cora.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CORA - Categorizing academic publications using getML\n",
    "\n",
    "In this notebook, we compare getML against extant approaches in the relational learning literature on the CORA data set, which is often used for benchmarking. We demonstrate that getML outperforms the state of the art in the relational learning literature on this data set. Beyond the benchmarking aspects, this notebooks showcases getML's excellent capabilities in dealing with categorical data.\n",
    "\n",
    "Summary:\n",
    "\n",
    "- Prediction type: __Classification model__\n",
    "- Domain: __Academia__\n",
    "- Prediction target: __The category of a paper__ \n",
    "- Population size: __2708__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background\n",
    "\n",
    "CORA is a well-known benchmarking dataset in the academic literature on relational learning. The dataset contains 2708 scientific publications on machine learning. The papers are divided into 7 categories. The challenge is to predict the category of a paper based on the papers it cites, the papers it is cited by and keywords contained in the paper.\n",
    "\n",
    "It has been downloaded from the [CTU Prague relational learning repository](https://relational.fit.cvut.cz/dataset/CORA) (Motl and Schulte, 2015)(Now residing at [relational-data.org](https://relational-data.org/dataset/CORA).)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get started with the analysis and set up your session:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getML engine is already running.\n",
      "\n",
      "Connected to project 'cora'\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "import os\n",
    "from pathlib import Path\n",
    "from urllib import request\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import Image\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "\n",
    "import getml\n",
    "\n",
    "getml.engine.launch(home_directory=Path.home(), allow_remote_ips=True, token='token')\n",
    "getml.engine.set_project('cora')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Download from source\n",
    "\n",
    "We begin by downloading the data from the source file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Connection(dbname='CORA', dialect='mysql', host='db.relational-data.org', port=3306)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = getml.database.connect_mysql(\n",
    "    host=\"db.relational-data.org\",\n",
    "    dbname=\"CORA\",\n",
    "    port=3306,\n",
    "    user=\"guest\",\n",
    "    password=\"relational\"\n",
    ")\n",
    "\n",
    "conn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 not getml.data.exists(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",
    "    else:\n",
    "        data_frame = getml.data.load_data_frame(name)\n",
    "    return data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "paper = load_if_needed(\"paper\")\n",
    "cites = load_if_needed(\"cites\")\n",
    "content = load_if_needed(\"content\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <p>\n",
       "    2708 rows x 2 columns<br />\n",
       "    memory usage: 0.09 MB<br />\n",
       "    name: paper<br />\n",
       "    type: getml.DataFrame<br />\n",
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       "name       paper_id   class_label           \n",
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       "   0             35   Genetic_Algorithms    \n",
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       "                ...   ...                   \n",
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    "paper"
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>5424</th>\n",
       "        \n",
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       "            <td class=\"char-align unused_float\">\n",
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       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "        <th>5425</th>\n",
       "        \n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
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       "          \n",
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       "              \n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">853155</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>5426</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "            <td class=\"char-align unused_float\">\n",
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       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>5427</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">853155</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">853118</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
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       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>5428</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">954315</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
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       "          \n",
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       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1155073</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",
       "    5429 rows x 2 columns<br />\n",
       "    memory usage: 0.09 MB<br />\n",
       "    name: cites<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name   cited_paper_id   citing_paper_id\n",
       "role     unused_float      unused_float\n",
       "   0               35               887\n",
       "   1               35              1033\n",
       "   2               35              1688\n",
       "   3               35              1956\n",
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       "                  ...               ...\n",
       "5424           853116             19621\n",
       "5425           853116            853155\n",
       "5426           853118           1140289\n",
       "5427           853155            853118\n",
       "5428           954315           1155073\n",
       "\n",
       "\n",
       "5429 rows x 2 columns\n",
       "memory usage: 0.09 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "            <td class=\"char-align unused_float\">\n",
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       "        <th>49211</th>\n",
       "        \n",
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       "            <td class=\"unused_string\">word815</td>\n",
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       "        <th>49215</th>\n",
       "        \n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1155073</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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       "</table>\n",
       "\n",
       "  <p>\n",
       "    49216 rows x 2 columns<br />\n",
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       "    name: content<br />\n",
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       "  </p>\n"
      ],
      "text/plain": [
       " name       paper_id   word_cited_id\n",
       " role   unused_float   unused_string\n",
       "    0             35   word100      \n",
       "    1             35   word1152     \n",
       "    2             35   word1175     \n",
       "    3             35   word1228     \n",
       "    4             35   word1248     \n",
       "                 ...   ...          \n",
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       "49214        1155073   word815      \n",
       "49215        1155073   word979      \n",
       "\n",
       "\n",
       "49216 rows x 2 columns\n",
       "memory usage: 1.20 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   "source": [
    "content"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Prepare data for getML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "getML requires that we define *roles* for each of the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "            <td class=\"categorical\">Genetic_Algorithms</td>\n",
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       "        \n",
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       "            <td class=\"join_key\">114</td>\n",
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       "            <td class=\"join_key\">117</td>\n",
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       "        \n",
       "          \n",
       "            <td class=\"categorical\">Reinforcement_Learning</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">128</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Reinforcement_Learning</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2703</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1154500</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Case_Based</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2704</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1154520</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Neural_Networks</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2705</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1154524</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Rule_Learning</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2706</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1154525</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Rule_Learning</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2707</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1155073</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Rule_Learning</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    2708 rows x 2 columns<br />\n",
       "    memory usage: 0.02 MB<br />\n",
       "    name: paper<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name   paper_id   class_label           \n",
       "role   join_key   categorical           \n",
       "   0         35   Genetic_Algorithms    \n",
       "   1         40   Genetic_Algorithms    \n",
       "   2        114   Reinforcement_Learning\n",
       "   3        117   Reinforcement_Learning\n",
       "   4        128   Reinforcement_Learning\n",
       "            ...   ...                   \n",
       "2703    1154500   Case_Based            \n",
       "2704    1154520   Neural_Networks       \n",
       "2705    1154524   Rule_Learning         \n",
       "2706    1154525   Rule_Learning         \n",
       "2707    1155073   Rule_Learning         \n",
       "\n",
       "\n",
       "2708 rows x 2 columns\n",
       "memory usage: 0.02 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paper.set_role(\"paper_id\", getml.data.roles.join_key)\n",
    "paper.set_role(\"class_label\", getml.data.roles.categorical)\n",
    "paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "          <th class=\"join_key\">citing_paper_id</th>\n",
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       "          \n",
       "            <td class=\"join_key\">8865</td>\n",
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       "            <td class=\"join_key\">853116</td>\n",
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       "        \n",
       "          \n",
       "            <td class=\"join_key\">853116</td>\n",
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       "          \n",
       "            <td class=\"join_key\">853155</td>\n",
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       "            <td class=\"join_key\">853118</td>\n",
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       "          \n",
       "            <td class=\"join_key\">1140289</td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>5427</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">853155</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">853118</td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>5428</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">954315</td>\n",
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       "          \n",
       "            <td class=\"join_key\">1155073</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    5429 rows x 2 columns<br />\n",
       "    memory usage: 0.04 MB<br />\n",
       "    name: cites<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name   cited_paper_id   citing_paper_id\n",
       "role         join_key          join_key\n",
       "   0               35               887\n",
       "   1               35              1033\n",
       "   2               35              1688\n",
       "   3               35              1956\n",
       "   4               35              8865\n",
       "                  ...               ...\n",
       "5424           853116             19621\n",
       "5425           853116            853155\n",
       "5426           853118           1140289\n",
       "5427           853155            853118\n",
       "5428           954315           1155073\n",
       "\n",
       "\n",
       "5429 rows x 2 columns\n",
       "memory usage: 0.04 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cites.set_role([\"cited_paper_id\", \"citing_paper_id\"], getml.data.roles.join_key)\n",
    "cites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to separate our data set into a training, testing and validation set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            <th class=\"sub-header categorical\">categorical  </th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "          \n",
       "            <td class=\"join_key\">35</td>\n",
       "          \n",
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       "          \n",
       "            <td class=\"categorical\">word100</td>\n",
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       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">35</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word1152</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">35</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word1175</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">35</td>\n",
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       "          \n",
       "            <td class=\"categorical\">word1228</td>\n",
       "          \n",
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       "      <tr>\n",
       "        <th>4</th>\n",
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       "          \n",
       "            <td class=\"join_key\">35</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word1248</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>49211</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1155073</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word75</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>49212</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1155073</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word759</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>49213</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1155073</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word789</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>49214</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1155073</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word815</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>49215</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1155073</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word979</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    49216 rows x 2 columns<br />\n",
       "    memory usage: 0.39 MB<br />\n",
       "    name: content<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       " name   paper_id   word_cited_id\n",
       " role   join_key   categorical  \n",
       "    0         35   word100      \n",
       "    1         35   word1152     \n",
       "    2         35   word1175     \n",
       "    3         35   word1228     \n",
       "    4         35   word1248     \n",
       "             ...   ...          \n",
       "49211    1155073   word75       \n",
       "49212    1155073   word759      \n",
       "49213    1155073   word789      \n",
       "49214    1155073   word815      \n",
       "49215    1155073   word979      \n",
       "\n",
       "\n",
       "49216 rows x 2 columns\n",
       "memory usage: 0.39 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.set_role(\"paper_id\", getml.data.roles.join_key)\n",
    "content.set_role(\"word_cited_id\", getml.data.roles.categorical)\n",
    "content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal is to predict seven different labels. We generate a target column for each of those labels. We also have to separate the data set into a training and testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "          <th>name</th>\n",
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       "          <th class=\"join_key\">paper_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">class_label=Case_Based</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">class_label=Genetic_Algorithms</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">class_label=Neural_Networks</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">class_label=Probabilistic_Methods</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">class_label=Reinforcement_Learning</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">class_label=Rule_Learning</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"target\">class_label=Theory</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\">role</th>\n",
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       "            <th class=\"sub-header target\">                target</th>\n",
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       "          \n",
       "            <th class=\"sub-header target\">                        target</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header target\">                     target</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header target\">                          target</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header target\">                          target</th>\n",
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       "        \n",
       "          \n",
       "            <th class=\"sub-header target\">                   target</th>\n",
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       "            <td class=\"join_key\">35</td>\n",
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       "            \n",
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       "            <span class=\"left\">0</span\n",
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       "            <span class=\"left\">0</span\n",
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       "            \n",
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       "            <span class=\"left\">0</span\n",
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       "            \n",
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       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
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       "            <td class=\"join_key\">40</td>\n",
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       "            <span class=\"left\">0</span\n",
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       "              >&nbsp;</span\n",
       "            >\n",
       "          </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: 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: 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: 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: 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: 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: 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\">114</td>\n",
       "          \n",
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       "          <td class=\"char-align numerical\">\n",
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       "              \n",
       "            \n",
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       "            <span class=\"left\">0</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
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       "              \n",
       "            \n",
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       "              \n",
       "              \n",
       "            \n",
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       "            <span class=\"left\">0</span\n",
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       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\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 numerical\">\n",
