consumer_expenditures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consumer expenditure categorization\n",
    "\n",
    "### 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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import os\n",
    "from pathlib import Path\n",
    "from urllib import request\n",
    "import time\n",
    "import zipfile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import getml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getML engine is already running.\n",
      "Loading pipelines... 100% |██████████| [elapsed: 00:52, remaining: 00:00]          \n",
      "\n",
      "Connected to project 'consumer_expenditures'\n"
     ]
    }
   ],
   "source": [
    "getml.engine.launch(in_memory=False, home_directory=Path.home(), 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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Connection(dbname='ConsumerExpenditures',\n",
       "           dialect='mysql',\n",
       "           host='db.relational-data.org',\n",
       "           port=3306)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = getml.database.connect_mysql(\n",
    "    host=\"db.relational-data.org\",\n",
    "    dbname=\"ConsumerExpenditures\",\n",
    "    port=3306,\n",
    "    user=\"guest\",\n",
    "    password=\"relational\"\n",
    ")\n",
    "\n",
    "conn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
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       "    \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",
       "            \n",
       "            <td class=\"char-align unused_float\">\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 unused_float\">\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 unused_float\">\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 unused_float\">\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 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",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>56810</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",
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       "          \n",
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       "              \n",
       "            \n",
       "            <td class=\"char-align unused_float\">\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 unused_float\">\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 unused_float\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 5ch\"\n",
       "                >.03955</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",
       "                >.04507</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",
       "                >.04607</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",
       "                >.02436</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",
       "                >.03558</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",
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       "    \n",
       "      <tr>\n",
       "        <th>56811</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",
       "                >.1616</span\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",
       "                >.03925</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",
       "                >.05741</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",
       "                >.04595</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",
       "                >.03789</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",
       "                >.05746</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",
       "                >.03931</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",
       "      </tr>\n",
       "    \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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          <th>  name</th>\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">66</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "            <td class=\"unused_string\">03111041</td>\n",
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       "          \n",
       "            <td class=\"unused_string\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1</td>\n",
       "          \n",
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       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
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       "    \n",
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       "        <th>1</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",
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       "              \n",
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       "              <span class=\"left\">66</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
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       "          \n",
       "            <td class=\"unused_string\">03111042</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">1</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">NULL</td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            \n",
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       "              \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",
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       "            <td class=\"unused_string\">03111091</td>\n",
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       "          \n",
       "        \n",
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       "          \n",
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       "                >&nbsp;</span\n",
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       "              <span class=\"left\">56</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "        \n",
       "          \n",
       "            <td class=\"unused_string\">03111092</td>\n",
       "          \n",
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       "          \n",
       "        \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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       "            <td class=\"char-align unused_float\">\n",
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       "              ><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",
       "          \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",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \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",
       "      </tr>\n",
       "    \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",
       "            </td>\n",
       "          \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",
       "            </td>\n",
       "          \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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "household_members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.89</span\n",
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       "              \n",
       "          \n",
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       "              ><span class=\"right\" style=\"width: 0ch\"\n",
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       "              <span class=\"left\">9</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.79</span\n",
       "              >\n",
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       "              \n",
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       "                >&nbsp;</span\n",
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       "        \n",
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       "            \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",
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       "          \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": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th> name</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"join_key\">HOUSEHOLD_ID</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">     YEAR</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">INCOME_RANK</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">INCOME_RANK_1</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">INCOME_RANK_2</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">INCOME_RANK_3</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">INCOME_RANK_4</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">INCOME_RANK_5</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">INCOME_RANK_MEAN</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"numerical\">  AGE_REF</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\"> role</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">    join_key</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">  numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">    numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">    numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">    numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">    numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">    numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">       numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03111041</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\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 numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.3044</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",
       "                >.1448</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",
       "                >.1427</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",
       "                >.1432</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",
       "                >.1422</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",
       "                >.1382</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",
       "                >.127</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">66</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03111042</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\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 numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.3063</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",
       "                >.1462</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",
       "                >.1444</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",
       "                >.1446</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",
       "                >.1435</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",
       "                >.1395</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",
       "                >.1283</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">66</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03111051</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\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 numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 4ch\"\n",
       "                >.6931</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",
       "                >.6222</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",
       "                >.6204</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",
       "                >.623</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",
       "                >.6131</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",
       "                >.6123</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",
       "                >.6207</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">48</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03111052</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\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 numerical\">\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 numerical\">\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 numerical\">\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 numerical\">\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 numerical\">\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 numerical\">\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 numerical\">\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 numerical\">\n",
