      "      ('690114', 0.006105834464043419),\n",
      "      ('110510', 0.005989518342899925),\n",
      "      ('160211', 0.005981308411214953),\n",
      "      ('150211', 0.005960568546538285),\n",
      "      ('130211', 0.005947955390334572),\n",
      "      ('520541', 0.005911778080945885),\n",
      "      ('120210', 0.005798018131983976),\n",
      "      ('040110', 0.005780346820809248),\n",
      "      ('260110', 0.005772763054316453),\n",
      "      ('070240', 0.005749668288367979),\n",
      "      ('090110', 0.005704227647576519),\n",
      "      ('110210', 0.005692403229145104),\n",
      "      ('030110', 0.005622410731899783),\n",
      "      ('260210', 0.0055542698449433),\n",
      "      ('080110', 0.005548549810844893),\n",
      "      ('120110', 0.005436931593515224),\n",
      "      ('040310', 0.005404077622205846),\n",
      "      ('250210', 0.005342831700801425),\n",
      "      ('010310', 0.005331627212625293),\n",
      "      ('440120', 0.005319148936170213),\n",
      "      ('100110', 0.005308219178082192),\n",
      "      ('470112', 0.005277044854881266),\n",
      "      ('110110', 0.005152378864284149),\n",
      "      ('160110', 0.005109489051094891),\n",
      "      ('270410', 0.00496031746031746),\n",
      "      ('060110', 0.004922542348342262),\n",
      "      ('520516', 0.004901960784313725),\n",
      "      ('270310', 0.004885574697865775),\n",
      "      ('120410', 0.004865350089766607),\n",
      "      ('220120', 0.004815409309791332),\n",
      "      ('040210', 0.004786324786324786),\n",
      "      ('070230', 0.004725554343874954),\n",
      "      ('130110', 0.004694835680751174),\n",
      "      ('140110', 0.004555336991406978),\n",
      "      ('340530', 0.004530011325028313),\n",
      "      ('060210', 0.00400114318376679),\n",
      "      ('230900', 0.003992015968063872),\n",
      "      ('520410', 0.003937007874015748),\n",
      "      ('140340', 0.003897369275738876),\n",
      "      ('490313', 0.003875968992248062),\n",
      "      ('009000', 0.002952029520295203),\n",
      "      ('350110', 0.002881844380403458),\n",
      "      ('140330', 0.002380952380952381),\n",
      "      ('130122', 0.002169197396963124),\n",
      "      ('150212', 0.001451378809869376),\n",
      "      ('130121', 0.001373626373626374),\n",
      "      ('190323', 0.0009389671361502347),\n",
      "      ('190311', 0.0008796003096193089),\n",
      "      ('200532', 0.0005934718100890207),\n",
      "      ('190312', 0.0005761198329252485),\n",
      "      ('190314', 0.0004549590536851683),\n",
      "      ('190324', 0.0004541326067211626),\n",
      "      ('200522', 0.0004464285714285714),\n",
      "      ('190212', 0.0004089793692629283),\n",
      "      ('190114', 0.0003787878787878788),\n",
      "      ('190112', 0.0003610760064993681),\n",
      "      ('190322', 0.0002765869174388052),\n",
      "      ('190211', 0.0002144925463840132),\n",
      "      ('190111', 0.0002058036633052068),\n",
      "      ('200512', 0.0001853911753800519),\n",
      "      ('190321', 7.427213309566251e-05),\n",
      "      ('440140', 0),\n",
      "      ('200112', 0),\n",
      "      ('620925', 0),\n",
      "      ('250110', 0),\n",
      "      ('200531', 0),\n",
      "      ('310242', 0),\n",
      "      ('600130', 0),\n",
      "      ('580901', 0),\n",
      "      ('200521', 0),\n",
      "      ('490316', 0),\n",
      "      ('200523', 0),\n",
      "      ('190113', 0),\n",
      "      ('310241', 0),\n",
      "      ('550340', 0),\n",
      "      ('450350', 0),\n",
      "      ('190214', 0),\n",
      "      ('300410', 0),\n",
      "      ('530903', 0),\n",
      "      ('200513', 0),\n",
      "      ('140410', 0),\n",
      "      ('002200', 0),\n",
      "      ('630900', 0),\n",
      "      ('680210', 0),\n",
      "      ('290210', 0),\n",
      "      ('140310', 0),\n",
      "      ('200533', 0),\n",
      "      ('440110', 0),\n",
      "      ('190313', 0),\n",
      "      ('190213', 0),\n",
      "      ('270311', 0),\n",
      "      ('270900', 0),\n",
      "      ('200511', 0);\n",
      "\n",
      "ALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\" REAL;\n",
      "\n",
      "UPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\n",
      "\n",
      "UPDATE \"POPULATION__STAGING_TABLE_1\"\n",
      "SET \"product_code__mapping_target_1_avg\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"value\"\n",
      "FROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"\n",
      "WHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"key\";\n",
      "\n",
      "DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\n"
     ]
    }
   ],
   "source": [
    "print(pipe3.features.to_sql()[pipe2.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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a folder named containing the SQL code.\n",
    "pipe3.features.to_sql().save(\"consumer_expenditures_pipeline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe3.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"consumer_expenditures_spark\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "getml.engine.shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Conclusion\n",
    "\n",
    "In this notebook, we have shown how you can use relational learning to predict whether items were purchased as a gift. We did this to highlight the importance of relational learning. Relational learning can be used in many real-world data science applications, but unfortunately most data scientists don't even know what relation learning is."
   ]
  }
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