       "            <span class=\"left\">0</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\">0</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>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">117</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: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">0</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\">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 numerical\">\n",
       "            <span class=\"left\">0</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 numerical\">\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 numerical\">\n",
       "            <span class=\"left\">0</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
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       "              \n",
       "            \n",
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       "            <span class=\"left\">0</span\n",
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       "            >\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">128</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">0</span\n",
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       "              >&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: 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: 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: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </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: 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: 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: 0ch\"\n",
       "              >&nbsp;</span\n",
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       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">...</span\n",
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       "              >&nbsp;</span\n",
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       "              >&nbsp;</span\n",
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       "              >&nbsp;</span\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",
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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\">...</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\">...</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    2708 rows<br />\n",
       "    \n",
       "    type: getml.data.View<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name   paper_id   class_label=Case_Based   class_label=Genetic_Algorithms   class_label=Neural_Networks\n",
       "role   join_key                   target                           target                        target\n",
       "   0         35                        0                                1                             0\n",
       "   1         40                        0                                1                             0\n",
       "   2        114                        0                                0                             0\n",
       "   3        117                        0                                0                             0\n",
       "   4        128                        0                                0                             0\n",
       " ...        ...                      ...                              ...                           ...\n",
       "\n",
       "name   class_label=Probabilistic_Methods   class_label=Reinforcement_Learning   class_label=Rule_Learning   class_label=Theory\n",
       "role                             target                             target                      target               target\n",
       "   0                                  0                                  0                           0                    0\n",
       "   1                                  0                                  0                           0                    0\n",
       "   2                                  0                                  1                           0                    0\n",
       "   3                                  0                                  1                           0                    0\n",
       "   4                                  0                                  1                           0                    0\n",
       " ...                                ...                                ...                         ...                  ...\n",
       "\n",
       "\n",
       "2708 rows x 8 columns\n",
       "type: getml.data.View"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_full = getml.data.make_target_columns(paper, \"class_label\")\n",
    "data_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    font-style: italic;\n",
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       "  td {\n",
       "    text-align: left !important;\n",
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       "  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>test</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>test</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td>test</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",
       "    infinite number of  rows<br />\n",
       "    \n",
       "    type: StringColumnView<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "          \n",
       " 0   train\n",
       " 1   test \n",
       " 2   train\n",
       " 3   test \n",
       " 4   test \n",
       "     ...  \n",
       "\n",
       "\n",
       "infinite number of  rows\n",
       "type: StringColumnView"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split = getml.data.split.random(train=0.7, test=0.3, validation=0.0)\n",
    "split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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\">paper</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">821</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\">paper</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">1887</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",
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       "  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\">cites</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">5429</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\">content</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">49216</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\">paper</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">2708</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": [
       "population\n",
       "    subset   name    rows   type\n",
       "0   test     paper    821   View\n",
       "1   train    paper   1887   View\n",
       "\n",
       "peripheral\n",
       "    name       rows   type     \n",
       "0   cites      5429   DataFrame\n",
       "1   content   49216   DataFrame\n",
       "2   paper      2708   DataFrame"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "container = getml.data.Container(population=data_full, split=split)\n",
    "container.add(cites=cites, content=content, paper=paper)\n",
    "container.freeze()\n",
    "container"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Predictive modeling\n",
    "\n",
    "We loaded the data and defined the roles and units. Next, we create a getML pipeline for relational learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define relational model\n",
    "\n",
    "To get started with relational learning, we need to specify the data model. Even though the data set itself is quite simple with only three tables and six columns in total, the resulting data model is actually quite complicated.\n",
    "\n",
    "That is because the class label can be predicting using three different pieces of information:\n",
    "\n",
    "- The keywords used by the paper\n",
    "- The keywords used by papers it cites and by papers that cite the paper\n",
    "- The class label of papers it cites and by papers that cite the paper\n",
    "\n",
    "The main challenge here is that `cites` is used twice, once to connect the _cited_ papers and then to connect the _citing_ papers. To resolve this, we need two placeholders on `cites`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "            <div style='margin-top: 15px; margin-bottom: 5px;'>\n",
       "            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\n",
       "            <div style=\"height:540px;width:1160px;position:relative;\"><svg height=\"530\" width=\"1150\"><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\">content</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\" 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       "            </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</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\">cites, paper</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">CITES__STAGING_TABLE_2</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">cites, paper</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">CITES__STAGING_TABLE_3</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">content</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">CONTENT__STAGING_TABLE_4</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "            </div>\n",
       "            "
      ],
      "text/plain": [
       "population:\n",
       "  columns:\n",
       "  - class_label: categorical\n",
       "  - paper_id: join_key\n",
       "\n",
       "  joins:\n",
       "  - right: 'cites'\n",
       "    on: (population.paper_id, cites.cited_paper_id)\n",
       "    relationship: 'many-to-many'\n",
       "    lagged_targets: False\n",
       "  - right: 'cites'\n",
       "    on: (population.paper_id, cites.citing_paper_id)\n",
       "    relationship: 'many-to-many'\n",
       "    lagged_targets: False\n",
       "  - right: 'content'\n",
       "    on: (population.paper_id, content.paper_id)\n",
       "    relationship: 'many-to-many'\n",
       "    lagged_targets: False\n",
       "\n",
       "cites:\n",
       "  columns:\n",
       "  - cited_paper_id: join_key\n",
       "  - citing_paper_id: join_key\n",
       "\n",
       "  joins:\n",
       "  - right: 'content'\n",
       "    on: (cites.citing_paper_id, content.paper_id)\n",
       "    relationship: 'many-to-many'\n",
       "    lagged_targets: False\n",
       "  - right: 'paper'\n",
       "    on: (cites.citing_paper_id, paper.paper_id)\n",
       "    relationship: 'many-to-one'\n",
       "    lagged_targets: False\n",
       "\n",
       "content:\n",
       "  columns:\n",
       "  - word_cited_id: categorical\n",
       "  - paper_id: join_key\n",
       "\n",
       "paper:\n",
       "  columns:\n",
       "  - class_label: categorical\n",
       "  - paper_id: join_key\n",
       "\n",
       "cites:\n",
       "  columns:\n",
       "  - cited_paper_id: join_key\n",
       "  - citing_paper_id: join_key\n",
       "\n",
       "  joins:\n",
       "  - right: 'content'\n",
       "    on: (cites.cited_paper_id, content.paper_id)\n",
       "    relationship: 'many-to-many'\n",
       "    lagged_targets: False\n",
       "  - right: 'paper'\n",
       "    on: (cites.cited_paper_id, paper.paper_id)\n",
       "    relationship: 'many-to-one'\n",
       "    lagged_targets: False\n",
       "\n",
       "content:\n",
       "  columns:\n",
       "  - word_cited_id: categorical\n",
       "  - paper_id: join_key\n",
       "\n",
       "paper:\n",
       "  columns:\n",
       "  - class_label: categorical\n",
       "  - paper_id: join_key\n",
       "\n",
       "content:\n",
       "  columns:\n",
       "  - word_cited_id: categorical\n",
       "  - paper_id: join_key"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm = getml.data.DataModel(paper.to_placeholder(\"population\"))\n",
    "\n",
    "# We need two different placeholders for cites.\n",
    "dm.add(getml.data.to_placeholder(cites=[cites]*2, content=content, paper=paper))\n",
    "\n",
    "dm.population.join(\n",
    "    dm.cites[0],\n",
    "    on=('paper_id', 'cited_paper_id')\n",
    ")\n",
    "\n",
    "dm.cites[0].join(\n",
    "    dm.content,\n",
    "    on=('citing_paper_id', 'paper_id')\n",
    ")\n",
    "\n",
    "dm.cites[0].join(\n",
    "    dm.paper,\n",
    "    on=('citing_paper_id', 'paper_id'),\n",
    "    relationship=getml.data.relationship.many_to_one\n",
    ")\n",
    "\n",
    "dm.population.join(\n",
    "    dm.cites[1],\n",
    "    on=('paper_id', 'citing_paper_id')\n",
    ")\n",
    "\n",
    "dm.cites[1].join(\n",
    "    dm.content,\n",
    "    on=('cited_paper_id', 'paper_id')\n",
    ")\n",
    "\n",
    "dm.cites[1].join(\n",
    "    dm.paper,\n",
    "    on=('cited_paper_id', 'paper_id'),\n",
    "    relationship=getml.data.relationship.many_to_one\n",
    ")\n",
    "\n",
    "dm.population.join(\n",
    "    dm.content,\n",
    "    on='paper_id'\n",
    ")\n",
    "\n",
    "dm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 getML pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- #### 2.1.1  -->\n",
    "__Set-up the feature learner & predictor__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the relboost algorithms for this problem. Because of the large number of keywords, we regularize the model a bit by requiring a minimum support for the keywords (`min_num_samples`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = getml.preprocessors.Mapping()\n",
    "\n",
    "fast_prop = getml.feature_learning.FastProp(\n",
    "    loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n",
    "    num_threads=1\n",
    ")\n",
    "\n",
    "relboost = getml.feature_learning.Relboost(\n",
    "    num_features=10,\n",
    "    num_subfeatures=10,\n",
    "    loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n",
    "    seed=4367,\n",
    "    num_threads=1,\n",
    "    min_num_samples=30\n",
    ")\n",
    "\n",
    "predictor = getml.predictors.XGBoostClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Build the pipeline__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='population',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['cites', 'content', 'paper'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['fast_prop'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='population',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['cites', 'content', 'paper'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['fast_prop'])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1 = getml.pipeline.Pipeline(\n",
    "    tags=['fast_prop'],\n",
    "    data_model=dm,\n",
    "    preprocessors=[mapping],\n",
    "    feature_learners=[fast_prop],\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=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['cites', 'content', 'paper'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=[],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['relboost'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='population',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['cites', 'content', 'paper'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=[],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['relboost'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2 = getml.pipeline.Pipeline(\n",
    "    tags=['relboost'],\n",
    "    data_model=dm,\n",
    "    feature_learners=[relboost],\n",
    "    predictors=[predictor]\n",
    ")\n",
    "\n",
    "pipe2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
      "The pipeline check generated 3 issues labeled INFO and 0 issues labeled WARNING.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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\">type</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">label                 </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">message                         </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">INFO</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">MIGHT TAKE LONG</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">The number of unique entries in column &#x27;word_cited_id&#x27; in CONTENT__STAGING_TABLE_4 is 1432. This might take a long time to fit. You should consider setting its role to unused_string or using it for comparison only (you can do the latter by setting a unit that contains &#x27;comparison only&#x27;).</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">INFO</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">FOREIGN KEYS NOT FOUND</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">When joining POPULATION__STAGING_TABLE_1 and CITES__STAGING_TABLE_2 over &#x27;paper_id&#x27; and &#x27;cited_paper_id&#x27;, there are no corresponding entries for 41.759406% of entries in &#x27;paper_id&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">INFO</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">FOREIGN KEYS NOT FOUND</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">When joining POPULATION__STAGING_TABLE_1 and CITES__STAGING_TABLE_3 over &#x27;paper_id&#x27; and &#x27;citing_paper_id&#x27;, there are no corresponding entries for 17.700053% of entries in &#x27;paper_id&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    type   label                    message                         \n",
       "0   INFO   MIGHT TAKE LONG          The number of unique entries in ...\n",
       "1   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING...\n",
       "2   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.check(container.train)"
   ]
  },
  {
   "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: 00:00, remaining: 00:00]          \n",
      "\n",
      "The pipeline check generated 3 issues labeled INFO and 0 issues labeled WARNING.\n",
      "To see the issues in full, run .check() on the pipeline.\n",
      "\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Trying 3780 features... 100% |██████████| [elapsed: 00:07, remaining: 00:00]          \n",
      "FastProp: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "\n",
      "Trained pipeline.\n",
      "Time taken: 0h:0m:14.893815\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='population',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['cites', 'content', 'paper'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['fast_prop', 'container-SfPngp'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='population',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['cites', 'content', 'paper'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['fast_prop', 'container-SfPngp'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.fit(container.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",
      "Checking... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n",
      "The pipeline check generated 3 issues labeled INFO and 0 issues labeled WARNING.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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\">type</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">label                 </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">message                         </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">INFO</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">MIGHT TAKE LONG</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">The number of unique entries in column &#x27;word_cited_id&#x27; in CONTENT__STAGING_TABLE_4 is 1432. This might take a long time to fit. You should consider setting its role to unused_string or using it for comparison only (you can do the latter by setting a unit that contains &#x27;comparison only&#x27;).</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">INFO</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">FOREIGN KEYS NOT FOUND</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">When joining POPULATION__STAGING_TABLE_1 and CITES__STAGING_TABLE_2 over &#x27;paper_id&#x27; and &#x27;cited_paper_id&#x27;, there are no corresponding entries for 41.759406% of entries in &#x27;paper_id&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">INFO</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">FOREIGN KEYS NOT FOUND</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">When joining POPULATION__STAGING_TABLE_1 and CITES__STAGING_TABLE_3 over &#x27;paper_id&#x27; and &#x27;citing_paper_id&#x27;, there are no corresponding entries for 17.700053% of entries in &#x27;paper_id&#x27; in &#x27;POPULATION__STAGING_TABLE_1&#x27;. You might want to double-check your join keys.</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    type   label                    message                         \n",
       "0   INFO   MIGHT TAKE LONG          The number of unique entries in ...\n",
       "1   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING...\n",
       "2   INFO   FOREIGN KEYS NOT FOUND   When joining POPULATION__STAGING..."