       "              <span class=\"left\">48</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">03111061</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\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 numerical\">\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 numerical\">\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 numerical\">\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 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",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
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       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
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       "          \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",
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       "          \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",
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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: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
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       "    \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",
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       "              \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",
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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",
       "                >.4106</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
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       "          \n",
       "            \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",
       "                >.3603</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
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       "          \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",
       "                >.3958</span\n",
       "              >\n",
       "            </td>\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",
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       "              >\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",
       "                >.3984</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",
       "                >.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",
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       "          \n",
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       "            \n",
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       "              \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",
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       "          \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",
       "                >.596</span\n",
       "              >\n",
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       "          \n",
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       "          \n",
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       "              \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",
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       "        \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",
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       "            \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",
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       "              \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",
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       "              \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",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <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",
       "        \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",
       "                >.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",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "            <td class=\"char-align numerical\">\n",
       "              <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": 9,
     "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": 10,
   "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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       "          \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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       "          \n",
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       "            <td class=\"categorical\">1</td>\n",
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       "            <td class=\"char-align numerical\">\n",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</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>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",
       "              \n",
       "          \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",
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       "          \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",
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       "        <th>137352</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362432</td>\n",
       "          \n",
       "        \n",
       "          \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",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">11</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\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",
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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",
       "            </td>\n",
       "          \n",
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       "          \n",
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       "              \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",
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       "    \n",
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       "        <th>137354</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">04362672</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">5</td>\n",
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       "          \n",
       "            <td class=\"categorical\">2</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",
       "  </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": 10,
     "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  }\n",
       "  th.sub-header {\n",
       "    font-weight: normal;\n",
       "    font-style: italic;\n",
       "  }\n",
       "  .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",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">MONTH      </th>\n",
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       "        \n",
       "          <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",
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       "        \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",
       "        \n",
       "          \n",
       "            <th class=\"sub-header target\">target</th>\n",
       "          \n",
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       "          \n",
       "            <th class=\"sub-header categorical\">categorical</th>\n",
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       "        \n",
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       "            <th class=\"sub-header categorical\">categorical</th>\n",
       "          \n",
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       "          \n",
       "            <th class=\"sub-header categorical\">categorical </th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header numerical\">numerical</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header unused_float\">unused_float</th>\n",
       "          \n",
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       "            <th class=\"sub-header unused_string\">unused_string </th>\n",
       "          \n",
       "        \n",
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       "    \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",
       "        \n",
       "          \n",
       "            <th class=\"sub-header target\">      </th>\n",
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       "          \n",
       "            <th class=\"sub-header categorical\">           </th>\n",
       "          \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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       "            <th class=\"sub-header unused_float\">            </th>\n",
       "          \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": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th.sub-header {\n",
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       "    font-style: italic;\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\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": 13,
     "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": 14,
   "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=\"int\">   rows</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">type</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">EXPENDITURES</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">387583</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=\"int\">1633051</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    387583   View\n",
       "  1   train    EXPENDITURES   1633051   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": 14,
     "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": 15,
   "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": 16,
   "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": 16,
     "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": 17,
   "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": 17,
     "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": 18,
   "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": 18,
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data model...\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:20, remaining: 00:00]          \n",
      "Checking... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "\n",
      "OK.\n"
     ]
    }
   ],
   "source": [
    "pipe1.check(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data model...\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n",
      "OK.\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Trying 418 features... 100% |██████████| [elapsed: 00:40, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 01:54, remaining: 00:00]          \n",
      "XGBoost: Training as feature selector... 100% |██████████| [elapsed: 06:58, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 07:29, remaining: 00:00]          \n",
      "\n",
      "Trained pipeline.\n",
      "Time taken: 0h:17m:4.359167\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-2IKXQ4'])</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-2IKXQ4'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.fit(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data model...\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:55, remaining: 00:00]          \n",
      "Checking... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n",
      "OK.\n"
     ]
    }
   ],
   "source": [
    "pipe2.check(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data model...\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:05, remaining: 00:00]          \n",
      "\n",
      "OK.\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:07, remaining: 00:00]          \n",
      "Relboost: Training features... 100% |██████████| [elapsed: 08:34, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 06:02, remaining: 00:00]          \n",
      "XGBoost: Training as feature selector... 100% |██████████| [elapsed: 02:55, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 06:32, remaining: 00:00]          \n",
      "\n",
      "Trained pipeline.\n",
      "Time taken: 0h:24m:12.258032\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-2IKXQ4'])</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-2IKXQ4'])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.fit(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data model...\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Checking... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n",
      "OK.\n"
     ]
    }