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.check(container.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training process seems a bit intimidating. That is because the relboost algorithms needs to train separate models for each class label. This is due to the nature of the generated features."
   ]
  },
  {
   "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",
      "\n",
      "The pipeline check generated 3 issues labeled INFO and 0 issues labeled WARNING.\n",
      "To see the issues in full, run .check() on the pipeline.\n",
      "\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Training subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Training subfeatures... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "Relboost: Training subfeatures... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "Relboost: Training subfeatures... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Training features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Training subfeatures... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
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      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
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      "Relboost: Training features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Training subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
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      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
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      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n",
      "Trained pipeline.\n",
      "Time taken: 0h:0m:26.081776\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='population',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['cites', 'content', 'paper'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=[],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['relboost', 'container-SfPngp'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='population',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['cites', 'content', 'paper'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=[],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['relboost', 'container-SfPngp'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.fit(container.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "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 subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
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       "  td.float {\n",
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       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\">  </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"datetime\">date time          </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">set used</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">target                          </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">accuracy</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">    auc</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">cross entropy</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:28</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Case_Based</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9979</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9999</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.02323</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:28</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Genetic_Algorithms</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.0</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.004915</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:28</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Neural_Networks</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9846</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9983</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.065852</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:28</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Probabilistic_Methods</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9958</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9998</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.02765</td>\n",
       "            \n",
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       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:28</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Reinforcement_Learning</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9995</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.009078</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">...</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>9</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:55</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Neural_Networks</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9513</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9787</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.163552</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>10</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:55</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Probabilistic_Methods</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9732</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9872</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.083174</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>11</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:55</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Reinforcement_Learning</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9805</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9736</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.074599</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>12</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:55</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Rule_Learning</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9842</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9937</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.052146</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>13</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:55</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Theory</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9574</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.977</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.128597</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 14:56:28   train      class_label=Case_Based               0.9979    0.9999        0.02323 \n",
       " 1   2024-02-21 14:56:28   train      class_label=Genetic_Algorithms       1.0       1.            0.004915\n",
       " 2   2024-02-21 14:56:28   train      class_label=Neural_Networks          0.9846    0.9983        0.065852\n",
       " 3   2024-02-21 14:56:28   train      class_label=Probabilistic_Method...     0.9958    0.9998        0.02765 \n",
       " 4   2024-02-21 14:56:28   train      class_label=Reinforcement_Learni...     0.9995    1.            0.009078\n",
       "     ...                   ...        ...                                     ...       ...             ...\n",
       " 9   2024-02-21 14:56:55   test       class_label=Neural_Networks          0.9513    0.9787        0.163552\n",
       "10   2024-02-21 14:56:55   test       class_label=Probabilistic_Method...     0.9732    0.9872        0.083174\n",
       "11   2024-02-21 14:56:55   test       class_label=Reinforcement_Learni...     0.9805    0.9736        0.074599\n",
       "12   2024-02-21 14:56:55   test       class_label=Rule_Learning            0.9842    0.9937        0.052146\n",
       "13   2024-02-21 14:56:55   test       class_label=Theory                   0.9574    0.977         0.128597"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.score(container.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
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      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\">  </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"datetime\">date time          </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">set used</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">target                          </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">accuracy</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">    auc</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">cross entropy</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:54</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Case_Based</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.0</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.008392</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:54</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Genetic_Algorithms</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.0</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.003788</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:54</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Neural_Networks</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9921</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9995</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.038185</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:54</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Probabilistic_Methods</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9979</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.014505</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:54</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Reinforcement_Learning</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.0</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.004389</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">...</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>9</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:57</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Neural_Networks</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9403</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.981</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.178774</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>10</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:57</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Probabilistic_Methods</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9708</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9877</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.092301</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>11</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:57</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Reinforcement_Learning</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9829</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9783</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.075957</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>12</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:57</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Rule_Learning</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9817</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9914</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.067588</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>13</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:57</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Theory</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9501</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9663</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.162815</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 14:56:54   train      class_label=Case_Based               1.0       1.            0.008392\n",
       " 1   2024-02-21 14:56:54   train      class_label=Genetic_Algorithms       1.0       1.            0.003788\n",
       " 2   2024-02-21 14:56:54   train      class_label=Neural_Networks          0.9921    0.9995        0.038185\n",
       " 3   2024-02-21 14:56:54   train      class_label=Probabilistic_Method...     0.9979    1.            0.014505\n",
       " 4   2024-02-21 14:56:54   train      class_label=Reinforcement_Learni...     1.0       1.            0.004389\n",
       "     ...                   ...        ...                                     ...       ...             ...\n",
       " 9   2024-02-21 14:56:57   test       class_label=Neural_Networks          0.9403    0.981         0.178774\n",
       "10   2024-02-21 14:56:57   test       class_label=Probabilistic_Method...     0.9708    0.9877        0.092301\n",
       "11   2024-02-21 14:56:57   test       class_label=Reinforcement_Learni...     0.9829    0.9783        0.075957\n",
       "12   2024-02-21 14:56:57   test       class_label=Rule_Learning            0.9817    0.9914        0.067588\n",
       "13   2024-02-21 14:56:57   test       class_label=Theory                   0.9501    0.9663        0.162815"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.score(container.test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make things a bit easier, we just look at our test results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            \n",
       "              <td class=\"float\">0.9861</td>\n",
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       "            \n",
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       "            \n",
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       "          \n",
       "            \n",
       "              <td class=\"float\">0.9854</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"float\">0.998</td>\n",
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       "            \n",
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       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9513</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9787</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.16355</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
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       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:55</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"str\">class_label=Probabilistic_Methods</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9732</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9872</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.08317</td>\n",
       "            \n",
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       "      <tr>\n",
       "        <th>4</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:55</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
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       "          \n",
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       "              <td class=\"str\">class_label=Reinforcement_Learning</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9805</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9736</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.0746</td>\n",
       "            \n",
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       "            \n",
       "          \n",
       "            \n",
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       "            \n",
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       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9842</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9937</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.05215</td>\n",
       "            \n",
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       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:55</td>\n",
       "            \n",
       "          \n",
       "            \n",
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       "            \n",
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       "            \n",
       "              <td class=\"float\">0.977</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.1286</td>\n",
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      ],
      "text/plain": [
       "    date time             set used   target                             accuracy       auc   cross entropy\n",
       "0   2024-02-21 14:56:55   test       class_label=Case_Based               0.9708    0.9861         0.08689\n",
       "1   2024-02-21 14:56:55   test       class_label=Genetic_Algorithms       0.9854    0.998          0.04898\n",
       "2   2024-02-21 14:56:55   test       class_label=Neural_Networks          0.9513    0.9787         0.16355\n",
       "3   2024-02-21 14:56:55   test       class_label=Probabilistic_Method...     0.9732    0.9872         0.08317\n",
       "4   2024-02-21 14:56:55   test       class_label=Reinforcement_Learni...     0.9805    0.9736         0.0746 \n",
       "5   2024-02-21 14:56:55   test       class_label=Rule_Learning            0.9842    0.9937         0.05215\n",
       "6   2024-02-21 14:56:55   test       class_label=Theory                   0.9574    0.977          0.1286 "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.scores.filter(lambda score: score.set_used == \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "              <td class=\"datetime\">2024-02-21 14:56:57</td>\n",
       "            \n",
       "          \n",
       "            \n",
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       "          \n",
       "            \n",