   ],
   "source": [
    "pipe3.check(star_schema.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data model...\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n",
      "OK.\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Retrieving features (because a similar feature learner has already been fitted)... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Training features... 100% |██████████| [elapsed: 05:33, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 01:36, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 05:17, remaining: 00:00]          \n",
      "XGBoost: Training as feature selector... 100% |██████████| [elapsed: 06:30, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 03:50, remaining: 00:00]          \n",
      "\n",
      "Trained pipeline.\n",
      "Time taken: 0h:22m:47.586342\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-2IKXQ4'])</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-2IKXQ4'])"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 00:05, remaining: 00:00]          \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
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       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">accuracy</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">    auc</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">cross entropy</th>\n",
       "        \n",
       "      \n",
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       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 16:40:54</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">GIFT</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9827</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9351</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.06029</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 17:29:07</td>\n",
       "            \n",
       "          \n",
       "            \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\">0.8658</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.07702</td>\n",
       "            \n",
       "          \n",
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       "    \n",
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      ],
      "text/plain": [
       "    date time             set used   target   accuracy       auc   cross entropy\n",
       "0   2024-02-21 16:40:54   train      GIFT       0.9827    0.9351         0.06029\n",
       "1   2024-02-21 17:29:07   test       GIFT       0.9804    0.8658         0.07702"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.score(star_schema.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:04, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 01:41, remaining: 00:00]          \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
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       "          <th class=\"float\">    auc</th>\n",
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       "          <th class=\"float\">cross entropy</th>\n",
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       "              <td class=\"datetime\">2024-02-21 17:06:09</td>\n",
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       "              <td class=\"str\">GIFT</td>\n",
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       "            \n",
       "              <td class=\"float\">0.9823</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9205</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.0637</td>\n",
       "            \n",
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       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 17:30:53</td>\n",
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       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">GIFT</td>\n",
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       "            \n",
       "              <td class=\"float\">0.9806</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.8619</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.07703</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    date time             set used   target   accuracy       auc   cross entropy\n",
       "0   2024-02-21 17:06:09   train      GIFT       0.9823    0.9205         0.0637 \n",
       "1   2024-02-21 17:30:53   test       GIFT       0.9806    0.8619         0.07703"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.score(star_schema.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 00:03, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:06, remaining: 00:00]          \n",
      "\n"
     ]
    },
    {
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       "              <td class=\"float\">0.9825</td>\n",
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       "              <td class=\"float\">0.9325</td>\n",
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       "              <td class=\"str\">test</td>\n",
       "            \n",
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       "              <td class=\"str\">GIFT</td>\n",
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       "    date time             set used   target   accuracy       auc   cross entropy\n",
       "0   2024-02-21 17:28:59   train      GIFT       0.9825    0.9325         0.0611 \n",
       "1   2024-02-21 17:31:04   test       GIFT       0.9806    0.868          0.07648"
      ]
     },
     "execution_count": 27,
     "metadata": {},
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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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 29,
   "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": 30,
   "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": 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 = 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 features look as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "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",
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       "      ('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",
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       "      ('620330', 0.06329113924050633),\n",
       "      ('420115', 0.06281407035175879),\n",
       "      ('380311', 0.0625),\n",
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       "      ('320370', 0.06196746707978312),\n",
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       "      ('380210', 0.06014492753623189),\n",
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       "      ('340110', 0.05929824561403509),\n",
       "      ('320901', 0.05747126436781609),\n",
       "      ('280110', 0.05726600985221675),\n",
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       "      ('340907', 0.05555555555555555),\n",
       "      ('600310', 0.05521472392638037),\n",
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       "      ('620913', 0.05333333333333334),\n",
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       "      ('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",
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       "      ('620221', 0.04615384615384616),\n",
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       "      ('590210', 0.03476151980598222),\n",
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       "      ('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",
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       "      ('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",
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       "      ('520310', 0.02),\n",
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       "      ('550210', 0.01761658031088083),\n",
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       "      ('150310', 0.01708217913204063),\n",
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       "      ('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",
       "```"
      ],
      "text/plain": [
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(\\'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\\nALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\" REAL;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\"\\nSET \"product_code__mapping_target_1_avg\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"value\"\\nFROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"key\";\\n\\nDROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.features.to_sql()[pipe1.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "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",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\\n\\nCREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\\n\\nINSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\\nVALUES(\\'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\\', 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   (\\'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     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(\\'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\\nALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\" REAL;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\"\\nSET \"product_code__mapping_target_1_avg\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"value\"\\nFROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"key\";\\n\\nDROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.features.to_sql()[pipe2.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "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",
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       "      ('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",
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       "      ('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",
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       "      ('560110', 0.009322560596643879),\n",
       "      ('170520', 0.009291360421578144),\n",
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       "      ('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",
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       "      ('110310', 0.006973269134982567),\n",
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       "      ('580000', 0.006857142857142857),\n",
       "      ('020210', 0.006824146981627296),\n",
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       "      ('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",
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       "      ('520516', 0.004901960784313725),\n",
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       "      ('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",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";\\n\\nCREATE TABLE \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"(\"key\" TEXT, \"value\" REAL);\\n\\nINSERT INTO \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\" (\"key\", \"value\")\\nVALUES(\\'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\\', 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(\\'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\\nALTER TABLE \"POPULATION__STAGING_TABLE_1\" ADD COLUMN \"product_code__mapping_target_1_avg\" REAL;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\" SET \"product_code__mapping_target_1_avg\" = 0.0;\\n\\nUPDATE \"POPULATION__STAGING_TABLE_1\"\\nSET \"product_code__mapping_target_1_avg\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"value\"\\nFROM \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\"\\nWHERE \"POPULATION__STAGING_TABLE_1\".\"product_code\" = \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\".\"key\";\\n\\nDROP TABLE IF EXISTS \"PRODUCT_CODE__MAPPING_TARGET_1_AVG\";'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe3.features.to_sql()[pipe3.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": 35,
   "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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe3.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"consumer_expenditures_spark\")"
   ]
  },
  {
   "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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   "name": "python3"
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    "name": "ipython",
    "version": 3
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