       "              <td class=\"float\">0.9403</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.981</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.17877</td>\n",
       "            \n",
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       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
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       "            \n",
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       "            \n",
       "          \n",
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       "          \n",
       "            \n",
       "              <td class=\"float\">0.9708</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9877</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.0923</td>\n",
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       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-02-21 14:56:57</td>\n",
       "            \n",
       "          \n",
       "            \n",
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       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9829</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9783</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.07596</td>\n",
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       "            \n",
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       "            \n",
       "              <td class=\"float\">0.9817</td>\n",
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       "            \n",
       "              <td class=\"float\">0.9914</td>\n",
       "            \n",
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       "              <td class=\"float\">0.06759</td>\n",
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       "          \n",
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       "            \n",
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       "            \n",
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       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9663</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.16282</td>\n",
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      ],
      "text/plain": [
       "    date time             set used   target                             accuracy       auc   cross entropy\n",
       "0   2024-02-21 14:56:57   test       class_label=Case_Based               0.9744    0.9787         0.10446\n",
       "1   2024-02-21 14:56:57   test       class_label=Genetic_Algorithms       0.9915    0.9989         0.03707\n",
       "2   2024-02-21 14:56:57   test       class_label=Neural_Networks          0.9403    0.981          0.17877\n",
       "3   2024-02-21 14:56:57   test       class_label=Probabilistic_Method...     0.9708    0.9877         0.0923 \n",
       "4   2024-02-21 14:56:57   test       class_label=Reinforcement_Learni...     0.9829    0.9783         0.07596\n",
       "5   2024-02-21 14:56:57   test       class_label=Rule_Learning            0.9817    0.9914         0.06759\n",
       "6   2024-02-21 14:56:57   test       class_label=Theory                   0.9501    0.9663         0.16282"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.scores.filter(lambda score: score.set_used == \"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take the average of the AUC values, which is also the value that appears in the getML monitor (http://localhost:1709/#/listpipelines/cora)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9849037843150564\n",
      "0.983174892778554\n"
     ]
    }
   ],
   "source": [
    "fastprop_auc = np.mean(pipe1.auc)\n",
    "relboost_auc = np.mean(pipe2.auc)\n",
    "print(fastprop_auc)\n",
    "print(relboost_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy for multiple targets can be calculated using one of two methods. The first method is to simply take the average of the pair-wise accuracy values, which is also the value that appears in the getML monitor (http://localhost:1709/#/listpipelines/cora)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9718113798503566\n",
      "0.9702453453975987\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(pipe1.accuracy))\n",
    "print(np.mean(pipe2.accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the benchmarking papers actually use a different approach: \n",
    "\n",
    "- They first generate probabilities for each of the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building subfeatures... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Relboost: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "probabilities1 = pipe1.predict(container.test)\n",
    "probabilities2 = pipe2.predict(container.test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- They then find the class label with the highest probability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_label = paper.class_label.unique()\n",
    "\n",
    "ix_max = np.argmax(probabilities1, axis=1)\n",
    "predicted_labels1 = np.asarray([class_label[ix] for ix in ix_max])\n",
    "\n",
    "ix_max = np.argmax(probabilities2, axis=1)\n",
    "predicted_labels2 = np.asarray([class_label[ix] for ix in ix_max])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- They then compare that value to the actual class label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Share of accurately predicted class labels (pipe1):\n",
      "0.8989037758830695\n",
      "\n",
      "Share of accurately predicted class labels (pipe2):\n",
      "0.8976857490864799\n",
      "\n"
     ]
    }
   ],
   "source": [
    "actual_labels = paper[split == \"test\"].class_label.to_numpy()\n",
    "fastprop_accuracy = (actual_labels == predicted_labels1).sum() / len(actual_labels)\n",
    "relboost_accuracy = (actual_labels == predicted_labels2).sum() / len(actual_labels)\n",
    "\n",
    "print(\"Share of accurately predicted class labels (pipe1):\")\n",
    "print(fastprop_accuracy)\n",
    "print()\n",
    "print(\"Share of accurately predicted class labels (pipe2):\")\n",
    "print(relboost_accuracy)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is the method the benchmark papers use, this is the accuracy score we will report as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Studying features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Feature correlations__\n",
    "\n",
    "We want to analyze how the features are correlated with the target variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "TARGET_NUM = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ffvttjo6O5sGDB/PAgQPZ6XTy+++/71XGivC2j0X7PvvsMw4NDeXU1FTu27cvh4WF8YgRI/iZZ57hxMREvvHGG/nEiRNeZRBdN6Ghofzzzz8zM/PevXs5KiqKExMT+cEHH+QmTZpwWFgY79692zSfjHEisqZF+5jFzzXdu3cv88fhcHBKSorrsaeIHsei2ytjnhE9v4ruE9HjTvTc1bhxY/7f//7HzMxTp07l6tWr88yZM3nNmjU8a9YsrlWrFr/44ose+5jF94nojKqPO9F9LON6p/o4Uf01jaxxJ/qaVxHetll0zYiuaxn3OqLnQtHjRPQ9vOh7btGvQWSMEdH33aLnBtE1KLpPRK+DMItf7xLdJ6qvF16PytzDi17fE70eBzwDGzDAZ0aNGlXhT9++fb3egPnpp5/4ww8/5HvvvZcDAwO5Ro0a/NRTT/G+ffu8zte+fXseO3YsMzMXFxfztGnTOCIigtesWcPM3k+oTZo04aVLlzIzc1ZWFgcGBvLChQtdxxcuXMht2rTxKmNMTAxv3brVlccwDF67dq3r+JYtW7hu3boe+4KDg/n7779nZuZGjRq52lrCpk2bODY21mNfYGAgd+3alSdNmuT6mThxIjscDh4+fLjrOU/585//zP/617/KPT5x4kS+6aabPPYZhuF64dGlSxceOHCg2/F//vOf3LlzZ499TZo04WXLlrkef/XVV1yvXj0uLi5mZuZevXp5dYNTknHv3r38yiuv8E033cQOh4NbtGjBc+bM4TNnznjlYmaePXt2hT9paWle1XX9+vX5888/Z2bm3Nxcdjgc/NFHH7mOr127luPi4jz2iW7vLbfcwkOHDnX1QWmKi4t56NCh3K5dO6+cKSkp/Oijj3JRURFfuHCBhw0bxtWrV+esrCxm9n5uaNmyJc+ePZuZmdetW8ehoaE8c+ZM1/GXX36ZO3bs6LFPdB+L9rVs2ZLnz5/verx27Vpu0qQJMzMXFBTwnXfeyQMGDPDYxyy+bkrPDffffz/fe++9rhfoRUVF3Lt3b+7WrZupPpHtFV3Ton3Mcuaa5ORkHjBggNuPw+Hgv/3tb67HniJ6HItur+h8zOLnV9F9InrciZ67Sr/mat68OS9fvtzt+KpVqzgxMdFjH7P4PhGdUfVxJ7qPZV3vVB8nKr+mkTHuRF/zRLdZxrgTWdeiX9Mwy7k+iRwnou/hRd9zi34NIuN1oej7btFzg+galPE6WOQ6CLP49S4Zr0FUXi8UvT7KLH59T/R6HPAMbMAAn3E4HNy6dWu+/fbby/xJSkry+sVlyUWEmfn48eM8depUbtiwITscDm7fvj2/+eabHvuqVKnChw8fdntu6dKlHB4ezitXrvR6Qg0NDXVNfsx/TIal//Lj0KFDHB0d7bGPmTksLIyPHj3qehwUFMR79uxxPc7Ly+Pw8HCPfXFxca5d+7p1617zVxn79u3zyrdlyxa+8cYbecKECVxUVOR6PjAwkPfu3euxp4SQkBA+cOBAuccPHDjAISEhHvtK10xMTAx/+eWXbse/++47vuGGGzz2hYaG8pEjR9yeCwwM5J9++omZmXfs2OF1H19d1zt27OChQ4dyVFQUh4aG8kMPPcTr16/3ylenTh2Oj48v86dOnTo+1XVQUJBbXR85coTDwsK8yieyvSEhIbx///5yj+/fv9+rmmFmrlq1Kh88eNDtufT0dK5atSrv3LnT67khPDyc8/LyXI+DgoLc/opu//79XL16dY99ovtYtC8kJMRtnBQXF3NQUBAfP36cmZn/97//cY0aNTz2lWQUPU5KfPXr13f99VUJWVlZHBMTo4SP2ff2iq5p0T5m8XPNu+++y/Xq1eO33nrL7fnKXp9Ej2PR7RWdj1n8/Cq6T0SPO9FzV+nXHbVq1XItRJWQk5PDoaGhHvtKMorsE9EZVR93ovtYxvVO9XGi+msaGeNO9DVPdJtF14zouhb9mqYko8i5UPQ4EX0PL/qeW/RrEFmvC0Xed4ueG0TXoMx7bhHrIGVl9HW9S3SfqL5eKHp9lFn8+p7o9TjgGfgOGOAziYmJNGrUKNqwYUOZP2+88YZXPsMw3B7HxMTQs88+Szk5ObR+/Xq68cYbaeTIkR77goOD6dy5c27P9enThxYuXEi9evWiDz/80Kt8YWFh9Pvvv7se16hRgyIiItz+jbdfKNawYUNatWoVERGtWbOGQkJCaO3ata7j//3vfykhIcFj38MPP0zjxo2jc+fO0SOPPELPPfcc5efnExHRxYsXadKkSdSxY0ePfR07dqRvvvmGcnJyqEOHDpSbm+vx75ZFfHw8ffrpp+Ue//TTTykuLs4r52+//UYXLlygkJAQCg4OdjsWEhJCFy9e9Crf119/7XqclZVFDoeDatWqRURE1apVo8LCQq/yXc3NN99Mr7/+Oh0/fpzmzZtHP/zwA3Xp0sXj34+Li6NXXnmFjhw5UuZPRee3LGJjY12fK/rVV1+RYRi0c+dO1/EdO3ZQ3bp1vXKWxtf21q5d2y3P1ezcudPVP95w6dIlt8djxoyhsWPH0l133UXbtm3zyhUUFOT2WcjBwcFuc0NwcLBXn4stuo9F++rWrUsHDx50Pc7NzaXi4mKqXr06ERHVq1fPNe9UFl/rxjAM1zXF4XBQVFSU2/Ho6Gg6e/asab6r8bW9RGJrWoZP9FzTu3dv2rx5M7355puUmprq0/knEj+ORbdXdD4i8fOr6D4RPe5Ez13du3enKVOmUFFREd1///00b948t88ynzNnDrVs2dJjH5H4PhGdUfVxJ7qPZVzvVB8nqr+mkTHuiMRe80S3WXTNyH4dJ+I1jei5UPQ4EX0PL/qeW8b9nejXhaLvu0XPDaJrUEafiFwHIRK/3iW6T1RfLxS9Pkokfn1Pxnoc8ABTt3+ALejTpw8/8cQT5R7ftWsXG4bhse/qHe2yOH/+vMe+Ll268EsvvVTmsczMTA4KCvJqB7pjx45ub5O9mpUrV3Lz5s099jEzL1myhAMCAjgxMZGDg4P5/fff5zp16nDPnj25d+/e7HQ6ee7cuR77Ll++zPfddx9XrVqVu3TpwiEhIRwWFsYNGzbk8PBwjo2NveavVzzlrbfe4tq1a/Prr7/OQUFBlfprnOXLl3NgYCDfe++9PHv2bF62bBkvW7aMZ8+ezffddx87nU5esWKFxz7DMNjhcLDD4WDDMHjBggVuxz/++GOv3tY6d+5cjoqK4rS0NJ4wYQLXqVOHBw0a5Dq+ZMkSrz5/tyTj9eramz5JTU3ltLS0co97O+5eeeUVDgkJ4ZSUFK5atSpnZGRw7dq1OS0tjceMGcNRUVH83HPPeewT3d65c+dycHAwjxw5kj/++GPevn07b9++nT/++GMeOXIkh4aG8quvvuqxj5n51ltvdfvohdJMmzaNg4ODvZobkpKS3N5Cfv78ebe3q3/++efcqFEjj32i+1i0b/LkyVyvXj2eP38+v/XWW9y8eXO3jwj44IMPuFmzZh77mMXXjWEYHB0dzVWrVuWgoCB+55133I6vXbuW4+PjTfWJbK/omhbtYxY/15RQVFTEEyZM4Pr16/Nnn31W6euT6HEsur2i8zHLmV+ZxfWJ6HEneu46d+4cJyUlcWJiIj/yyCMcEhLCcXFx3KVLF05ISOCoqCjX53Z7iug+kZGRWd1xJ7qPZVzvSlB1nKj+mkZGTYu+5olucwmiakZ0XYt+TcMsfi4UPU5E38OXRsQ9t+jXIDJeF4q+7xY9N4iuQRn33CLXQZjFr3eJ7hPV1wtFr48yi1/fE70eBzwDGzDAZ06cOOH21ltfGTBgAF+4cEGY74MPPqhwAly6dCnffvvtHvu2bNnC2dnZ5R5/9dVXec6cOd5EdHlffvll1+fI7t27lx955BFOTU3lRYsWee1jZl6zZg0PHz6cu3btynfddRf379+fFyxYwPn5+ZXylZCTk8Nt27Z1fdZvZdi6dSv36tWLY2Nj2el0stPp5NjYWO7Vqxdv27bNK9fGjRvdfq6++MyaNYunT5/ulXPevHncoUMHbtOmDY8dO5b/7//+z3UsJyenwrcil8Xtt9/OZ8+e9ep3KmLv3r0VfuFfQUGB1+Ny6dKlPGLECM7MzGRm5g0bNvCtt97Kbdq04UmTJrm9Ff56iG4vM/OyZcv4lltu4cDAQDYMw/Vln7fccgu/9957XvveeOMN7tu3b7nHX3zxRa9uwj744IMKv+QwPT2dx48f77FPdB+L9hUWFnJaWhrXqVOHq1evzn369OFffvnFdXzHjh1ef+mj6LpZtGiR28/Vb8t/7rnneNSoUab5RLdXdE2L9pUgcq65ms2bN3NCQgI7HI5KXZ9Ej2Nmse2VkY9Z/PxaGl/7RPS4kzF3FRQU8Pz58/kvf/kLN2nShBs1asTJyck8duxY/uGHH7xylSC6T2RkLEG1cSe6j2XUzNWoNk5Uf01T8jsia1r0NU9Gm0vja82IrmsZr/2Zxc6FoscJs5x7+BJE3HOLfA0i63Wh6Ptu0XOD6OuxyD6RsQ4iY71LZJ+ovl4oen20NCLX90SuxwHPwAYM8DuZmZk+bwDo7JPhrIyvqKiIz507V+YX0slos0hUOH/XY8uWLXzp0iX4yqCgoICPHz/Ox48f54KCAmEZroedzqEdfDKc8Knlq6zzt99+4127dvHly5eF+CrCbj5Z8yv6pPI+M655GHfm+HAOreOT4cS4qxirzIUVofo9t9l97AlmtlnXGvS3U/U2q+6T5QS+gw0Y4HciIyM5NzcXPoWcqvtEY4X2qp5RdZ8MVG+zbj4ZTvjU8slwwqceqrdZdZ8MVG+zbj4ZTvjUc8KnHqq3WTefDKfqdWiF9qqeUTefLCfwHYfZ30ED9INLfeEWfGo4VfPt37+fGjRoICjNtajWXn847e7Lzc2lzp07C0pTNqq1WXefDCd8avlkOOHzHtnzq4ptVt2HPtHLJ8MJn3pO+LwHc6FePhlO1e9BVWuvP5zwme+UvR6nK9iAAQAoR0FBAX3//fdmxwAWIj8/nzZt2mR2DAAAsB2YX9UDfQIAAJgLgfmgBoEdwXqcHALNDgAA0I8nn3yywuO//PKLn5IAq5CRkVHh8Z9++slPSQAAwF5gflUP9AkAAGAuBOaDGgR2BOtx5oANGACA35k9eza1bNmSqlSpUubx/Px8PycCqvPEE09QTEwMOZ3OMo8XFBT4OREAANgDzK/qgT4BAADMhcB8UIPAjmA9zhywAQMA8DuJiYk0atQo6tu3b5nHd+3aRW3atPFzKrUwDAO+UsTFxdG0adOoZ8+eZR73R81Y/RzazSfDCZ9aPhlO+K7F7PnVDudQtA99Ap9sJ3zqOeG7FrvNhaqjes3IQPV7UCucQ9Ho2GZ/g/U4c8B3wAC/ExcXR0FBQfAp5PS3Lykpib755ptyjxuGIeXLyEpQ/fwRqf/lbv72tWnTxtSaIbL+ObSbT4YTPrV8MpzwXYvZ86sdzqFoH/oEPtlO+NRzwnctdpsLVb8HVb1miPzfZtSg/52qt1l1nydOs9fjdMVgnFUAgJ85efIkXb58meLi4syOYjrMTMXFxRQQEKCFb9GiRdS9e3eKiory6vf27dtHFy9epKSkpDKPFxYW0vHjx4XUlOrnUDSV7ZPyUL29ROrUtVWxQntVr0PR59AXn7/mVyvUjUh8qUH0iTXA+QNALv58/S+KQ4cO0bFjxyguLo4SExOV81kBldqMGpTnFInq4061PsF6nEkwAJLZtWsXOxwO+BRyqu67mszMTM7Pz6/076vQ3sLCQh43bhzfdtttPGHCBGZmnj59OoeFhbHT6eR+/frx5cuXbeMrj6CgIN63b5/PnuuxZcsWvnTpUoX/xgrn8NNPP+VBgwbx008/zfv373c7dubMGb7jjju88pVFZftEVs2IbLPqdR0REcEDBw7krVu3+pyhBJVrpgTV+9gK51C2ryI8mV/LwpeMIvtE9Ljz1zxTEVbvkx07dvCVK1dcj1euXMm33XYb16lTh9u0acOLFy82NV95+HL+RNeh6j4r9LHojKqfQ9V9leF6c6HojFOnTuV169Yx8x81d+edd7JhGGwYBjscDu7atSufPXvWNJ/q8wKz+m32FqvXoAyn6m1W3SfL6Q2+rseBP8AGDJDOrl272DAM+BRyqu67msjISM7Nza3076vQ3vHjx3OtWrX4ySef5GbNmvFjjz3G9evX5yVLlvDixYu5bt26PG3aNNv4qlatWuaPYRgcFRXleiwLT2pG9XO4dOlSDggI4L/+9a/cqVMnDgkJ4SVLlriOnzx50quNQNF9Irq9Mtqsel0bhsF/+tOf2DAMbtKkCb/88sv8888/e/z7V6N6zcjIqNu4M3tuZb7+/Co6o+g+ET3uZMyF3mL1PnE4HHzq1ClmZv7kk0/Y4XBwv379+NVXX+XBgwdzYGAgf/DBB6blkzHuRNeh6j7V+1hGRtXPoeq+ynC9uVB0xnr16nFWVhYzMw8ePJhbtWrFWVlZ/H//93+8a9cubteuHQ8aNMg0n+rzArP6bfYWq9egDKfqbVbdJ8vpDb6ux4E/wAYM8Jnu3btX+NO5c2evXgDr5rNCRhlt9oaIiIgKJ3wrtLdBgwa8cuVKZmY+dOgQOxwOXrZsmev4e++9x82bN7eNLyIigv/617/yokWLXD9vv/02BwQE8JQpU1zPyeJ6NcOs/jls2bIlz5492+33w8PDeeHChczs/eKC6D4R3V5m8W1Wva4Nw+BTp07xrl27eMSIEVytWjV2Op3co0cPXr16NRcXF3vsYla/ZmRk1G3cmT23lmSoaH4VnVF0n4gedzLmQm+xS58wM3fq1InHjBnjdnzKlCncrl070/LJGHei69AqPmY1+1hGRtXPoeq+ynC9uVB0xuDgYD569CgzM8fHx/OmTZvcjn/99dccExNjmk/1eYFZ/TZ7i9VrUIZT9Tar7pPl9AZP1lbA9cEGDPCZwMBAvueee3jAgAFl/tx3331evQDWzWeFjDLa7A3Xm/Ct0N6QkBA+duyY2+PSH5eQl5fHkZGRtvEdOnSI27Zty/369ePffvvN9XxgYCDv3bvXY09l8eRFgurnMDw8nPPy8tye++KLLzgiIoLnz5/v9eKC6D4R3V5m8W1Wva5L35AwM1+6dIkzMzP5zjvvZIfDwfXq1eN//etfHvtUrxkZGXUbd2bPrczXn19FZxTdJ6LHnYy50Fvs1Cc1a9bkr7/+2u34gQMHODo62rR8Msad6Dq0kk/FPpaRUfVzqLqvMniz+C0iY6NGjXjVqlXMzJyQkHDNx15lZ2dzlSpVTPOpPi8wq99mb7F6Dcpwqt5m1X2ynN6ADRgxYAMG+MxNN93k+mujssjOzvbqBbBuPitklNFmb7jehG+F9taqVYu//fZb1+MOHTrwjz/+6Hq8f/9+ry6aqvuY//hc/LS0NL7xxht5y5YtzKzWBozq5zAmJoa//PLLa57fuHEjR0RE8Lhx4yr1XUSi+kRGzYhus+p1Xfot+Vdz5MgRHj9+PNevX99jn+o1IyOjbuNOhs9bPJlfRWYU3Seix52MecZbrN4nhmHwhg0bePfu3RwXF8c7d+50O37gwAGOiIgwLR+z+HEnug5V91mhj0VnVP0cqu6rDJ4sfovM+NJLL3HTpk350KFDPGPGDG7fvj0fPnyYmf/YfL/99tv5gQceMM2n+rzArH6bvcXqNSjDqXqbVffJcnoDNmDEgA0Y4DMDBgzg4cOHl3t83759HB8fD5+FM8poszdcb8K3QnvvuOOOCj+eYvny5dymTRvb+Eqzfv16jo2N5WeffZaDgoKU2YBR/Rzef//9ri90vpoNGzZweHh4pTc+RfSJjJoR3WbV6/rqv9IrC28+KkH1mpGRUbdxJ9PnKd7chInIKLpPRI87mfOMp9ihTxwOh+sLZV955RW34++++y43a9bMtHylETXuRNehFXyq97HojKqfQ9V9lcGTxW/RGR9//HEOCgriJk2acEhICDscDnY6nexwODgpKYlPnDhhmk/1eaEEldvsLXaoQdFOK7RZdZ8sp6dgA0YMgQSAj7z22mtUVFRU7vGmTZvSkSNH4LNwRhltFokV2vvaa69RUFBQuccLCwspLS3NNr7SdO7cmbKysmjIkCEUHh5OAQEBlfJ4g2EY1/03qp/DUaNG0bZt28o8dvvtt9PKlSvp3//+t8e+0ojoExk1I7rNqtf1xIkTKSIiosJ/40ktl6B6zcjIqNu4k+nzFG9qUkRG0X0ietzJnGc8xep9cvVrqqv7p6CggJ555hnT8pVG1LgTXYeq+6zQx6Izqn4OVfdVhuudTxkZMzIyaNiwYbRq1SrKy8uj4uJiiomJoY4dO1JKSopXfSzap/q8UILKbfYWO9SgaKcV2qy6T5YT+Bmzd4CAfqSnp/PZs2fhU8ipuu9Pf/qT2+er+4rq7ZXh1M0n4680VG+zbj4ZTvjU8slwwuc7oudX1dusuo8ZfWJ3nwwnfOo54fMdzIX29slwqn4Pqnp7ZTjhU88pej1OV7ABA/xOZGSk0IuSbj4ZTtV9orFCe1XPqIqvuLiYr1y5IixHRajSZvjkOeFTyyfDCZ/nvP3223zu3DlhWcpDpTZb0ScD1dusm0+GEz71nPCph+pt1s0nw6l6HVqhvapn1M1XWWdOTg6vW7eODx06JDQL+P84zH4HDtAPZoZPMadqvt27d0v9KBXV2usPp9V9V65cofHjx1NycjJNnDiRiIheeuklioiIoLCwMOrfvz8VFBQIzeRtRvj865PhhE8tnwwnfJ4zdOhQOn78uMA0ZaNSm1XyrV69mgYPHkxpaWl04MABt2Nnz56lzp07C81VGrucQ7v4ZDjhU88J37VERkbSoEGDyv24OdnY4RzaySfDeT3fzp073T6afNWqVZScnEx169alpKSkSn+cpah8ZvtkOOGT70xPT6f169cT0R+vKVNSUqhx48bUpUsXaty4Md1zzz107tw54bl0BxswAAAlkXEhAtZl8uTJtHDhQkpKSqIVK1bQsGHDaM6cObRgwQJ64403aP369TRr1iyzYwIAgOWoVq1amT9Xrlyh9u3bux4D/5GZmUn33XcfnTx5kr788ktq1aoVLV261HW8oKCANm3aZGJCAACQz++//047duygTp06UdOmTWnGjBn0yy+/mB0LaET79u3p119/JSKilStX0v3330/x8fE0btw4atWqFQ0aNIg+/PBDk1MC4B3z5s1zvbZPS0ujM2fO0DfffEMXL16krKwsOnfuHI0ePdrklPYj0OwAAAD96NGjR4XHz58/jy8RA25kZmbSwoULqVu3bjRs2DBq3LgxZWZmUq9evYiIKCQkhJ5//nnpX3QMAAB2o7CwkJKTk+nBBx90PcfMrndf1K1b18R0evLSSy/RzJkzaeTIkUREtHz5cho4cCBdunSJBg0aZHI6AADwH1988QWdOHGCFi5cSFOnTqWxY8dSt27daPDgwdS1a1fcMwKplP6j0OnTp1NaWhqlp6e7nktISKDp06dT9+7dzYgHQKX45ZdfXBsw69ato8WLF1OrVq2IiKhFixY0d+5cuvfee82MaEvwDhgAgN9ZuXIlXbp0iaKiosr8iYiIMDsiUIzjx49TixYtiIgoMTGRnE6n6zERUdu2ben77783Kx4AAFiW7Oxs+vnnn+mLL76g1NRU6t+/Pw0YMIAMw6C//e1v1L9/f+rfv7/ZMbXi0KFDbje+PXv2pJUrV9ITTzxBr732monJAADA/7Ro0YLmzJlDx48fp0WLFtH58+epW7duFBsbSxMmTDA7HtCEnJwceuCBB9yeS01NveZjQgFQnbi4OPruu++IiMgwDAoMdH9vRkBAAP3+++9mRLM1eAcMAMDvNG3alFJTU8v9K85du3bRqlWr/JwKqExUVBSdO3eO6tevT0RErVu3psjISNfxy5cv4y/gAACgEiQmJtK2bdto3Lhx1LJlS1q8eDF17NjR7FhaU6VKFTp16hQlJCS4nrvjjjto1apV1K1bN/rxxx9NTAcAAP7h6tf2wcHB9NBDD9FDDz1ER48epTfffJMWLVpEzz33nEkJgQ7s27ePTp48SaGhoVRcXHzN8StXrpiQCoDKM2TIEHr66aepcePGNGLECBo9ejS98847dOONN9KRI0do1KhRdNddd5kd03bgHTDA79x6660UGhoKn0JOf/vatGlDWVlZ5R4PDg6m2NhYYXmuRvXzJ8NpdV+zZs3cambr1q1uH4uzZ88eatiwobA8lckIn399MpzwqeWT4YSvbAIDA2natGm0YMEC6tOnD40dO9Zvm9p2OYcifTfffDOtWbPmmueTk5Np5cqV0r/zzA7n0E4+GU741HPCdy0VfSdofHw8Pf/881LfAW+Hc2gnnwynJ74777yTWrZsSceOHaOtW7e6HcvOzsa6heIZdfN54hw9ejSlpKRQs2bN6I033qDs7Gxq1KgRBQcHU2JiIuXn59OcOXOEZgJExAAI5PDhwzxu3Dju3bs3nzp1ipmZV69ezd999x18Nskownfp0iX+/fffK/X/90c+mT4rZFTRd/DgQc7Lyyv3+NKlS/m9996rVD5RGeHDOIHPXhl185Vw+vRp7t69O0dHR/OBAwd8cqneZlV9Gzdu5KlTp5Z7/IsvvuABAwaYmhE+zIV29Vkhoy6+SZMmaXvPqJtP1YxHjx51+zl9+rTb8cWLF/PixYtNyyfTZ4WMuvlEO/ft28fTp0/nxx57jIcOHcoTJ07ktWvXcnFxcaXzgfLBBgwQxsaNGzk0NJRTUlLY6XRybm4uMzOnp6dzamoqfDbIKKPNnpCens5nz571ez70sXo+T/G0ZmRkhA/jBD7rZ9TN5w12uSar7vMG9Ik1fVbIqJvPChl183kD5kJr+qyS0RPsUoNWyKibT5bTE7xZWwHlgw0YIIx27drxjBkzmJk5IiLCNRns2LGD69atC58NMsposydERka6/l/+zIc+Vs/nKZ7WjIyM8GGcwGf9jLr5vMEu12TVfd6APrGmzwoZdfNZIaNuPm/AXGhNn1UyeoJdatAKGXXzyXJ6gjdrK6B8sAEDhBEeHu76iKDSk8GRI0c4ODgYPhtklNFmTyj9//JnPvSxej5P8bRmZGSED+MEPutn1M3nDXa5Jqvu8wb0iTV9Vsiom88KGXXzeQPmQmv6rJLRE+xSg1bIqJtPltMTvFlbAeXjMPs7aIB9iI6OphMnTlzzfHZ2ttuXZcNn3Ywy2iwSK7RX9Yyq+2Sgept181khI3y++ayQUTefDFRvs+o+GajeZt18Vsiom88KGXXzyUD1Nuvms0pGkVihvapn1M0nywn8BzZggDB69+5NzzzzDJ08eZIMw6Di4mLaunUrjR49mvr16wefDTLKaLNIrNBe1TOq7pOB6m3WzWeFjPChj+3mk4HqbVbdJwPV26ybzwoZdfNZIaNuPhmo3mbdfFbJKBIrtFf1jLr5ZDmBHzH7LTjAPly+fJkHDx7MgYGBbBgGBwUFscPh4L59+/KVK1fgs0FGGW32BE/f8miF9qqeUXWfp3jzNlnV26ybzwoZ4UMf283nDXa5Jqvu8wb0iTV9Vsiom88KGXXzeQPmQmv6rJLRE+xSg1bIqJtPltMT8BFkYjCYmc3eBALWh5nphx9+oBo1atDp06dpz549lJ+fT61ataKGDRvCZ4OMMtrsKZGRkbR7925q0KCB3/Khj9XzeYMnNSMjI3wYJ/BZP6NuPm+xwzVZdZ+3oE+s57NCRt18Vsiom89bMBdaz2eVjJ5ihxq0QkbdfLKcnuLp2gq4Dj5v4QDAzEVFRRwUFMQ5OTnwKeJU3ecN99xzDx8/frzCf2OF9qqeUXWfN3hSM8zqt1k3nwwnfGr5ZDjh8y92uCar7vMW9In1fDKc8KnnhM+/YC60nk+GU/V7UCu0V/WMuvlkOT3F07UVUDH4DhggBIfDQQ0bNqRff/0VPkWcqvtKyM3NpfHjx9NDDz1EP//8MxERrVmzhvbu3ev6N6tXr6aYmBi/5kMfq+crQVTNyMgIn3pO+NTyyXDCJw5drsmq+0qDPrGnT4YTPvWc8IkDc6E9fTKcqt+DWqG9qmfUzSfLSSR2bQVcB7N3gIB9+OSTT7hTp068Z88e+BRxqu7buHEjh4aGckpKCjudTtfnSqanp3Nqaqrp+dDH6vlE14yMjPCp54RPLZ8MJ3y+o9s1WXUfM/rE7j4ZTvjUc8LnO5gL7e2T4VT9HlT19spwwqeeU8baCigffAcMEEbVqlXp4sWLdOXKFXI6nRQaGup2/MyZM/BZPKNoX/v27enBBx+kJ5980u1zJXfu3Ek9evSgH3/80dR86GP1fKJrRkZG+DBO4LN+Rt18RPpdk1X3EaFP7O6zQkbdfFbIqJuPCHOh3X1WyKhbDVoho24+GU4ZayugfALNDgDsw6xZs+BTzKm6b8+ePZSZmXnN8zVr1qTTp0977VO9vTKcuvlE1wyR+m3WzSfDCZ9aPhlO+HxHt2uy6j4i9IndfTKc8KnnhM93MBfa2yfDqfo9qOrtleGETz2njLUVUD54BwwAwDTq1atHy5cvpw4dOrjtuH/44Yc0evRoys3NNTsiUAzUDAAAyAHzq3qgTwAAAHMhMB/UILAjqGv/gnfAAGEcO3aswuOxsbHw+dmpuq937970zDPP0Pvvv0+GYVBxcTFt3bqVRo8eTf369fPKJSMf+lg9n+iakZERPt98MpzwqeWT4YTPNx+Rftdk1X1E6BO7+2Q44fPNJ8MJn28+IsyFdvfJcKp+D6p6e2U44fPNJ8MpY20FlA/eAQOE4XA4yDCMco8XFRXB52en6r6CggL6xz/+QYsWLaKioiIKDAykoqIi6tOnDy1atIgCAgJMzYc+Vs8numZkZITPN58MJ3xq+WQ44fPNR6TfNVl1HxH6xO4+GU74fPPJcMLnm48Ic6HdfTKcqt+Dqt5eGU74fPPJcMpYWwHlg3fAAGFkZ2e7PS4sLKTs7GyaOXMmTZkyBT4bZBTpY2Y6efIkZWRk0IQJE2jPnj2Un59PrVq1ooYNG3qdTXQ+GT4rZFTZJ6NmRGeED+MEPntk1M2n4zVZdR/6xP4+K2TUzWeFjLr5MBfa36d6Rh1r0AoZdfOJdspaWwEVwABIZtWqVZycnAyfQk4VfEVFRRwUFMQ5OTnCcpSHCu31t9OOPn/WDLMabYZPrhM+tXwynPB5Bq7J6vnQJ/r6ZDjhU88Jn2dgLtTXJ8Op+j2oCu31txM+c5z+XlsBzNiAAdI5dOgQh4WFwaeQUxVfs2bN+MsvvxSWozxUaa8/nXb1+atmmNVpM3zynPCp5ZPhhM9zcE1Wz4c+0dMnwwmfek74PAdzoZ4+GU7V70FVaa8/nfCZ5/Tn2gpgxkeQAWFcuHDB7TEz04kTJ2jSpEmVegubbj4rZBTte/HFF+npp5+m+fPnU/Pmzb3+fdn50Mfq+UTXjIyM8GGcwGf9jLr5iPS7JqvuI0Kf2N1nhYy6+ayQUTcfEeZCu/uskFG3GrRCRt18Mpwy1lZABQjYxAGAmZkNw2CHw+H2YxgGx8bG8rZt2+CzQUbRvujoaHY6nexwODgkJISrVq3q9mN2PvSxej7RNSMjI3wYJ/BZP6NuPmb9rsmq+5jRJ3b3WSGjbj4rZNTNx4y50O4+K2TUrQatkFE3nwynjLUVUD54BwwQxoYNG9weOxwOqlGjBiUmJlJgoPelppvPChlF+2bNmuX171SE6u21QkbVfaJrhkj9Nuvms0JG+NDHdvMR6XdNVt1HhD6xu88KGXXzWSGjbj4izIV291kho241aIWMuvlkOGWsrYAKMHsHCNiHTZs2cWFh4TXPFxYW8qZNm+CzQUYZbRaJFdqrekbVfTJQvc26+ayQET7ffFbIqJtPBqq3WXWfDFRvs24+K2TUzWeFjLr5ZKB6m3XzWSWjSKzQXtUz6uaT5QT+w2BmNnsTCNiDgIAAOnHiBNWsWdPt+V9//ZVq1qxJRUVF8Fk8o2jfsWPHKjweGxtraj70sXo+0TUjIyN8vvmskBE+33xWyKibj0i/a7LqPiL0id19Vsiom88KGXXzEWEutLvPChl1q0ErZNTNJ8MpY20FlA8+ggwIg5nJMIxrnv/1118pPDwcPhtkFO2Lj48v01eCtxcQ1dtrhYyq+0TXDJH6bdbNZ4WM8Pnms0JG3XxE+l2TVfcRoU/s7rNCRt18Vsiom48Ic6HdfVbIqFsNWiGjbj4ZThlrK6B8sAEDfKZHjx5ERGQYBg0YMICCg4Ndx4qKiujbb7+lDh06wGfhjDLaTESUnZ3t9riwsJCys7Np5syZNGXKFNPyoY/V85UgqmZkZIQP4wQ+62fUzVcaXa7JqvtKgz6xp88KGXXzWSGjbr7SYC60p88qGYn0qUErZNTNJ8tJJHZtBVwfbMAAn4mKiiKiP3ZjIyMjKTQ01HXM6XRSu3btaMiQIfBZOKOMNhMRtWjR4prnkpKSqE6dOvTSSy+5LjT+zoc+Vs9XgqiakZERPowT+KyfUTdfaXS5JqvuKw36xJ4+K2TUzWeFjLr5SoO50J4+q2Qk0qcGrZBRN58sJ5HYtRXgAQyAICZNmsT5+fnwKeRU3Vcehw4d4rCwMK9/zwrtVT2j6r7yqGzNMKvfZt18MpzwqeWT4YRPHna9Jqvuqwj0iT18MpzwqeeETx6YC+3hk+FU/R7UCu1VPaNuPlnOsvBlbQWUj8HMbPYmEABATy5cuOD2mJnpxIkTNGnSJDpw4ADt2rXLnGBAWVAzAAAgB8yv6oE+AQAAzIXAfFCDwI6grv0LPoIMCGXFihW0fPlyOnbsGBUUFLgdy8rKgs8GGUX6oqOjr/nSL2am+vXr07Jly7zOJjqfDJ8VMqrsk1EzojPCh3ECnz0y6ubT8Zqsug99Yn+fFTLq5rNCRt18mAvt71M9o441aIWMuvlEO2WtrYCycZgdANiHjIwMevTRR6lWrVqUnZ1NN998M1WvXp3y8vLonnvugc8GGUX7NmzYQF988YXrZ+PGjbRv3z7Kzc2l9u3bm54PfayeT3TNyMgIH8YJfNbPqJuPSL9rsuo+IvSJ3X1WyKibzwoZdfMRYS60u88KGXWrQStk1M0nwyljbQVUgP8/9QzYlcaNG3NmZiYzM0dERHBubi4zM//rX//if/zjH/DZIKNo36ZNm7iwsPCa5wsLC3nTpk2m50Mfq+cTXTMyMsKHcQKf9TPq5mPW75qsuo8ZfWJ3nxUy6uazQkbdfMyYC+3us0JG3WrQChl188lwylhbAeWDDRggjNDQUD569CgzM9eoUYN37drFzMw5OTlcrVo1+GyQUbTP4XDwqVOnrnn+9OnT7HA4TM+HPlbPJ7pmZGSED+MEPutn1M3HrN81WXUfM/rE7j4rZNTNZ4WMuvmYMRfa3WeFjLrVoBUy6uaT4ZSxtgLKBx9BBoRRu3ZtOnPmDBERxcbG0vbt24mI6MiRI8TM8Nkgo2gfM1/zmZNERL/++iuFh4ebng99rJ5PdM3IyAgfxgl81s+om49Iv2uy6j4i9IndfVbIqJvPChl18xFhLrS7zwoZdatBK2TUzSfDKWNtBVSAL7s3AJRm0KBBPGnSJGZmnjt3LoeGhnJKSgpHR0fzwIED4bNBRlG+7t27c/fu3dnhcPBf/vIX1+Pu3bvzfffdx/Hx8Xz33Xeblk+WzwoZVfXJqhmRGeHDOIHPPhl18ul6TVbZhz7Rw2eFjLr5rJBRJx/mQj18KmfUtQatkFE3n0inzLUVUD4GcyW33gC4iuLiYiouLqbAwEAiIlq2bBlt27aNGjZsSH//+9/J6XTCZ/GMonyPPvooEREtXryYevbsSaGhoa5jTqeT4uPjaciQIXTDDTeYkk+WzwoZVfXJqhmRGeHDOIHPPhl18ul6TVbZhz7Rw2eFjLr5rJBRJx/mQj18KmfUtQatkFE3n0inzLUVUD7YgAEAmMbkyZNp9OjReHsj8BjUDAAAyAHzq3qgTwAAAHMhMB/UILAjqGv/gu+AAULZvHkz9e3bl9q3b08//fQTERG98847tGXLFvhsklGkb+LEicIne5Xba5WMKvtk1AyR2m3W0WeFjPChj+3m0/GarLoPfWJ/nxUy6uazQkbdfJgL7e9TPaOONWiFjLr5RDtlra2AssEGDBDGf/7zH7r77rspNDSUsrOz6fLly0REdP78eZo6dSp8Nsgoo80rVqygnj17Urt27ah169ZuP2bnQx+r5yMSWzMyMsKHcQKf9TPq5itBp2uy6r4S0Cf29Vkho24+K2TUzVcC5kL7+qySUacatEJG3XyynKLXVkAFmPsVNMBOtGzZkhcvXszMzBEREZybm8vMzFlZWVyrVi34bJBRtG/27NkcERHBI0aMYKfTyX//+985JSWFo6KieOzYsabnQx+r5xNdMzIywodxAp/1M+rmY9bvmqy6jxl9YnefFTLq5rNCRt18zJgL7e6zQkbdatAKGXXzyXDKWFsB5YN3wABhHDx4kG677bZrno+KiqJz587BZ4JTdd+8efNowYIFNGfOHHI6nZSWlkaff/45jRw5ks6fP296PvSxej7RNSMjI3y++WQ44VPLJ8MJn28+Iv2uyar7iNAndvfJcMLnm0+GEz7ffESYC+3uk+FU/R5U9fbKcMLnm0+GU8baCigfbMAAYdSuXZsOHz58zfNbtmyhBg0awGeDjKJ9x44dow4dOhARUWhoKP32229ERPTII4/Qu+++a3o+9LF6PtE1IyMjfL75rJARPt98Vsiom49Iv2uy6j4i9IndfVbIqJvPChl18xFhLrS7zwoZdatBK2TUzSfDKWNtBVSA2W/BAfZh6tSp3KxZM96+fTtHRkby5s2becmSJVyjRg3OyMiAzwYZRfsSEhI4KyuLmZnbtGnDr732GjMz//e//+WqVauang99rJ5PdM3IyAgfxgl81s+om49Zv2uy6j5m9IndfVbIqJvPChl18zFjLrS7zwoZdatBK2TUzSfDKWNtBZQPNmCAT+zevZuLiopcj1944QUODw9nwzDYMAwOCQnh8ePHw2fhjDLaXMKgQYN40qRJzMw8d+5cDg0N5ZSUFI6OjuaBAweakg99rJ6vNCJqRkZG+DBO4LN+Rt18V6PDNVl139WgT+zns0JG3XxWyKib72owF9rPZ5WMJehQg1bIqJtPlrMEUWsrwDOwAQN8wuFw8KlTp5j5j93T06dP8+XLl3nv3r28Y8cO/u233+CzeEYZbS6hqKiICwsLXY/fffddfvzxxzkjI4MvX75sSj70sXq+0oioGRkZ4cM4gc/6GXXzXY0O12TVfVeDPrGfzwoZdfNZIaNuvqvBXGg/n1UylqBDDVoho24+Wc4SRK2tAM/ABgzwiWrVqvH27duZmdkwDP7555/hs1lGGW0WiRXaq3pG1X0yUL3NuvmskBE+9LHdfDJQvc2q+2Sgept181kho24+K2TUzScD1dusm88qGUVihfaqnlE3nywnMIdAs7+DBlib1NRUSk5OppiYGDIMg5KSkiggIKDMf5uXlwefBTPKaHNpNm/eTK+//jrl5ubSihUrqG7duvTOO+9QQkICderUye/50Mfq+a7G15qRkRE+jBP4ro/qGXXzlYXdr8mq+8oCfWIvnxUy6uazQkbdfGWBudBePqtkLI3da9AKGXXzyXKWRsTaCvAMbMAAn1iwYAH16NGDDh8+TCNHjqQhQ4ZQZGQkfDbKKKPNJfznP/+hRx55hB5++GHKzs6my5cvExHR+fPnaerUqbR69Wq/50Mfq+crjYiakZERPowT+KyfUTff1ehwTVbddzXoE/v5rJBRN58VMurmuxrMhfbzWSVjCTrUoBUy6uaT5SxB1NoK8BCz34ID7MOAAQP4woUL8CnkVN3XsmVLXrx4MTMzR0REcG5uLjMzZ2Vlca1atUzPhz5Wzye6ZmRkhE89J3xq+WQ44fMd3a7JqvuY0Sd298lwwqeeEz7fwVxob58Mp+r3oKq3V4YTPvWcMtZWQPkYzMxmbwIBAPQkLCyM9u3bR/Hx8RQZGUm7d++mBg0aUF5eHjVr1owuXbpkdkSgGKgZAACQA+ZX9UCfAAAA5kJgPqhBYEdQ1/7FYXYAAIC+1K5dmw4fPnzN81u2bKEGDRqYkAioDmoGAADkgPlVPdAnAACAuRCYD2oQ2BHUtX/BBgwAwDSGDBlC//znP2nHjh1kGAYdP36cli5dSqNHj6Zhw4aZHQ8oCGoGAADkgPlVPdAnAACAuRCYD2oQ2BHUtX8JNDsAAEAvvv32W2revDk5HA569tlnqbi4mO688066ePEi3XbbbRQcHEyjR4+mxx9/3OyoQBFQMwAAIAfMr+qBPgEAAMyFwHxQg8COoK7NA98BAwDwKwEBAXTixAmqWbMmNWjQgL766iuKjIykw4cPU35+PjVr1owiIiLMjgkUAjUDAABywPyqHugTAADAXAjMBzUI7Ajq2jzwDhgAgF+Jjo6mI0eOUM2aNeno0aNUXFxMTqeTmjVrZnY0oCioGQAAkAPmV/VAnwAAAOZCYD6oQWBHUNfmgQ0YAIBfSU1NpeTkZIqJiSHDMCgpKYkCAgLK/Ld5eXl+TgdUBDUDAABywPyqHugTAADAXAjMBzUI7Ajq2jywAQMA8CsLFiygHj160OHDh2nkyJE0ZMgQioyMNDsWUBjUDAAAyAHzq3qgTwAAAHMhMB/UILAjqGvzwHfAAABM49FHH6WMjAxM+MBjUDMAACAHzK/qgT4BAADMhcB8UIPAjqCu/Qs2YAAAAAAAAAAAAAAAAAAAAATjMDsAAAAAAAAAAAAAAAAAAACA3cAGDAAAAAAAAAAAAAAAAAAAgGCwAQMAAAAAAAAAAAAAAAAAACAYbMAAAAAAAAAAAAAAAAAAAAAIBhswAAAAAAAAAEsyYMAAMgzjmp/Dhw/77F60aBFFR0f7HhIAAAAAAACgLYFmBwAAAAAAAACAytK1a1d6++233Z6rUaOGSWnKprCwkIKCgsyOAQAAAAAAAPAzeAcMAAAAAAAAwLIEBwdT7dq13X4CAgLo448/ptatW1NISAg1aNCAJk+eTFeuXHH93syZM+mmm26i8PBwql+/Pg0fPpzy8/OJiGjjxo306KOP0vnz513vqpk0aRIRERmGQR999JFbhujoaFq0aBERER09epQMw6D33nuPkpOTKSQkhJYuXUpERAsXLqSmTZtSSEgINWnShObNm+dyFBQU0IgRIygmJoZCQkIoLi6O0tPT5Z04AAAAAAAAgHTwDhgAAAAAAACArdi8eTP169ePMjIy6NZbb6Xc3FwaOnQoERFNnDiRiIgcDgdlZGRQQkIC5eXl0fDhwyktLY3mzZtHHTp0oFmzZtGECRPo4MGDREQUERHhVYYxY8bQjBkzqFWrVq5NmAkTJtDcuXOpVatWlJ2dTUOGDKHw8HDq378/ZWRk0CeffELLly+n2NhY+uGHH+iHH34Qe2IAAAAAAAAAfgUbMAAAAAAAAADLsmrVKrfNkXvuuYfOnj1LY8aMof79+xMRUYMGDej555+ntLQ01wbME0884fqd+Ph4euGFF+ixxx6jefPmkdPppKioKDIMg2rXrl2pXE888QT16NHD9XjixIk0Y8YM13MJCQm0b98+ev3116l///507NgxatiwIXXq1IkMw6C4uLhK/X8BAAAAAAAA6oANGAAAAAAAAIBlueOOO2j+/Pmux+Hh4fTnP/+Ztm7dSlOmTHE9X1RURJcuXaKLFy9SWFgYrVu3jtLT0+nAgQN04cIFunLlittxX0lKSnL99++//065ubk0aNAgGjJkiOv5K1euUFRUFBERDRgwgLp06UKNGzemrl27Urdu3eiuu+7yOQcAAAAAAADAPLABAwAAAAAAALAs4eHhlJiY6PZcfn4+TZ482e0dKCWEhITQ0aNHqVu3bjRs2DCaMmUKVatWjbZs2UKDBg2igoKCCjdgDMMgZnZ7rrCwsMxcpfMQEb3xxht0yy23uP27gIAAIiJq3bo1HTlyhNasWUP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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, correlations = pipe2.features.correlations(target_num=TARGET_NUM)\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, correlations)\n",
    "\n",
    "plt.title('Feature correlations with class label ' + class_label[TARGET_NUM])\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Correlations')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Feature importances__\n",
    " \n",
    "Feature importances are calculated by analyzing the improvement in predictive accuracy on each node of the trees in the XGBoost predictor. They are then normalized, so that all importances add up to 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "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.features.importances()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, importances)\n",
    "\n",
    "plt.title('Feature importances for class label ' + class_label[TARGET_NUM])\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importances')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Column importances__\n",
    "\n",
    "Because getML uses relational learning, we can apply the principles we used to calculate the feature importances to individual columns as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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(target_num=TARGET_NUM)\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, importances)\n",
    "\n",
    "plt.title('Columns importances for class label ' + class_label[TARGET_NUM])\n",
    "plt.xlabel('Columns')\n",
    "plt.ylabel('Importances')\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important features look as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_64\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_64\" AS\n",
       "SELECT AVG( t2.\"t4__class_label__mapping_2_target_6_avg\" ) AS \"feature_1_64\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"POPULATION__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"CITES__STAGING_TABLE_3\" t2\n",
       "ON t1.\"paper_id\" = t2.\"citing_paper_id\"\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_64\";\\n\\nCREATE TABLE \"FEATURE_1_64\" AS\\nSELECT AVG( t2.\"t4__class_label__mapping_2_target_6_avg\" ) AS \"feature_1_64\",\\n       t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"CITES__STAGING_TABLE_3\" t2\\nON t1.\"paper_id\" = t2.\"citing_paper_id\"\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.features.to_sql()[pipe1.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_6_1\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_6_1\" AS\n",
       "SELECT AVG( \n",
       "    CASE\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" > 5.518669 ) AND ( f_6_2.\"feature_6_2_1\" > 15.824859 ) THEN 19.11484833926196\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" > 5.518669 ) AND ( f_6_2.\"feature_6_2_1\" <= 15.824859 ) THEN 16.25336464210706\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" <= 5.518669 ) AND ( f_6_2.\"feature_6_2_16\" > 0.759650 ) THEN 13.54988592990512\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" <= 5.518669 ) AND ( f_6_2.\"feature_6_2_16\" <= 0.759650 ) THEN 7.015267202459227\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_5\" > 1.818403 ) THEN 5.178143055435354\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_5\" <= 1.818403 ) AND ( f_6_2.\"feature_6_2_13\" > 0.586650 ) THEN 3.066163439590274\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_5\" <= 1.818403 ) AND ( f_6_2.\"feature_6_2_13\" <= 0.586650 ) THEN -0.674856124411419\n",
       "        ELSE NULL\n",
       "    END\n",
       ") AS \"feature_6_1\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"POPULATION__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"CITES__STAGING_TABLE_3\" t2\n",
       "ON t1.\"paper_id\" = t2.\"citing_paper_id\"\n",
       "LEFT JOIN \"FEATURES_6_2\" f_6_2\n",
       "ON t2.rowid = f_6_2.\"rownum\"\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_6_1\";\\n\\nCREATE TABLE \"FEATURE_6_1\" AS\\nSELECT AVG( \\n    CASE\\n        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" > 5.518669 ) AND ( f_6_2.\"feature_6_2_1\" > 15.824859 ) THEN 19.11484833926196\\n        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" > 5.518669 ) AND ( f_6_2.\"feature_6_2_1\" <= 15.824859 ) THEN 16.25336464210706\\n        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" <= 5.518669 ) AND ( f_6_2.\"feature_6_2_16\" > 0.759650 ) THEN 13.54988592990512\\n        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" <= 5.518669 ) AND ( f_6_2.\"feature_6_2_16\" <= 0.759650 ) THEN 7.015267202459227\\n        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_5\" > 1.818403 ) THEN 5.178143055435354\\n        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_5\" <= 1.818403 ) AND ( f_6_2.\"feature_6_2_13\" > 0.586650 ) THEN 3.066163439590274\\n        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_5\" <= 1.818403 ) AND ( f_6_2.\"feature_6_2_13\" <= 0.586650 ) THEN -0.674856124411419\\n        ELSE NULL\\n    END\\n) AS \"feature_6_1\",\\n       t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"CITES__STAGING_TABLE_3\" t2\\nON t1.\"paper_id\" = t2.\"citing_paper_id\"\\nLEFT JOIN \"FEATURES_6_2\" f_6_2\\nON t2.rowid = f_6_2.\"rownum\"\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.features.to_sql()[pipe2.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 Productionization\n",
    "\n",
    "It is possible to productionize the pipeline by transpiling the features into production-ready SQL code. Please also refer to getML's `sqlite3` and `spark` modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a folder containing the SQL code.\n",
    "pipe1.features.to_sql().save(\"cora_pipeline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe1.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"cora_spark\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.7 Benchmarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "State-of-the-art approaches on this data set perform as follows:\n",
    "\n",
    "| Approach                      | Study                      | Accuracy     | AUC     |\n",
    "| :--------------------------   | :------------------------  | -----------: | ------: |\n",
    "| RelF                          | Dinh et al (2012)          | 85.7%        |     --  |\n",
    "| LBP                           | Dinh et al (2012)          | 85.0%        |     --  |\n",
    "| EPRN                          | Preisach and Thieme (2006) | 84.0%        |     --  |\n",
    "| PRN                           | Preisach and Thieme (2006) | 81.0%        |     --  |\n",
    "| ACORA                         | Perlich and Provost (2006) | --           |   97.0% |\n",
    "\n",
    "\n",
    "As we can see, the performance of the relboost algorithm, as used in this notebook, compares favorably to these benchmarks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Approach</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FastProp</td>\n",
       "      <td>89.9%</td>\n",
       "      <td>98.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Relboost</td>\n",
       "      <td>89.8%</td>\n",
       "      <td>98.3%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Approach Accuracy    AUC\n",
       "0  FastProp    89.9%  98.5%\n",
       "1  Relboost    89.8%  98.3%"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data={\n",
    "    'Approach': ['FastProp', 'Relboost'],\n",
    "    'Accuracy': [f'{score:.1%}' for score in [fastprop_accuracy, relboost_accuracy]],\n",
    "    'AUC': [f'{score:,.1%}' for score in [fastprop_auc, relboost_auc]]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Conclusion\n",
    "\n",
    "In this notebook we have demonstrated that getML outperforms state-of-the-art relational learning algorithms on the CORA dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Dinh, Quang-Thang, Christel Vrain, and Matthieu Exbrayat. \"A Link-Based Method for Propositionalization.\" ILP (Late Breaking Papers). 2012.\n",
    "\n",
    "Motl, Jan, and Oliver Schulte. \"The CTU prague relational learning repository.\" arXiv preprint arXiv:1511.03086 (2015).\n",
    "\n",
    "Perlich, Claudia, and Foster Provost. \"Distribution-based aggregation for relational learning with identifier attributes.\" Machine Learning 62.1-2 (2006): 65-105.\n",
    "\n",
    "Preisach, Christine, and Lars Schmidt-Thieme. \"Relational ensemble classification.\" Sixth International Conference on Data Mining (ICDM'06). IEEE, 2006."
   ]
  }
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